repo_name
stringlengths 6
92
| path
stringlengths 7
220
| copies
stringclasses 78
values | size
stringlengths 2
9
| content
stringlengths 15
1.05M
⌀ | license
stringclasses 15
values |
|---|---|---|---|---|---|
amueller/advanced_training | 03.1 Cross Validation and Grid Searches.ipynb | 1 | 252997 | {
"cells": [
{
"cell_type": "code",
"execution_count": 10,
"metadata": {
"collapsed": false,
"hide_input": false
},
"outputs": [],
"source": [
"from preamble import *\n",
"%matplotlib inline\n",
"import matplotlib as mpl\n",
"mpl.rcParams['legend.numpoints'] = 1"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Model evaluation and improvement"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"0.88"
]
},
"execution_count": 2,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"from sklearn.datasets import make_blobs\n",
"from sklearn.linear_model import LogisticRegression\n",
"from sklearn.model_selection import train_test_split\n",
"\n",
"# create a synthetic dataset\n",
"X, y = make_blobs(random_state=0)\n",
"# split data and labels into a training and a test set\n",
"X_train, X_test, y_train, y_test = train_test_split(X, y, random_state=0)\n",
"# Instantiate a model and fit it to the training set\n",
"logreg = LogisticRegression().fit(X_train, y_train)\n",
"# evaluate the model on the test set\n",
"logreg.score(X_test, y_test)\n",
"# we predicted the correct class on 88% of the samples in X_test"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Cross-validation"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {
"collapsed": false,
"hide_input": false
},
"outputs": [
{
"data": {
"image/png": "iVBORw0KGgoAAAANSUhEUgAAA3wAAACqCAYAAAD2m02NAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x7fd69855c908>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"mglearn.plots.plot_cross_validation()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Cross-validation in scikit-learn"
]
},
{
"cell_type": "code",
"execution_count": 1,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"cross-validation scores: [ 0.961 0.922 0.958]\n"
]
}
],
"source": [
"from sklearn.model_selection import cross_val_score\n",
"from sklearn.datasets import load_iris\n",
"from sklearn.linear_model import LogisticRegression\n",
"\n",
"iris = load_iris()\n",
"logreg = LogisticRegression()\n",
"\n",
"scores = cross_val_score(logreg, iris.data, iris.target)\n",
"print(\"cross-validation scores: \", scores)"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"array([ 1. , 0.96666667, 0.93333333, 0.9 , 1. ])"
]
},
"execution_count": 2,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"scores = cross_val_score(logreg, iris.data, iris.target, cv=5)\n",
"scores"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"0.96000000000000019"
]
},
"execution_count": 3,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"scores.mean()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Stratified K-Fold cross-validation and other strategies"
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"[0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0\n",
" 0 0 0 0 0 0 0 0 0 0 0 0 0 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1\n",
" 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 2 2 2 2 2 2 2 2 2 2 2\n",
" 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2\n",
" 2 2]\n"
]
}
],
"source": [
"from sklearn.datasets import load_iris\n",
"iris = load_iris()\n",
"print(iris.target)"
]
},
{
"cell_type": "code",
"execution_count": 8,
"metadata": {
"collapsed": false,
"hide_input": false
},
"outputs": [
{
"data": {
"image/png": "iVBORw0KGgoAAAANSUhEUgAAA/wAAAFjCAYAAACADgBEAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\ndVs6AEsAAAAASUVORK5CYII=\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x7fd69855c780>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"mglearn.plots.plot_stratified_cross_validation()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## More control over cross-validation"
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"from sklearn.model_selection import KFold\n",
"kfold = KFold(n_folds=5)"
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"array([ 1. , 0.93333333, 0.43333333, 0.96666667, 0.43333333])"
]
},
"execution_count": 5,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"cross_val_score(logreg, iris.data, iris.target, cv=kfold)"
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"array([ 0., 0., 0.])"
]
},
"execution_count": 6,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"kfold = KFold(n_folds=3)\n",
"cross_val_score(logreg, iris.data, iris.target, cv=kfold)"
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"array([ 0.9 , 0.96, 0.96])"
]
},
"execution_count": 7,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"kfold = KFold(n_folds=3, shuffle=True, random_state=0)\n",
"cross_val_score(logreg, iris.data, iris.target, cv=kfold)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Leave-One-Out cross-validation"
]
},
{
"cell_type": "code",
"execution_count": 8,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"number of cv iterations: 150\n",
"mean accuracy: 0.953333333333\n"
]
}
],
"source": [
"from sklearn.model_selection import LeaveOneOut\n",
"loo = LeaveOneOut()\n",
"scores = cross_val_score(logreg, iris.data, iris.target, cv=loo)\n",
"print(\"number of cv iterations: \", len(scores))\n",
"print(\"mean accuracy: \", scores.mean())"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Shuffle-Split cross-validation"
]
},
{
"cell_type": "code",
"execution_count": 11,
"metadata": {
"collapsed": false,
"hide_input": false
},
"outputs": [],
"source": [
"mglearn.plots.plot_shuffle_split()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
""
]
},
{
"cell_type": "code",
"execution_count": 25,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"array([ 0.893, 0.947, 0.907, 0.947, 0.92 , 0.933, 0.88 , 0.947,\n",
" 0.84 , 0.947])"
]
},
"execution_count": 25,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"from sklearn.model_selection import ShuffleSplit\n",
"shuffle_split = ShuffleSplit(test_size=.5, train_size=.5, n_iter=10)\n",
"cross_val_score(logreg, iris.data, iris.target, cv=shuffle_split)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Cross-validation with groups"
]
},
{
"cell_type": "code",
"execution_count": 26,
"metadata": {
"collapsed": false,
"hide_input": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"label_kfold\n"
]
},
{
"data": {
"image/png": "iVBORw0KGgoAAAANSUhEUgAAAzwAAACLCAYAAACk0Zy+AAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x7f89a8861080>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"print(\"label_kfold\")\n",
"mglearn.plots.plot_label_kfold()"
]
},
{
"cell_type": "code",
"execution_count": 27,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"array([ 1. , 0.8, 1. ])"
]
},
"execution_count": 27,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"from sklearn.model_selection import LabelKFold\n",
"from mglearn.datasets import make_blobs\n",
"# create synthetic dataset\n",
"X, y = make_blobs(n_samples=12, random_state=0)\n",
"# assume the first three samples belong to the same group, then the next four etc.\n",
"labels = [0, 0, 0, 1, 1, 1, 1, 2, 2, 3, 3, 3]\n",
"cross_val_score(logreg, X, y, labels, cv=LabelKFold(n_folds=3))"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Grid Search"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Simple Grid-Search"
]
},
{
"cell_type": "code",
"execution_count": 29,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Size of training set: 112 size of test set: 38\n",
"best score: 0.973684210526\n",
"best parameters: {'gamma': 0.001, 'C': 100}\n"
]
}
],
"source": [
"# naive grid search implementation\n",
"from sklearn.svm import SVC\n",
"from sklearn.model_selection import train_test_split\n",
"X_train, X_test, y_train, y_test = train_test_split(iris.data, iris.target, random_state=0)\n",
"print(\"Size of training set: %d size of test set: %d\" % (X_train.shape[0], X_test.shape[0]))\n",
"\n",
"best_score = 0\n",
"\n",
"for gamma in [0.001, 0.01, 0.1, 1, 10, 100]:\n",
" for C in [0.001, 0.01, 0.1, 1, 10, 100]:\n",
" # for each combination of parameters\n",
" # train an SVC\n",
" svm = SVC(gamma=gamma, C=C)\n",
" svm.fit(X_train, y_train)\n",
" # evaluate the SVC on the test set \n",
" score = svm.score(X_test, y_test)\n",
" # if we got a better score, store the score and parameters\n",
" if score > best_score:\n",
" best_score = score\n",
" best_parameters = {'C': C, 'gamma': gamma}\n",
" \n",
"print(\"best score: \", best_score)\n",
"print(\"best parameters: \", best_parameters)"
]
},
{
"cell_type": "code",
"execution_count": 30,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"0.97368421052631582"
]
},
"execution_count": 30,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"best_score"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### The danger of overfitting the parameters and the validation set"
]
},
{
"cell_type": "code",
"execution_count": 31,
"metadata": {
"collapsed": false,
"hide_input": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"threefold_split\n"
]
},
{
"data": {
"image/png": "iVBORw0KGgoAAAANSUhEUgAAA1MAAABsCAYAAACPfOh5AAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x7f89a87a6390>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"print(\"threefold_split\")\n",
"mglearn.plots.plot_threefold_split()"
]
},
{
"cell_type": "code",
"execution_count": 32,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Size of training set: 84 size of validation set: 28 size of test set: 38\n",
"best score on validation set: 0.964285714286\n",
"best parameters: {'gamma': 0.001, 'C': 10}\n",
"test set score with best parameters: 0.921052631579\n"
]
}
],
"source": [
"from sklearn.svm import SVC\n",
"# split data into train+validation set and test set\n",
"X_trainval, X_test, y_trainval, y_test = train_test_split(iris.data, iris.target, random_state=0)\n",
"# split train+validation set into training and validation set\n",
"X_train, X_valid, y_train, y_valid = train_test_split(X_trainval, y_trainval, random_state=1)\n",
"\n",
"print(\"Size of training set: %d size of validation set: %d size of test set: %d\" % (X_train.shape[0], X_valid.shape[0], X_test.shape[0]))\n",
"best_score = 0\n",
"\n",
"for gamma in [0.001, 0.01, 0.1, 1, 10, 100]:\n",
" for C in [0.001, 0.01, 0.1, 1, 10, 100]:\n",
" # for each combination of parameters\n",
" # train an SVC\n",
" svm = SVC(gamma=gamma, C=C)\n",
" svm.fit(X_train, y_train)\n",
" # evaluate the SVC on the test set \n",
" score = svm.score(X_valid, y_valid)\n",
" # if we got a better score, store the score and parameters\n",
" if score > best_score:\n",
" best_score = score\n",
" best_parameters = {'C': C, 'gamma': gamma}\n",
"\n",
"# rebuild a model on the combined training and validation set, and evaluate it on the test set\n",
"svm = SVC(**best_parameters)\n",
"svm.fit(X_trainval, y_trainval)\n",
"test_score = svm.score(X_test, y_test)\n",
"print(\"best score on validation set: \", best_score)\n",
"print(\"best parameters: \", best_parameters)\n",
"print(\"test set score with best parameters: \", test_score)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Grid-search with cross-validation"
]
},
{
"cell_type": "code",
"execution_count": 33,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"SVC(C=100, cache_size=200, class_weight=None, coef0=0.0,\n",
" decision_function_shape=None, degree=3, gamma=0.01, kernel='rbf',\n",
" max_iter=-1, probability=False, random_state=None, shrinking=True,\n",
" tol=0.001, verbose=False)"
]
},
"execution_count": 33,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# reference: manual_grid_search_cv\n",
"for gamma in [0.001, 0.01, 0.1, 1, 10, 100]:\n",
" for C in [0.001, 0.01, 0.1, 1, 10, 100]:\n",
" # for each combination of parameters\n",
" # train an SVC\n",
" svm = SVC(gamma=gamma, C=C)\n",
" # perform cross-validation\n",
" scores = cross_val_score(svm, X_trainval, y_trainval, cv=5)\n",
" # compute mean cross-validation accuracy\n",
" score = np.mean(scores)\n",
" # if we got a better score, store the score and parameters\n",
" if score > best_score:\n",
" best_score = score\n",
" best_parameters = {'C': C, 'gamma': gamma}\n",
"# rebuild a model on the combined training and validation set\n",
"svm = SVC(**best_parameters)\n",
"svm.fit(X_trainval, y_trainval)"
]
},
{
"cell_type": "code",
"execution_count": 34,
"metadata": {
"collapsed": false,
"hide_input": false
},
"outputs": [
{
"data": {
"image/png": "iVBORw0KGgoAAAANSUhEUgAAAzIAAAE4CAYAAACAMcZiAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\ngAXOZAAAAABJRU5ErkJggg==\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x7f89a88126d8>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"mglearn.plots.plot_cross_val_selection()"
]
},
{
"cell_type": "code",
"execution_count": 35,
"metadata": {
"collapsed": false,
"hide_input": false,
"scrolled": false
},
"outputs": [
{
"data": {
"image/png": "iVBORw0KGgoAAAANSUhEUgAAApwAAAGnCAYAAAAaHodWAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nSURERES6+j+GCoLsRdQW1wAAAABJRU5ErkJggg==\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x7f89a854b438>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"mglearn.plots.plot_grid_search_overview()"
]
},
{
"cell_type": "code",
"execution_count": 36,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"{'C': [0.001, 0.01, 0.1, 1, 10, 100], 'gamma': [0.001, 0.01, 0.1, 1, 10, 100]}"
]
},
"execution_count": 36,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"param_grid = {'C': [0.001, 0.01, 0.1, 1, 10, 100],\n",
" 'gamma': [0.001, 0.01, 0.1, 1, 10, 100]}\n",
"param_grid"
]
},
{
"cell_type": "code",
"execution_count": 37,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"from sklearn.model_selection import GridSearchCV\n",
"from sklearn.svm import SVC\n",
"grid_search = GridSearchCV(SVC(), param_grid, cv=5)"
]
},
{
"cell_type": "code",
"execution_count": 38,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"X_train, X_test, y_train, y_test = train_test_split(iris.data, iris.target, random_state=0)"
]
},
{
"cell_type": "code",
"execution_count": 39,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"GridSearchCV(cv=5, error_score='raise',\n",
" estimator=SVC(C=1.0, cache_size=200, class_weight=None, coef0=0.0,\n",
" decision_function_shape=None, degree=3, gamma='auto', kernel='rbf',\n",
" max_iter=-1, probability=False, random_state=None, shrinking=True,\n",
" tol=0.001, verbose=False),\n",
" fit_params={}, iid=True, n_jobs=1,\n",
" param_grid={'gamma': [0.001, 0.01, 0.1, 1, 10, 100], 'C': [0.001, 0.01, 0.1, 1, 10, 100]},\n",
" pre_dispatch='2*n_jobs', refit=True, scoring=None, verbose=0)"
]
},
"execution_count": 39,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"grid_search.fit(X_train, y_train)"
]
},
{
"cell_type": "code",
"execution_count": 40,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"0.97368421052631582"
]
},
"execution_count": 40,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"grid_search.score(X_test, y_test)"
]
},
{
"cell_type": "code",
"execution_count": 41,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"{'gamma': 0.01, 'C': 100}\n",
"0.973214285714\n"
]
}
],
"source": [
"print(grid_search.best_params_)\n",
"print(grid_search.best_score_)"
]
},
{
"cell_type": "code",
"execution_count": 42,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"SVC(C=100, cache_size=200, class_weight=None, coef0=0.0,\n",
" decision_function_shape=None, degree=3, gamma=0.01, kernel='rbf',\n",
" max_iter=-1, probability=False, random_state=None, shrinking=True,\n",
" tol=0.001, verbose=False)"
]
},
"execution_count": 42,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"grid_search.best_estimator_"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Analyzing the result of cross-validation"
]
},
{
"cell_type": "code",
"execution_count": 43,
"metadata": {
"collapsed": false,
"scrolled": true
},
"outputs": [
{
"data": {
"text/plain": [
"[mean: 0.36607, std: 0.01137, params: {'gamma': 0.001, 'C': 0.001},\n",
" mean: 0.36607, std: 0.01137, params: {'gamma': 0.01, 'C': 0.001},\n",
" mean: 0.36607, std: 0.01137, params: {'gamma': 0.1, 'C': 0.001},\n",
" mean: 0.36607, std: 0.01137, params: {'gamma': 1, 'C': 0.001},\n",
" mean: 0.36607, std: 0.01137, params: {'gamma': 10, 'C': 0.001},\n",
" mean: 0.36607, std: 0.01137, params: {'gamma': 100, 'C': 0.001},\n",
" mean: 0.36607, std: 0.01137, params: {'gamma': 0.001, 'C': 0.01},\n",
" mean: 0.36607, std: 0.01137, params: {'gamma': 0.01, 'C': 0.01},\n",
" mean: 0.36607, std: 0.01137, params: {'gamma': 0.1, 'C': 0.01},\n",
" mean: 0.36607, std: 0.01137, params: {'gamma': 1, 'C': 0.01},\n",
" mean: 0.36607, std: 0.01137, params: {'gamma': 10, 'C': 0.01},\n",
" mean: 0.36607, std: 0.01137, params: {'gamma': 100, 'C': 0.01},\n",
" mean: 0.36607, std: 0.01137, params: {'gamma': 0.001, 'C': 0.1},\n",
" mean: 0.69643, std: 0.01333, params: {'gamma': 0.01, 'C': 0.1},\n",
" mean: 0.91964, std: 0.04442, params: {'gamma': 0.1, 'C': 0.1},\n",
" mean: 0.95536, std: 0.03981, params: {'gamma': 1, 'C': 0.1},\n",
" mean: 0.36607, std: 0.01137, params: {'gamma': 10, 'C': 0.1},\n",
" mean: 0.36607, std: 0.01137, params: {'gamma': 100, 'C': 0.1},\n",
" mean: 0.69643, std: 0.01333, params: {'gamma': 0.001, 'C': 1},\n",
" mean: 0.92857, std: 0.04278, params: {'gamma': 0.01, 'C': 1},\n",
" mean: 0.96429, std: 0.03405, params: {'gamma': 0.1, 'C': 1},\n",
" mean: 0.94643, std: 0.03251, params: {'gamma': 1, 'C': 1},\n",
" mean: 0.91964, std: 0.06507, params: {'gamma': 10, 'C': 1},\n",
" mean: 0.50893, std: 0.04666, params: {'gamma': 100, 'C': 1},\n",
" mean: 0.92857, std: 0.04278, params: {'gamma': 0.001, 'C': 10},\n",
" mean: 0.96429, std: 0.03405, params: {'gamma': 0.01, 'C': 10},\n",
" mean: 0.96429, std: 0.01793, params: {'gamma': 0.1, 'C': 10},\n",
" mean: 0.93750, std: 0.04556, params: {'gamma': 1, 'C': 10},\n",
" mean: 0.91964, std: 0.06507, params: {'gamma': 10, 'C': 10},\n",
" mean: 0.56250, std: 0.04966, params: {'gamma': 100, 'C': 10},\n",
" mean: 0.96429, std: 0.03405, params: {'gamma': 0.001, 'C': 100},\n",
" mean: 0.97321, std: 0.02234, params: {'gamma': 0.01, 'C': 100},\n",
" mean: 0.95536, std: 0.04983, params: {'gamma': 0.1, 'C': 100},\n",
" mean: 0.94643, std: 0.05199, params: {'gamma': 1, 'C': 100},\n",
" mean: 0.91964, std: 0.06507, params: {'gamma': 10, 'C': 100},\n",
" mean: 0.56250, std: 0.04966, params: {'gamma': 100, 'C': 100}]"
]
},
"execution_count": 43,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"grid_search.grid_scores_"
]
},
{
"cell_type": "code",
"execution_count": 44,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"<matplotlib.collections.PolyCollection at 0x7f89a8579f60>"
]
},
"execution_count": 44,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"image/png": "iVBORw0KGgoAAAANSUhEUgAAAR0AAAEKCAYAAAAvuYFYAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz\nAAALEgAACxIB0t1+/AAAIABJREFUeJzt3Xl8FPX9x/HXd48cJOEIkIsjQA6OoIDcV0HLfUqrgogC\nYrVY6Q+otyD1qgcCFbUKigotAlpbwSqKVNR6VVQQKgghGiD3SW6y1/f3xy5JlgAJ2WUS8fN8PPbB\nzs53Zt+Znf3sd2bY7yqtNUIIYRRTYwcQQvy8SNERQhhKio4QwlBSdIQQhpKiI4QwlBQdIYShLI0d\n4EJSSsn/BxCikWit1Zkev6iLDkB5Rie/rOfhJwtZcnsrn9cTqKx+SOP2wJP5LLu9tc/rKXSV+yGN\n2+MrirnrD819Xk+5dvohjdvKFSUs/kOYz+s57gj2Qxq3dasKmbfI9/1pwSML/JCmWsbX7xLTd5zP\n6/nmhcVnnSeHV0IIQ0nREUIYSopOPf1iSFBjR6hlxBD/dff9ZejgwMaOUMvgwQGNHaGWPoOa3v4E\nEBYdf8GfQ4pOPf2iCb7BRw5p1tgRahk2pAkWnSaY6bLBTW9/AgiLkaIjhLjISNERQhhKio4QwlBS\ndIQQhpKiI4QwlBQdIYShpOgIIQwlRUcIYagmUXSUUuuUUtlKqX01HmullNqhlDqklHpPKdWixrx7\nlFLJSqmDSqkxvjz3jl3l9B6exqXD0ljxTFGt+SeKnMyYl8PAUemMmJTBwcO2qnlFxS6uuzmHPr9I\np+/IdL78ptKXKFXe/aCMHsNS6TY0lSeeKThjpl/fmEGfXx5lyMTjHDjkft7DKTb6jj5Gv9HH6Dv6\nGK0SU3j6xRN+yfTvXScZ9ItsBgzLYvWzJbXmFxW5mH1TPiNGZTN2Ug6HDtur5hUXu7jx5nwGj8hm\n6OXZfP2NrdbyDfXhrkouH5HLiOG5/OXZ0jPmuvmmQsaOzmPq5HwOH3ZUzRsyKIexo/MYPzaPyRPz\n/Zbpiw/LufaKNGZcfpy/PVd7+5cUObnnlmxmj0vnN1dm8GOye3vkZDpYcG0m141O4/qxabz+cu39\nsaEGJ8XyxoOz+cdDc5g9tl+t+ZcltuPDP89n45KZbFwyk3kTBlTNCw0O4PFbJvL3B27gtT/eQM/O\nUT5laSrfMn8ZeBrYUOOxu4GdWusnlFJ3AfcAdyulegDXAN2B9sBOpVSCbsDPWrhcmsX3FfDOa1FE\nR5oZNj6DSWOD6ZpQ/d/ml68uolfPADavi+DwETuL7s3n7dfcG/2O+/MZe0UwG9dG4HBoyit8H0nD\n5dL8/r5c3n+tHTFRFgaOP8aUsaF0q5Hp0dWF9OkZyBsvxXDoiI0F9+aw47X2JMYF8PX7HavW0/Gy\nH7lyfIhfMt295AT/2NKGqEgzoyfkMn5sEAnx1d+YX/V0CZckWVn/YmuSj9i5674i/rGlDQD33l/E\nqF8G8dLaEBwOTYUfttOpXEuXFLNpSziRkSYmT8xnzNgg4uOrd+tnni4jqaeVtS+2IiXFwZL7itm0\nORwAZVJseT2cli3999nrcmlWLstn9cYo2kRamDclg+GjmxEbX/36bfhLEYlJATy6JpKjKTZW3p/P\nUxujMZthwZJwEpMCKS9zcePkDAYMD/ZatiGUgruuvYLfrvw7eUVlbLj3Wj76NoXUrEKvdt8kp7P4\n2W21lr99+kg+2f8jd615G7NJERTg20gJTaKno7X+BCg87eGpwHrP/fXAlZ77U4DNWmuH1joVSAYG\n0ABf7akkrrOVju0tWK2Kq6eG8K/3KrzaHEy2M3Ko+3syifFWjqY5yM13Ulzi4tP/VnLDDPeQCRaL\nonmY75vzyz0nie9sJbaDFatVMX1qGNve8/4EP3C4ksuHub8C0TU+gNTjDnLzHF5tdn5cTlyslQ7t\nfB9K45s9drp0ttDBs52mTQ1m+3snvdocOmxn+DD31w0S4q0cT3OQl++kpMTFF19WMnO6u/hZLIow\nP2wngL177HTubKZ9ezNWq2LylCB2nJYrOdnBkKHuN21cnIW0407y813umRr8/QtMB/ZW0qGTlaj2\nVixWxajJIfznfe+hQ1KTbfT1fK0mNi6AzDQHhflOWkdYSExyb8NmISY6xVnJzfZ9iI+kTlEcyykk\nq6AEh9PFjt2HGdErrlY7Re3hb0KCAuiT0I63PjsAgNOlKTvpW0+1SRSds4jQWmcDaK2zgAjP4+2A\n4zXapXseO28ZWU7ax5irptvFWMjI8n7zXtIjgK3b3TvN7j2VHE93kJ7hIPWYg9bhJm5emMfgMRn8\n7o48KipcDYnhJT3TQYeY6k/qdtEW0k/L1KtHIP98x12IvtxzkmPpdtIyvdu8tq2UGdN8H0MGIDPL\nSUyN7RQdbSYzy/vN0LOHlX+9437Df7PHRlq6k4xMJ0ePOQkPN7FgUSGXj81h0Z2FfuvpZGW5iD4t\nV1aW92vQo4eFd7e7c+3dYyMjw0lmpie7guuuLWDSxDxe3eifMYVys51E1Hj9IqIttQpHfPcAPnq3\nDHAXqewMB7mnvX6Zx+0kH7DRo7fv3xuLaBVKdkH1B1d2YQkRLUNrtbs0LppXl17HUwum0jna3RuM\nadOcE6UVLJs9ho1LZnLfrFEEWs21lj0fTbnonK5Be+rDTxZW3T7+rKLuBU5z+20tKDzhYvCYDNa+\nUkyvngGYzQqHU7N3v41b5obx+Y4YmgUrnjzDOaEL4a4F4RQWueg3+hh/efkEfXoGYjZXf0rZ7Zq3\n3ivjqkm1d6wL5fe3hVFU5OLysTmse6WUS3paMZvc22nffjs3zglh13sRNAtSPHWGc0IXyq2/C6Go\nSDNhXB7r15eTlGTF7Nnr//HPcN55tw3rN4SzYX05X37pv3NN5zJrfktKilzMnZjOGxuKSewRiKnG\n61de5uK+W3NYuCycZiHGvEUPHs1h4t0vMvOhjWzZ9S0rbp0CgNlkolvHCF778Fuue/hVTtrszBlX\n+8CiJOMIGV+/W3U7l6ZyTudMspVSkVrrbKVUFJDjeTwd6FCjXXvPY2d0rtH+YqLMHE+v/oRJz3AQ\nE+W9ScJCTaxZ1aZquvvANDrHWigr17SPsdC3l/uTaNrEEFY863vRaRdt4VjNTJkO2p0h07pVkVXT\ncQN+pEts9WHU9g/KuOzSQNq28c/LGx1lJj29+tM6M9NJdJT3p11YqInVK6u39WWDsoiNNVNermkX\nY6ZPL/chzuRJwaw+wwnfhoiKMpFxWq6oKO83aWioiSdXVF2DYOjgXDrGurNHRrr/bd3axNhxQXy7\n186AAb6dP2kbaSa7xuuXk+mgbaT3tgoJNXHv8rZV01cNO05MR/dr5XBolszPYdy0UIaP8f18HEBO\nYSlR4dW93shWYeSc8H4NKiqrT/x/9r9ULDNNNG8WSE5hKVkFpRw8mg3Av79JZvbY/rWeIywm3usb\n6lnf7DhrnqbU01Ge2ynbgDme+7OBrTUen6GUClBKdQbigS8b8oR9ewfyQ6qDY2kObDbN61vLmDjW\ne8iBomIXdru7k/XSxhKGDw4kNMREZFsz7WPMJKe4X6xdn5yke6Lv50/69w4iJdXO0eN2bDbNlq0l\nTB7j3WMpKnZWZXrhb0X8YnAwoTU+ETe/WeK3QyuAPr2t/Jjq4LhnO/1zawXjxniPB1NcYztt2FjG\nkEHu7RTR1kxMjJkjnu308SeVdE30TzHs1dtKaqqTtDQnNpvmrW0nGX2OXK9uLGfgICshISYqKjRl\nZe5DsfJyFx9/XEnXrr7n6t4rkLSjdrLS7Nhtmp1vlTFstPcQJKXFLhyeTNs2FdN7YFBVj+bRO/Po\nlGDlmhtb1Fp3Qx1IzaZDREuiwsOwmE2M6Z/IR9/+4NUmPKw6Y1KnSBRQXF5JQUk52YUldIxoCUD/\nbh35IdO3K31NoqejlHoVGAm0VkodA5YBjwGvK6VuBI7ivmKF1vqAUuo14ABgB25tyJUrALNZsfKR\ncCbPyMKlYfYM91WiF/9aglIwb1YYh5Jt/Ob/8jCZFN27WnluRXWv58mHWjP3tlzsdk3nWKtXj6ih\nzGbF6kfaMu7adFwuuPHa5nRPDGDNhiKUgpuvb8HBZBtz/y8bk4IeXQN5cWVE1fLl5S7+/XEFa5ZH\nnuNZzj/TYw+35Kpr83G5NLOuDSExwcorfy1DKZg9K4TDyQ5+t7AQkwm6JVp4akV1r+fRB1vy2wWF\nOOwQG2vm6ZW+jw18KtdDDzdn1swCXC6YPiOYhAQLf/tbOQq4blYzjiQ7WLyoCJMJEhItLH/S/WbO\nzXVy800nUAocDrhyWhC/GOH7+ROzWbH4gdYsvCEb7dJMuiaMTvEBvLmxGKVg6szmpB6x8fDteZgU\ndE60cs/j7l7Pvq9OsuPNUrp0DWDOhHSUglvuaMWgkb6Nm+TSmsc3fcCzC3+FSSm2fvodqVkF/OoX\nl6A1/PM/+/ll3wSuGnEpDqeLSruDe154p2r55Zt38fBN47GYTaTnFvHA+rP3YupDNfD9+pOglNL+\nGpjdX/w5MLu/+HNgdn/x58Ds/uLPgdn9xd8Ds/vLNy8sPuuvQTSlwyshxM+AFB0hhKGk6AghDCVF\nRwhhKCk6QghDSdERQhhKio4QwlBSdIQQhpKiI4QwlBQdIYShpOgIIQwlRUcIYSgpOkIIQ0nREUIY\nqkmMp3MhHbY76m5kID+M8+V3zZrgcBt5Tt/HmxZNk/R0hBCGkqIjhDCUFB0hhKGk6AghDCVFRwhh\nKCk6QghDSdERQhhKio4QwlBSdIQQhpKiI4QwlBQdIYShpOgIIQwlRUcIYagm/S1zpdQ6YBKQrbW+\n1PNYK2ALEAukAtdorYsa+hyffniS5Q+ewOWCadObMXd+c6/5xUUu/nhHAWnHnAQGKf74RCviEq3Y\nKjU3XpOD3QZOp2bUhGb8dmHzszzL+dmxq5w77y/ApWH2jDD+cFsLr/knipz8dnE+Px61ExSkeH5l\nG7onBgBQVOzi1tvzOPC9HZMJnlvZhgGXBV6UmQD+8+FJ/vRAMS4XXDW9Gb+5NdRrfnGRi/vuOMGx\no06CghSPLG9BfI2v+rtcmqsm5REZZea5l8L9kumLD8t56sECtNZMuiaMWfNbes0vKXLypzvzyDjq\nICBIce/yNnROCCAn08FDi3MpyHNiMsGUGWFcPbfFWZ7l/AxOiuX26SNRSrH1k/+x/r2vvOZfltiO\nlbdOIT3P/Vb64JsjrHvnSwBCgwNYesNo4mJa49Lw4Pod/O/HrAZnadJFB3gZeBrYUOOxu4GdWusn\nlFJ3Afd4HjtvLpfmsfsLWfNqW9pGmrluSg4jRwfTOb56p1z3bDHdkgJYubY5qSl2Hl16gjWvtiUg\nUPHC5rYEB5twOjVzfp3L0JFBXNI7wIc/151p8X0FvPNaFNGRZoaNz2DS2GC6JlSvd/nqInr1DGDz\nuggOH7Gz6N583n4tCoA77s9n7BXBbFwbgcOhKa/QPuVpqplO5XpoaTEvbwonItLM1ZPz+OWYILrE\nV+/Wa54ppXuSlafXhvNDioOHlhTx8qbWVfM3vFRGXIKF0hL/ZVq5LJ/VG6NoE2lh3pQMho9uRmx8\n9bba8JciEpMCeHRNJEdTbKy8P5+nNkZjNsOCJeEkJgVSXubixskZDBge7LVsQygFd117Bb9d+Xfy\nisrYcO+1fPRtCqlZhV7tvklOZ/Gz22otf/v0kXyy/0fuWvM2ZpMiKMC3oVCa9OGV1voToPC0h6cC\n6z331wNXNnT9/9tro2MnCzHtLVitinGTg/nw/QqvNj8kO+g/xP2p3CnOSkaag4J8JwDBwe7NZ7Np\nnA6NUg1NUu2rPZXEdbbS0ZPp6qkh/Os970wHk+2MHBoEQGK8laNpDnLznRSXuPj0v5XcMCMMAItF\n0TzM95e4KWYC2LfXTmxnC+08uSZMDubfO056tTmS7GCQ5/XrEmchPc1Z9fplZTr5+INKrprRzC95\nAA7sraRDJytR7a1YrIpRk0P4z/vlXm1Sk230HRIMQGxcAJlpDgrznbSOsJCY5M7aLMREpzgrudlO\nnzMldYriWE4hWQUlOJwuduw+zIhecbXaKWrvwCFBAfRJaMdbnx0AwOnSlJ20+ZSnSReds4jQWmcD\naK2zgIiGrigny0lkTPWnYmSUmZws78GjErtb+eBd9xts/14bmRlOcjLdO4LLpZk+PptR/TIZNDyI\nnr18+0QCyMhy0j7GXDXdLsZCRpb3QGSX9Ahg63b3jrx7TyXH0x2kZzhIPeagdbiJmxfmMXhMBr+7\nI4+KCt8Hw2qKmQCys5xERVfvwlHRJrKzvN+k3XpY2PGuuxDt87x+WZnu53/0gWLuuK+5Xz4sTsnN\ndhJRY5+KiLbUKhzx3QP46N0ywF2ksjMc5GZ6b8/M43aSD9jo0dv3w9CIVqFkF5RWTWcXlhDRMrRW\nu0vjonl16XU8tWAqnaPdh5oxbZpzorSCZbPHsHHJTO6bNYpAq7nWsufjp1h0TnfOfvFzq4qqbrs/\nP3mupmd0461hFBe5mDEhmy0bSumWZMXk2eYmk2LL9kje+yKa/XtspBy2N+gPOF+339aCwhMuBo/J\nYO0rxfTqGYDZrHA4NXv327hlbhif74ihWbDiyWcafLrrJ58J4De3hlJc5OJXE3LZuL6c7klWzGb4\n8N8nad3WRPckK1qD9s/RVb3Mmt+SkiIXcyem88aGYhJ7BGIyV1e+8jIX992aw8Jl4TQLMeYtevBo\nDhPvfpGZD21ky65vWXHrFADMJhPdOkbw2offct3Dr3LSZmfOuAG1li/JOELG1+9W3c6lqZ/TOZNs\npVSk1jpbKRUF5Jyr8fxFZz8RFxFlJiuj+hMmO8tJRJT3ixwSauKBJ6tPME4Ymkn7jt6bLTTMRP/B\ngXz20UnifByPNCbKzPH06kzpGQ5ioryfLyzUxJpVbaqmuw9Mo3OshbJyTfsYC317uT8dp00MYcWz\nvr/Bm2ImcPdMMzOqe01ZmS4io7w/hUNDTfzpyeoTuaOG5tC+o5m3t1Ww6/2TfPxBJZUnNWVlmrsW\nnuDxP3uf9D1fbSPNZNfYVjmZDtpGemcKCTVx7/K2VdNXDTtOjGefcjg0S+bnMG5aKMPHhPiUpSpD\nYSlR4WFV05Gtwsg5UerVpqKy+gPzs/+lYplponmzQHIKS8kqKOXg0WwA/v1NMrPH9q/1HGEx8YTF\nxFdNZ32z46x5fgo9HeW5nbINmOO5PxvY2tAVJ/UK4Hiqg4w0B3ab5t23KhgxKtirTUmxC7vd/TH4\nxqZS+g4KpFmIicICJyXF7h3+5EnNF5+cpFOc7zW8b+9Afkh1cCzNgc2meX1rGRPHemcqqpHppY0l\nDB8cSGiIici2ZtrHmElOce9Auz45SXc/DMrcFDMBXNLLyrFUB+meXO+8VcEVo70PR2q+fq+9Wk6/\ngQGEhJhYfFdzdn0Ryc5PI1jxbEsGDgnwueAAdO8VSNpRO1lpduw2zc63yhg22vucUWmxC4cn07ZN\nxfQeGFTVo3n0zjw6JVi55kb/XLUCOJCaTYeIlkSFh2ExmxjTP5GPvv3Bq014WHXGpE6RKKC4vJKC\nknKyC0voGOHeNv27deSHzHyf8jTpno5S6lVgJNBaKXUMWAY8BryulLoROApc09D1m82Kux9sxfzr\n86oumXdJsPL3jaWg4KqZofx4xM7SPxRiUhCXaGXZE60AyMtxsXSx+xKydsGYScEMvyK4jmesX6aV\nj4QzeUaW5/J0KN0SAnjxryUoBfNmhXEo2cZv/i8Pk0nRvauV51ZU9zCefKg1c2/LxW7XdI61evU+\nLqZMp3Itfag582YVoF3w6+nNiEuwsvlvZSilmH5dM1KOOLh78QlMJohPsPLIcv+9mc+WafEDrVl4\nQzba5b5k3ik+gDc3FqMUTJ3ZnNQjNh6+PQ+Tgs6JVu553N3r2ffVSXa8WUqXrgHMmZCOUnDLHa0Y\nNNK3E90urXl80wc8u/BXmJRi66ffkZpVwK9+cQlawz//s59f9k3gqhGX4nC6qLQ7uOeFd6qWX755\nFw/fNB6L2UR6bhEPrD97L6Y+lDbyYNZgSim992j7xo7hJdHapOt8k3HMUdnYEWrJdwU1doRaFjyy\noLEjnNE3LyxGa33GU/Q/hcMrIcRFRIqOEMJQUnSEEIaSoiOEMJQUHSGEoaToCCEMJUVHCGEoKTpC\nCENJ0RFCGEqKjhDCUFJ0hBCGkqIjhDCUFB0hhKGk6AghDHXRj7MwZcfvGzuCl21jVjd2hFqa4nAb\nzUxNb8iVfP8M7exXIdmOuhs1MdLTEUIYSoqOEMJQUnSEEIaSoiOEMJQUHSGEoaToCCEMJUVHCGEo\nKTpCCENJ0RFCGEqKjhDCUFJ0hBCGkqIjhDCUFB0hhKGa3teL60kptQ6YBGRrrS9t6HpGtO/E/YOu\nwKQUWw7t5/l9X3rNv/mS/lwZ1x0NWEwm4lu2ps/fnqHYVlnnsg316YcnWf7gCVwumDa9GXPnN/ea\nX1zk4o93FJB2zElgkOKPT7QiLtGKrVJz4zU52G3gdGpGTWjGbxc2P8uznJ8du8q58/4CXBpmzwjj\nD7e18Jp/osjJbxfn8+NRO0FBiudXtqF7YgAARcUubr09jwPf2zGZ4LmVbRhwWaBfcn24q5IH/liM\nywXTZwRz6+9CveYXFbm44w9FHD3qJChIsXxFCxIT3bv9kEE5hIWZMJnAYlG89XZrv2T64sNynnqw\nAK01k64JY9b8ll7zS4qc/OnOPDKOOggIUty7vA2dEwLIyXTw0OJcCvKcmEwwZUYYV89tcZZnOT8D\n+nRiwU3uffXtnft59R/e+2qvpPb86d5pZGYXAfDx54fZ8PoXANx121gG94+j8EQ5c//vFZ+z/GSL\nDvAy8DSwoaErUMCDg0cxc/trZJeVsu3K63n/6BFSigqq2qzdv5u1+3cDcEWHLszr2Y9iW2W9lm0I\nl0vz2P2FrHm1LW0jzVw3JYeRo4PpHG+tarPu2WK6JQWwcm1zUlPsPLr0BGtebUtAoOKFzW0JDjbh\ndGrm/DqXoSODuKR3gM+ZFt9XwDuvRREdaWbY+AwmjQ2ma0L1epevLqJXzwA2r4vg8BE7i+7N5+3X\nogC44/58xl4RzMa1ETgcmvIK/wxb4XJpli4pZtOWcCIjTUyemM+YsUHEx1fv1s88XUZSTytrX2xF\nSoqDJfcVs2lzOADKpNjyejgtW/qvw+9yaVYuy2f1xijaRFqYNyWD4aObERtfva02/KWIxKQAHl0T\nydEUGyvvz+epjdGYzbBgSTiJSYGUl7m4cXIGA4YHey3bEErBwltGsWjpa+QVlLL2yev55L9HOJbu\nva/uO5DGPY/8s9by7/z7f7zx9h7uWzjBpxyn/GQPr7TWnwCFvqyjd9toUosLSS8txqFdvJVykNGx\n8WdtPzWuO9tSDjZo2fr6314bHTtZiGlvwWpVjJsczIfvV3i1+SHZQf8h7p5CpzgrGWkOCvKdAAQH\nu19Sm03jdGiU8jkSX+2pJK6zlY6eTFdPDeFf73lnOphsZ+TQIAAS460cTXOQm++kuMTFp/+t5IYZ\nYYC7R9E8zD+73d49djp3NtO+vRmrVTF5ShA73jvp1SY52cGQoe43bVychbTjTvJPDYyjQft52J4D\neyvp0MlKVHsrFqti1OQQ/vN+uVeb1GQbfYcEAxAbF0BmmoPCfCetIywkJrlf12YhJjrFWcnNdvqc\nqXtCNGkZhWTnFuN0uvjgk4MMG1j/fXX/wXRKSk/W3bCefrJFxx+iQsLIKCupms4qLyUqJPSMbQPN\nFka078z21MPnvez5yMlyEhlT/UkdGWUmJ8t79KjE7lY+eNf9pt+/10ZmhpOcTPfO6XJppo/PZlS/\nTAYND6JnL98+JQEyspy0jzFXTbeLsZCR5T141CU9Ati63f3m2r2nkuPpDtIzHKQec9A63MTNC/MY\nPCaD392RR0WFf0bDyspyEV0jV3S0mazTtlWPHhbe3e5+w+zdYyMjw0mmZ1uh4LprC5g0MY9XN3oX\nhobKzXYSUeP1i4i21Coc8d0D+OjdMsBdpLIzHORmem/PzON2kg/Y6NHb98PQtq3DyMmr3ldz8kpp\n07r2vprULYZ1q2bz+NJfEdvBP4eaZ/KzLjrnY1THOHZnp1Fsq2zsKNx4axjFRS5mTMhmy4ZSuiVZ\nMXneeyaTYsv2SN77Ipr9e2ykHLYbkun221pQeMLF4DEZrH2lmF49AzCbFQ6nZu9+G7fMDePzHTE0\nC1Y8+UyRIZkAbv1dCEVFmgnj8li/vpykJCtmz17/j3+G8867bVi/IZwN68v58kubIZlmzW9JSZGL\nuRPTeWNDMYk9AjGZq7uk5WUu7rs1h4XLwmkWYsxb9HBKNlfPW8O8Rev5x9t7+NM9V16w5/opn9Op\nlxPbdlTdD+oaR1DXuKrprLISYkKqT7RGNQslq6z0jOuZEteNbSnfN2jZ8xERZSYro/pTLzvLSUSU\n944XEmrigSfDq6YnDM2kfUfvlzI0zET/wYF89tFJ4hKt+CImyszx9OpM6RkOYqK8ny8s1MSaVW2q\nprsPTKNzrIWyck37GAt9e7k/sadNDGHFs/4pOlFRJjLSq3sRmZlOok7bVqGhJp5cUX0ydujgXDrG\nuit0ZKT739atTYwdF8S3e+0MGOBbz7BtpJnsGtsqJ9NB20izV5uQUBP3Lm9bNX3VsOPEeF4/h0Oz\nZH4O46aFMnxMiE9ZTsnNLyGybfW+GtEmlLx873214mT1h9N/v/kRi8VEWGhQvQ+rTuSlUJSXUq+2\nP/WejvLczqrllDFVt5oFB+DbvCw6NW9Ju9DmWE0mJsd1Z+exI7XWEWYNYGBUB3YcPXLey56vpF4B\nHE91kJHmwG7TvPtWBSNGBXu1KSl2Ybe7T0a8samUvoMCaRZiorDASUmx+/Di5EnNF5+cpFOc758r\nfXsH8kOqg2NpDmw2zetby5g41jtTUY1ML20sYfjgQEJDTES2NdM+xkxyinun3vXJSbr7WARP6dXb\nSmqqk7Q0Jzab5q1tJxk9JsirTXGNXK9uLGfgICshISYqKjRlZe5tVV7u4uOPK+na1fdt1b1XIGlH\n7WSl2bHp5S/CAAAWzklEQVTbNDvfKmPY6GZebUqLXTg8mbZtKqb3wKCqHs2jd+bRKcHKNTf656oV\nwPdHsmgX3ZLIts2xWExcMaw7n37pva+2alGdsXtCFKC8Co5S536jtWwTR2y3MVW3c/nJ9nSUUq8C\nI4HWSqljwDKt9cvnsw6X1tz/+U7+Ou5qz2XvfRw5UcDMbr3QWrPp0D4AxnRK4OO0H6l0Oupc1ldm\ns+LuB1sx//q8qkvmXRKs/H1jKSi4amYoPx6xs/QPhZgUxCVaWfZEKwDyclwsXey+rK1dMGZSMMOv\nCK7jGeuXaeUj4UyekeW5ZB5Kt4QAXvxrCUrBvFlhHEq28Zv/y8NkUnTvauW5FdW9nicfas3c23Kx\n2zWdY61ePSJfcz30cHNmzSyoumSekGDhb38rRwHXzWrGkWQHixcVYTJBQqKF5U+638y5uU5uvukE\nSoHDAVdOC+IXI3w/f2I2KxY/0JqFN2SjXe5L5p3iA3hzYzFKwdSZzUk9YuPh2/MwKeicaOWex929\nnn1fnWTHm6V06RrAnAnpKAW33NGKQSOb1fGs5+Zyaf68ZicrHrjac8l8H0fTCpgy1r2fv7VjHyOH\ndmXquF44nC5sNgd/fPKtquWXLp5In0s60jwsiNdfvIWXNn3K9n//r8F5lPb36fsmRCmlO77wRGPH\n8CK/BlE/eS7/XS3xl+MO3wu4v939+/mNHeGM/rPtTrTWZ+wc/dQPr4QQPzFSdIQQhpKiI4QwlBQd\nIYShpOgIIQwlRUcIYSgpOkIIQ0nREUIYSoqOEMJQUnSEEIaSoiOEMJQUHSGEoc5ZdJRS8UqpoWd4\nfKhSKu5MywghxLnU1dP5M1B8hseLPfOEEOK81FV0IrXW+09/0PNYpwuSSAhxUatrIJWW55jX9AYX\nOYPEm/3zW1T+MmXt7xs7Qi2bRj/X2BFq6Wo1193IYMs6923sCLUE0rT27/qoq6fzlVLqN6c/qJS6\nCfj6wkQSQlzM6urpLAT+qZS6juoi0w8IAKZdyGBCiIvTOYuO1jobGKKUuhzo6Xn4ba31Bxc8mRDi\nolSvwXG11ruAXRc4ixDiZ0D+c6AQwlBSdIQQhpKiI4QwlBQdIYShpOgIIQwlRUcIYSgpOkIIQ0nR\nEUIYql7/OdBoSqlxuIfOMAHrtNaPnza/K/AycBlwr9Z6ZUOfq9/Y3sxfNQeTSfHuSx+w5YmtXvMH\nT+7H7Aeno10ah93J84tf4bvPDtEuIZolmxehtUYpRXSXSF65fzNvPr29oVGqjGjfifsHXYFJKbYc\n2s/z+7y/1HfzJf25Mq47GrCYTMS3bE2fvz1Dsa2yzmUb6osPy3nqwQK01ky6JoxZ872/C1xS5ORP\nd+aRcdRBQJDi3uVt6JwQQE6mg4cW51KQ58Rkgikzwrh6bgu/ZAL4966T3LesCJdLM+vaEH7/uzCv\n+UVFLn7/h0JSUx0EBSlWr2xF10QrAMXFLhbeXsjBQw5MJli9ohV9LwvwOVNT3KeaUialtfb17/Er\npZQJOAz8EsgAdgMztNbf12jTBogFrgQKz1Z0lFJ6lLrqXM/Fy4dWc+eoB8jPKOTZLx/jkWtXcfxQ\nRlWbwOAAKitsAHTq2ZGlWxYxL2lRrfVsOv48CwbdS25a/jn/vsNrB5xzvgI+vPomZm5/jeyyUrZd\neT0LPniLlKKCM7a/okMX5vXsx3XbXzvvZU+p61vmLpdmxuVprN4YRZtIC/OmZPDg022Jja9+gz77\naAHNQhRzf9+Koyk2Vt6fz1Mbo8nPcZCf6yQxKZDyMhc3Ts7g8bURXsueSVer7ZzzT+UaODybf2xp\nQ1SkmdETcnnhuVYkxFur2vzx4SJCQxS3L2pO8hE7d91XxD+2tAHgtoWFDBkcwMzpITgcmooKTVjY\n2Tv/M9oPrjNTY+xTTTHTTv13tNbqTPOa4uHVACBZa31Ua20HNgNTazbQWudprb8GHL48UbcB8aQn\nZ5JzLA+nw8muLZ8yZGp/rzanXgiA4NAgXK7aRfqyUZeQkZLt884B0LttNKnFhaSXFuPQLt5KOcjo\n2Piztp8a151tKQcbtGx9HdhbSYdOVqLaW7FYFaMmh/Cf98u92qQm2+g7xD3aSWxcAJlpDgrznbSO\nsJCYFAhAsxATneKs5GY7fc4E8M0eO106W+jQ3oLVqpg2NZjt7530anPosJ3hw9zPnxBv5Xiag7x8\nJyUlLr74spKZ00MAsFjUOQtOfTXFfaqpZWqKRacdcLzGdJrnMb9r3S7cawPmpeXTul14rXZDpvZn\n3XereGjbXayY95da80dMH8quzZ/4JVNUSBgZZSVV01nlpUSFhJ6xbaDZwoj2ndmeevi8lz0fudlO\nImKqj8Qjoi21Ckd89wA+ercMcBep7AwHuZnenwmZx+0kH7DRo3egz5kAMrOcxMRUj7sTHW0mM8s7\nV88eVv71jrsQfbPHRlq6k4xMJ0ePOQkPN7FgUSGXj81h0Z2FVFT43utvivtUU8vUFItOk/PZ1t3M\nS1rEsmnLmfPQtV7zzBYzgyf34+PXvzA816iOcezOTqPYVmn4c59u1vyWlBS5mDsxnTc2FJPYIxCT\nubp3XV7m4r5bc1i4LJxmIcbtdr+/LYyiIheXj81h3SulXNLTitmkcDg1+/bbuXFOCLvei6BZkOKp\nZ0vqXqGfNMV9yqhMTfFEcjrQscZ0e89jDZKiv6u634q2hKuIqun89AIiOrSpmm7TvjX56Wc///Hd\np98T3SWCsFahlBSWAjBgfB+Sv/6BorwzDSV9/rLKSogJaV41HdUslKyy0jO2nRLXjW0pVae6zmvZ\n89E20kx2enWvJSfTQdtI75H9QkJN3Lu8bdX0VcOOE9PRvXs5HJol83MYNy2U4WNCfM5zSnSUmfT0\n6p5NZqaT6CjvXGGhJlavbFU1fdmgLGJjzZSXa9rFmOnTy31uafKkYFY/6/u2aor7lBGZCnQOheTW\nK09T7OnsBuKVUrFKqQBgBrDtHO3PeLLqlDiVVHWrWXAADu1OISY+ioiObbBYLVw+fSifb/vKq010\nl8iq+/F9OmMJsFS9EACXz/BfNxjg27wsOjVvSbvQ5lhNJibHdWfnsSO12oVZAxgY1YEdR4+c97Ln\nq3uvQNKO2slKs2O3aXa+Vcaw0c282pQWu3DY3Ycn2zYV03tgUFWP5tE78+iUYOWaG/131QqgT28r\nP6Y6OJ7mwGbT/HNrBePGBHm1KS52Yffk2rCxjCGDAgkNMRHR1kxMjJkjKXYAPv6kkq6Jvn8GN8V9\nyohM4SrC6712Lk2up6O1diqlbgN2UH3J/KBS6hb3bL1WKRUJfAWEAS6l1P8BPbTW5/VR5XK5eGbB\nOh57bykmk2L7Sx9w7Pt0Jt48Cq3hnRd2MvzXAxl1/QgcNge2ChsPT19VtXxgcAB9Rl3KqlvW+O3v\nd2nN/Z/v5K/jrvZc9t7HkRMFzOzWC601mw7tA2BMpwQ+TvuRSqejzmV9ZTYrFj/QmoU3ZKNd7kvm\nneIDeHNjMUrB1JnNST1i4+Hb8zAp6Jxo5Z7H3b2efV+dZMebpXTpGsCcCekoBbfc0YpBI5vV8az1\ny/XYwy256tr8qkvmiQlWXvlrGUrB7FkhHE528LuFhZhM0C3RwlMrqns9jz7Ykt8uKMRhh9hYM0/X\n6BE1VJPcp5pYpiZ3ydyf6rpk3hjqumTeGJrmwOx1XzI3Wn0umQu3n9olcyHERUyKjhDCUFJ0hBCG\nkqIjhDCUFB0hhKGk6AghDCVFRwhhKCk6QghDSdERQhhKio4QwlBSdIQQhpKiI4QwlBQdIYShpOgI\nIQwlRUcIYSgZT0cI4Xcyno4QosmQoiOEMJQUHSGEoaToCCEMJUVHCGEoKTpCCENJ0RFCGEqKjhDC\nUFJ0hBCGkqIjhDCUFB0hhKGk6AghDCVFRwhhKEtjPbFSahzwZ9yFb53W+vEztFkNjAfKgLla6z2e\nx9cBk4BsrfWlvuToN7Y381fNwWRSvPvSB2x5YqvX/MGT+zH7welol8Zhd/L84lf47rNDtEuIZsnm\nRWitUUoR3SWSV+7fzJtPb/cljmS6CHJJpnNrlKEtlFIm4DDwSyAD2A3M0Fp/X6PNeOA2rfVEpdRA\n4Cmt9SDPvGFAKbDhXEWnrqEtlFK8fGg1d456gPyMQp798jEeuXYVxw9lVLUJDA6gssIGQKeeHVm6\nZRHzkhbVWs+m48+zYNC95Kbln+fWkEwXUy7J5NYUh7YYACRrrY9qre3AZmDqaW2mAhsAtNb/BVoo\npSI9058Ahb6G6DYgnvTkTHKO5eF0ONm15VOGTO3v1ebUCwEQHBqEy1W7SF826hIyUrL98kaSTD/t\nXJKpbo11eNUOOF5jOg13ITpXm3TPY9n+CtG6XbjXBsxLy6frgPha7YZM7c+8P82kRdvmLJn0aK35\nI6YPZdfmTySTgZmaai7JVLeL/kRyiv6u6lagcxq0js+27mZe0iKWTVvOnIeu9ZpntpgZPLkfH7/+\nhT/iSqafSa6LLVOBzvF6r51LY/V00oGONabbex47vU2HOtrUKU4lnXVefnoBER3aVE23ad+a/PSC\ns7b/7tPvie4SQVirUEoKSwEYML4PyV//QFFe8flGk0wXYa6fa6ZwFUE4EVXTP+qDZ11/Y/V0dgPx\nSqlYpVQAMAPYdlqbbcANAEqpQcAJrXXNQyvluTXYod0pxMRHEdGxDRarhcunD+XzbV95tYnuEll1\nP75PZywBlqoXAuDyGf49ZJBMP+1ckqlujdLT0Vo7lVK3ATuovmR+UCl1i3u2Xqu1fkcpNUEpdQTP\nJfNTyyulXgVGAq2VUseAZVrrl883h8vl4pkF63jsvaWYTIrtL33Ase/TmXjzKLSGd17YyfBfD2TU\n9SNw2BzYKmw8PH1V1fKBwQH0GXUpq25Z49P2kEwXTy7JVDf5NQghhN81xUvmQoifKSk6QghDSdER\nQhhKio4QwlBSdIQQhpKiI4QwlBQdIYShpOgIIQwlRUcIYSgpOkIIQ0nREUIYSoqOEMJQUnSEEIaS\noiOEMJQUHSGEoaToCCEMJUVHCGEoKTpCCENJ0RFCGEqKjhDCUFJ0hBCGkqIjhDCUFB0hhKGk6Agh\nDCVFRwhhKCk6QghDSdERQhhKio4QwlCWC/0ESqlxwJ9xF7h1WuvHz9BmNTAeKAPmaK33nmtZpdRV\nwB+B7kB/rfU3Dc3Xb2xv5q+ag8mkePelD9jyxFav+YMn92P2g9PRLo3D7uT5xa/w3WeHaJcQzZLN\ni9Bao5Qiukskr9y/mTef3t7QKJLpIsklmc5Naa19/XvOvnKlTMBh4JdABrAbmKG1/r5Gm/HAbVrr\niUqpgcBTWutB51pWKdUVcAFrgNvPVnSUUnqUuupc+Xj50GruHPUA+RmFPPvlYzxy7SqOH8qoahMY\nHEBlhQ2ATj07snTLIuYlLaq1nk3Hn2fBoHvJTcs/z60kmS6mXJLJbaf+O1prdaZ5F/rwagCQrLU+\nqrW2A5uBqae1mQpsANBa/xdooZSKPNeyWutDWutk4Ix/VH11GxBPenImOcfycDqc7NryKUOm9vdq\nc+qFAAgODcLlql2kLxt1CRkp2X55I0mmn3YuyVS3C3141Q44XmM6DXcxqatNu3ou65PW7cK9NmBe\nWj5dB8TXajdkan/m/WkmLdo2Z8mkR2vNHzF9KLs2fyKZDMzUVHNJpro1xRPJPvVeTpeiv6u6Feic\nBq3js627mZe0iGXTljPnoWu95pktZgZP7sfHr3/hj7iS6WeS62LLVKBzvN5r53KhezrpQMca0+09\nj53epsMZ2gTUY9k6xamks87LTy8gokObquk27VuTn15w1vbfffo90V0iCGsVSklhKQADxvch+esf\nKMorPt9okukizPVzzRSuIggnomr6R33wrOu/0D2d3UC8UipWKRUAzAC2ndZmG3ADgFJqEHBCa51d\nz2XBh57Rod0pxMRHEdGxDRarhcunD+XzbV95tYnuEll1P75PZywBlqoXAuDyGf49ZJBMP+1ckqlu\nF7Sno7V2KqVuA3ZQfdn7oFLqFvdsvVZr/Y5SaoJS6gjuS+Zzz7UsgFLqSuBpoA3wL6XUXq31+PPN\n53K5eGbBOh57bykmk2L7Sx9w7Pt0Jt48Cq3hnRd2MvzXAxl1/QgcNge2ChsPT19VtXxgcAB9Rl3K\nqlvW+LSdJNPFk0sy1e2CXjJvbHVdMhdCXBiNeclcCCG8SNERQhhKio4QwlBSdIQQhpKiI4QwlBQd\nIYShpOgIIQwlRUcIYSgpOkIIQ0nREUIYSopOPTV0WIwLSTLVj2SqPyNySdGpp0JyGztCLZKpfiRT\n/RmRS4qOEMJQUnSEEIa66Ie2aOwMQvxcnW1oi4u66Aghmh45vBJCGEqKjhDCUD/LoqOUGqeU+l4p\ndVgpdddZ2qxWSiUrpfYqpXrXtaxS6iql1P+UUk6l1GUG5+tT4/F1SqlspdQ+XzL4mlEp1VUp9ZlS\n6qRSavGFzHKOjIZsi4bkUEq1UkrtUEodUkq9p5Rq0dRyKKXu8exjB5VSY/wWRGv9s7rhLrRHgFjA\nCuwFup3WZjzwtuf+QOCLupYFugIJwAfAZY2RzzM9DOgN7GvkbdgG6As8BCxupNf6gm+LhuYAHgfu\n9Ny/C3isKeUAegB7cP94QyfP6638kePn2NNp0j917GM+tNafAIU+ZvA5o9Y6T2v9NeC4wFnOyqBt\n0dAcU4H1nvvrgSubWI4pwGattUNrnQok46df2P05Fp2z/YxxfdrUZ9nGyJd+AXKcixHb4WIXod2/\n74bWOgtq/FJd08hxwfaxn2PRaQi//tSxEGfQVP7vygXP8XMsOr781HF9lm3MfEYxYjtc7LJPHRIr\npaKAxvoG6NlyXLB97OdYdJr0Tx37mK/m81/I3ll9t0PNPI3lQm+L+jo9xzZgjuf+bGBrE8uxDZih\nlApQSnUG4oEv/ZKgMc/qN9YNGAccwn1y7G7PY7cAN9do8wzuM/bfUuNq1JmW9Tx+Je5j4AogE9je\nSPleBTKASuAYMLcxtiEQ6dkeJ4ACT5ZQg19nQ7ZFQ3IArYCdnm24A2jZ1HIA93j2sYPAGH/lkK9B\nCCEM9XM8vBJCNCIpOkIIQ0nREUIYSoqOEMJQUnSEEIaSoiOEMJQUHSGEoaToCCEMJUVH+J1Saqln\ngK+PlVKvKqX+oJS6SSn1pVJqj1LqdaVUkKfty0qpvyilPldKHVFKjfAMNnVAKfVSjXWWKKWe8AyU\ntkMp1V8ptcuzzCRPm1jPc37luQ1qrG0gzk6KjvArpVQ/YBpwCTAB6If7m8tvaK0HaK37AN8D82os\n1lJrPRhYjPs7Pyu01j2AS5VSl3rahAA7tdY9gVLcg4P9EviV5z64v6w4SmvdD/f3wZ6+cH+paChL\nYwcQF52hwFbtHtzLrpR6y/P4JUqph4GWuAvIezWWOdVmP5CltT7gmf4O96h1+4BKrfWOGu1Oaq1d\nSqn9uEcwBPcohms8w8s6cY/kKJoYKTrCCAp4BZiitf6fUmo2MKLG/ErPv64a909Nn9pH7ac9Xgmg\ntdZKqVNtFuEuWpcqpcy4v3wrmhg5vBL+9ikwWSkVqJQKBSZ5Hg8FspRSVuC6cyx/tmEozjU8xal5\nLXB/wx/cQ3+Y6xdZGEmKjvArrfVXuM/LfAu8jfvQ6ASwFPd4LP/BPVRC1SKnr6Ie92s9reffvwBz\nlFJ7gESg7HzziwtPhrYQfqeUCtFalymlgoGPgd9orfc2di7RNMg5HXEhrFVK9QACgVek4IiapKcj\nhDCUnNMRQhhKio4QwlBSdIQQhpKiI4QwlBQdIYShpOgIIQz1/5iHNf6H3y0SAAAAAElFTkSuQmCC\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x7f89a8540b00>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"scores = [score.mean_validation_score for score in grid_search.grid_scores_]\n",
"scores = np.array(scores).reshape(6, 6)\n",
"\n",
"# plot the mean cross-validation scores\n",
"mglearn.tools.heatmap(scores, xlabel='gamma', ylabel='C', xticklabels=param_grid['gamma'],\n",
" yticklabels=param_grid['C'], cmap=\"viridis\")"
]
},
{
"cell_type": "code",
"execution_count": 45,
"metadata": {
"collapsed": false,
"scrolled": true
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"gridsearch_failures\n"
]
},
{
"data": {
"image/png": "iVBORw0KGgoAAAANSUhEUgAAAsIAAAElCAYAAADuu4R+AAAABHNCSVQICAgIfAhkiAAAAAlwSFlz\nAAALEgAACxIB0t1+/AAAIABJREFUeJzs3Xd81EX++PHXbEkPLZBGSMCEQCiCSgfFgnTEcgp46ol4\nKsU7sd8JqOidCoL3U7Cg2FGwnIKnSPnCiWABFI4iNUBCyqZBCmmb3Z3fHxuSbEiym0ISzPvpYx/u\n7mdm35+3ZmZnZ2fno7TWCCGEEEII0dIYmvoEhBBCCCGEaAoyEBZCCCGEEC2SDISFEEIIIUSLJANh\nIYQQQgjRIslAWAghhBBCtEgyEBZCCCGEEC2SDISFEEIIIUSdde5k1kqputxONPW5K9lHWAghhBBC\n1JVSSpekRte6njksHq21qvRao4F/4ZysXa61fqHS8TbA20A0UAjcpbX+rfRYa+AtoBfgKD32c03n\nYKr1WQshhBBCCFGBXTvq/RpKKQOwBLgGSAF2KKVWa60PVij2d2CX1vpGpVQ3YCkwovTY/wO+0Vrf\nrJQyAX7uYsrSCCGEEEII0RwMAI5orRO01iXASmBipTI9gE0AWutDQGelVAelVCvgcq31O6XHbFrr\nXHcBZSAshBBCCCHqxYGu9a0KHYGTFR4nlT5X0f+AGwGUUgOASCAC6AJkKqXeUUr9qpRappTydXfe\nMhAWQgghhBD14qjDP3X0PNBWKfUrMBPYBdhxLve9FFiqtb4UKAAed/diskZYCCGEEELUi92DzRe+\n/6GIrT8W11QkGecM71kRpc+V0VrnAXedfayUOg4cA/yBk1rrnaWHPgMec3dOsmuEEEIIIYSoM6WU\nPp0cUet6bTsmuewaoZQyAodw/lguFdgOTNFaH6hQpjVQoLUuUUr9GRiqtb6z9Nh3wJ+11oeVUk8C\nflrrGgfDMiMshBBCCCHqxV71mt9a0VrblVKzgPWUb592QCl1r/OwXgbEAe8ppRzAfmBahZf4C7BC\nKWXGOUs81V1MmREWQgghhBB1ppTS6cnhta4X3DHlnH2EG5vMCAshhBBCiHrxZI1wcyQDYSGEEEII\nUS/1v5xG05CBsBBCCCGEqJeGWCPcFGQgLIQQQggh6sV+YY6DZSAshBBCCCHq50JdGiFXlhNCCCGE\nEC2SzAgLIYQQQoh6sdOku6DVmQyEhRBCCCFEvThkjbAQQgghhGiJZEZYCCGEEEK0SDIQFkIIIYQQ\nLZKjaa+UXGcyEBZCCCGEEPUiM8JCCCGEEKJFsl+gO/LKQFgIIYQQQtSLLI0QQgghhBAtkiyNEEII\nIYQQLZJdy9IIIYQQQgjRAjku0DXCF+ZZCyGEEEIIUU8yIyyEEEIIIepF1ggLIYQQQogWSdYICyGE\nEEKIFskhM8JCCCGEEKIlkgtqCCGEEEKIFkmWRgghhBBCiBbpQt0+TQbCQgghhBCiXuxyiWUhhBBC\nCNESyRphIYQQQgjRIjlkjbAQQgghhGiJLtQZ4QvzrIUQQgghRLNh16rWt6oopUYrpQ4qpQ4rpR6r\n4ngrpdQapdRupdRepdSdFY7NVkrtU0rtUUqtUEp5uTtvGQgLIYQQQogmp5QyAEuAUUBPYIpSqnul\nYjOB/VrrvsBVwCKllEkpFQ7cD1yqtb4Y56qHye5iytIIIYQQQghRLw20fdoA4IjWOgFAKbUSmAgc\nrFBGA4Gl9wOBLK21TSkFYAT8lVIOwA9IcRdQBsJCCCGEEKJeGuiCGh2BkxUeJ+EcHFe0BFijlEoB\nAoBJAFrrFKXUIiARKADWa603ugsoSyOEEEIIIUS9OFC1vtXRKGCX1jocuARYqpQKUEq1wTl7HAWE\nAwFKqVvdvZjMCAshhBBCiHrxZEb40M/ZHN6eXVORZCCywuOI0ucqmgo8B6C1jldKHQe6A52BY1rr\nUwBKqX8DQ4CPagooA2EhhBBCCFEvnmyfFjOwHTED25U9/nppYuUiO4AYpVQUkIrzx25TKpVJAEYA\n25RSIUAscAznKodBSikfoBi4pvT1aiQDYSGEEEIIUS+OBrjEstbarpSaBazHObBdrrU+oJS613lY\nLwOeBd5VSu0prfZo6SzwdqXUZ8AuoKT038vcxVRa63qfuBBCCCGEaJmUUvpfB66pdb0H4v4PrRtg\nBF0PMiMshBBCCCHqRS6xLIQQQgghWiR73XeBaFIyEBZCCCGEEPUiM8JCCCGEEKJFulBnhC/M4bsQ\nQgghhBD1JDPCQgghhBCiXmRphBBCCCGEaJE8ubJcc3TBDISVUrLhsbhgNPW+iCBtRlxYpM0IUTvN\noc1U5LhA1whfMANhgBHqD7WuE6/3E616noezaT4xJcfmFXOj/uw8nE3dxD2xuNZ1MrZ8S4crRte6\n3sbpC2td56xFi/J46KHAWtUJNQbUOd7TL2bx5MNBta5nsZ+pc8y65HiwpHblK3rvX5n86YH2ta63\nYFzt+1mAo+lbiAm+ok511/32zzrVOx9+z+8zh5cNqHO87DXraXPdyFrXWzPy5TrFe+2lHKbPbl3r\nerHmug9rnn3xNHMeblvret7KXKd4de2HAIxhR+pU73ySGWEhhBBCCNEiNcQllpuCDISFEEIIIUS9\n2C/Qjch+9wPhtnT43ceUHH8/MZsDv6iYRo85eLBXo8YbPsS3UeNB4+fYZ5Bfo8Zr5x/VqPGak5bQ\nP/l0i27UeP0GeTdqPIArhvg0arym6IfOJ5kRbqbaqeDffUzJ8fcTsznwb4KB8JAhjfumd+WQxh0k\nQuPn2FcGwo2mJfRPjT0Q7j+4cQelAFc08sC0Kfqh88khM8JCCCGEEKIlssuMsBBCCCGEaIlkaYQQ\nQgghhGiRLtQry12YZy2EEEIIIUQ9ndcZYaVUBPA+EAI4gDe11ufsrq2UehkYA+QDd2qtd3vy+v1G\n9WX6S3diMCi+fXsTqxasdjnu39qPh5fPICw6BGuhlRenvUbigSQAPji2lPycArRDYyuxc/+gvzW7\neJLj7ydHT53vNjOsaxSPj70Sg1J8/ss+ln+/0+X41KGXMb5PdzQak8HIRR3aMfS518krKnZbtyqb\nNxfx5FO5OBwwZbIfM2e6XgQjJ8fBQw9lcyLBjo+PYvGi1sTGOjenHzgoncBAhcEAZpPi6689uzjE\nt5vyeXBeBg4Nd01pxaOz2rkcz86xM212GscSSvD1MfDW4mB6dHP+kC0n186fH0pn/8FiDAbFWy+F\nMPDSmn+009g5bv8un1fnp+NwwJhJrZlyn2t+Z3LtLHw0jZQEK94+Bh5eEELnrt5kpJbw/EMWTmfa\nMRhg7KTW3DjVs4sHXDasK/c9Ng5lUKz7904+Xf69y/He/Trz5Cu3YUk6DcC2jfv5+I3/AuAX4M3s\n+TcQFROC1prFc/7Nob1JVcZRSi0HxgNpWuuLS59rC6wCooATwC1a65zSY38D7gJswF+11uureM1q\n67vjrq8YPKEff5o/qaw/eP3Bd9n/wyE6dg1jzsrZaK1RShF2UQjvzlvJl6+sbXYxh0d0Zt6gqzEo\nxapDe3l9z3aX4/f07s/10XFowGQwENMmiEs+XEKutdht3eps+28RC+dn43DADZP8mDq9lcvx3BwH\nTz1yiqREO94+iqcWtCU61oy1WHPXLemUWMFu14wY68d9D7SqJkq59ZsLeHTeKRwa/jQ5kIdmuV6o\nIzvHzn0PZnE8oQQfH8Xri9sTF+vc5SUn18GMhzP57WAJBgO8trg9Ay51/8PXuvZDh+OtTLnPggI0\ncCyhhPmPBnH/3W3cxmxqdrmyXJVswINa691KqQDgF6XUeq31wbMFlFJjgGitdVel1EDgdWCQuxdW\nSjHrlWk8OuJpslJOs3T78/ywegcnD6WUlZny9xs5uvs4T//hRSJiw7l/yTQeG/kMANqhefiqpziT\nne9RIo0dT3L8/eRYS+exzcCc8Vdz1zufkZ6bzyfTp7DpQDzHM0+XlXln2y+8s+0XAIZ368IdQy4l\nr6jYo7qVORyaOXNyWbWqHSEhRsaOy2TUKB9iYsq7nVdeOUPPXmbeeqsdR+NtPPFEDqtWOq+0ZDDA\nZ58G0aaN519cORyavzyRwYZPOhIeamLgmESuGxVA967lW5c99/JpLunlzedvh3PoqJX7/57O+k8i\nAHhgbgZjrvHjkzfDsNk0BYUOt/EaM0eHQ/PKk+m8+GEEQSEmZkxMZOi1AURGl+e3YukpYnp48/Tr\n4ZyMt/Lyk+ks/DACo0kxfU4HYnr4UJjv4L4JCfS7wt+lblWUUsx8YgKP3/U2WRm5vLxqBj9uOkDS\n8UyXcvt+OcFTsz48p/70v41n+5bD/OPBlRiMBnx8arwK1zvAKzg/DJ71OLBRa71AKfUY8DfgcaVU\nD+AWIA6IADYqpbpqrStfJrnK+jUmjWd9xa8b9/DjV84PhJ17RTJ31Wym9ZxN8pFUpl/2aNnrfHzy\ndbZ94X6Q2NgxFTB/8AhuXfsJaflnWHP97WxIOEp8zqmyMsv27mDZ3h0AXN3pIqb16keutdijulVx\nODTPzzvNGx91oEOIkT9el86V1/rSJab872L50ly69/Ri8bJWnIgv4bm52bzxUQe8vBVvruyAr68B\nu11z500ZDL3Sh959q/8bdjg0Dz5xim8+CSUsxMiwMSmMH+VLtwp9wsKXc+jTy4uVy4M5fLSE2X/P\n4utPQgF4ZF4Wo672ZcWy4NI+wf1VuOvTD8VGe/HLhsiy14m89DjXj/F3G7M5uFDXCJ/XpRFaa8vZ\nmSqt9RngANCxUrGJlHZ6WuufgdZKqRB3r919QAzJR1JJT8zEbrOzedU2hkzs71ImKi6C3Zv2AZB0\nOIWQzsG0bl/66VEplMHz/2mNHU9y/P3kWBvns8307hhKQtZpUrLzsDkcfLP3MFfHVb8l0riLu/H1\nnoN1qguwa1cJXbqYiIgwYTYrJl7ny7p1RS5lDh+xMWyoc3YlJtpE0kk7WVn20v8W4HD/nuNi+64i\nYrqYiepkxmxWTJoYyJp1rpdF/u1wMVcNc25b1C3GixMnbWRk2sjNs7P15yKmTnbOFplMilaBxmaV\n48H/FdGxs5mQCDMms+KqCYFs2+CaX8JRK5eUbsvUKdoLS1IJ2Vk22nUwEdPDObvt628gMsaLTIvN\nbcxuvSNITsgiPTUbu83Bd2v3MPjquHMLqnPbhZ+/Nz0vjWLDl78C4LA7KMgvrjaW1norUPnT1UTg\nvdL77wHXl96/DliptbZprU8AR4CqriFcXf0aedJXFBday+77BvjgqOJ/5qUjepMSn0ZGUlazi9m3\nQxgnck+TfCYXm3bwVfwBrq1h+8SJ0XGsiT9Qp7pn7dttJbKzifDSNjN6gi//3VDoUubYERv9S7cb\n7BxtJiXJxqnSNuPr6xy2WK0au01X9WfnYueuYqK7mIksjXfzRH/+s8413oEjJVw51Nk2YmPMJCTZ\nyMiyk5vnYNvPxdwx2Xmpc2ef4H7YVJ9+qKKNWwqIjjLTqWPdLuHc2BzaUOtbc9BoZ6GU6gz0BX6u\ndKgjcLLC42TOfeM/R1DHdi6NPDMpi6COrl89HNuTwLAbBwLQrX8MwZHtaR9RWkZrXlg/lyU/P8eY\nu69xe/6NHU9y/P3kWFcN3WZCWgWQmlPeGafl5BHSKqDKst4mI8O6dmbD/qO1rnuWxWInPLy8iwkL\nM2Cx2F3K9Ohh4pu1zoHjrl1WklPspKY6Z2GVgilTshg7LpMVKwrcpQdAcqqNTuHls7Edw0wkVxrs\n9enhzRffOHPZvquIxOQSklJtHE+00b6dgbsesNDv2kTufTiNQjczwo2dY6bFRnBY+Zti+1ATWZXy\ni+7uzfffOvM7uLuQ9JQSMiqVsSSVEP9bMXF93e/VGhTSigxL+UqCTEsO7YPP/To6rk8nln4+i/mv\n3kFktPNiDyERbcnNLuDBZ29kyacz+ctT1+PlXesvIoO11mng/KAInN1A19N2UF39GnnSVwAMmdif\n5ftf4pk1j7Fo2qvnHB8+aSibV271JGSjxwz1DyQlP6/ssaXgDKH+1fQJRhPDI7qw9sThWtetKN1i\nJ6RCGw0JNZJucW1nsXFmNn3rHKzu3W0lNcVOeqqzXTkcmklj0hjRL5VBl/vQq0/N32ikWOxEhJd/\noO0YbiKlUnvo3cOL1Wud7W/HrmJOJttITrFxItFGUDsD9zyQyeCRKcx8JNNtnwD164cq+mTNGSbf\nEOg2XnPhQNX61hw0yq4RpV/xfoZzDdcZd+WrE6/3l93vq7u7Lb/y+S+Y8f+m8urOFzi+N5Gju47j\nsDv/iB8YNodTlmxat2/FC+vnknggmf3bDrp5xeYVT3JsHjl+v3ULp8mod54VNVSbydjybdn94g5/\ngK6dPap3Vfdofk1IIa+o+tm7hjBrZgDznsxl1OgMunc306unGUPpuPLLL4IICTGSlWVn8pRTdO1q\nYsCA+l+d7bH72/HA3Az6XZtIrzgvLunljdGoKCnR/Lq3mFf+GUy/vj7MnpvBC0tO89QjQRdUjlOm\nt2Pp/HTuHZ9Al27exPTwwVDhW4zCfAdPz0hh5rxgfP0bZi7kyG8p3DFiIcVFJfQbFsu8l2/j7nEv\nYTQaiIkLZ+mzX3FkfzL3Pj6WW+4ezssL3uZUfkJdw9Xye4La1T/7PtNVh1Osu7l9sR9W7+CH1Tvo\nObQ7dz4zhcdHPVN2zGgyMnhCP5Y/vqKep9z0MUdERrMjLYlc6/ntEwDumhHIgqeymTw2jZjuZrr3\nNGMoHcsaDIpVa0M4k+dg9p+ziD9cQnRs/WZMH57VmofnnmLwyBR6xZnp08vL2SfYNLv3Wnnpn0Fc\n1sebR+Zl8eKSHOY+4tna+ppU1w+dVVKi+WpdPs/93dn//PeHAr77obC6l2sWZB/haiilTDjf0D/Q\nWq+uokgy0KnC44jS584RrXqW3S9OsRPcqfyHJe0jgshKdl2bVHimiEXTXit7/H78UlKPpQNwypIN\nQE5mLlu//JnuA2JqHEBlJZ9q1HhNEVNyrHu8dhUmmY7rAzXm7E5DtpkOV4wuu5/tHUBY6/LZhZDW\ngaTlVj3GHtM7tmxZBEBa7hmP654VGmokObl89iQ11UFoqOtSg4AAA4sXlf8IZNDgdKKinGVCQpz/\nDgoyMma0D7t2W90OEjuGmUhMLp9VSU610THUtZsLDDCw/KXylSTRA45zUZSZ/AIHncJN9CudJb1p\nfAALlla/BropcmwfaiI9paTscabFRlCl/PwCDDyyILTs8R8vP0Z4pHOgYLdpnp6RwogbWjF0pPvZ\nO4CstFyCw8p/XNQ+tDWZ6bkuZYoKyr+u37n1MCbTBAJa+5KZlkOGJYcj+51/nlvX7+fmaVfQzj/K\n5Up08Zk1zl6mKaVCtNZpSqlQIL30eU/bQXX1q3T2fcaU4k+nyPKXr6qvqGj/toOEXRRMYNsA8k47\n28aAMZdw5Jdj5GTmVluvIk/6p4aMacnPI9y/fHY/1C8AS37V7fq66O6siS/vE2pTt6LgUCOWlPI2\nmmaxExzq+oHMP8DA0y+Wz4SPHZpKRKTr33lAoIH+g7354buiGgfC4aFGTlbsE1JshFfRJ7zxUvl/\n97iBSXSJMpFfoIkIN3FZH+cyjRvG+bNoqfvfWdanHzpr7aZ8Lr3Ymw7tnfWuHOLnciW6+YtqXovd\nFJrLUofaaoyzfhv4TWv9/6o5vga4A0ApNQjIPvs1Vk0O7YgnPCaU4Mj2mMwmrpo0lB/XuP6K3a+V\nH0aT841mzN3XsPe73yjKL8Lb1wsff+ebnY+fN/2u7cOJfYnNKp7k+PvJsQ7OS5vZl5xGVFAbwtsE\nYjYaGNs7ls0Hj51TLsDbi/5dIth0IL7WdSvq29fMiRM2kpJsWK2a1WsKGTnS9dfWubkOSkqcE3Qr\nVhQwaJAX/v4GCgs1+fnOAWZBgYPvthTTvZv7WZ/+fX2IP1FCwskSrFbNqtV5TKg04MvJtZfFfPPD\nHK4Y7EuAv4GQDiY6hZs5HO8c1G3aWkCP2JoH3o2dY7eLfUhOKCEtqYQSq2bzV3kMGeH6Q5ozuXZs\npfG+/jibiwf6lc38LnzUQmRXL27ycLcIgMP7kgiPDCI4rA0ms5HhYy7mp82uHzbbBJWfQ2zvCJRS\nnMkpJDsrnwxLDh2jnLNafQdGkxhf4zgUnL/hqji1tAa4s/T+n4DVFZ6frJTyUkp1AWKAqn4dVl39\nGnnSV4RdVD6QibmkCyYvU9mAFOCqyZ4vi2iKmP/LtNC5VRs6BrTCbDAwITqOjYlHzykXaPZiYGgn\n1iccrXXdynr28eLkCRspSTZKrJpvvypk+AjXSxvnVWgzn398hssGeePnb+D0KTt5uc42U1Sk+Wlr\nEZ2ja57Pu6yvN8dO2EgsbaOfrs5n3CjXeDkV4r29Io/LB3uX9glGIsKNHIl3fvjcvLWIOA9mn+vT\nD5218su8C2pZBDh/LFfbW3NwvrdPGwr8EdirlNqF8yupv+PcxkZrrZdprb9RSo1VSh3FuRXUVE9e\n2+FwsOT+5Ty/bi4Gg2Lt25tIPJjMuHtGoDV88+ZGouI68si7s9AOByf2J7HobucsX9uQNjz170fQ\nWmM0Gfm/j77nlw17mlU8yfH3k2NtnNc2ozXP/mcTb/7pRucWaL/u51jGKW7p3xut4dOdewG4pkcM\nW48kUGyzu61bE6NR8eyzrZhy66myrcW6djXzwYf5KBS33ebHkSM2HpidjcEAsbFmFr3onHnMyLAz\n7e7TKAV2G9xwgy/Dh7vfsshoVLz8jw6MnpKMw+Hctigu1os33s9BKbjn9tYcOGJl6l/TMCjo0c2b\ntxaXz+b/69kO3D7TQolN0yXSzNv/qvk3iI2do9GouP/pYB69IwmtYcwtrYmK8earj7JRwPhb25AY\nb+WFhywYDIqorl48/IIzh307C/m/1Xl06ebNveMSQMG0R9ozYHjNv0h3ODRL//EV/3zzztLt037h\n5LEMxt7cHw2s/XQHl4/sxbhJA7HZ7FiLSvjnQyvL6r/23H94bMEtGE1GLCdPsWjO59XGUkp9BFwJ\nBCmlEoEngeeBT5VSdwEJOHeKQGv9m1LqE+A3oASYcXbHCKXUm8BrWutfgReATyrXd8eTvuLymwYy\n4vbh2Kw2rIVWnp30Ull9b18vLhlxMS/d+4Yn4ZokpkNr5v24kQ9G31y6Bdoejmaf4tbufdBa8/Eh\nZ/82snNXtiQdp9huc1vXHaNR8fj8tky/PbNs+7SLupr5bMUZUPCHWwM4frSEuQ+dxqAgOtbMkwuc\nH9wy0x3MfdC5DZp2wMjxvlx+ta/beIv/0Y4Jky2l26c5d29464M8lIJptwVy6IiVP/81E4NBEdfN\nzGuLymeHX3wmiKmzMigp0XSJMrvMHNcUsz79UEGBg//bUsgbC93+BrpZaS5rfmtLnbvTTPOklNIj\n1B+a+jSEcGuj/gytm/6jrlJKxz2xuNHibZy+sNFiAYQaPftqvyFZ7HVerl0nB0saf0ZowbjG72fX\n/fbPZtNmfs/vM4eXVbWpxvm1ZuQ526CfV7Hmxr9grrdq/F0djGFHmkWbOUsppSf/+Oda11s5+M0m\nz0MusSyEEEIIIerlQl0jLANhIYQQQghRL81lzW9tXZjDdyGEEEII0Ww01D7CSqnRSqmDSqnDpVeD\nrHy8lVJqjVJqt1Jqr1LqTk/rVkUGwkIIIYQQoskppQzAEmAU0BOYopSqfOGImcB+rXVf4CpgkVLK\n5GHdc8hAWAghhBBC1EsDbZ82ADiitU7QWpcAK3FeJr0iDZz9JXEgkKW1tnlY9xyyRlgIIYQQQtRL\nA60Rrnzp9CScA9yKlgBrlFIpQAAwqRZ1zyEDYSGEEEIIUS+eDIQzdyWRtavKC6HWxihgl9b6aqVU\nNLBBKXVxXV9MBsJCCCGEEKJePBkIt+vbiXZ9yy9dfuTdHZWLJAORFR5Xden0qcBzAFrreKXUcaC7\nh3XPIWuEhRBCCCFEvTTQrhE7gBilVJRSyguYjPMy6RUlACMAlFIhQCxwzMO655AZYSGEEEIIUS8N\nsUZYa21XSs0C1uOcrF2utT6glLrXeVgvA54F3lVK7Smt9qjW+hRAVXXdxZSBsBBCCNECxN6zvdFj\nXrfsL40ar7Ev6QwQ2/hXWG6WGuqCGlrrb4FulZ57o8L9VJzrhD2q644MhIUQQgghRL1cqFeWk4Gw\nEEIIIYSoFxkICyGEEEKIFknLQFgIIYQQQrRE1ewC0ezJ9mlCCCGEEKJFkhlhIYQQQghRL7JGWAgh\nhBBCtEiyRlgIIYQQQrRIMiNcBaXUcmA8kKa1vriK462AD3FeG9oILNJav+vp6/cb1ZfpL92JwaD4\n9u1NrFqw2uW4f2s/Hl4+g7DoEKyFVl6c9hqJB5IA+ODYUvJzCtAOja3Ezv2D/tbs4kmOv58cPXW+\n28ywrlE8PvZKDErx+S/7WP79TpfjU4dexvg+3dFoTAYjF3Vox9DnXievqNht3aps3lzEk0/l4nDA\nlMl+zJwZ4HI8J8fBQw9lcyLBjo+PYvGi1sSW7k4/cFA6gYEKgwHMJsXXX7f3KMdvN+Xz4LwMHBru\nmtKKR2e1czmenWNn2uw0jiWU4Otj4K3FwfTo5u08n1w7f34onf0HizEYFG+9FMLAS32aVY7bv8vn\n1fnpOBwwZlJrptznmt+ZXDsLH00jJcGKt4+BhxeE0LmrNxmpJTz/kIXTmXYMBhg7qTU3Tm3rNh7A\nZcO6ct9j41AGxbp/7+TT5d+7HO/drzNPvnIblqTTAGzbuJ+P3/gvAH4B3syefwNRMSForVk8598c\n2pvkUdyKqmobSqm2wCogCjgB3KK1zqmi7mjgX5RfbeoFT2K66ysGT+jHn+ZPKusPXn/wXfb/cIiO\nXcOYs3I2WmuUUoRdFMK781by5Strm13MpshxeERn5g26GoNSrDq0l9f3uF7o457e/bk+Og4NmAwG\nYtoEccmHS8i1FrutW5Vt/y1i4fxsHA64YZIfU6e3cjmem+PgqUdOkZRox9tH8dSCtkTHmrEWa+66\nJZ0SK9jtmhFj/bjvgVbVRHG1fnMBj847hUPDnyYH8tCs1i7Hs3Ps3PdgFscTSvDxUby+uD1xsV4A\n5OQ6mPHShmZ9AAAgAElEQVRwJr8dLMFggNcWt2fApd4exW1KMiNctXeAV4D3qzk+E9ivtb5OKdUe\nOKSU+lBrbXP3wkopZr0yjUdHPE1WymmWbn+eH1bv4OShlLIyU/5+I0d3H+fpP7xIRGw49y+ZxmMj\nnwFAOzQPX/UUZ7LzPUqkseNJjr+fHGvpPLYZmDP+au565zPSc/P5ZPoUNh2I53jm6fLg237hnW2/\nADC8WxfuGHIpeUXFHtWtzOHQzJmTy6pV7QgJMTJ2XCajRvkQE1Pe7bzyyhl69jLz1lvtOBpv44kn\ncli1MggAgwE++zSINm08/02vw6H5yxMZbPikI+GhJgaOSeS6UQF07+pVVua5l09zSS9vPn87nENH\nrdz/93TWfxIBwANzMxhzjR+fvBmGzaYpKHS4jdeYOTocmleeTOfFDyMICjExY2IiQ68NIDK6PL8V\nS08R08Obp18P52S8lZefTGfhhxEYTYrpczoQ08OHwnwH901IoN8V/i51q6KUYuYTE3j8rrfJysjl\n5VUz+HHTAZKOZ7qU2/fLCZ6a9eE59af/bTzbtxzmHw+uxGA04ONT58twVdU2Hgc2aq0XKKUeA/5W\n+lzF8zcAS4BrgBRgh1Jqtdb6YE3BPOkrft24hx+/cn4g7NwrkrmrZjOt52ySj6Qy/bJHy17n45Ov\ns+0L9wO2xo7ZJDkC8weP4Na1n5CWf4Y119/OhoSjxOecKiuzbO8Olu3dAcDVnS5iWq9+5FqLPapb\nmcOheX7ead74qAMdQoz88bp0rrzWly4x5X+Hy5fm0r2nF4uXteJEfAnPzc3mjY864OWteHNlB3x9\nDdjtmjtvymDolT707ltzm3E4NA8+cYpvPgklLMTIsDEpjB/lS7cK/dDCl3Po08uLlcuDOXy0hNl/\nz+LrT0IBeGReFqOu9mXFsuDSfki7/e/aHFyoM8LnddcIrfVWoPp3StBAYOn9QCDLkzd0gO4DYkg+\nkkp6YiZ2m53Nq7YxZGJ/lzJRcRHs3rQPgKTDKYR0DqZ1+9JPc0qhDJ7/T2vseJLj7yfH2jifbaZ3\nx1ASsk6Tkp2HzeHgm72HuTouutry4y7uxtd7DtapLsCuXSV06WIiIsKE2ayYeJ0v69YVuZQ5fMTG\nsKHOmY6YaBNJJ+1kZdmdiWpw1LL/376riJguZqI6mTGbFZMmBrJm3RmXMr8dLuaqYX4AdIvx4sRJ\nGxmZNnLz7Gz9uYipk50zNyaTolWgsVnlePB/RXTsbCYkwozJrLhqQiDbNrjml3DUyiVDnPl1ivbC\nklRCdpaNdh1MxPRwzm77+huIjPEi0+L+T6db7wiSE7JIT83GbnPw3do9DL467tyC6tx24efvTc9L\no9jw5a8AOOwOCvKLPU+4gmraxkTgvdL77wHXV1F1AHBEa52gtS4BVpbWq5EnfUVxobXsvm+AD44q\n/mdeOqI3KfFpZCRluQvZ6DGbIse+HcI4kXua5DO52LSDr+IPcG1UTLXlJ0bHsSb+QJ3qAuzbbSWy\ns4nw0jY6eoIv/91Q6FLm2BEb/Yc422jnaDMpSTZOlbZRX1/nMMlq1dhtuqo/83Ps3FVMdBczkaUx\nb57oz3/WucY8cKSEK4c622NsjJmEJBsZWXZy8xxs+7mYOyY7u3lnP3RhbPClde1vzUFT/9ddAvRQ\nSqUA/wP+6mnFoI7tXBpdZlIWQR1dvyI8tieBYTcOBKBb/xiCI9vTPqK0jNa8sH4uS35+jjF3X9Ps\n4kmOv58cG1id20xIqwBSc8oHTWk5eYS0CqiyrLfJyLCundmw/2it655lsdgJDy/vYsLCDFgsdpcy\nPXqY+Gatc+C4a5eV5BQ7qanOWVilYMqULMaOy2TFigKPckxOtdEpvHw2tmOYieRKg70+Pbz54htn\nLtt3FZGYXEJSqo3jiTbatzNw1wMW+l2byL0Pp1HoZka4sXPMtNgIDiufyWofaiKrUn7R3b35/ltn\nfgd3F5KeUkJGpTKWpBLifysmrm/Nyz4AgkJakWEpX22QacmhffC5Xw/H9enE0s9nMf/VO4iM7gBA\nSERbcrMLePDZG1ny6Uz+8tT1eHk36BeRwVrrNACttQUIrqJMR+BkhcdJpc/VyJO+AmDIxP4s3/8S\nz6x5jEXTXj3n+PBJQ9m8cqu7cE0SsylyDPUPJCU/r+yxpeAMof7V9ENGE8MjurD2xOFa1z0r3WIn\npEKfEBJqJN3i2q5j48xs+tY5UN2720pqip30VGc7djg0k8akMaJfKoMu96FXn5pngwFSLHYiwss/\nRHcMN5FSqQ327uHF6rXONr9jVzEnk20kp9g4kWgjqJ2Bex7IZPDIFGY+kum2H2ouHKha35qDph4I\njwJ2aa3DgUuApUqpmv+qa2Hl818Q0NafV3e+wHUzRnF013Ecducf1APD5jCj32M8Me45Js4YTc+h\n3S+4eE0RU3I8PznWwnltM2dd1T2aXxNSyCuq2+ydp2bNDCAnx8Go0Rm8+14BvXqaMZT2Sl9+EcS6\nbzvwwfttefe9fLZvt9b8Yh567P52nM5x0O/aRF59J5tLenljNCpsNs2ve4uZcWcbdm6IxM/XwAtL\napqc90xj5zhlejvO5Nq5d3wCX36QQ0wPHwwVvsUozHfw9IwUZs4Lxte/Yd4CjvyWwh0jFjLzpiWs\n+egn5r18GwBGo4GYuHC++vhnZt28lOIiK7fcPbxBYlaj0eeYfli9g2k9Z/PkDQu585kpLseMJiOD\nJ/Rjy6c/XdAxmyJHgBGR0exISyLXen77obtmBJKb42Dy2DRWvX+G7j3NGErHsQaDYtXaENb9FMbe\nXVbiD5c0SMyHZ7XmdLaDwSNTWPZuLn16eTn7Ibtm914r904N5Mf14fj5Kl5ccs6y92ZJa1XrW3PQ\n1LtGTAWeA9BaxyuljgPdgSp/hROv95fdD09qz9hOI8oet48IIivZdZ1Q4ZkiFk17rezx+/FLST2W\nDsApSzYAOZm5bP3yZ7oPiGH/tuqXjGUlnyK4U/kPWc53vKaIKTnWLd73W7dwmowa82xAtWozGVu+\nLbufYBrBH/r1Lnsc0jqQtNwzVVVjTO/YsmURAGm5ZwhrHVj2uKa6Z4WGGklOLp/JSE11EBrqutQg\nIMDA4kVtyh4PGpxOVJSzTEiI899BQUbGjPZh124rAwbUPBvTMcxEYnL5zEtyqo2Ooa7dXGCAgeUv\nhZQ9jh5wnIuizOQXOOgUbqJf6SzpTeMDWLC05oFwY+fYPtREekr5G3GmxUZQpfz8Agw8siC07PEf\nLz9GeKRzFtlu0zw9I4URN7Ri6EjPPj9lpeUSHFb+Q5/2oa3JTM91KVNUUD6A37n1MCbTBAJa+5KZ\nlkOGJYcj+5MB2Lp+PzdPu4JT+Qmcyk/wKL4baUqpEK11mlIqFEivokwyzh+XnhVR+lyVzr7PBCYZ\nubbT5WXPV9VXVLR/20HCLgomsG0AeaedbWPAmEs48ssxcjJzq61XkSf9U0PGbOx4AJb8PML9y79R\nCPULwJJfdV9yXXR31sSX90O1qXtWcKgRS0p5n5BmsRMc6voB0D/AwNMvls+Ejx2aSkSka7sKCDTQ\nf7A3P3xXRHRszevcw0ONnKzYD6XYCK+iH3rjpfL/9nEDk+gSZSK/QBMRbuKyPs6lGjeM82fR0hy2\n/FDIlh9cl101N7JGuHqq9FaVBGAEgFIqBIgFjlX3QtGqZ9nNsvMU4TGhBEe2x2Q2cdWkofy4xnUs\n4NfKD6PJ+UYz5u5r2PvdbxTlF+Ht64WPv/PNzsfPm37X9uHEvsQakzi0I75R4zVFTMmxbvHaqWCX\nv80G0GBtpsMVo8tuR+1mooLaEN4mELPRwNjesWw+eG7VAG8v+neJYNOB+LLn9iWneVS3or59zZw4\nYSMpyYbVqlm9ppCRI11/+Zyb66CkxDmJt2JFAYMGeeHvb6CwUJOf7xxgFhQ4+G5LMd27uf+RVf++\nPsSfKCHhZAlWq2bV6jwmVBrw5eTay2K++WEOVwz2JcDfQEgHE53CzRyOdw7qNm0toEdszQPvxs6x\n28U+JCeUkJZUQolVs/mrPIaM8HcpcybXjq003tcfZ3PxQL+ymd+Fj1qI7OrFTR7uFgFweF8S4ZFB\nBIe1wWQ2MnzMxfy02fXDZpug8nOI7R2BUoozOYVkZ+WTYcmhY5Tzx4F9B0aTGJ9OO/8oYoKvKLvV\nQuW2sQa4s/T+n4DVlSsAO4AYpVSUUsoLmFxar0pn23H+Tk1UTFSNfUXYReUfqGIu6YLJy1Q2QAS4\narLnSwbAs/6pIWM2djyA/2Va6NyqDR0DWmE2GJgQHcfGxKPnlAs0ezEwtBPrE47Wum5FPft4cfKE\njZQkGyVWzbdfFTJ8hK9LmbwKbfTzj89w2SBv/PwNnD5lJy/X2UaLijQ/bS2ic7T7+cPL+npz7ISN\nxNJ+4dPV+Ywb5Rozp0LMt1fkcflg79J+yEhEuJEj8c4PvJu3FhEXa+aKIb7Mebht2a05ulDXCJ/v\n7dM+Aq4EgpRSicCTgBegtdbLgGeBd5VSe0qrPKq1rv7jaAUOh4Ml9y/n+XVzMRgUa9/eROLBZMbd\nMwKt4Zs3NxIV15FH3p2Fdjg4sT+JRXc7Z/nahrThqX8/gtYao8nI/330Pb9s2NOs4kmOv58ca+O8\nthmtefY/m3jzTzc6t0D7dT/HMk5xS//eaA2f7twLwDU9Yth6JIFim91t3ZoYjYpnn23FlFtPlW0t\n1rWrmQ8+zEehuO02P44csfHA7GwMBoiNNbPoRefMY0aGnWl3n0YpsNvghht8GT7c/fZBRqPi5X90\nYPSUZBwO5/ZpcbFevPF+DkrBPbe35sARK1P/moZBQY9u3ry1uHxZ6b+e7cDtMy2U2DRdIs28/a+Q\nGqI1fo5Go+L+p4N59I4ktIYxt7QmKsabrz7KRgHjb21DYryVFx6yYDAoorp68fALzhz27Szk/1bn\n0aWbN/eOSwAF0x5pz4Dh/jXGdDg0S//xFf98887S7dN+4eSxDMbe3B8NrP10B5eP7MW4SQOx2exY\ni0r450Mry+q/9tx/eGzBLRhNRiwnT7Fozuc1xqtONW3jeeBTpdRdOD8k3lJaNgx4U2s9XmttV0rN\nAtZTvn3aAXfxPOkrLr9pICNuH47NasNaaOXZSS+V1ff29eKSERfz0r1veJxjY8dskhy1Zt6PG/lg\n9M2lW6Dt4Wj2KW7t3getNR8fcnZtIzt3ZUvScYrtNrd1a2I0Kh6f35bpt2eWbZ92UVczn604Awr+\ncGsAx4+WMPeh0xgURMeaeXKBc6CZme5g7oPOLdC0A0aO9+Xyq31rjHc25uJ/tGPCZEvp9mnOnWve\n+iAPpWDabYEcOmLlz3/NxGBQxHUz89qi8tnhF58JYuqsDEpKNF2izC4zx81Zc1nqUFtKN5chuRtK\nKT1C/aGpT0MItzbqz9DNoEdQSum4JxY3WryN0xc2WiyAUGODL412y2Kv+WvYhnawJNB9oQa2YFzj\n97Prfvtns2kz8j7TsA4vG9Co8daMfLlR4wHEmht/lalf+Ilm0WbOUkrp3mvm1rre3uueafI8mnqN\nsBBCCCGEuMA1o3F5rchAWAghhBBC1MuF+mM5GQgLIYQQQoh6uUBW2p5DBsJCCCGEEKJeZGmEEEII\nIYRokWQgLIQQQgghWqQLdGWEDISFEEIIIUT9yIywEEIIIYRomS7QKWEZCAshhBBCiHqRGWEhhBBC\nCNEiyfZpQgghhBAVxN6zvVHjXbfsL40aD5rmss7N0YU6I2xo6hMQQgghhBACQCk1Wil1UCl1WCn1\nWBXHH1ZK7VJK/aqU2quUsiml2iilIpRSm5RS+0uf9+hTkQyEhRBCCCFE/WhV+1slSikDsAQYBfQE\npiiluruE0fpFrfUlWutLgb8B/9VaZwM24EGtdU9gMDCzct2qyEBYCCGEEELUi9a1v1VhAHBEa52g\ntS4BVgITawg7BfjYGV9btNa7S++fAQ4AHd2dtwyEhRBCCCFE/eg63M7VEThZ4XES1QxmlVK+wGjg\n8yqOdQb6Aj+7O235sZwQQgghhKgXT34sV/jbMYoOHGuokBOAraXLIsoopQKAz4C/ls4M10gGwkII\nIYQQon482D7NN+4ifOMuKnuc88WmykWSgcgKjyNKn6vKZEqXRZyllDLhHAR/oLVe7f6MZGmEEEII\nIYSoJ61VrW9V2AHEKKWilFJeOAe7ayoXUkq1BoYDlQe7bwO/aa3/n6fnLQNhIYQQQghRPw2wRlhr\nbQdmAeuB/cBKrfUBpdS9Sql7KhS9HlintS48+4RSaijwR+DqCturjXZ32ud1aYRSajkwHkjTWl9c\nTZkrgZcAM5Chtb7K09fvN6ov01+6E4NB8e3bm1i1wPWDgX9rPx5ePoOw6BCshVZenPYaiQeSAPjg\n2FLycwrQDo2txM79g/7W7OJJjr+fHD11vtvMsK5RPD72SgxK8fkv+1j+/U6X41OHXsb4Pt3RaEwG\nIxd1aMfQ514nr6jYbd2qbN5cxJNP5eJwwJTJfsycGeByPCfHwUMPZXMiwY6Pj2LxotbExpoBGDgo\nncBAhcEAZpPi66/be5Tjt5vyeXBeBg4Nd01pxaOz2rkcz86xM212GscSSvD1MfDW4mB6dPN2nk+u\nnT8/lM7+g8UYDIq3Xgph4KU+zSrH7d/l8+r8dBwOGDOpNVPuc83vTK6dhY+mkZJgxdvHwMMLQujc\n1ZuM1BKef8jC6Uw7BgOMndSaG6e2dRsP4LJhXbnvsXEog2Ldv3fy6fLvXY737teZJ1+5DUvSaQC2\nbdzPx2/8FwC/AG9mz7+BqJgQtNYsnvNvDu1N8iiuJzxsMy8DY4B84M6zvyx3x11fMXhCP/40f1JZ\nf/D6g++y/4dDdOwaxpyVs9Fao5Qi7KIQ3p23ki9fWdvsYraEHIdHdGbeoKsxKMWqQ3t5fY/rRT7u\n6d2f66Pj0IDJYCCmTRCXfLiEXGux27rV2fbfIhbOz8bhgBsm+TF1eiuX47k5Dp565BRJiXa8fRRP\nLWhLdKwZa7HmrlvSKbGC3a4ZMdaP+x5oVU2U5qZhLqihtf4W6FbpuTcqPX4PeK/Sc9sAY23jne81\nwu8ArwDvV3WwdGp7KTBSa52slPLsnc5Zl1mvTOPREU+TlXKapduf54fVOzh5KKWszJS/38jR3cd5\n+g8vEhEbzv1LpvHYyGcA0A7Nw1c9xZns/GYZT3L8/eRYS+exzcCc8Vdz1zufkZ6bzyfTp7DpQDzH\nM0+XB9/2C+9s+wWA4d26cMeQS8krKvaobmUOh2bOnFxWrWpHSIiRseMyGTXKh5iY8m7nlVfO0LOX\nmbfeasfReBtPPJHDqpVBABgM8NmnQbRp4/kXVw6H5i9PZLDhk46Eh5oYOCaR60YF0L2rV1mZ514+\nzSW9vPn87XAOHbVy/9/TWf9JBAAPzM1gzDV+fPJmGDabpqDQ4TZeY+bocGheeTKdFz+MICjExIyJ\niQy9NoDI6PL8Viw9RUwPb55+PZyT8VZefjKdhR9GYDQpps/pQEwPHwrzHdw3IYF+V/i71K2KUoqZ\nT0zg8bveJisjl5dXzeDHTQdIOp7pUm7fLyd4ataH59Sf/rfxbN9ymH88uBKD0YCPj9mjXGvBXZsZ\nA0RrrbsqpQYCrwOD3L2oJ33Frxv38ONXzg+EnXtFMnfVbKb1nE3ykVSmX/Zo2et8fPJ1tn3hfgDV\n2DFbRI7A/MEjuHXtJ6Tln2HN9bezIeEo8Tmnysos27uDZXt3AHB1p4uY1qsfudZij+pWxeHQPD/v\nNG981IEOIUb+eF06V17rS5eY8r/95Utz6d7Ti8XLWnEivoTn5mbzxkcd8PJWvLmyA76+Bux2zZ03\nZTD0Sh969625nTYLF+glls/r0git9Vag+ndKuBX4XGudXFo+s4ayLroPiCH5SCrpiZnYbXY2r9rG\nkIn9XcpExUWwe9M+AJIOpxDSOZjW7Us/WSmFMnj+6aWx40mOv58ca+N8tpneHUNJyDpNSnYeNoeD\nb/Ye5uq46GrLj7u4G1/vOVinugC7dpXQpYuJiAgTZrNi4nW+rFtX5FLm8BEbw4Y6Z2Njok0knbST\nlWXHmRs4atmxbt9VREwXM1GdzJjNikkTA1mzzvVHw78dLuaqYX4AdIvx4sRJGxmZNnLz7Gz9uYip\nk1sDYDIpWgXWPLnQ2Dke/F8RHTubCYkwYzIrrpoQyLYNrvklHLVyyRBnfp2ivbAklZCdZaNdBxMx\nPZyz277+BiJjvMi02NzG7NY7guSELNJTs7HbHHy3dg+Dr447t6A6t134+XvT89IoNnz5KwAOu4OC\n/GLPE/aAB21mIqWDZK31z0BrpVSIu9f1pK8oLrSW3fcN8MFRxf/MS0f0JiU+jYykLLe5NHbMlpBj\n3w5hnMg9TfKZXGzawVfxB7g2Kqba8hOj41gTf6BOdc/at9tKZGcT4aX9wugJvvx3Q6FLmWNHbPQf\n4uwXOkebSUmycaq0X/D1dQ7NrFaN3aaralrNU8Nsn9bomnqNcCzQTim1WSm1Qyl1u6cVgzq2c2kA\nmUlZBHV0/Yrw2J4Eht04EIBu/WMIjmxP+4jSMlrzwvq5LPn5OcbcfU2ziyc5/n5ybGB1bjMhrQJI\nzSkfNKXl5BHSKqDKst4mI8O6dmbD/qO1rnuWxWInPLy8iwkLM2Cx2F3K9Ohh4pu1zoHjrl1WklPs\npKY6Z2GVgilTshg7LpMVKwo8yjE51Uan8PLZ2I5hJpIrDfb69PDmi2+cuWzfVURicglJqTaOJ9po\n387AXQ9Y6HdtIvc+nEahmxnhxs4x02IjOKx8Vql9qImsSvlFd/fm+2+d+R3cXUh6SgkZlcpYkkqI\n/62YuL41L/sACAppRYYlp8I55NA++NyvauP6dGLp57OY/+odREZ3ACAkoi252QU8+OyNLPl0Jn95\n6nq8vBt9s6LK+5Im48Em+570FQBDJvZn+f6XeGbNYyya9uo5x4dPGsrmlVs9OtHGjtkScgz1DyQl\nP6/ssaXgDKH+1fR7RhPDI7qw9sThWtetKN1iJ6RCPxQSaiTd4tqXxMaZ2fStc3C8d7eV1BQ76anO\nvsPh0Ewak8aIfqkMutyHXn0ugNlgaJAryzWFph4Im4BLca7dGg3MVUq5/7jloZXPf0FAW39e3fkC\n180YxdFdx3HYnX+MDwybw4x+j/HEuOeYOGM0PYe6vQpfs4vXFDElx/OTYy2c1zZz1lXdo/k1IYW8\nooadvats1swAcnIcjBqdwbvvFdCrpxlDaa/05RdBrPu2Ax+835Z338tn+3ZrzS/mocfub8fpHAf9\nrk3k1XeyuaSXN0ajwmbT/Lq3mBl3tmHnhkj8fA28sKSmiUbPNHaOU6a340yunXvHJ/DlBznE9PDB\nUOFbjMJ8B0/PSGHmvGB8/RvmLeDIbyncMWIhM29awpqPfmLey7cBYDQaiIkL56uPf2bWzUspLrJy\ny93DGyRmc/HD6h1M6zmbJ29YyJ3PTHE5ZjQZGTyhH1s+/emCjtkScgQYERnNjrQkcq3nt98DuGtG\nILk5DiaPTWPV+2fo3tOMofQLKINBsWptCOt+CmPvLivxh0vO+/m0ZE29j3ASkKm1LgKKlFJbgD7A\n0aoKx+v9ZffDk9ozttOIssftI4LISnZdt1N4pohF014re/x+/FJSj6UDcMri3H85JzOXrV/+TPcB\nMezfdrDaE81KPkVwp/LlmOc7XlPElBzrFu/7rVs4TUaNeTagWrWZjC3flt1PMI3gD/16lz0OaR1I\nWm7Ve42P6R1btiwCIC33DGGtAz2qe1ZoqJHk5PJZkNRUB6GhrksNAgIMLF7UpuzxoMHpREU5y4SE\nOP8dFGRkzGgfdu22MmBAzTMjHcNMJCaXz34mp9roGOrazQUGGFj+Uvk349EDjnNRlJn8Agedwk30\nK50lvWl8AAuW1jwQbuwc24eaSE8pf1PMtNgIqpSfX4CBRxaElj3+4+XHCI90ziLbbZqnZ6Qw4oZW\nDB3pfmYLICstl+Cw1hXOoTWZ6bkuZYoKygfwO7cexmSaQEBrXzLTcsiw5HBkv3Mb0K3r93PztCs4\nlZ/AqfwEj+I3gGSgU4XHNe1LWvY+E5hk5NpOl5c9X1VfUdH+bQcJuyiYwLYB5J12to0BYy7hyC/H\nyMnMrbZeRZ70Tw0Zs7HjNUVMS34e4f7l32CE+gVgya+677ouujtr4sv7vdrUrSg41IglpbwfSrPY\nCQ51/dDpH2Dg6RfLZ8LHDk0lItK1LQcEGug/2JsfviviVJadnT+d/wF6fVRzyeRmrzFmhBXV/5Rw\nNTBMKWVUSvkBA3FeG7pK0apn2c2y8xThMaEER7bHZDZx1aSh/LjG9Vfsfq38MJqcbzRj7r6Gvd/9\nRlF+Ed6+Xvj4O9/sfPy86XdtH07sS6wxiUM74hs1XlPElBzrFq+dCnb522wADdZmOlwxuux21G4m\nKqgN4W0CMRsNjO0dy+aD517hJ8Dbi/5dIth0IL7suX3JaR7VrahvXzMnTthISrJhtWpWrylk5Ehv\nlzK5uQ5KSpy954oVBQwa5IW/v4HCQk1+vnOAWVDg4LstxXTv5v5HVv37+hB/ooSEkyVYrZpVq/OY\nUGnAl5NrL4v55oc5XDHYlwB/AyEdTHQKN3M43jmo27S1gB6xNQ+8GzvHbhf7kJxQQlpSCSVWzeav\n8hgywt+lzJlcO7bSeF9/nM3FA/3KZn4XPmohsqsXN3m4WwTA4X1JhEcGERzWBpPZyPAxF/PTZtcP\nm22Cys8htncESinO5BSSnZVPhiWHjlHOHwf2HRhNYnw67fyjiAm+ouzWAGpqM2uAOwCUUoOAbK11\nWnUvdLYd5+/URMVE1dhXhF1U/oEq5pIumLxMZYM1gKsme75kADzrnxoyZmPH+//s3Xd4VFX6wPHv\nOyU9BAmkEQiQAoSq0hQVUUSqrBVx7VgWhF0buLuCimIBBf0J2FFXRcEOrCLqwqpgA4QFkRogIb2R\nQl4VnHgAACAASURBVEibmfP7Y4ZJIclMSCaN83meeZiZe+597xvmnHvmzJkzzRHzf9npdGvXns4B\n7TAbDEyM7s23SaeOGwSavRga1oWvEw/We9/q+gzw4ugRC6nJFsrLFF+tLWbEKN8qZQortQuffHCc\nc4d54+dv4FiulcICe7tQUqL4eVMJ3aJNDD7Ph2n3BTlvLVIrnSPs6eXT3gcuBoJFJAl4FPAClFLq\nNaXUXhFZD+wErMBrSqk/3Dm2zWZj6czlPLN+LgaDsO7NDSTtTWH8XaNQCr58/Vuiendm1tszUDYb\nR3Yns+gO+yjfWaHteezTWSilMJqM/Of9H9j2zc4WFU/n2HZyrA+P1hmlmP/vDbx+y1X2JdB+282h\nrFyuG9wPpeCjrbsAuDQ+hk0HEim1WF3uWxejUZg/vx1Tbsh1Li0WG2vm3feKEIQbb/TjwAEL996X\nh8EAcXFmFj1nb+CzsqxMveMYImC1wJVX+jJihHed8U7GfPHJToyZkoLNZl8+rXecF6++k48I3HVT\nEHsOlHHb3zIwCMT39OaNxSHO/V+Y34mb7kmn3KLo3tXMmy/U/Z2qps7RaBRmzgth9s3JKAVjrwsi\nKsabte/nIcCEG9qTlFDGggfSMRiEqFgvHlxgz+H3rcX8Z3Uh3Xt6c/f4RBCYOqsjQ0b41xnTZlMs\ne3ItT71+q2P5tG0cPZTFuGsHo4B1H23hwtF9GT95KBaLlbKScp56YKVz/5ef/jcPLbwOo8lI+tFc\nFs35pM549eVGnflSRMaJyEHsy6fd5s5x3WkrLrx6KKNuGoGlzEJZcRnzJz/v3N/b14uzR/Xn+btf\nrSNK88Y8I3JUikd++pZ3x1zrWAJtJwfzcrmh1wCUUnywz96Gj+4Wy/fJhym1Wlzu64rRKPz98bOY\ndlO2c/m0HrFmPl5xHASuuSGAwwfLmfvAMQwC0XFmHl1of3OanWlj7v252BQoG4ye4MuFl/i6iNhC\ntJA5v/UlqpWMZYuIGiXXNPdpaJpL36qPUe786LqHiYjq/fDiJov37bRnmywWQJjRvY/2G1O61fXH\noo1pb3mg60KNbOH4pm9n1//xVIupM/o607rtf21Ik8dcM/rFJo85MCq5RdSZk0RERb22sN77Jd41\nu9nzaO45wpqmaZqmaVpr1zrGVU+hO8Kapmmapmlaw7ScAep60R1hTdM0TdM0rWH0iLCmaZqmaZp2\nRtIdYU3TNE3TNO2MpDvCmqZpmqZp2hlJzxHWNE3TNE3TzkTSSkeEm+KX5TRN0zRN0zStxamzIywi\nMSIyvIbnh4tItOdOS9NapxOq5h9c0HVGOxMUleVy7MTRU54/duIoJ8qONcMZaZrWZFrpTyy7GhF+\nASio4fkCxzZN0yrZz47aNuk6o7V5+9K/xWQ49WeiTQZv9qZ/0wxnpGmaVjdXc4RDlVK7qj+plNol\nIt08ckaa1oqVUVrj87rOaGeCUksRgT4hpzwf6BNCcXl+M5yRpmlNpbXOEXbVEW5fxzbfxjwRTWsL\nyimva3ObrjOFtqaNF2Zs2niaaxZbSa3brDZLE56JdqaKu+vXJo95xWt/bfKYMLsZYrrQSleNcDU1\nYquI3Fn9SRG5A9jmmVPStNarHWfV+LyuM9qZoJ1POEePbT/l+eRjOwjyDWuGM9I0rcm00jnCrkaE\n7wU+E5E/U3ERHwR4AVd68sQ0rTWKYwAZHEVE/ouuM9oZplfYKHYc/YS0/N2087F3fAtK0rApG2d3\nubqZz07TNI9qIR3b+qqzI6yUygDOF5GRQF/H018opTZ4/Mw0rRXyFp+TjcE8dJ3RzjDepgCGdr+F\nnKIjHC/NAqBTYAzB/t2a98Q0TfO4tjpHGACl1EZgo4fPRdPaDF1ntDNZsH833fnVtDNNW+4Ia5qm\naZqmaVqtdEdY0zRN0zRNOxO16akRmqZpmqZpmlarNrp8mqZpmqZpmqY1CREZIyJ7RWS/iDxUS5mL\nRWS7iPwuIhurbTOIyG8issadeB4dERaR5cAEIEMp1b+OcoOBH4HJSqlP3T3+oMsHMu35WzEYhK/e\n3MCqhaurbPcP8uPB5dMJjw6lrLiM56a+TNKeZADePbSMovwTKJvCUm5l5rB/tLh4Ose2k6O7PF1n\nLoiN4u/jLsYgwifbfmf5D1urbL9t+LlMGNALhcJkMNKjUweGP/0KhSWlLvetyQ//LeGpeQXYbHDN\nZD/unB5QZXtBvo2HZ+WRlGjFx0d48tkgYuLMzu02m+KaCdmEhhl5+c0ObuX41YYi7n8kC5uC26e0\nY/aMqvvl5VuZel8GhxLL8fUx8MbiEOJ72n8WOL/Ayp0PZLJ7bykGg/DG86EMPcenzngbN5bw6GP2\nHKdc78c991TNMT/fxgMP5HHEkePiRUHEOXIcOiyTwEDBYACzSfjii44u8/v1uyJeejwTmw3GTg5i\nyl+q5ne8wMqzszNITSzD28fAgwtD6RbrTVZaOc88kM6xbCsGA4ybHMRVt9W87nV1514Qy18eGo8Y\nhPWfbuWj5T9U2d5vUDceXXIj6cnHANj87W4+ePW/APgFeHPf41cSFROKUorFcz5l365kt+K6Q0TG\nYP/5cgOwXCm1oNr2nsBbwDnAP5VSi909tqu24ryJg7jl8cnO9uCV+99m94/76BwbzpyV96GUQkQI\n7xHK24+s5PMl61pcTJ2jZ3IcEdmNR4ZdgkGEVft28crOqj/0cVe/wfwpujcKMBkMxLQP5uz3llJQ\nVupy3xarEaZGiIgBWApcCqQCW0RktVJqb6UyQcAyYLRSKkVEqjecfwP+ANq5E9PTUyPeApYA79RW\nwJH0M8D6+hxYRJixZCqzR80jJ/UYy359hh9Xb+HovlRnmSn/vIqDOw4z75rniIyLYObSqTw0+gkA\nlE3x4MjHOJ5X1CLj6RzbTo715ME6A3MmXMLtb31MZkERH06bwoY9CRzOPlYRfPM23tpsX/54RM/u\n3Hz+ORSWlLq1b3U2m+KJuQW89UEHQkKNXDsxm0tH+9AjpqLZeXXpcXr3MbPktQ4cSrDwxJx83vog\n2Ln9nTeLiI41cbzQvRbWZlP89eEsvvmwMxFhJoaOTeKKywPoFevlLPP0i8c4u683n7wZwb6DZcz8\nZyZffxgJwL1zsxh7qR8fvh6OxaI4UVz3z+XZbIo5cwpYtaoDoaFGxo3P5vLLfYiplOOSJcfp09fM\nG2904GCChYcfzmfVSnuOBgN8/FEw7du79+GczaZY8mgmz70XSXCoiemTkhh+WQBdoyvyW7Esl5h4\nb+a9EsHRhDJefDSTZ9+LxGgSps3pREy8D8VFNv4yMZFBF/lX2bcmIsI9D0/k77e/SU5WAS+ums5P\nG/aQfDi7Srnftx3hsRnvnbL/tH9M4Nfv9/Pk/SsxGA34+JhPKXO63LloAjnATOBP9Ty2y7bit293\n8tNa+xvCbn27MnfVfUztcx8pB9KYdu5s53E+OPoKmz9z3Zlp6pg6Rw/lCDx+3ihuWPchGUXHWfOn\nm/gm8SAJ+bnOMq/t2sJru7YAcEmXHkztO4iCslK39m2pGmmO8BDggFIqEUBEVgKTgMp1+gbgE6VU\nCoBSytkYiUgkMA54ErjfnYAenRqhlNoE1H6ltJsJfAxk1ufYvYbEkHIgjcykbKwWKxtXbeb8SYOr\nlInqHcmODb8DkLw/ldBuIQR1dLxBEEEM7s9naep4Ose2k2N9eLLO9OscRmLOMVLzCrHYbHy5az+X\n9I6utfz4/j35Yufe09oXYOeOcqK6m+gcacJsFsZN9OU/X1f9Cd6DBywMO98+Gtsj2kRKspXcHCsA\n6WlWvt9QyjXX+7md46/bS4jpbiaqixmzWZg8KZA1649XKfPH/lJGXmA/Zs8YL44ctZCVbaGg0Mqm\nX0q47fogAEwmoV1g3b/jvH17Od27m4h05DjpCl/Wr6+a4/4DFi4Ybs8xJtpE8lErOY4clQJbPS4e\ne/9XQuduZkIjzZjMwsiJgWz+pmp+iQfLOPt8e35dor1ITy4nL8dCh04mYuLto9u+/ga6xniRne76\nZ4979oskJTGHzLQ8rBYb363byXmX9D61oJxaL/z8velzThTffP4bADarjRNFpe4n7JrzoqmUKgdO\nXjSdlFLZSqltQL1+49mdtqK0uMx53zfAB1sN/5nnjOpHakIGWck5LS6mztEzOQ7sFM6RgmOkHC/A\nomysTdjDZVExtZafFN2bNQl7TmvfFqVxflmuM3C00uNkx3OVxQEdRGSjiGwRkZsqbXsemFXr0WvQ\nrHOERSQC+JNS6mXsb6LcFty5Q5UXZHZyDsGdq35EeGhnIhdcNRSAnoNjCOnakY6RjjJKseDruSz9\n5WnG3nFpi4unc2w7OTamhtSZ0HYBpOVXdJoy8gsJbRdQY1lvk5ELYrvxze6D9d7XWSbdSlh4RRMT\nFm4gI91apUyveBNff2XvOO7cUUZaqpX0NPso7NPzCpj1cLua+le1Skmz0CWiYjS2c7iJlGqdvQHx\n3nz2pT2XX7eXkJRSTnKahcNJFjp2MHD7vekMuiyJux/MoNjFiHB6upWIiIocw8MNpFfLMT7exJfr\n7Dlu315GSqqVNEeOIjBlSg7jxmezYsUJl/llp1sICa8YUe0YZiKnWn7Rvbz54St7fnt3FJOZWk5W\ntTLpyeUk/FFK74F1T/sACA5tR1Z6fqVzyKdjyKmfOPYe0IVln8zg8Zdupmt0JwBCI8+iIO8E98+/\niqUf3cNfH/sTXt6N+kGkOxfN0+JOWwFw/qTBLN/9PE+seYhFU186ZfuIycPZuHJTi4ypc2z8eABh\n/oGkFhU6H6efOE6Yfy1trdHEiMjurDuyv977tjSi6n87TSbsU53GAmOAuSISIyLjsU8r3IH9+ujW\n1aO5vyz3AlB5InSjDrWtfOYzAs7y56WtC7hi+uUc3H4Ym9V+Abr3gjlMH/QQD49/mknTx9BneK9W\nF685YuocPZNjPXi0zpw0slc0vyWmUljSqKN3p7hzegAF+TauGpfFin+doHcfM0Yj/Pc/JQR3MtC7\njxml7COnjeWhmR04lm9j0GVJvPRWHmf39cZoFCwWxW+7Spl+a3u2ftMVP18DC5a6Gpx3bcY9AeTn\n27h8TBZv/+sEffuYMTha3s8/C2b9V514952zePtfRfz6a1ndB3PDlGkdOF5g5e4JiXz+bj4x8T4Y\nKn2KUVxkY970VO55JARf/8a5BBz4I5WbRz3LPVcvZc37P/PIizcCYDQaiOkdwdoPfmHGtcsoLSnj\nujtGNErMluLH1VuY2uc+Hr3yWW59YkqVbUaTkfMmDuL7j35u1TF1jp7JEWBU12i2ZCRTUObZtrZJ\nuDECfCLhIDnffuW81SAF6FrpcaTjucqSgfVKqRKlVA7wPTAAGA5cISKHgA+AkSJS6zTDk5p7+bRB\nwEoREaAjMFZEypVSNX7TL0Htdt6PSO7IuC6jnI87RgaTk1J1Dk3x8RIWTX3Z+fidhGWkHbJ/mpyb\nngdAfnYBmz7/hV5DYti9eS+1yUnJJaRLxXxsT8drjpg6x9OL98Om7zlGVp15NqJ61Zms7ysamkTT\nKK4Z1M/5ODQokIyC4zXtxth+cc5pEQAZBccJDwp0a19nmTAjaakVI6rpaTZCw6pONQgIMPDUc+2d\nj0cNzySyq5Ev1hSz8ZsSvt9QSmmJoqhI8dC9eSx4oT116RxuIimlYvQzJc1C57CqzVxggIHlz4c6\nH0cPOUyPKDNFJ2x0iTAxyDFKevWEABYuq7sjHBZmJCWlIse0NBthNeS4eFHFeQ87L5OoKHuZ0FD7\nv8HBRsaO8WH7jjKGDKl9zm7HMBOZqeXOx9npFoKr5ecXYGDWwjDn4z9feIiIrvZRZKtFMW96KqOu\nbMfw0e6NMuVkFBASHlTpHILIziyoUqbkREUHfuum/ZhMEwkI8iU7I5+s9HwO7LZfxzZ9vZtrp15E\nblEiuUWJbsV3wZ2LZr2cvM4EJhu5rMuFzudraisq2715L+E9Qgg8K4DCY/a6MWTs2RzYdoj87IJa\n96vMnfapMWM2dbzmiNkcOaYXFRLhX/GpSZhfAOlFNbeXV0T3Yk1CRVtb274l+xIo2ZfgVvxm48aA\nhV/3GPy6V0z1yN34dfUiW4AYEYkC0oDrgSnVyqwGloiIEfAGhgKLlVKfAP8EEJERwANKqZtdnVNT\njAjXOjytlOrhuHXHPudxem0XdIBo6eO8pW/NJSImjJCuHTGZTYycPJyf1lT9FrtfOz+MJvuFZuwd\nl7Lruz8oKSrB29cLH3/7xc7Hz5tBlw3gyO9JdSaxb0tCk8Zrjpg6x9OL10FCqrw2G0Gj1ZlOF41x\n3g5azUQFtyeifSBmo4Fx/eLYuPfQKfsEeHsxuHskG/ZUNLq/p2S4tW9l/QaYSTpiISXZQlmZ4su1\nxVxymXeVMoUFNsrL7a3nh++fYNBQL/z9Ddz/UDs2/hzKt5tDWLSsPUPP93LZCQYYPNCHhCPlJB4t\np6xMsWp1IROrdfjyC6zOmK+/l89F5/kS4G8gtJOJLhFm9ifYO3UbNp0gPq7uL5INHGjmyBELyY4c\nV68pZvToqjkWVMpxxYoTDBtmz7G4WFFUZO9Enzhh47vvS+nVs+4vkvXs70NKYjkZyeWUlyk2ri3k\n/FH+VcocL7BiccT74oM8+g/1c478Pjs7na6xXlzt5moRAPt/TyaiazAh4e0xmY2MGNufnzdWfbPZ\nPrjiHOL6RSIiHM8vJi+niKz0fDpH2b8cOHBoNEkJmXTwjyIm5CLnrQGcF00R8cJ+0axrySSXn6Cc\nrMdFWxVRMVF1thXhPSreUMWc3R2Tl8nZeQIYeb37H6eDe+1TY8Zs6nhnSo7/y06nW7v2dA5oh9lg\nYGJ0b75NOnhKuUCzF0PDuvB14kGX+/r0jKb9FaOdt5aoMaZGKKWswAzga2A3sFIptUdE7haRuxxl\n9mL/svhO4GfgNaXUH6d73p5ePu194GIgWESSgEcBL0AppV6rVrxeH37abDaWzlzOM+vnYjAI697c\nQNLeFMbfNQql4MvXvyWqd2dmvT0DZbNxZHcyi+6wj/KdFdqexz6dhVIKo8nIf97/gW3f7GxR8XSO\nbSfH+vBonVGK+f/ewOu3XGVfAu233RzKyuW6wf1QCj7auguAS+Nj2HQgkVKL1eW+dTEahblPtGPq\njbkoG1w92Y/oWDMr3ytCRJj8Zz8SDlr4+/15GAwQE2vmyWeD6jymK0aj8OKTnRgzJQWbzb58Wu84\nL159Jx8RuOumIPYcKOO2v2VgEIjv6c0bi0Oc+78wvxM33ZNOuUXRvauZN18IrSOaPd78+e2YckOu\nc/m02Fgz775XhCDceKMfBw5YuPc+e45xcWYWPWfPMSvLytQ7jiECVgtceaUvI0Z4u4w3c14Is29O\nRikYe10QUTHerH0/DwEm3NCepIQyFjyQjsEgRMV68eACew6/by3mP6sL6d7Tm7vHJ4LA1FkdGTLC\nv86YNpti2ZNreer1Wx3Lp23j6KEsxl07GAWs+2gLF47uy/jJQ7FYrJSVlPPUAyud+7/89L95aOF1\nGE1G0o/msmjOJ3XGqw+llFVETl40Ty6ftkdE7sZRZ0QkFNgKBAI2EfkbEK+UqvMjDXfaiguvHsqo\nm0ZgKbNQVlzG/MnPO/f39vXi7FH9ef7uV93Op6lj6hw9lKNSPPLTt7w75lrHEmg7OZiXyw29BqCU\n4oN99uvG6G6xfJ98mFKrxeW+ZxKl1FdAz2rPvVrt8XPAc3Uc4zvgO3fiiWrMyXceJCJqlFzT3Keh\naS59qz5Gqeb/iR0RUb0fdnvJ1Ab77O5nmywWQKy56b9Akm6tezpIY9tbHui6UCNbOL7p29n1fzzV\nYuqMvs5o9bX/tSFNHjPpztktos6cJCIqbl79rzf7H72/2fNo7jnCmqZpmqZpWivXSOsINzndEdY0\nTdM0TdMappV2hJt7+TRN0zRN0zRNaxZ6RFjTNE3TNE1rmFY6Iqw7wpqmaZqmaVqD6DnCmqZpmqZp\n2plJd4Q1TdM0TdO0M5EeEdY0TdM0TdPOTLojrGmapmmapp2RdEdY0zRN0zRNOxPpqRGapp3RCpRX\nc5+Cpmlak4u769cmj5nU5BHdoDvCmqZpmqZp2hlJd4Q1TdM0TdO0M5GeGqFpmqZpmqadmXRHWNM0\nTdM0TTsTtdYRYUNzn4CmaZqmaZqmNQc9IqxpmqZpmqY1TCsdEdYdYU3TNE3TNK1hdEdY0zRN0zRN\nOxNJc5/AafJoR1hElgMTgAylVP8att8APOR4WAhMU0rtcvf4gy4fyLTnb8VgEL56cwOrFq6ust0/\nyI8Hl08nPDqUsuIynpv6Mkl7kgF499AyivJPoGwKS7mVmcP+0eLi6RzbTo7u8nSduSA2ir+PuxiD\nCJ9s+53lP2ytsv224ecyYUAvFAqTwUiPTh0Y/vQrFJaUuty3Jj/9t5jFj+dis8EVkwO4ZVpQle2F\n+TaemJVNcpIFHx9hzsJgesR5UVaquPu6dMrLFFYrXDLOjzvvbe9Wjl9tKOL+R7KwKbh9Sjtmz+hQ\nZXtevpWp92VwKLEcXx8DbywOIb6nNwD5BVbufCCT3XtLMRiEN54PZeg5PnXG27ixhEcfK8BmgynX\n+3HPPQFVtufn23jggTyOJFrx8REWLwoiLs4MwNBhmQQGCgYDmE3CF190dJnfr98V8dLjmdhsMHZy\nEFP+UjW/4wVWnp2dQWpiGd4+Bh5cGEq3WG+y0sp55oF0jmVbMRhg3OQgrrrtLJfxAM69IJa/PDQe\nMQjrP93KR8t/qLK936BuPLrkRtKTjwGw+dvdfPDqfwHwC/DmvsevJComFKUUi+d8yr5dyW7FrUxE\nxgAvYP9uy3Kl1IIayrwIjAWKgNuUUtsdz9dZr2rjqq04b+Igbnl8srM9eOX+t9n94z46x4YzZ+V9\nKKUQEcJ7hPL2Iyv5fMm6FhdT59g2cmwR9Ihwjd4ClgDv1LL9EHCRUirf0ci9Dgxz58AiwowlU5k9\nah45qcdY9usz/Lh6C0f3pTrLTPnnVRzccZh51zxHZFwEM5dO5aHRTwCgbIoHRz7G8bwitxJp6ng6\nx7aTYz15sM7AnAmXcPtbH5NZUMSH06awYU8Ch7OPVQTfvI23Nm8DYETP7tx8/jkUlpS6tW91Npvi\n2UdyWfZ+KJ1CjdxyRRojLvOjW4y5It6yfHr28WLhayEkJpSzcK69vJe38PLKUHx8DVitijuvTuf8\ni33pM9C7zhxtNsVfH87imw87ExFmYujYJK64PIBesRW/evf0i8c4u683n7wZwb6DZcz8ZyZffxgJ\nwL1zsxh7qR8fvh6OxaI4UWxzGW/OnAJWrepAaKiRceOzufxyH2JiKprWJUuO06evmTfe6MDBBAsP\nP5zPqpXBABgM8PFHwbRv7973lm02xZJHM3nuvUiCQ01Mn5TE8MsC6Bpdkd+KZbnExHsz75UIjiaU\n8eKjmTz7XiRGkzBtTidi4n0oLrLxl4mJDLrIv8q+NRER7nl4In+//U1ysgp4cdV0ftqwh+TD2VXK\n/b7tCI/NeO+U/af9YwK/fr+fJ+9ficFowMfHfEoZV0TEACwFLgVSgS0isloptbdSmbFAtFIqVkSG\nAi9TUTdc1auaYrpsK377dic/rbW/IezWtytzV93H1D73kXIgjWnnznYe54Ojr7D5M9e/PtbUMXWO\nbSPHlkKvGlEDpdQmoNYrpVLqZ6VUvuPhz0Bnd4/da0gMKQfSyEzKxmqxsnHVZs6fNLhKmajekezY\n8DsAyftTCe0WQlDHdvaNIojB/YH8po6nc2w7OdaHJ+tMv85hJOYcIzWvEIvNxpe79nNJ7+hay4/v\n35Mvdu49rX0Bdu8oo0s3E+GRJkxmYfREf7775kSVMocPlDPofPuIa1S0mbRkC8dyrAD4+Nqbp/Iy\nhcVi78i78uv2EmK6m4nqYsZsFiZPCmTN+uNVyvyxv5SRF/gB0DPGiyNHLWRlWygotLLplxJuu94+\nam0yCe0CjXXG2769nO7dTURGmjCbhUlX+LJ+fUmVMvsPWLhguL0DHxNtIvmolRxHjkqBrR4Xj73/\nK6FzNzOhkWZMZmHkxEA2f1M1v8SDZZx9vj2/LtFepCeXk5djoUMnEzHx9r+1r7+BrjFeZKdbXMbs\n2S+SlMQcMtPysFpsfLduJ+dd0vvUgjX8B/n5e9PnnCi++fw3AGxWGyeKSt1PuMIQ4IBSKlEpVQ6s\nBCZVKzMJR0dXKfULECQioY7HddarmrjTVpQWlznv+wb4YKvhP/OcUf1ITcggKzmnxcXUObaNHFsM\ndRq3FqAlLZ92B+D22H9w5w5VXhzZyTkEd676EeGhnYlccNVQAHoOjiGka0c6RjrKKMWCr+ey9Jen\nGXvHpS0uns6x7eToQfWqM6HtAkjLr+g0ZeQXEtouoMay3iYjF8R245vdB+u970lZ6RZCIypGRkPC\njGSlW6uUie1tZuNX9s7x7h2lpKdayEyzl7HZFDeOTWXsoGSGXuhD/IC6R4MBUtIsdKkUs3O4iZRq\nnb0B8d589qU9l1+3l5CUUk5ymoXDSRY6djBw+73pDLosibsfzKDYxYhwerqViIiKZjQ83EB6tRzj\n4018uc7eOd6+vYyUVCtpafbjisCUKTmMG5/NihVV3yTUJDvdQkh4xYhqxzATOdXyi+7lzQ9f2fPb\nu6OYzNRysqqVSU8uJ+GPUnoPrHvaB0BwaDuy0vOdj7PT8+kY0u6Ucr0HdGHZJzN4/KWb6RrdCYDQ\nyLMoyDvB/fOvYulH9/DXx/6El/dpfRDZGTha6XEyp74JrF4mpYYybnOnrQA4f9Jglu9+nifWPMSi\nqS+dsn3E5OFsXLmpRcbUOTZ+vOaK2SLojvDpE5GRwG1UzH1sFCuf+YyAs/x5aesCrph+OQe3H8Zm\ntV+A7r1gDtMHPcTD459m0vQx9Bneq9XFa46YOkfP5FhfnqozJ43sFc1viakUlpzW6J3bbpkeb6OX\n5wAAIABJREFURGG+jZvGpfHRO4X07OOFwTEIazAI762LYO3Pkfy+vZRD+8vqPpibHprZgWP5NgZd\nlsRLb+Vxdl9vjEbBYlH8tquU6be2Z+s3XfHzNbBgab0GEWs0454A8vNtXD4mi7f/dYK+fcwYHC3v\n558Fs/6rTrz7zlm8/a8ifv214TlOmdaB4wVW7p6QyOfv5hMT74Oh0qcYxUU25k1P5Z5HQvD1b5xL\nwIE/Url51LPcc/VS1rz/M4+8eCMARqOBmN4RrP3gF2Zcu4zSkjKuu2NEo8RsKX5cvYWpfe7j0Suf\n5dYnplTZZjQZOW/iIL7/6OdWHVPn2DZy9DRR9b+1BM2+aoSI9AdeA8Yopeq86iSo3c77EckdGddl\nlPNxx8hgclJyq5QvPl7CoqkvOx+/k7CMtEOZAOSm5wGQn13Aps9/odeQGHZv3kttclJyCelS8UUW\nT8drjpg6x9OL98Om7zlGVp15Nqb61Jms779y3k80jeKaQf2cj0ODAskoOF7TboztF+ecFgGQUXCc\n8KBAt/Y9qVOYifTUipHIzHQrncKqTjXwDzAw97mK/49Jw5Pp3LVqsxQQaGDQeT789F0JPeLqns/a\nOdxEUkpFzJQ0C53Dqh4vMMDA8udDnY+jhxymR5SZohM2ukSYGOQYJb16QgALl9XdEQ4LM5KSUjFq\nnJZmI6xajgEBBhYvqvii37DzMomKspcJDbX/GxxsZOwYH7bvKGPIkNpz7BhmIjO13Pk4O91CcLX8\n/AIMzFoY5nz85wsPEdHVPopstSjmTU9l1JXtGD667hH9k3IyCggJr/iSY8ewILIzC6qUKTlR0YHf\numk/JtNEAoJ8yc7IJys9nwO7UwDY9PVurp16EblFieQWJboV3yEF6FrpcaTjueplurgo49LJ60xg\nspHLulzofL6mtqKy3Zv3Et4jhMCzAig8Zq8bQ8aezYFth8jPLqh1v8rcaZ8aM2ZTx2uOmG0lx1yV\n2aTXmdPSQjq29dUUI8JCLatqiEhX4BPgJqVUgqsDRUsf5y19ay4RMWGEdO2IyWxi5OTh/LSm6rfY\n/dr5YTTZLzRj77iUXd/9QUlRCd6+Xvj42y92Pn7eDLpsAEd+T6oz9r4tCU0arzli6hxPL14HCany\n2mwEjVZnOl00xnk7aDUTFdyeiPaBmI0GxvWLY+PeQ6fsE+DtxeDukWzYU3H431My3Nq3svgBXiQf\nsZCWbKG8TPH12iIuGuVXpczxAhuWcnvr+fkHhZwzzAc/fwN5uVaOF9g7mCUlNn7ZVEK3aNdfsho8\n0IeEI+UkHi2nrEyxanUhE6t1+PILrJQ7Yr7+Xj4XnedLgL+B0E4mukSY2Z9g79Rt2HSCeBcd74ED\nzRw5YiE52UJZmWL1mmJGj646haOgwOaMt2LFCYYN88Lf30BxsaKoyJ7jiRM2vvu+lF49686xZ38f\nUhLLyUgup7xMsXFtIeeP8q9S5niB1fk3/eKDPPoP9XOO/D47O52usV5c7eZqEQD7f08momswIeHt\nMZmNjBjbn583Vn2z2T644hzi+kUiIhzPLyYvp4is9Hw6R9m/HDhwaDRJCZl08I8iJuQi580NW4AY\nEYkSES/gemBNtTJrgJsBRGQYkKeUyqi0vdZ6VdnJely0VREVE1VnWxHeo+INVczZ3TF5mZwdGYCR\n19fvo2132qfGjNnU8XSOpx/TA9eZRqdHhGsgIu8DFwPBIpIEPAp4AUop9RowF+gAvCQiApQrpYa4\nc2ybzcbSmct5Zv1cDAZh3ZsbSNqbwvi7RqEUfPn6t0T17syst2egbDaO7E5m0R32Ub6zQtvz2Kez\nUEphNBn5z/s/sO2bnS0qns6x7eRYHx6tM0ox/98beP2Wq+xLoP22m0NZuVw3uB9KwUdb7auwXRof\nw6YDiZRarC73rYvRKMx6vAMzb8pAOZZP6x5r5tMVhYjAlTcEcvhgOfMeyMYg0CPOizkL7R2m7Ewr\n8+7PwaYUygajJvgz/BJflzkajcKLT3ZizJQUbDb78mm947x49Z18ROCum4LYc6CM2/6WgUEgvqc3\nbywOce7/wvxO3HRPOuUWRfeuZt58IbSOaPZ48+e3Y8oNuc7l02Jjzbz7XhGCcOONfhw4YOHe+/Iw\nGCAuzsyi5+yjq1lZVqbecQwRsFrgyit9GTGi7nnQRqMwc14Is29ORikYe10QUTHerH0/DwEm3NCe\npIQyFjyQjsEgRMV68eACew6/by3mP6sL6d7Tm7vHJ4LA1FkdGTLCv86YNpti2ZNreer1Wx3Lp23j\n6KEsxl07GAWs+2gLF47uy/jJQ7FYrJSVlPPUAyud+7/89L95aOF1GE1G0o/msmjOJ3XGq4lSyioi\nM4CvqVg+bY+I3I2jbiilvhSRcSJyEMfyaSf3r6leKaXeqjtv123FhVcPZdRNI7CUWSgrLmP+5Oed\n+3v7enH2qP48f/erbufZ1DF1jm0jxxajhXRs60uUah1nLiJqlFzT3KehaS59qz5GKdXsa4uLiOr9\n8OImi/evu/6vyWIBnOtV92itJ6Rb654O0tj2lge6LtTIFo5v+nZ2/R9PtZg6o68zWmvQUq4zJ4mI\nGjit/tebHS/ff0oertYMF5ERwGrsy4kCfKqUmu/YFgS8AfQFbMDtjlVkatXsc4Q1TdM0TdO01q0x\npjq4s2a4w/dKqStqOMT/AV8qpa4VERPgV0OZKlrEqhGapmmapmlaK9Y4y6e5s2Y41DDfX0TaARee\nnPaklLIopVx+w1F3hDVN0zRN07SGaZyOsDtrhgOcJyI7ROQLEYl3PNcdyBaRt0TkNxF5TURcfrlE\nd4Q1TdM0TdO0BmnCVSO2AV2VUgOxT6P43PG8CTgHWKaUOgc4Afzd1cH0HGFN0zRN0zStYdzo2Bam\nHqQw7WBdRVyuGa6UOl7p/joReUlEOmAfPT6qlDq5Vt3HuPGjU7ojrGmapmmapjWIuLEKWbvwaNqF\nRzsfp2//unoR55rhQBr2NcOr/OyeiISeXCNcRIbYQ6tcx+OjIhKnlNqP/Qt3f7g6J90R1jRN0zRN\n0xqmEVaNcGfNcOAaEZkGlAPFwORKh/grsEJEzNiXV7sNF3RHWNM0TdM0TWuQxvqlOKXUV0DPas+9\nWun+MmBZLfv+Dxhcn3i6I6xpmqZpmqY1TOv4fbZT6I6wpmmapmma1iCNNSLc1HRHWNM0TdM0TWsY\n3RHWNE3TWjvrnv3NfQqapmlNRneENU3TNE3TtAbRUyM0TdM0TdO0M5PuCGuapmmapmlnIj0irGma\npmmapp2Z3PhluZZId4Q1TdM0TdO0BtEjwpqmaZqmadqZSXeENU3TNE3TtDOR2Jr7DE6P7ghrmqZp\nmqZpDaNHhE8lIsuBCUCGUqp/LWVeBMYCRcCtSqkd7h5/0OUDmfb8rRgMwldvbmDVwtVVtvsH+fHg\n8umER4dSVlzGc1NfJmlPMgDvHlpGUf4JlE1hKbcyc9g/Wlw8nWPbydFdnq4zF8RG8fdxF2MQ4ZNt\nv7P8h61Vtt82/FwmDOiFQmEyGOnRqQPDn36FwpJSl/vW5Kf/FrP48VxsNrhicgC3TAuqsr0w38YT\ns7JJTrLg4yPMWRhMjzgvykoVd1+XTnmZwmqFS8b5cee97d3K8asNRdz/SBY2BbdPacfsGR2qbM/L\ntzL1vgwOJZbj62PgjcUhxPf0BiC/wMqdD2Sye28pBoPwxvOhDD3Hp854GzeW8OhjBdhsMOV6P+65\nJ6DK9vx8Gw88kMeRRCs+PsLiRUHExZkBGDosk8BAwWAAs0n44ouOLvP79bsiXno8E5sNxk4OYspf\nquZ3vMDKs7MzSE0sw9vHwIMLQ+kW601WWjnPPJDOsWwrBgOMmxzEVbed5TIeuK4z/S+KZ97ns0k7\nlAHAps9+4f0nPwXg/jemMWz8ORzLyOfugQ+6jCUiY4AXAAOwXCm1oIYyNdaB2vYVkWuAx4DewGCl\n1G+Nkfd5Ewdxy+OTne3BK/e/ze4f99E5Npw5K+9DKYWIEN4jlLcfWcnnS9a1uJg6x7aRY0ug5wjX\n7C1gCfBOTRtFZCwQrZSKFZGhwCvAMHcOLCLMWDKV2aPmkZN6jGW/PsOPq7dwdF+qs8yUf17FwR2H\nmXfNc0TGRTBz6VQeGv0EAMqmeHDkYxzPK3IrkaaOp3NsOznWkwfrDMyZcAm3v/UxmQVFfDhtChv2\nJHA4+1hF8M3beGvzNgBG9OzOzeefQ2FJqVv7VmezKZ59JJdl74fSKdTILVekMeIyP7rFmCviLcun\nZx8vFr4WQmJCOQvn2st7eQsvrwzFx9eA1aq48+p0zr/Ylz4DvevM0WZT/PXhLL75sDMRYSaGjk3i\nissD6BXr5Szz9IvHOLuvN5+8GcG+g2XM/GcmX38YCcC9c7MYe6kfH74ejsWiOFFc92d9NptizpwC\nVq3qQGiokXHjs7n8ch9iYiqa1iVLjtOnr5k33ujAwQQLDz+cz6qVwQAYDPDxR8G0b2+oM07leEse\nzeS59yIJDjUxfVISwy8LoGt0RX4rluUSE+/NvFciOJpQxouPZvLse5EYTcK0OZ2IifehuMjGXyYm\nMugi/yr71sSdOgOw6/s9PPKnU/qsrH9rI6uXrGP2v2a4zE9EDMBS4FIgFdgiIquVUnsrlamxDrjY\ndxdwJfCqy5OoR96/fbuTn9ba3xB269uVuavuY2qf+0g5kMa0c2c7j/PB0VfY/NmvLS6mzrFt5Nhi\ntNJVI9xrfU+TUmoTUPuVEibhuOArpX4BgkQk1J1j9xoSQ8qBNDKTsrFarGxctZnzJw2uUiaqdyQ7\nNvwOQPL+VEK7hRDUsZ19owhiELdzaep4Ose2k2N9eLLO9OscRmLOMVLzCrHYbHy5az+X9I6utfz4\n/j35Yufe09oXYPeOMrp0MxEeacJkFkZP9Oe7b05UKXP4QDmDzrePuEZFm0lLtnAsxwqAj6+9eSov\nU1gs9o68K79uLyGmu5moLmbMZmHypEDWrD9epcwf+0sZeYEfAD1jvDhy1EJWtoWCQiubfinhtuvt\no9Ymk9Au0FhnvO3by+ne3URkpAmzWZh0hS/r15dUKbP/gIULhts78DHRJpKPWslx5KgU2Opx7dj7\nvxI6dzMTGmnGZBZGTgxk8zdV80s8WMbZ59vz6xLtRXpyOXk5Fjp0MhETb/9b+/ob6BrjRXa6xWVM\nd+oMALX8/+zevJfCY8dr3niqIcABpVSiUqocWIn9NV9ZbXWg1n2VUvuUUgdqP8tTuZN3aXGZ875v\ngA+2Gv4zzxnVj9SEDLKSc1pcTJ1j28ixpRBV/1tL4NGOsBs6A0crPU5xPOdScOcOVV4c2ck5BHeu\n+hHhoZ2JXHDVUAB6Do4hpGtHOkY6yijFgq/nsvSXpxl7x6UtLp7Ose3k2MhOu86EtgsgLb+iQ5KR\nX0hou4Aay3qbjFwQ241vdh+s974nZaVbCI2oGBkNCTOSlW6tUia2t5mNX9k7x7t3lJKeaiEzzV7G\nZlPcODaVsYOSGXqhD/ED6h4NBkhJs9ClUszO4SZSqnX2BsR789mX9lx+3V5CUko5yWkWDidZ6NjB\nwO33pjPosiTufjCDYhcjwunpViIiKprR8HAD6dVyjI838eU6e+d4+/YyUlKtpKXZjysCU6bkMG58\nNitWVH2TUJPsdAsh4RUj6h3DTORUyy+6lzc/fGXPb++OYjJTy8mqViY9uZyEP0rpPbDuaR/gXp0B\niD8vjld+e5b5a/9B196RLo9bi+qv72ROfX3XVsadfd3mbt7nTxrM8t3P88Sah1g09aVTto+YPJyN\nKze1yJg6x8aP11wxtdPXqr4sl6B2O+8PVL1cll/5zGdM/7/beGnrAg7vSuLg9sPYrPYL0L0XzCE3\nPY+gju1Y8PVckvaksHvzXhdHbFnxdI4tI8cfNn3PMbIanKcnZH3/lfN+aadrILabW/uN7BXNb4mp\nFJaUeujM7G6ZHsTix3K5aVwa0b3M9OzjhcExCGswCO+ti+B4oY1Zd2ZyaH8ZPeLq/hjfHQ/N7MC9\nc7MYdFkSfXt7cXZfb4xGobxc8duuUpY8FcKggT7cNzeLBUuP8dis4AbFm3FPAI88WsDlY7Lo1ctM\n3z5mDI6+8+efBRMaaiQnx8r1U3KJjTUxZEjDcpwyrQPLHs/k7gmJdO/pTUy8D4ZKn2IUF9mYNz2V\nex4Jwde/ccZC9m87xJ+jplFaXMbgMQOZ99ksbuv1t1rL56rMxqwzjfoRzcnrTKyKoFT1dFn+x9Vb\n+HH1FvoM78WtT0zh75c/4dxmNBk5b+Iglv99RWOeYpPH1Dk2f46NXGc8o4WM8NZXc3eEU4AulR5H\nOp6rUbT0cd4vTbUS0qXiiyUdI4PJScmtUr74eAmLpr7sfPxOwjLSDmUCkJueB0B+dgGbPv+FXkNi\n6uxA5aTkNmm85oipczz9eB0IcZY/rPbUmXMD1avOdLpojPN+nncA4UGBzsehQYFkFNT8kfXYfnHO\naREAGQXH3d7XGTvMRHpqxUhkZrqVTmFVpxr4BxiY+1zF/8ek4cl07lq1WQoINDDoPB9++q7EZUe4\nc7iJpJSKmClpFjqHVT1eYICB5c9XzCaJHnKYHlFmik7Y6BJhYpBjlPTqCQEsXFbXLBUICzOSklIx\napyWZiOsWo4BAQYWL6r4ot+w8zKJirKXCQ21/xscbGTsGB+27yirsyPcMcxEZmq583F2uoXgavn5\nBRiYtTDM+fjPFx4ioqt9FNlqUcybnsqoK9sxfHTdI/onuVNnSooqpoNs+WoHRrOJwLMCap0S0UFC\naqszKUDXSkVren3XVge83NjXpZPXGVOqP126VoSpKe/Kdm/eS3iPkCp5Dxl7Nge2HSI/u8Ct2O78\nrRszZlPHa46YbSXHOupMi9FSpjrUV1NMjRBqf8e+BrgZQESGAXlKqQx3DrpvSwIRMWGEdO2IyWxi\n5OTh/LSm6rfY/dr5YTTZLzRj77iUXd/9QUlRCd6+Xvj42y92Pn7eDLpsAEd+T2pR8XSObSfH0+CR\nOvN7SgZRwe2JaB+I2WhgXL84Nu49dEq5AG8vBnePZMOehHrvW1n8AC+Sj1hIS7ZQXqb4em0RF43y\nq1LmeIENS7m99fz8g0LOGeaDn7+BvFwrxwvsHcySEhu/bCqhW7T5lBjVDR7oQ8KRchKPllNWpli1\nupCJ1Tp8+QVWyh0xX38vn4vO8yXA30BoJxNdIszsT7DP3duw6QTxLjreAweaOXLEQnKyhbIyxeo1\nxYweXXUKR0GBzRlvxYoTDBvmhb+/geJiRVGRPccTJ2x8930pvXrWnWPP/j6kJJaTkVxOeZli49pC\nzh/lX6XM8QKr82/6xQd59B/q5xz5fXZ2Ol1jvbjazdUiwL060z6kYjWQnoNjEKFKJ1hEEHcmecMW\nIEZEokTEC7ge+2u+strqgDv7gpsjyO7kHd6j4g1VzNndMXmZquQ98vr6fbTd1DF1jm0jxxZDqfrf\nWgBPL5/2PnAxECwiScCj2N+1K6XUa0qpL0VknIgcxL4Mzm3uHttms7F05nKeWT8Xg0FY9+YGkvam\nMP6uUSgFX77+LVG9OzPr7Rkom40ju5NZdId9lO+s0PY89ukslFIYTUb+8/4PbPtmZ4uKp3NsOznW\nh0frjFLM//cGXr/lKvsSaL/t5lBWLtcN7odS8NHWXQBcGh/DpgOJlFqsLveti9EozHq8AzNvykA5\nlk/rHmvm0xWFiMCVNwRy+GA58x7IxiDQI86LOQvt0xCyM63Muz8Hm1IoG4ya4M/wS3xd5mg0Ci8+\n2YkxU1Kw2ezLp/WO8+LVd/IRgbtuCmLPgTJu+1sGBoH4nt68sbhilOWF+Z246Z50yi2K7l3NvPlC\n3d9DNBqF+fPbMeWGXOfyabGxZt59rwhBuPFGPw4csHDvfXkYDBAXZ2bRc/ZOY1aWlal3HEMErBa4\n8kpfRoyoex600SjMnBfC7JuTUQrGXhdEVIw3a9/PQ4AJN7QnKaGMBQ+kYzAIUbFePLjAnsPvW4v5\nz+pCuvf05u7xiSAwdVZHhozwrzOmO3XmomuGMeEvo7GWWyktLuPJ65937v+P9/7GgIvjCQwOZMWR\nl3nnsVWsf/u/NcZSSllFZAbwNRVLoO0RkbtxUQdq2xdARP6EfTWWjsC/RWSHUmpsQ/O+8OqhjLpp\nBJYyC2XFZcyfXJG3t68XZ4/qz/N3u71QRZPH1Dm2jRxbitY6IiyqhfTIXRERNUquae7T0DSXvlUf\no5Ty3NISbhIR1fvhxU0W7193/V+TxQI416vh84XrK93q9uoHjWJveaDrQo3s6R41Ll/tUS2pzujr\njNYatJQ6c5KIqAsnLqz3fj+snd3seTT3HGFN0zRN0zStlWutI8K6I6xpmqZpmqY1TH0WRW9BdEdY\n0zRN0zRNa5jW2Q/WHWFN0zRN0zStYfTUCE3TNE3TNO3M1EoWX6hOd4Q1TdM0TdO0BmmtI8JN8YMa\nzSpXZbb5mDrHthOzJShKPNjkMbf9VOK6UCP6748nmjQewI8/evbnqqvb8XPT5nim1hc4M9qnth6v\nOWKeyXWmJWnzHeHm+G3upo6pc2w7MVuCE83REf65aTvC3/1Y3KTxAH76qaxJ4/2viTvCZ2p9gTOj\nfWrr8ZojZpurM+o0bjUQkTEisldE9ovIQ7WFE5HBIlIuIldVeu4+EfldRHaKyArHr0zWqc13hDVN\n0zRN0zTPEqXqfTvlGCIGYClwOdAHmCIivWop9wywvtJzEcBM4BylVH/s03+vd3XeuiOsaZqmaZqm\nNYztNG6nGgIcUEolKqXKgZXApBrKzQQ+BqrPLzEC/iJiAvyAVFen3ap+Yrm5z0HT3NXcPxkJus5o\nrYuuM5pWPy2hzpwkIuqSkU/Ve78NG/9ZJQ8RuRq4XCl1l+PxjcAQpdRfK5WJAFYopUaKyFvAWqXU\np45tfwWeBE4AXyulbnJ1Dq1m1YiW9B+uaa2BrjOaVj+6zmhaA7jxNvLYsUMcyzvU0EgvAJXnDguA\niLTHPnocBeQDH4vIDUqp9+s6WKvpCGuapmmapmktlBszDM5q352z2nd3Pj6SuKF6kRSga6XHkY7n\nKhsErBQRAToCY0WkHPACDimlcgFE5FPgfEB3hDVN0zRN0zTPaaSJRVuAGBGJAtKwf9ltSuUCSqke\nzpgVUyPWiMgQYJiI+AClwKWO49WpzXxZTkSWi0iGiOyso8yLInJARHaIyEBPxhORG0Tkf47bJhHp\n58l4lcqdspyIJ2OKyMUist2xXMlGT8YTkXYissbx/7dLRG5tYLxIEdkgIrsdx/trLeUa7XXT1NxZ\nhqa2/GrbV0Sucfx/W0XknEaOf3al5916zTckvoj0FJEfRaRERO4/3TiVjufxdqimGCJyloh8LSL7\nRGS9iATVsq+7yxLVK4aI/MOR0x4RGV3LMd06x+bWkNediJwj9mWb9ovIC5WeX+xoJ39z5J/ryXiO\nbddVatvea4IcbxGRTEeOvzlybuy2p8bXkIh0EHtbXigiL9Z1jAbGbzVtX7NQqv63Uw6hrMAM4Gtg\nN7BSKbVHRO4Wkbtqilpp31+xf4FuO/A/7FMmXnPjvFWbuAEXAAOBnbVsHwt84bg/FPjZw/GGAUGO\n+2M8Hc9RxgD8B/g3cFUT/E2DHC/Uzo7HHT0c7x/A0ydjATmAqQHxwoCBjvsBwD6glydfN015c7we\nDmKfL2UGdribX137Aj2BWGAD9mVqGj2+u6/5Rsi/I3Au8ARwfyP8zT3eDtUUA1gAzHbcfwh45nT+\nHqcTA4jHfuExAd0cMaSGY7o8x5Zwa8jrDvgFGOy4/yX2L/1ULzMDeMOT8YAYYBvQ7uTr3NM5ArcA\nL7r7WqutLtS1bx2vQT/sH4HfBbzowfitou1rpnqjRg2fX+8boJr73NvMiLBSahNwrI4ik4B3HGV/\nAYJEJNRT8ZRSPyul8h0PfwY6n24sd+I51LaciKdi3gB8opRKcZTP9nA8BQQ67gcCOUopSwPipSul\ndjjuHwf2cOr/U6O+bpqYO8vQ1JZfrfsqpfYppQ7g+IKCh+K7+5pvUHylVLZSahtw2q+jasfzeDtU\nS4xJwL8c9/8F/KmGXd1dlqi+Ma7APmpjUUodAQ44YlXnzjk2u5pyF5EeIrJORLaIyHciEld9PxEJ\nAwKVUic/in2HmnOcAnzg4Xh3AsuUUgWOGFXaZg/meLJN8EjbQy2vIaXUCaXUj9g/DvdY/FbU9jUP\n1fAR4ebQZjrCbugMHK30OIUGdk7r4Q5gnScDiH05kT8ppV7GdSVtLHFABxHZ6Gg8XS5T0kBLgXgR\nScX+scffGuvAItIN+zvwX6ptas7XTUNVP/dkTj332sq4s68n4jfm37cxcmhsnso3RCmVAfY3eECI\nG7Hr+/eoLYa7Oblzji3Va8AMpdRgYBbwcg1lOmP/m550yt9XRLpiHzU/5RtCjRwvDugp9ml5P4rI\n5S7iNUZMgKtE5H/AIiC3jnInj1XftifUzdfQmd72NY/qvxrnzq0F0F+W8zARGQnchv2jDk+qcTkR\nDzMB5wCXAP7ATyLyk1LKU7/RezmwXSl1iYhEA9+ISH/HaO5pE5EA7CPpf2vosdoAvXxU29AUl5iG\nxmghl8G6iYg/9o/dPxKRk/XDfJqHux74WKnah8IaKZ4J+/SIi7B/A/97Eel7coTYQzHXAO8rpcpF\n5GXsnxTUx+m0PY35GtJt3xnqTOoIpwBdKj2uaUmORiUi/bG/yx6jlPL0xxw1LieilFrjwZjJQLZS\nqgQoEZHvgQHY50Z5wm3A0wBKqQQROQz0Arae7gHF/uszHwPvKqVW11CkyV83jcidZWhqy8/LjX09\nGb8xuBO/qXkq3wwRCVVKZTg+vq5pelRD/x61xXA3J3fOsSUyAMeUUlW+HCX2n3jdhr0ztgZ4Bdd/\nh+uB6U0QLxn7nFMbcERE9mOf27rNUzGrXePewf5JaE3ndtLptD3pbr6GzvS2r1nU9JMOkldwAAAH\ncklEQVTJrUFbmxoh1P6ubg1wM4CIDAPyTn7E4ol4jo/APgFuUkolNDCOy3hKqR6OW3fsHbvpjdQJ\nrutvuhq4QESMIuKHfcL/Hg/GSwRGATjmUsUBDV2Z+03gD6XU/9Wy3ROvm6biXIZGRLywX4SrvyZq\ny8+dfaHuUZSGxK98/NMdqXE3h8qxGkNTtEPVY6wBbnXcvwV73azudP4e7sRYA1wvIl4i0h37SOSv\nNRzPnXNsKZy5K6UKgcMico1zo/2TKJtS6myl1DlKqcccH9Xni8gQx4DEzVTKUUR6Ae2VUj83QbzP\ngZGOfTti7wRXbysbNaajY3pSBGDxQNvjzmtIXByjIfGrx/n/9u4uxI6zjuP499c0VG2LweiFUUzx\nBSTaYCEWSi8UlL5R29oLtRQqtKgX1leKghos2osiCkKl0ZSQgCCh2AvbUpLgleCFmtZqbAzaglX7\nomCpaLTpmv17MbPt6Sa7eyZzNmcn5/uBw87Mmec8M7vzPPs/zzzzPEuZdt03HQPtI3zGtAgn+RHw\nfmBjkj8DX6f5ZldVtbOqHkpyVZLHgaM0rYurlh+wHXgdcHdbYcxV1ckeIJlUfqMmcnWN8Ts9kmQ/\n8FvgOLCzqg6vVn7AHcCevDykzJeqHTj7FPO7FLgROJTk1zS/t6/QPOm7KtfN6VRVx5MsDENzFrCr\n2mFoWOH8lkoLkOQ64C6aOw8PJnm0qq6cZP5tPidcD1W1e5Ln336hOkjz8OV8ks8BW061i8zpqIeW\nyONOmtvaN9N8YfxIu+8bgXuq6url/qZ98qiqw0nuBQ4DczRfwqv9nHuAHVX1CM0T//cuTr/WLHHu\nNwLfT/I1mv+be2nqvcU+DewBXgU8VFX7Rt77aJtu1fOrqv1JLkvyGM2DoLeNttiu0jl+Nsk1NNfA\nc7w8BNbE6h6WuYbS3CE8n6a8XQt8Y9L5D6Xum5r5aR/AqckyXZUkSZKkZSWpy7bd3jndgYO3U1Oe\n2vyMaRGWJEnSlAy0YdVAWJIkSf0YCEuSJGkmDbSPsIGwJEmSehnq8GkGwpIkSerHQFiSJEkzyUBY\nkiRJM2mggfCZNrOcJEmSNBZbhAcqyXaamYD+TjOv/MPAP4FPAuuBx2mmd34hyW7gv8BFwBuAW2im\ndryEZj76m9vP/BewA7gKeBr4KvAtmvnQP19VDybZDPwQeE17KLcuMWWotKZYZqRuLDPqZKCjRtgi\nPEBJtgEfBi6kqUy20UwPfF9VXVxVFwFHaCqiBRuq6hLgizRznH+nqrYAW5Nsbfc5F/hpVb0b+Dfw\nTeADwPXtMjQV4gerahvN/Ol3rd6ZSpNhmZG6scyoq1R1fq0FtggP06XAT6pqDphL8kC7/cIkdwAb\naCqb/SNpFvY5BDxbVYfb9ceAC2jmlD9WVQdG9nuhquaTHAI2t9vXAz9I8h7gOPCOiZ+dNHmWGakb\ny4y6WSOBbVcGwmeOAHuAa6rqd0k+Drxv5P1j7c/5keWF9YXrYG7R9mMAVVVJFvb5Ak0FtzXJOppb\nYdIQWWakbiwzWtr8MANhu0YM08+BDyU5J8l5wNXt9vOAZ5Osp+nXtZR03D763muBZ9rlm4B14x2y\nNFWWGakby4y6qer+WgNsER6gqjqY5H7gN8DfaG43PQ9sB35J07/qF8D5C0kWf8QYyydk2/68G7gv\nyU3APuDoqZyDdDpZZqRuLDPqbI0Etl2lBnrgsy7JuVV1NMmrgZ8Bn6iqR6d9XNJaZZmRurHMaFxJ\n6oq33dY53b4nvk1VLXeXYNXZIjxcO5NsAc4B9lg5SSuyzEjdWGY0voH2ETYQHqiqWq5vlqRFLDNS\nN5YZdVLDHEjYQFiSJEn9DLSrrYGwJEmS+hlo1wiHT5MkSdJMskVYkiRJ/Qy0a4QtwpIkSepnQhNq\nJLkiyZEkf0jy5aWyS/LeJHNJru+adpSBsCRJkvqZQCCc5Czge8DlwLuAG5K8c4n97gT2d027mIGw\nJEmS+pmf7/460cXAH6vqyaqaA/YC155kv88AP6aZ4bBr2lcwEJYkSVI/k+ka8SbgLyPrf223vSTJ\nJuC6qtoBpEvak/FhOUmSJPUzxsNy/3jxKZ578em+OX0XGKv/7zgMhCVJktTPGOMIbzx7ExvP3vTS\n+hP/eXjxLk8BbxlZf3O7bdQ2YG+SAK8HrkzyvzHTnsBAWJIkSb3UZKZY/hXw9iSbgWeAjwE3vDKf\neuvCcpLdwANVdX+SdSulPRkDYUmSJPUzgZnlqup4kluBAzTPse2qqt8n+VTzdu1cnGSltCvlmRro\nAMiSJEmaviR1+YZbOqfb//wuqior77l6bBGWJElSPycfDm3NMxCWJElSPwPtYeA4wpIkSZpJtghL\nkiSpl7JrhCRJkmbSQLtGGAhLkiSpnwkMnzYNBsKSJEnqZzITapx2BsKSJEnqpWwRliRJ0kyyRViS\nJEmzyBZhSZIkzaaBtginBjrchSRJkqYvyZ+AzaeQ9MmqumCyR9ONgbAkSZJmklMsS5IkaSYZCEuS\nJGkmGQhLkiRpJhkIS5IkaSYZCEuSJGkm/R8+UK7D0Mg1HQAAAABJRU5ErkJggg==\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x7f89a84d8128>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"fig, axes = plt.subplots(1, 3, figsize=(13, 5))\n",
"\n",
"param_grid_linear = {'C': np.linspace(1, 2, 6),\n",
" 'gamma': np.linspace(1, 2, 6)}\n",
"\n",
"param_grid_one_log = {'C': np.linspace(1, 2, 6),\n",
" 'gamma': np.logspace(-3, 2, 6)}\n",
"\n",
"param_grid_range = {'C': np.logspace(-3, 2, 6),\n",
" 'gamma': np.logspace(-7, -2, 6)}\n",
"\n",
"for param_grid, ax in zip([param_grid_linear, param_grid_one_log,\n",
" param_grid_range], axes):\n",
" grid_search = GridSearchCV(SVC(), param_grid, cv=5)\n",
" grid_search.fit(X_train, y_train)\n",
" scores = [score.mean_validation_score for score in grid_search.grid_scores_]\n",
" scores = np.array(scores).reshape(6, 6)\n",
"\n",
" # plot the mean cross-validation scores\n",
" scores_image = mglearn.tools.heatmap(scores, xlabel='gamma', ylabel='C', xticklabels=param_grid['gamma'],\n",
" yticklabels=param_grid['C'], cmap=\"viridis\", ax=ax)\n",
" \n",
"plt.colorbar(scores_image, ax=axes.tolist())\n",
"print(\"gridsearch_failures\")"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Nested cross-validation"
]
},
{
"cell_type": "code",
"execution_count": 35,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Cross-validation scores: [ 0.967 1. 0.967 0.967 1. ]\n",
"Mean cross-validation score: 0.98\n"
]
}
],
"source": [
"scores = cross_val_score(GridSearchCV(SVC(), param_grid, cv=5), iris.data, iris.target, cv=5)\n",
"print(\"Cross-validation scores: \", scores)\n",
"print(\"Mean cross-validation score: \", scores.mean())"
]
},
{
"cell_type": "code",
"execution_count": 36,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"def nested_cv(X, y, inner_cv, outer_cv, Classifier, parameter_grid):\n",
" outer_scores = []\n",
" # for each split of the data in the outer cross-validation\n",
" # (split method returns indices)\n",
" for training_samples, test_samples in outer_cv.split(X, y):\n",
" # find best parameter using inner cross-validation:\n",
" best_parms = {}\n",
" best_score = -np.inf\n",
" # iterate over parameters\n",
" for parameters in parameter_grid:\n",
" # accumulate score over inner splits\n",
" cv_scores = []\n",
" # iterate over inner cross-validation\n",
" for inner_train, inner_test in inner_cv.split(X[training_samples], y[training_samples]):\n",
" # build classifier given parameters and training data\n",
" clf = Classifier(**parameters)\n",
" clf.fit(X[inner_train], y[inner_train])\n",
" # evaluate on inner test set\n",
" score = clf.score(X[inner_test], y[inner_test])\n",
" cv_scores.append(score)\n",
" # compute mean score over inner folds\n",
" mean_score = np.mean(cv_scores)\n",
" if mean_score > best_score:\n",
" # if better than so far, remember parameters\n",
" best_score = mean_score\n",
" best_params = parameters\n",
" # build classifier on best parameters using outer training set\n",
" clf = Classifier(**best_params)\n",
" clf.fit(X[training_samples], y[training_samples])\n",
" # evaluate \n",
" outer_scores.append(clf.score(X[test_samples], y[test_samples]))\n",
" return outer_scores"
]
},
{
"cell_type": "code",
"execution_count": 37,
"metadata": {
"collapsed": false,
"hide_input": false
},
"outputs": [
{
"data": {
"text/plain": [
"[0.96666666666666667, 1.0, 0.96666666666666667, 0.96666666666666667, 1.0]"
]
},
"execution_count": 37,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"from sklearn.model_selection import ParameterGrid, StratifiedKFold\n",
"nested_cv(iris.data, iris.target, StratifiedKFold(5), StratifiedKFold(5), SVC, ParameterGrid(param_grid))"
]
},
{
"cell_type": "markdown",
"metadata": {
"collapsed": true
},
"source": [
"### Parallelizing cross-validation and grid-search"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Exercises\n",
"Load the bank campaign dataset, and split it into training and test set.\n",
"Apply grid-search to the training set, searching for the best C for Logistic Regression, also search over L1 penalty vs L2 penalty."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"X[::113], y[::113]"
]
}
],
"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": "3.5.1"
}
},
"nbformat": 4,
"nbformat_minor": 0
}
| bsd-2-clause |
computational-class/cjc2016 | code/08.06-regression.ipynb | 2 | 180144 | {
"cells": [
{
"cell_type": "markdown",
"metadata": {
"slideshow": {
"slide_type": "slide"
}
},
"source": [
"# Simple Linear Regression"
]
},
{
"cell_type": "markdown",
"metadata": {
"slideshow": {
"slide_type": "fragment"
}
},
"source": [
"We used the correlation function to measure the strength of the linear relationship between two variables. For most applications, knowing that such a linear relationship exists isn’t enough. We’ll want to be able to understand the nature of the relationship. This is where we’ll use simple linear regression.\n"
]
},
{
"cell_type": "markdown",
"metadata": {
"slideshow": {
"slide_type": "subslide"
}
},
"source": [
"## The Model\n",
"\n",
"$$y_i = \\beta x_i + \\alpha + \\epsilon_i$$\n",
"\n",
"\n",
"where \n",
"\n",
"- $y_i$ is the number of minutes user i spends on the site daily, \n",
"- $x_i$ is the number of friends user i has\n",
"- $\\alpha$ is the constant when x = 0.\n",
"- $ε_i$ is a (hopefully small) error term representing the fact that there are other factors not accounted for by this simple model."
]
},
{
"cell_type": "markdown",
"metadata": {
"slideshow": {
"slide_type": "slide"
}
},
"source": [
"# Least Squares Fit\n",
"\n",
"最小二乘法"
]
},
{
"cell_type": "markdown",
"metadata": {
"slideshow": {
"slide_type": "subslide"
}
},
"source": [
"$$ y_i = X_i^T w$$\n",
"\n",
"The constant could be represent by 1 in X\n",
"\n",
"The squared error could be written as: \n",
"\n",
"$$ \\sum_{i = 1}^m (y_i -X_i^T w)^2 $$"
]
},
{
"cell_type": "markdown",
"metadata": {
"slideshow": {
"slide_type": "subslide"
}
},
"source": [
"If we know $\\alpha$ and $\\beta$, then we can make predictions."
]
},
{
"cell_type": "markdown",
"metadata": {
"ExecuteTime": {
"end_time": "2018-11-14T07:00:38.067857Z",
"start_time": "2018-11-14T07:00:38.062774Z"
},
"slideshow": {
"slide_type": "fragment"
}
},
"source": [
"Since we know the actual output $y_i$ we can compute the error for each pair."
]
},
{
"cell_type": "markdown",
"metadata": {
"slideshow": {
"slide_type": "fragment"
}
},
"source": [
"Since the negative errors cancel out with the positive ones, we use squared errors."
]
},
{
"cell_type": "markdown",
"metadata": {
"slideshow": {
"slide_type": "fragment"
}
},
"source": [
"The least squares solution is to choose the $\\alpha$ and $\\beta$ that make **sum_of_squared_errors** as small as possible."
]
},
{
"cell_type": "markdown",
"metadata": {
"slideshow": {
"slide_type": "subslide"
}
},
"source": [
"The choice of beta means that when the input value increases by standard_deviation(x), the prediction increases by correlation(x, y) * standard_deviation(y). \n",
"\n",
"- In the case when x and y are perfectly positively correlated, a one standard deviation increase in x results in a one-standard-deviation-of-y increase in the prediction.\n",
"- When they’re perfectly negatively correlated, the increase in x results in a decrease in the prediction. \n",
"- And when the correlation is zero, beta is zero, which means that changes in x don’t affect the prediction at all."
]
},
{
"cell_type": "markdown",
"metadata": {
"slideshow": {
"slide_type": "subslide"
}
},
"source": [
"> In this case, the slope of the fitted line is equal to the correlation between y and x corrected by the ratio of standard deviations of these variables. \n",
"\n",
"$$ y_i = \\alpha + \\beta x_i + \\varepsilon_i $$\n",
"\n",
"$$ \\hat\\varepsilon_i =y_i-a -b x_i $$\n",
"\n",
"$$ \\text{Find }\\min_{a,\\, b} Q(a, b), \\quad \\text{for } Q(a, b) = \\sum_{i=1}^n\\hat\\varepsilon_i^{\\,2} = \\sum_{i=1}^n (y_i -a - b x_i)^2\\ $$\n"
]
},
{
"cell_type": "markdown",
"metadata": {
"slideshow": {
"slide_type": "subslide"
}
},
"source": [
"By expanding to get a quadratic expression in $a$ and $b$, we can derive values of $a$ and $b$ that minimize the objective function $Q$ (these minimizing values are denoted $\\hat{\\alpha}$ and $\\hat{\\beta}$):\n",
"\n",
"\n",
"\n",
"\\begin{align}\n",
" \\hat\\alpha & = \\bar{y} - \\hat\\beta\\,\\bar{x}, \\\\\n",
" \\hat\\beta &= \\frac{ \\sum_{i=1}^n (x_i - \\bar{x})(y_i - \\bar{y}) }{ \\sum_{i=1}^n (x_i - \\bar{x})^2 } = \\frac{ \\operatorname{Cov}(x, y) }{ \\operatorname{Var}(x) } = r_{xy} \\frac{s_y}{s_x}. \\\\[6pt]\n",
"\\end{align}\n",
"\n",
"- $r_{xy}$ as the sample correlation coefficient between x and y\n",
"- $s_x$ and $s_y$ as the uncorrected sample standard deviations of x and y\n",
"\n",
"\n",
"> Kenney, J. F. and Keeping, E. S. (1962) \"Linear Regression and Correlation.\" Ch. 15 in ''Mathematics of Statistics'', Pt. 1, 3rd ed. Princeton, NJ: Van Nostrand, pp. 252–285"
]
},
{
"cell_type": "markdown",
"metadata": {
"slideshow": {
"slide_type": "subslide"
}
},
"source": [
"Substituting the above expressions for $\\hat{\\alpha}$ and $\\hat{\\beta}$ into\n",
"\n",
"$$f = \\hat{\\alpha} + \\hat{\\beta} x,$$\n",
"\n",
"yields\n",
"\n",
"$$\\frac{ f - \\bar{y}}{s_y} = r_{xy} \\frac{ x - \\bar{x}}{s_x} .$$\n",
"\n",
"\n",
"> Kenney, J. F. and Keeping, E. S. (1962) \"Linear Regression and Correlation.\" Ch. 15 in ''Mathematics of Statistics'', Pt. 1, 3rd ed. Princeton, NJ: Van Nostrand, pp. 252–285"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {
"ExecuteTime": {
"end_time": "2019-06-08T13:54:24.977792Z",
"start_time": "2019-06-08T13:54:24.954921Z"
},
"slideshow": {
"slide_type": "subslide"
}
},
"outputs": [],
"source": [
"num_friends_good = [49,41,40,25,21,21,19,19,18,18,16,15,15,15,15,14,14,13,13,13,13,12,12,11,10,10,10,10,10,10,10,10,10,10,10,10,10,10,10,9,9,9,9,9,9,9,9,9,9,9,9,9,9,9,9,9,9,8,8,8,8,8,8,8,8,8,8,8,8,8,7,7,7,7,7,7,7,7,7,7,7,7,7,7,7,6,6,6,6,6,6,6,6,6,6,6,6,6,6,6,6,6,6,6,6,6,6,5,5,5,5,5,5,5,5,5,5,5,5,5,5,5,5,5,4,4,4,4,4,4,4,4,4,4,4,4,4,4,4,4,4,4,4,4,3,3,3,3,3,3,3,3,3,3,3,3,3,3,3,3,3,3,3,3,2,2,2,2,2,2,2,2,2,2,2,2,2,2,2,2,2,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1]\n",
"daily_minutes_good = [68.77,51.25,52.08,38.36,44.54,57.13,51.4,41.42,31.22,34.76,54.01,38.79,47.59,49.1,27.66,41.03,36.73,48.65,28.12,46.62,35.57,32.98,35,26.07,23.77,39.73,40.57,31.65,31.21,36.32,20.45,21.93,26.02,27.34,23.49,46.94,30.5,33.8,24.23,21.4,27.94,32.24,40.57,25.07,19.42,22.39,18.42,46.96,23.72,26.41,26.97,36.76,40.32,35.02,29.47,30.2,31,38.11,38.18,36.31,21.03,30.86,36.07,28.66,29.08,37.28,15.28,24.17,22.31,30.17,25.53,19.85,35.37,44.6,17.23,13.47,26.33,35.02,32.09,24.81,19.33,28.77,24.26,31.98,25.73,24.86,16.28,34.51,15.23,39.72,40.8,26.06,35.76,34.76,16.13,44.04,18.03,19.65,32.62,35.59,39.43,14.18,35.24,40.13,41.82,35.45,36.07,43.67,24.61,20.9,21.9,18.79,27.61,27.21,26.61,29.77,20.59,27.53,13.82,33.2,25,33.1,36.65,18.63,14.87,22.2,36.81,25.53,24.62,26.25,18.21,28.08,19.42,29.79,32.8,35.99,28.32,27.79,35.88,29.06,36.28,14.1,36.63,37.49,26.9,18.58,38.48,24.48,18.95,33.55,14.24,29.04,32.51,25.63,22.22,19,32.73,15.16,13.9,27.2,32.01,29.27,33,13.74,20.42,27.32,18.23,35.35,28.48,9.08,24.62,20.12,35.26,19.92,31.02,16.49,12.16,30.7,31.22,34.65,13.13,27.51,33.2,31.57,14.1,33.42,17.44,10.12,24.42,9.82,23.39,30.93,15.03,21.67,31.09,33.29,22.61,26.89,23.48,8.38,27.81,32.35,23.84]\n",
"alpha, beta = 22.9475, 0.90386\n"
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {
"ExecuteTime": {
"end_time": "2019-06-08T13:54:49.498421Z",
"start_time": "2019-06-08T13:54:48.852023Z"
},
"slideshow": {
"slide_type": "subslide"
}
},
"outputs": [
{
"data": {
"image/png": "iVBORw0KGgoAAAANSUhEUgAAAYwAAAEpCAYAAACA6BUXAAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADl0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uIDMuMC4xLCBodHRwOi8vbWF0cGxvdGxpYi5vcmcvDW2N/gAAIABJREFUeJztnXmYFNX1v9/DOMCAwIDgwsgmKigxQkRx32Lk65bgEnHNL8YlMUaNGiIao5IYg6IRt0SJC1HUuKO4m4BLVFAQFVFAQbYBlG1YBxhmzu+PWw09TVVNV0/1dM/0eZ9nnp6+td1b3X1P3Xs+9xxRVQzDMAyjLprlugKGYRhG48AMhmEYhpEWZjAMwzCMtDCDYRiGYaSFGQzDMAwjLcxgGIZhGGlhBiMGRGRHEZkrIpNFpHmu6wMgIkeKiIrIS1k492jv3Kcllf1QRFaJyL1xX8/IL/Lx+57PiMjn3u+lez3OoSKyNr5aZcZ2ua5AE6E1sBPQCigGNuW2OjlhR6At0D3H9TCyj33fCxQzGDGgqt+ISBmwSVXX5bo+uUBVnxCRt4Hvcl0XI7vY971wMYMRE6q6Itd1yDWquijXdTAaBvu+FybmwzAKGhEpyuCYZiIi2ahPY7i+UbiYwUgDETlIRJ4XkYUissFz+N2etL2j55Sam3LcW155fxE5V0Q+E5FKEflCRM709ikVkftEZLF37ikicqxPHbY4vUTkoqRzfSsiD4rIzhHaUyYio0SkXEQ2ishsEblZRFrV4x6d5tVxtF+9vU7udyIyy2vnHBG5IuBcLUXkOhGZ4dXvWxF5VES6+ex7tIg85X02m0Rkmfe+R8p+W0QAItJLRN4RkY3A7annTDlui4NfRH4hIvOAamDfpH3Svp8iUiIi13tt2yAii0TkDhHpFvAdiu364jhLRN717tM6zyH7s5T9Mvq+e9vaeJ/dVO9zX+99V/8QcD8ifz/8iPO35u3bXUT+4d3LDSJS4X1nzg6pw09F5D2vPRUiMk5EetVR77NF5APvs1glIm+KyGHptrvBUVX7C/kDfor7ga4DngZGAxOBr5L26QgoMDfl2Le88gdwc/uPJZXVACcDU4F5wCPAZG/bJqBPyrkUWAvcAqwBxgGPAwu9bV8DpUn7H+mVv5Rynr2AJd62t7z2zPDevwNsl8Y9Ge3tf1pS2Wle2eiAet8HLPLuwZteuQKXpOzfGnjf2zbdu9a73vslQJeUfRVYDjzn3efpXtk8oLXP/XgN+Ap4G3gQuDLNtt7rtWMM8Aywd9T7CbRIasti7zN/FqgAPgj4DsV5/bu88kXefk8Cs4AHYvq+7wJ86W2bDTzstXGBVzYZ2L4+34+Qz+kt4vutHQms8rZPBEZ5n9Mar+x+n+v/ydu2ztv3UWCu99mUe9u6pxyT+DwWe/V90atPFXC8333KeX+Y6wrk+x/wofdhHZVS3i3p/7oMxkKgLKn8Jq98IzAFaOuVC64zUOAOny9MDa4z6J5U3gaY4G2/Nan8SFIMBm5EOQ3XIZyaVF4MvODtf34a92Q00QyGApOA9knlV3nlX6fsf2+i/UCzpPLfe+WPJpWVAFcDLZPKtmOrwTnX535sBO6M8Pkn2roe2D9lW6T7CdzolY2ntjHrgjNiYQajXtfHKdg244xT8ucgQNeYvu9veOV/B4qSylsk1eeu+nw/Qj6nt4jhtwa0A5Z65WelXKMzzhAqcEpS+WG43+YSYPek8pZJ16hlMHCGOWHU2yWVH+x9TnNT7qEZjMbwh3sqUeDQkH3qMhhXppTvnPQlOjZl2wle+X9SyhP7H+Nz/X29bfOSyo5kW4NxPD6durftQG/buDTuyWiiG4yDU8pbAhu8be28slLvhz07+cfibSvCPb2tIsmQBNQv0dnc4nM/KoGOET7/RFuf9tmW9v3EdVBLvI6lj8/+Pwn4DsV1/Xbe+5VAm7i/78D3vLL5JBnwpO1dcU/Pa4HiTL4fdXxObxHDbw34TdD99raf4W1/PansSa/s1z77t8cZgFSD8aH3Xejhc8xr3v77pdynnBsMU0nVzb+AvsDLIjISuE9VF0c8x9TkN6q6REQ2Ac1xQ95kyr3Xtj7n2Yx7IqmFqn4qIpVAVxFpoaobA+qRmBvtIiL3pGxr7b32DDi2PlTjpgC2oKobRGQR0APXma0CBuDuSRVwp2zr163y9u2IJ98V57TuDxwK7AHsDvTx9vfzycxQ1WUZtOF/PmVR7uduuLULX6nqdJ9zfZTN66vqKhF5AWeYporILcBjqro+5bhMv++HeK+vqeqG1I2qOl9EvgL2BnoBnydtTvf7kQ71/a0l2vF8wPn/673u53PM2NSdVXWliMzBfTcB58fyjl8HXOXzPe/ivfbEjYryBjMYdaCqI0VkFW464XrgWhF5EhgSwXCs8SmrApqr6uqU8sSPzU+QsEpVgxZJrcNN0TTHPaX70dF7Pdr786MkoLw+bAiod6KzSrQ1Ub9e3l8QJQAisgfwFK6DWw18BnyDu4cn4J7qU4lq7MOOi3I/d/JeFwTsV9fit/peH+BM4I+4p+hRwAgRuRO4OfGQUY/ve6IuQe1LtGFvtn0YSvf7kQ71/a2FtkNVl4rIZmq3YUfcw1zQ/UltWwfvmtsDlwQcA9n5LdYLU0mlgao+jHtCHIR7Sjkb+J+f6iPLtPAr9J5YOgLrVdXvB5Mgse1XqioBfz1Cjs82ifr9O6R+oqrzvP0ewRmLX+Hmvw9T1Z/hnIdB1GRYN7/jotzPKu+1Q8D5d8zy9VHVSlW9FigDLsZNT12PG1WQtF8m3/dEZ7xLyD4JJd/ykH1yTWg7RKQj7kE7uQ0bvbJ2AedM/WwTn9uSOr7n/0o9Ua4xg5Emqlqtqi+o6mE4hc1uwA8buBrbB8j0TvZeU4fcqXzivR4UX5Vi5VPvdX+pY32EiLTBzdPPV9X7VTW5Q9034LC4iXI/Z+E6/d7iL4EOGiHEdf0tqOoaVb0P6Ieb6hksIm1T9on6ff/Qez1WRIpTN4rIrrhpme9w9yJfSbTjxIDtic8peYrwS+/1qNSdReR7QKfkMm+k8w2ws9QjvlQuMINRByLyExFJnbpLPC1ubuj6AP9I/nGLyC7AnxPb6jh2LK6DOFdETk7e4Gn0B4lI0FNS1vFGDm/h5m7/mnrfRWRfEennvd2M64A7iUjnpH0OAs5vmBqnfz9VdRXwKs6ZOzK5bSLSFzcFlLXri0h7EUk1StU4Z2o13ggm0++7qk7CdbY9gVtEZEvfIiLb476bzYG/qefFzVMexjnmzxGRU5I3iEhP4Gbcvfpb0qYnvdfhItIlaf+OOJmvH4nRwwMiUmvUKS644/GZNyF7mA+jbu4E7hWRyTi5XT+cw2oKWx1gDcUG3NzpTBGZgJujPw43FH5UVZ8JO1hVV4vIhcATwHMi8hFu3UIxTs7XAzdtkK6DMRv8Evf0NgQ4VUQ+xM1lfx/n3D4DmKqqlZ4T92ScE/dV3JPcD3HrB87JdkUzuJ+/xY2KBgM/EJF3cNNDR+PkxFfgOvBsXL8d8F8RmY7z9WzCPRGXAveoaiISan2+72fhDP4VwEki8i7u+3owbornGWBEuu3LBar6nbc47yngWRF5H3dPu+BEBq1wSqwPkg67B/eZ7g9M836bVbj7OwU3Atkr5VK34H67PwRmich7wLe4z+xw4CHglaw0sj7kWqaV73+4H/lU3NzmKty0ydUkLUCiblltf5/zrnW3f5vy3t4xk1PK1TumA+6pZRlOIvoJznHWLGX/I0mR1SZtOxi38G857ov9HfAycHKa92Q00WS1vnJAnFKmltzQK+8K/BOnqa/y7vv7wOVAi6T9SnE/1gXevZgI/Aj4uXfee9K5H1HbWp/7iVNxPQms8Or8MXAurkNV4ItsXB+nmroLt96j0tvnHVwnL3F8371tnXBG4Svc3P5q4D3gvOTr1Of7EbDvW8T0W/O27YNbTLfIu6dLcWtJDg+4fltc1IAFXrvn4NaAtAhqB8743IAzKBtwD0afe/dvp3TvU0P+iVcZI88REQXWqer2ua6LET8iMgBn8MarakP7xgwjLcyHYRj5QWK+/P2c1sIwQrARRiPBRhiNHxE5AlihqtNSygfhpqlqgN66VTZsGHmFOb0No+E4CKf++gznG1CcBHhf3Dz5hWYsjHzGRhiNBBthNH68NTSX41RRZbgHtsU45/MdqvppyOGGkXOalMHo2LGjdu/ePdfVMAzDaFRMmTJlmap2qmu/JjUl1b17dyZPnlz3joZhGMYWxCXmqhNTSRmGYRhpYQbDMAzDSIu8MBgiMlREvk762yAix4vI5SIyX0Rmishxua6nYRhGIZMXPgxVHQ4MB/CCpU3FZV0biUuG0wX4j4h0U9WqwBMZhmEYWSMvRhgpnI0LUnYS8JS6UMxf4HLc7pe6s4hcJCKTRWTy0qVLG7amhmEYBUQ+GozzcZEauwDJnvuF+CQ1UdVRqtpfVft36lSnKswwDKPJMHZqOYcMH0+PoS9zyPDxjJ1aXvdB9SAvpqQSiMh+uHSNM0SkObWzjNXg4vYbhmEUPGOnlnPNc9OorHLdYnlFJdc856LODOpXlpVr5tsI40LgQe//xbjVsAl2JTxfsGEYRsEw4vWZW4xFgsqqaka8PjNr18wbgyEirfH8Fl7Ry8AZItJKRPbG5YH4JOh4wzCMQmJRRWWk8jjIpympwcBr6mX+UtUpIjIGl+1qA3CBNqU4JoZhGPWgc2kJ5T7GoXNpSdaumTcjDFV9SFXPTym7WVV7qOpeqvperupmGIaRbwwZ2IuS4qJaZSXFRQwZ2Ctr18ynEYZhGIaRJgnH9ojXZ7KoopLOpSUMGdgraw5vMINhGIbRaBnUr4wDdirj/vvh2iugRYvsXi9vpqQMwzCM9Jk1Cy68EHr0gJtugnffzf41zWAYhmE0Ij76CE47DXr3hkcfhfPPd8bjmGOyf22bkjIMw8hzVOHNN+GWW2D8eGjXDq65Bi67DHbaqeHqYQbDMAwjT9m8GZ591hmKqVOhc2cYMQIuugjatm34+pjBMAzDyDM2bIDRo51xmDMHevWCBx6Ac87JvmM7DDMYhmEYeUJFBfzjH3DnnfDtt3DAAXDbbfCTn0CzPPA4m8EwDMPIMYsWwciRcN99sGYNDBwIV18NRx4JIrmu3VbMYBiGYeSImTPdtNOjjzp/xemnw+9/D/365bpm/pjBMAzDaGA++giGD4fnn3c+ifPPh9/9DnbbLdc1C8cMhmEYRgOQkMYOHw4TJkBpaW6ksfXBDIZhGEYW2bwZnnkGbr11qzT2ttucNLZNm1zXLhpmMAzDMLJAZSX861+1pbEPPghnn51baWx9MINhGIYRIxUV8Pe/O2nsd9/lnzS2PpjBMAzDiIFFi+COO+D++7dKY4cOhSOOyC9pbH0wg2EYhlEPUqWxgwc7aWzfvrmuWfyYwTAMw8iADz90MZ4S0tgLLoCrrsp/aWx9MINhGIaRJqrwxhvOUCSksdde66SxO+6Y69plHzMYhmEYdZCQxt5yC3zySeOWxtaHSD57EXlCRI7OVmUMwzDyicpKFwywVy8480z3/sEHnUz2qqsKy1hA9Ix7g4E3ReRrEblGRHbJRqUMwzBySUUF3HwzdO8Ov/41dOwIzz0HX3wBv/hF411HUV+iGowTgaeAXYC/APNEZKyInCgijVxhbBhGoVNeDkOGQJcu8Ic/wA9+4HwVEyfCySc3/nUU9SVS81X1FVU9E9gJ+AXwDs6IvADMF5E/i0iPTCoiIu1E5N8iUi4is0WkuYhcLiLzRWSmiByXyXkNwzDqYuZMp3Lq0QP+9jc46SQXxuPVV/MvxHguychequpaVR2tqscAXYGhwDLgD8BXIvKmiJwuIsURTns38DmwK9AH6AJc4v1/MvBgxPMZhmGE8uGHcOqpsNde8NhjcOGF8NVX8PjjTXMdRX2p9wBLVRep6gjg/wFPAAL80Pt/gYj8TkSah51DRHYGDgZuVscGnJF4SlXXqOoXwFxgv/rW1zCMwkYVXn8djjoKBgyA8eOdNHbePLj33qa9jqK+1EtWKyJ7AmfgnOG9ccbiM+Ah3AjhPOAW4FQROV5VVwacqg/wDfCsiOwNjAOKcSOOBAtxvpPUOlwEXATQtWvX+jTHMIwmTKo0tqysMKWx9SGywRCRrjgjcQawL85IrAb+CTygqpOT9v0jcDVwAzACuCDgtDsCewMDgJXAf4CdccYnQQ1QnXqgqo4CRgH0799fo7bHMIymTWUljB7twnd8842TyD70kIsa2zx07sNIJZLBEJH3cZ06OEPxHvAAbuqoMnV/b2ppmIgcAvw45NTfAVNUdaF3nTdxxqEsaZ9dgQVR6msYRuGycqWLGnvXXS5q7IABzqH94x+b2ilTot62A4GlwO3AXqp6mKr+y89YpLARZ2CCmAjsLSKdRaQFcAywFjhDRFp501QdgE8i1tcwjAKjvNylO+3aFa67zklj33oLPvgABg0yY1Efok5JnQqMU9XNEY+7iBCDoarrRORS4E2gBTBaVW/3jMd0YANwgaralJNhGL7MmLE1amx1NZxxhosau+++ua5Z00GaUh/cv39/nTx5ct07GobRZJg0yTmyx451K7DPP9+F7eiR0YqwwkREpqhq/7r2y8TpfQLwW+B7QCv8Rw6qqu2intswGpKxU8sZ8fpMFlVU0rm0hCEDezGoX1ndBxo5JyGNveUWN91UWupWZl96aWFEjc0VUZ3ep+JCg9i6R6NRM3ZqOdc8N43KKie8K6+o5JrnpgGY0chjNm+Gp592huLTT5009vbb3YI7k8Zmn6jun6sBBX4NdASm4RzgLXDqqf8Bo7Gw6UaeM+L1mVuMRYLKqmpGvD4zRzUywqisdIqnPfeEs86CjRudNHbOHLjySjMWDUVUg/E94BVVvU9VVwBfA7upapWqfoSLK3UscF3M9TSMWFlU4S/sCyo3csPKlfCXv0C3bnDJJW666fnnYfp0OO88W0fR0EQ1GAosSXo/G9h9y0bV1cAEXGBCw8hbOpeWRCo3GpZUaWz//iaNzQei3vbZwJ5J76cDe4lIaVJZO9x0lWHkLUMG9qKkuKhWWUlxEUMG9spRjQxw0tjzz3cKpzvucIvsPvkEXnkFjjjCosbmmqi+hleBK0Skm6rOA8YDRcAjInI70Bc4HufbMIy8JeHYNpVUfpAqjb3oIpPG5iNRDcYdQA+cn+KfqrpARO4DLgZO8PYRXNwow8hrBvUrMwORQxLS2OHD4e23oX17k8bmO5EMhqouAU5PKf4NLkjgMUAV8ISqjouneoZhNDVMGtt4qbf81QvXcb/3ZxiG4UtlJTz8sAsp/s030Lu3e3/WWaZ2aizYegnDMLJKImrsnXfC0qVw4IEWNbaxEmgwROShepxXVfX8ehxvFAAWmqNps3ChUzqNGgVr18Jxx8HQoXDYYaZ2aqyEjTB+HrJN2TY8SCKKoXj/m8EwArHQHE2XGTPg1lthzBioqYHBgy1qbFMhzGD81KdMgGtw2fHuBaYAm3AZ834InALcA7wdbzWNpkZYaA4zGI2TiROdI/uFF0wa21QJNBiq+mxqmYhcCXwfOFJV30vZ/A8ROReXgW9MrLU0mhwWmqNpoAqvveYMRUIae911ThrbqVOua2fETVSX0y+B//oYCwBU9VFgMvCn+lbMaNpYaI7GzebN8MQT0K8fHH88zJ7tHNnz58Of/mTGoqkS1WB0A+bXsc8sXCpXwwjEQnM0Ttavh3vvrR019uGHncG44grYfvtc19DIJlFltd/hwpiH8X1cuBDDCMRCczQuVq50huKuu7ZKY++4A046KX+lsabCi5+oBuMl4JciMhz4o6pWJW8Ukd8D/TCnt5EGFpoj/0lIY++/H9atc9NPV1+d/9JYU+Flh6gG40ZczoshwJki8iqwANgBOBLYF6gG/hxfFQ2jbuxpMl6+/BJGjNgqjT3jDCeN/f73c12z9DAVXnaIGkvqOxE5FHgIOBq4KGWXpcCvVXV8TPUzjDqxp8n4SEhjx46FkhL45S+dNLZ791zXLBp1qfDsASMzIocGUdX5wDEi0hs4BLcGYyPwJTBeVTfGW0XDCMeeJuuHnzT2j39s3NLYzqUllPsYjc6lJfaAUQ8yjiWlqjOAGTHWxTAywtZ0ZMbmzfDUU85QfPYZ7Lqrk8ZeeGHjVzsNGdirllGArSo8e8DInDzVNxhG+tiajmgkpLF77AFnnw1VVTB6dNOSxg7qV8ZfT9mHstISBCgrLeGvp+zDoH5l9oBRD/ImWq2IzAU2e28Xq+phInI5cBVQCfxWVV/NVf2M/CXsadLYyooVW6PGLlsGBx0EI0fmtzS2PgSp8MKmq4xw8sZgAKjq7on/RaQncAnQB+gC/MdLDVsVdLxRmNiajnAWLnRTTaNGbZXGDh0Khx6a39LYbGEPGJmTVwYjhZOBp1R1DfCFNwLZD5iY01oZeYmt6diWL790UWMfe6xxSmOzhT1gZE4+GYxKEZmNk+begBtVfJ60fSGwS+pBInIRnry3a9euDVBNIyomYWxYJk50ebJfeKFxS2OziT1gZEbaBkNEmuHydy9T1cfjroiq7uVd5zDgeeBpoCZplxrcosDU40YBowD69++vqduNeIna+ZuEsWFQhVdfdYqnd95x0tjrr4ff/KbxSmON/CNtV5eq1gC3A8dkrzqgqu8Cc4HFQHKPsituVbmRIxKdf3lFJcrWzn/s1PLAY8IkjEb92bwZHn8c+vaFE06AOXNcKI/582HYMDMWRrxE1UZMA2L/CopIaxHZxfu/H27q6b/AGSLSSkT2BjoAn8R9bSN9Mun8TcKYHdavh3vu8ZfG/va3TUMaa+QfUX0YvwZeEpHLgHtVdZspogxpBbwtIkXAKuAcVX1PRMYA04ENwAWqalNOOSSTzt8kjPGyYsXWqLEJaeydd8KJJzZNaayRX0Q1GGcBrwG3AL8XkUm4qaOalP1UVS9P96SquhTY06f8ZuDmiHU0skQmnf+Qgb0Y8synVFVvtfXFRWISxogsWOCmmhLS2BNOcFFjC1Uaa+SGqAbjN4Dicnt3xklf/VAgbYNhNA4y1q+njgttnJg2CWnsmDHOsX3mmU4au88+ua6ZUYhENRjnZaUWRiwEKZjikrVmol8f8fpMqmpqW4iqGrW4PXXwwQdO8ZSQxl58MVx5pUljjdwSNbz5v7JVEaN+BMlXJ89bwbNTynMmazWnd/qkSmM7dDBprJFfmJusiRCkYHpi0oLYZK2ZyGotMGDdbN7sVmPvu29taey8eSaNNfKLyAbDk8AOE5FPRWStiGxO2tZNRPaPt4pGOgQ9sVcHCMsyecLPRFY7ZGAvSoprp3i3uD2OZGnsOedAdbVJY438JtKUlIjsALwL9MI5vlN7o27ABBE5T1UfiaeKRjoEKZiKRHyNRiZP+JlML1ncnm1JlcYefLBJY43GQVSn9zCgN3AvcBPwR+DixEZVfUdEvgR+BpjBaECCFEyn7ldWy4eRKM/kCT/TNRUWt8fhJ41NRI01jMZA1OeZnwAfq+qlqvot/gLJj4B9610zIxJBCWNuGrRPYCKZqNj0UmZ8+SWcdx7stpsbVZx8sstw99JLZiyMxkXUEUYn4I069ikC2mVWHSNBJlLYoCf5uJ7wbXopGh984KLGvviiSWONpkFUg7EIl9AojH7A8syqY0B+R3htStNL2Qi7npDGDh8O7767VRp76aXQsWNMFTeMHBHVYDwHXCEil6nqXakbReRSnEH5dxyVKwT8Oi1LUp994jbKmzfDk0+6NRTTpkGXLi796fnnm9rJaDpENRh/Bk4C7hCRQbiggYjIX4CDgCOAdd5+RhJ+hgHw7bRSjUUCW+wWH3EZ5fXr4cEH4fbb3bqJvfeGf/3LhfAoLo671oaRW6Ku9F4lIocDDwHHJW26xnudDfxcVb+MqX5NgqCn2RbbNfPttOKUwhr+1HcF+vLlThp7991bpbF33+2UTyaNNZoqkVO0euqoE0RkL+AwoCOwBper4n8Wgnxbgp5mg0YS1aqUFBdZkvoskqlEeMEC+Nvf4J//dNLYE0/cGjXWMJo6Gef09kYRNpJIg6hTSWVJvgxTI2WHqJF3v/jCRY197DHn2D7rLBc19nvfa6gaG0buychgiEh74Me4HBbNgW+BD1T1vRjr1mQIeppt36qYDVU1vp1WU1Ij5SPpSoTff985shPS2F//2klju3XLRa0NI7dENhgi8mvgVqAEFx4EvAV8IjIN+JmqfhZbDZsAQU+zN5zkFMo2ksgNQUZZFV55xRmKhDT2hhtc1FiTxhqFTNRYUoOBe3AG4gVgIrAW6IobcXwfF0tqgKp+HXNdGy1hT7NhkV6NhqWqykljb721tjT2ggugdetc184wco9E8VGLyEe4hXmDVPWllG0CDAGGA4+p6rlxVjQd+vfvr5MnT27oy2ZMqnoK3Mgj09AdRmakSmP79HH+CZPGGoWCiExR1f517Rd1SqoP8HaqsQCXxBu4VUROBX4U8bwFiS3Qi49MVm0npLF33eX+P+QQk8YaRhhRDcZaYH4d+3wGWMbhNGhq2eiyEWoj3etGWbWdkMaOGuVGFyaNNYz0iPoc9V/clFQYu2Ny27RoStnoMsnGFxfpJnb64gv4+c9d1Ni774ZTT3W+inHjzFgYRjpEHWFcDXwkIr9X1VtTN4rIj4HDgVPiqFxT56jenRgzcdsB21G9M8vJed3YaTwxaQHVqhSJcOaALtw0aJ8GefLP5fRaXSO19993wQDHjYNWrUwaaxiZEtVgnIYLLHiziJwATAC+A1oA/YHTgfeAniJyZerBqvq3+lW3aTFhxtJI5WFcN3ZaLeNTrcqYifP5ZulaPp6/KuuRb3M5vea3zkUVSr7dlcMOg//9z6SxhhEHUQ3GbThJreDCghzG1iRKiTUZhwKHpByXSOcaaDBEpDkuvMj7qnqBiFwOXAVUAr9V1Vcj1jXv8VvMF1YexhOTFviWvzd7xTZl2XjyzzTURhwkr3PRamHdjM6sndSTjUvb0KWLS396/vkmjTWM+hLVYPwJ/yx7cXAtMBdARHoCl+BUWV2A/4hIN1WtytK1c0IzgRqfu9lMti2rC79ghWHdOjdRAAAgAElEQVRkYpTCiBpqI04G9StjQ6Vw7fC1zH97V6pXt6JLzypuus2ksYYRJ1Gj1d6YjUp4gQz3B57CjVBOBp5S1TXAFyIyF9gPt1Aw9diLgIsAunbtmo3qZQ0/YxFWHkZQhNuw/eMkV9n4li+He+6Bu+/uvEUaO3QoHH98sUljDSNmMg4+GBfegr+7gItxxgLcqOLzpN0WArv4Ha+qo4BR4BbuZa+m+c2ZA7r4OtCDiDoiSYegUBvZcLrPn781aqxJYw2jYci5wQB+Bbylql+LSOLn3hyoSdqnBvCPBZ4DcrXeIIybBrmlL6kqqQkzlvpOP5U1kHQ37sx206e70B2PP+7en3mmRY01jIYiHwzGuUAbEfkp0AFojRtxJPcmuwL+Xt0GJs4OsH2rYlau39Yt075V+KR7kMG6adA+WwxHUH2hYXNrxCW3fe89FwwwIY295BInjc33Wch8fLgwjEzJ+Syvqh6sqvuoal/geuB54CXgDBFpJSJ74wzJJ7msZ4J0F4mlww0n9aG4qLYvobhItkSx9WPs1HKGPPNprQVyQ575NHCB3KB+Zfz1lH0oKy1BcCOLhoxVVR+5rSq8/DIcdpibanr/fbjxRjcdNXJk4zAWuVrMaBjZIB9GGNugqlNEZAwwHdgAXJAvmfziXG+QiaN42LjpVFXXvhVV1cqwcdMDj8tlbo1M5LZVVfDvf7upp88/p9FKYy1WmNHUyCuDoaqjgdHe/zcDN+eyPn6EdYCZTD9E7cz9prDCynNNFLntunVbo8bOn++ixj7yCJxxRuOUxja1WGGGkfMpqcbGkIG9KCkuqlVWUlzEUb072fSDD+lMiS1fDsOGuVAdl1/upprGjYPPPoNzz22cxgKaVqwww4DoCZSOxYUAeVhVFyeV3wxcCFQBt6rqyFhrmUcETSPVNf0Ql/NTxM3t+5Xn2sEadP2gUVSqNPakk5w09pDUOAGNlFwuZjSMbJBJAqUdge4Jn4KIXAaMpPYK8ONV9fU4K5oOuUyg1H3oy4HbRg7uG1uipLDrlBQX+V4Dsr+gLkoyqFRp7FlnwZAhTVMam2sjbhjpkK0ESnsCbyQZi+2AobgAhAfi1kt8BFwGNLjByFfqUlZF6VCCpLgi+F5j2LjpbKiqyXrwwXQcvI1VGlsfcik4MIy4ierDqAEqkt6fAuwM3Kaqc1V1PvAG0Dem+jUJgpycCUlsuhJZgA1V/usXgwaKK9dXxSYDDiOwjSsreeklJ4ttjNJYwzC2EnWE8RVwuDeyqMGNLjYADyXtUw20j6d6TYMgZVUzIbJEtrKqxrc8KpkqdYKmWFLbqNXCui87s37y7px0qzMMd90Fv/hF45LGGoaxlagG42HgXlycpxqgF3C/qibH0N4bWBJP9XJLUEKiqAQ5P1Of/BOsXF+V0dy3nw+jxXbNqKjcdgorE6VO2Cr3RBvXrYO1n3Vh9Uc9qF7diq67V3HTiMYrjTUMYytRDcY/geOAE733U3FhyQEQkR/gVFQPbXto4yIoIREQ2WgEKat++2Tw4vUhT39KlRe2tryikiFPfwqEhxM54fu71DJwp+5XRv9uHTJS6vgZrDA/xQsXHM2+37Xh2TEt2by+OW26V3DZn9fz50s7EnNgXMMwckQkldSWg1w48mJgWvIKbBHpBnQHZqpqg48y4lRJ9bzmFd+IrkUizP7r8duU9xj6sm+iEAG+GX6C7zXCFE9+lJYUc+OP+zDkmU9rTWUVFwmD9+/Cs1PKY1FJBSme/EZEm1e3ZPWHu1H9ZY8Gkcaa6sgw4idbKikAVPXLgPJ5wLxMzplvBIX/Dio/+8CuvuHFzz4wPq9uRWVVRutA3ht6dKRONehcyTk3Ni3dntWTerLuy84IcO45Lmpsn+AwWPUm7si3hmFEIyODISJHAYOAnkBLVT0m1lo1QoLCi2fi86gLP6nmFQHTW5k4t4OOqVZFF+/A0vd6UDl7J6R4M+37z+eWG1py4fE7R75OVCw2k2HklqgrvZsB/wLOYmsO7+QpqXOA+4H/U9V346pkY8EvvHichIU9jzPG1TaKJ4XK2TuyYfIerJlXynatNlF66Ez2OPJbrj2lJ4P6Zd9YgMVmMoxcE3WEcRVwNm5x3t+An+LSqSZ4HLgF+A3QKAxGkBKqLKADLsswyKAfQWlVmwkUNZNt/BRhYc+DlFiJGFdRpnES51q/oYZ1X3Zm9aSeVC1rQ6edN/OXu+AXv2hO69a9cCK5hiOTyLeGYcRHVIPxc2A+cKSqViZlyANAVWtE5F2gUUQDClNChXXAQQqmMKPhZ5iC0qqeNaAr/bt1iGSUMo1x5ceP9izj74uECc+0Z/PqEoo7reHHly/gmRFdciqNtdhMhpFbohqM3YAnVTVsDmANLt5U3vP4JP8c2I9Pmr9laim1A77xxelbjEWCqhrlxhen++4/qF9ZoGE658CunHNg10C/R9RRS319G8uWwT33wO0ja1i7qjMtdl1O+2M/p2S375jVvIiXP2+WU19BJvlDDMOIj6gGYzXQqY599gDWZ1adhqUmQFGcKPfrgIPWTlRUVgVO/TwxyT+77BOTFnD76fsyYcZSFlVUsnO7lvTv1iGDlgSTzjTOvHkuauwDD7iose33Ws5OJ35Fy11XbtknX5zLFpvJMHJHVIMxHjhVRA5U1YmpG0XkIOAwb7+CI2jqJ0yiG2RkIPqTtJ9vJWwa5/PPt0aNFYGzz3ZRY0989EPfNSXmXDaMwiZq8MEbgI3Af0TkeqAMQESO8d4nItT+Lb4qZo+gBchxLkxeVFFJUchS56AIs0OeTglK+HR4UMKg/NHANgmMzu2xHw9eX8Y++8Bzz8Gll8Ls2TB6tFtHYYl/DMPwI5LBUNVZwAnAWuBG4Ce4/vV1731L4CpVfTXWWmaJoDXucSYP71xawpkDukQ6ZuX6qlA/iR91hVDXGlj/9Y5Mu+8H/OH8TnzwgctyN28e3HFH7aixQVkFzblsGIVN5IV7qvqOiOwO/Aw4FOiIc3R/AjymqnPirWL2CIvLBP5TPIEZ74CWPsH/kqeSUp3bE2Ys9fUvBOEXRDBB0HTRwuUb+OX1y1g5cX+qlrWhqO16Ov5oOnff2J4zDu7se0w+O5ctNIhh5I6MYknlK1FjSfUd9oZvJxwWsyk1HHkyIwf3zVrMpgRzh58QGBgw2fjUbCryosbuRvXqEoo7rqbtgXNo3XsRUqSUlhTzyQ3HBl4nH4mS1c8wjPTJSiwpERkPPK+qd4fsMxIoUtVLo5w7FwQ9sVdUVjFs3HTfXBVBI4yy0pLICp6gJ/lh46YHjnyC4in9oGs7yisqqV5fzJop3VnzcXdqNjSnxa7L2eHYabTcbWmtqLFho5V8xUKDGEZuiToldSQuF0YY2wODgbw3GGH4ddjgjIVf3olM5/eDjIzf6OaGk/oEdprvTq2k4sO9WftpV3RzESW7L6HtgDm1pLGNHQsNYhi5JapKKhBx7A38CIj0CxaRZiLypojMEpGZIjLQK79cROZ7ZcfFVdf6kqo6intKZFC/Mkactm+ta4w4bV8G9SvbpnPctLQNy17alwX3Hcmaqd1o1XsRu5z/NjueOiXUWDRrhDkqTL1lGLkldIQhIm2AZUn7KXCJiFwSdhgwJmI9FPiZqi4Wkf8D/iIiXwOXAH2ALjgpbzdVjW0uJSiWU1B5gqBRQZhDNqqzNuga7UqKqaisYsPC9qye2HNL1Ng2+82l7f7fsF3bDek0PXDRYj5joUEMI7eEGgxVXSMibwJHeEWtgM3AJr/dge+AN4FrolTCS8K02HvbDfgUF9TwKVVdA3whInOB/YBtFgxmSlAspzMHdOHlzxaHKqhSCcvVAMSSx6GmBlbP6sSSt7uxsbwDzUo20e7QmbT5wTyKSqLZ0bJG+FSez+otwygEIqmkRKQGuEdVL4u9IiK/B64GlgIDgSuBz1X1n972p4AnVPX5lOMuAi4C6Nq1637z5kXL3xQUrXbs1HJfP8KI0/Zl8rwVaUtkEx1z0Lb3hh5dZx2rqtxq7FtvhS++gKK262l7wBy2//4CmhXXhB7bunkRNYopiwzDCCRdlVRUgzEBeC5MJVVfROQU4GZgAjBZVR/0yv8NPK6qLwYdG2eKVvCfRpo8b4XvqCQMwX8xYFj61rFTyxn+4tfMeqcj66b0ZNOqluyzD6za8zPosRAp0m3O5XeNhETYnsoNwwgiK7JaVT0q8yqlfY3nROQu3BRVcq+2K+Afxa8BCQokGESRCDu3axkpj8Mj4xdz5Y2VrPzoICeN7bKcXY/7nGFDdmHYuCVUVG5rGoLM/iovrasZCMMw6ktGKVrjRkR2A9ar6hIvgOEG4GXgURG5DegOdMCtJm8QgnwSYc5wP6pV03bWzpsHt98O9963IzVVRZTssYR2A2bToqwCgNveWB15/UTcCiJbaW0YhUvUhXvBS5Bro6oa5dylwGsiUgR8CwxW1SkiMgaYjjMgF2gWlqUHdYBB6x2i0rp5UZ3O2mnTnH/iiSdc1NhWvRfRZsAcmndcW+tciUCGUbL0xakgCnPsm9EwjKZP1BFGJf6zHy2SzrUuaiVU9WNgT5/ym3H+jKyQ6tgur6hkyDMue15ci8HWbXKd6+R5K1iyagMKLFm1gY/mrmCHtWXccgu8/DK0bg2XXQZXXAFnPPZV4BRWUOypGoWi1E8mZvOa6UprG5UYRtMgarTa7VW1TeofUAIcALwNvAHskIW6xk5Q+I9h46YHTuWUlZZwSM/aSY5S36eSyLhXrYoqrJnVidsvLePww2HSJPjTn2D+fJfEqEuX8GixQXLYIhHfCLcjXp/J2KnlHDJ8PD2Gvswhw8eHhkkPI5OV1kFh1zOtg2EYuSOWld6qWq2qk4ETgR8Af4rjvNkmKPzHyvVVHNXbP7Fg9x1K+HBu7RXUqe9TeWzSfLRaWDutjMUPHs7S5/Zn85qWdPjR58ybB3/8I3RIsjmD+pUFriYPMiZBvpVEBx1Hh53JSuu6wq4bhtF4iNXprarrROQd4GxgaJznbmgmzFjqW/7BnBXbrJIOi2BbQnO+/bCzixq7poTiTqvZ4cSptO69GClSWrX6nu9xQcqmIH/IjS9O93WIC8GZAKNOC2Wy0triPxlG0yEbKqky6s77nReErY8I6tDCQmo0k63bq9cXs/bj7iyftjtrVzejRZfl7DBw26ixQYTN+/sZk2Hj/JMrBVU3kw47k5XW6eQUNwyjcRCbwRCRYuAy4GhgRlznzSZhGffKQhzMQRSJsKmiJas/6rElauwBR1Yyf9ePt0hjU/EzDBA9lEjQ9FoQiQ47rhhXQVj8J8NoOkSV1X4WsKkEN7Jo4b0fWZ9KNRRBRqHM6zj9OjpQKqu2DcdRtbQNyyb2ZN2Xu4BA673LaTtgDtvtXk2LEMPjZxhabNcs8jRSkNw2aBR1VO9ODSKTtfhPhtF0iDrC8J9w38pM4HZVfSDD+jQoYU+/QR1dcmgQVdi4sD2rJ+5O5ZwdfaPGLqoINkxFIr6GIWi9R9iIJ8jpHTSKmjBjKRNmLG2QhES20twwmgZRDUaPgPIaYKWqrg3YnpfU9fTr19GNeH0mqlD59Y6snrg7Gxe1p1nJRkoPm8n2/baNGlvaqpghA3sx5OlPa8lei5ttK4Oti7AcFlGn0MJ8GOaQNgzDj6ixpKKFgm0ERHn63bQJZr3bgVWTelK1vA1F7dbT/pjPQ6PGbnnwT+3sxQUGjBLqI8y+HNW7k29QxJLiZr5TaAkfhjmkDcNIl7yIJZVL0kl6tPC7TTSb1ZM1k3uw/Nu+FHdaTceTptKq92KkWfgoYVVlFSNen+m7QLCqOjw0eRSCZMAti4sACXQ6m0PaMIx0iWwwROT7wG9wC/RK8V/8p6ras551yzp1JT0a8ugMlk7qypqPu1GzoTklXVdw5KXfMqdkzjbS2OJm4PMgT7uS4sCpokTYkHRJXNLPyAVNI1Wsr+LsA7vWyt9x6n61R1XmkDYMIx2i5sP4EfASztCErSZQVS0K2Z4VoubDOGT4eN/OfAdtz6J3u/DtR53RzbWjxoYF//ObMmrfqpjVlZsjR7kNYuTgvr6jghbbNfOd3mrfqpgNVTWWQMkwjECykg8D+CtQDLwA3AnMBtZHr15+kGosNn3XhtWTejIvIY3tU067A+ZQnBQ1NqjjD/IvVKyvii0GYJFIYKiNlsXNtnGkFzcT56BvACWUYRhNn6gGow8u3/Yp2Qg13tCIeNLY8lJWvb/HFmls2/5z2eOHi1mm2y62CxphBJWHOZeDnv6DZLXVqoFTTyvXV1FclDLoEwKd6lEXJRqGYUQNPrgS+KQpGAvYqmCqnNuRjYvb0e6wmZRdPJ72R3/Jdad39w3yd+aALpHKhwzsFRgw8IaT+vgGGQyKSFtWWhKoYCoS8XWsG4ZhxEXUEcZY4NBsVCSXtN3/G9oeMKeWNDZsjUb/bh0ilScIW++RSph6yW9bJsmdDMMwohDV6d0WeAv4APidqubVvEZUp3f3oS8Hbps7/IQ4qpQxYXLf68ZOq6V6OnNAFybMWBppmqlIhNl/PT5b1TcMoxGRLaf3GGAZ8CvgDBGZBqz22U9V9ScRz93ghEWrDSOoM48zs1zQgsKxU8t5dkr5Fn9JtSrPTinnB13b+RqMPXZszVffbZsE8cwBXTKql2EYhUtUg3Fi0v/tgcMD9msUk+dh0WqDCFq7MXneCp6dUh4YyC8uYxKkkpo4xz+J0/pNNRzSswPvzV6xpeyQnh24adA+ka9tGEZhE9VgHJWVWuSI9q2KfcOCt29VHHhMUIedmCJKLU9klosrKmyQSios496KdZtqlX08fxVjp5abrNYwjEhEjSX1drYqkguC3Ddhbp2oHfaiisrQNKVRO+2ghERBCwdFbB2GYRjxEEtO78bKqoA1CkHlEByYryggjV7n0pJY05QG5RpPXYKRIMj4WURawzCiEjjCEJEzgc6qentS2c/SPbGqPlLPumWdutKH+vkdgnJonLpfWS0fRqJ8yMBeDBs33XfqqzRk6iuIoCCDfnGswrCItIZhRCVsSup+oLWIPKGqi7yy0dTt0E6Ij/LeYIQlUBo7tbxWDovyikqGPP0pI366L389ZZ9I6zCuec4/UeGGDNZORB0ZtG5e5BvkMGikYhiGEUSYwbgF6JJkLAD+ThYUUCLSErgLOBKX5nWkqt4hIpcDVwGVwG9V9dU4rxu2OK/vsDe2SXBUVaPc+OJ0PrnhWN/5/yAprF8+irByCJbuBo2KgsKMFBc1A7Y1GEEjFcMwjCACDYaq/sWn7DdZqkdr4HXgl8AOwHQR+Ri4BBe/qgvwHxHppqrpZxxKg6BOPigGU5SER+ngZxggWFUVNCq64aQ+wLbG74onP/G9rvkwDMOISl4kUFLV5cCz3ttlIrIAt8bjKVVdA3whInOB/YCJuall3QSNCoLku62bF/kahhbbNQtUNr039GjAf1Q0dmr5Nteoy09jGIaRLhkZDBEpBfYG2hKgtFLVVzI89/eAlkBH4POkTQuBXXz2vwi4CKBr166ZXDIyfqE5+nfrEDgquOGkPgx55tNawQCLi4Tiom1zWFRWVQfGhUp0/H6joqAFhWHOeMMwjChEktWKSCsReRT4DngXeBkYF/AXGRHpCDwKnAc0B5In+WvwmYxX1VGq2l9V+3fqFJ8jN2jxXovtmjFm4vxaoTnGTJzPNc99Frre4YDu7WttO6B7+1D5rh9B0l0IXlA4YcZS34i4tgbDMIyoRB1h3A6cjXN8TwHKgQ1xVERE2uOy+V2rqh+JyHFAcq+2K7AgjmulQ9CoYOPmaA7sRRWVXDd2Wq3QHADvzV5BSXGzUMd3KmFZ+8LWegT5aQzDMKIQ1WCcAqwCjlBVf61oBnhRcMcBNyUpoV4GHhWR24DuQAfA34ObBQb1K2PyvBW1pp4G79+FMRPnRzpP59ISnpjkb+c2bq6huEi2MUqtm2/n61wPypMBLne43zHtSqKv9TAMw/Aj6krv7YHX4jQWHpcB/YCRIvK1iHyNS9Y0BpiOc4hf2JCJm4KiwkblqN6dwtO6pm5SOHHfXQKTMQURNFsVMotlGIYRiagjjGk4R3esqOpNwE0+m272/rJGkLIpyCcQFBI9iAkzlgambwV813ok/A5RottW+KiwwsoNwzCiEnWEcTNwrBc2pNGTUBaVV1SibFUWjZ1aHugTULa9aWE3cVFFZeTcE5mskQiSyZp81jCMuIhkMFT1ReBc4CEReVdEbhOR633+/pid6sZLWBTZoI62rLSEvw3uW0t1lHjvR+fSEm4atA/nHNh1i8qpSIRzDuwaeExpq+JAQxZEUN5wk88ahhEXUVO0dgReBA6sY1dV1aI69omdqClaewx9OTDj3h2D+9aKJQVQ3EwY8dN9AzPh+a3ADpOwBh3TYrtt12eAM06JhXtB54sr459hGIVDtlK03oUzFiuAp4H5wKbQI/KYOldBpzqMQxzIYXGpwo5JVWKdul8ZjwUoseqaqjL5rGEY2SSqwTgOWAL0VdXvslCfBiUsWu2I12fWkrsCVFVraOKhqB322KnlPPnhglpKrCc/XBAokTV/hGEYuSSq07sa+E9TMBbgOvigVdBxJj0K4sYXp/uqpDZtrjZ/hGEYeUfUEcZrQMMEbGoggkYFDRG0Lyjy7fqqGkYO7hs4vWW+CsMwckFUg3EV8I6IXAcMV9XNWahTgxLU+daVXCnbHXaQIQsKMpg4xjAMI1tENRj/BBYDw4BLReQLYI3PfqqqP6lv5bJNOp1vlFwVfvuHdeLNxFvt7VMeRJgU2AyGYRjZJKrBODHp/07AEQH7NVgIj/pQV+fr95R/yPDxvscMGze9Vsa7dJ78/YxFWDmEBxk0DMPIJlENxlFZqUWOyKTzDdrmlyCprif/sgA/SViQQUuIZBhGrohkMFT17WxVJBeUBmTCKw3IhRF2TBCJzt3P73FU706+0W+P6h2c1yPMt2IYhpFN8iJFa64IWuQetvh97YboSY+CfCUttvNXNU+YsTTQsZ7JAkHDMIw4KGiDEZTxLlHu12lHyHcEuMV4Qb6SsFSsYc54W9FtGEYuiLpwr0kRFuE1KJJtVMpKSyI7pItEAp3xhmEYuaKgDUZYhNegUUEQ4h3rd64gw9S+VbHvMUG5M0wJZRhGLilog5FJaJAgzj6wa+C5ggzTDSf18T0mLFS6YRhGrihoHwZEDw1SVlrCUb071Yowe+aALtw0aJ8t5/O7BgQ7qv2OMSWUYRj5RsEbjCDCJK83Ddpni4FIlyiOalNCGYaRj5jBCGDCjKWRyuPGlFCGYeQbBe3DCMNCcBiGYdTGDEYAYZJbwzCMQsQMRgBhklvDMIxCJK98GCJSAnRR1VkNdU0LwWEYhpEeeWEwRKQt8AhwNPAUcIFXfjkuaVMl8FtVfTXO646dWs6Qpz/dkia1vKKSIU9/ClgIDsMwjFTyZUqqBrgbuDJRICI9gUuAPsDJwIMiEhxGNgOCcmrf+OL0OC9jGIbRJMgLg6Gqa1X1v0ByyteTgadUdY2qfgHMBfaL87pBObWDyg3DMAqZvJiSCqAL8HnS+4XALqk7ichFwEUAXbt2jbUCDZG72zAMo7GQFyOMAJrjpqoS1ADbRP9T1VGq2l9V+3fqFJx4yI/2AYmS2rcqDoxWO3ZqeaRrGIZhNBXy2WAsBpIf53cFFsR5gRtO6kMzqV3WTFx5WL5vwzCMQiSfDcbLwBki0kpE9gY6AJ/EfZGiFIuReG8rvQ3DMGqTFz4MEWkDTAXaAC1F5EjgQmAMMB3YAFygGpY8NTojXp9JVXWKSqraZcgLilZrK70NwyhU8mKE4SmhdlfVnVS1nff/BFW9WVV7qOpeqvpe3NcNG0XYSm/DMIza5IXByBVh8aLCkisZhmEUInkxJZUrhgzsFZqoyFZ6G4ZhbKWgDYbFizIMw0ifgjYYYKMIwzCMdCloH4ZhGIaRPmYwDMMwjLQwg2EYhmGkhRkMwzAMIy3MYBiGYRhpITFH28gpIrIUmFfHbh2BZQ1QnXykkNsOhd1+a3vhkk77u6lqneG+m5TBSAcRmayq/XNdj1xQyG2Hwm6/tb0w2w7xtt+mpAzDMIy0MINhGIZhpEUhGoxRua5ADinktkNht9/aXrjE1v6C82EYhmEYmVGIIwzDMAwjA8xgGIZhGGlhBqOJIyIlIrJnruthGEbjp2AMhoicLiLfiMjXIvKLXNcn24hIWxEZC3wL/D6p/HIRmS8iM0XkuNzVMHuISEsRGSUis0Rknohc4ZU3+bYDiEgzEXnTa/9MERnolRdE+wFEpLmIfCEiD3jvC6ntc71+7msRedcri6f9qtrk/4A2wAKgDNgZWAJ0ynW9stzm7YEfAhcAD3hlPYFZ3v3YG1gEFOe6rllo+w7AqYDgVrl+CxxRCG332i/ALt7//wdMLpTPPuke3Ai8AjxQgG2fm/I+tvYXyghjIPC2qpar6hJgPK4zbbKo6lpV/S+wOan4ZOApVV2jql8Ac4H9clG/bKKqy1X1WXUswz0sHE4BtB3Aa/di72034FMK5LMHEJG9gP2Bp7yigml7ALG1v1AMRhdqx5haCOySo7rkkoK7DyLyPaAlbqRRMG0Xkd+LyHLgCuBPFMhnLyIC3AVcnlRcEG1PolJEZovIRG86Mrb2F4rBaA7UJL2vAapzVJdcUlD3QUQ6Ao8C51FgbVfVW1V1B+Ba4HUKp/2/At5S1a+Tygql7QCo6l6q2hMYAjxGjO0vFIOxGOe/SLArbpqi0CiY+yAi7YGXgGtV9SMKqO3JqOpzOH9WobT/XOAMEfkEN7I6GeezLIS210JV38VNP8X22ReKwXgDGCgiO4rIzsDBXlmh8TLux9RKRPYGOgCf5LhOsSMibYFxwE2q+qpXXBBtBxCR3bzvOSJyELCBAmm/qh6sqvuoal/geuB53INDk287gIi0FpFdvP/74aae/rjxOC8AAAa7SURBVEtM7d8utprmMaq6RET+AHzgFV2lqutyWadsIyJtgKk4ZURLETkSuBAYA0zHdSIXqCejaGJcBvQDRorISK/sWAqj7QClwGsiUoRTiA1W1SkiUijtr0WBtb0V8Lb32a8CzlHV9+Jqv8WSMgzDMNKiUKakDMMwjHpiBsMwDMNICzMYhmEYRlqYwTAMwzDSwgyGYRiGkRZmMAzDMIy0MINhGCmISDsRudMLE71JRK5P45g9ROR5EVkqIutFpM7gliLyBxFZIyJnx1Pz+iEiHUVERWRuruti5CdmMIy8RUTai0iNiJzhvS/13p+TxWsKLsrpZcBK4BFgaR3H7AC8AwwCpgBPAlVpXK4rLmxH53pU2TAajIJY6W00WgbgcjtM8t4f4L2fmMVr7oVbFT4L6K+q6QRpG4zLs/Kgql4Q4VoXA39W1YXRq2kYDY8ZDCOfORBYqqrfeO8HAMtSIpHGzR7e6+Q0jUXyMZNC90pBVWtwoaYNo1FgU1JGPnMgtTvhAUTslDOg2HtNZ0opo2O8OD+G0egwg2HkFSLylud4VVymxBOT3p8AnJB4LyJ1Zg3zcjtf5iWTWSUiG7y8xreKSIek/bp713jaK/p/SdfpHnDun3vHXOIVPZzsNBaRI733L4lILxF5R0Q2Ard72+/xtv/c59wDRWS8iKwWkXUi8r6I/MRnv7neOTp69flMRCpFZKGI3OxnnLx7MlRczusNIrJYRP4OtA1oZ7GIXCwiH4lIhVenySLyf0H33Wia2JSUkW+8DXyOi7L7M1xo6nlAa+DnuDzNiSmqGWEnEpHtccmDDsblBHgRNwo4FJdc5jQROVRVFwGrgXtx+Y//zzv3f71TrQ64xJfeMYcD+3j7zwCWp+y3ndeORbiIufPrqPeVOKNSAbwKFOGM51gR+aWqjvI5bCjwC+A/uBwIxwHX4EY/Q5LOXeyd82hcnogncY73s3AjOj+eBU4Cvvb+L8X5k/oDr4W1xWhi5Dphuf3Zn98frtNWYDfv/THe+z0jnGOUd8wLQElSeTNcR6/AiynHnOaVj45wnXu8Y36eUn6kV74RuDOd43CddjXOGO2SVL47znCtAdollc/1zjEb6JJUfqpXvhZokVR+nVf+FtA6qbwMmOltm5tUvrdXNivlPEVAWa6/J/bXsH82JWXkKwcC36rqnKT3y1V1VjoHexn3zsN1mBepamVimzpn89W4kcBJIpJtWWsN8Oc0970SZ9AuVtXFiUJ1jv7HcaOBY3yO+6OqLkja/1ncSKY10Au2SIZ/gzMAv9GknDCqWg78zue8Cf9MDbA5af9q7xijgLApKSNvEJGjgVO8twOBahG5x3v/I2BT0vtXVPWVkNMdgPt+f6Cq36ZuVNW1IjIJOB74AW66KFvMUNVlae57GK5DP11ETkvZ1td77elznJ/UeDZurUdp0nE7AV+p6uc++0/xKZuGS8TVD5gsIsOB51Q1iijAaCKYwTDyiZ+w1YGcIOj95DrO1dF7DctdnHiC93X2xsjiunfZQkfcWpOLQ/Yp8Snz87Os914TMwk7eq9B92RTaoGq1ojIMcDNOB/Sv4ElnuG4S1UtA1sBYVNSRt6gqperqgCdvKKfeu874J66z1RV8f5G13G6RAe6S8g+O3uvqU7quKmJsO8aXFtbJbU19W9YhvXY6L3uELB9R79CVV2hqr8CdsVN5RUBI4G/ZlgPo5FiBsPIRw7wXhNrLlJXfKfDR7iO96Bk+WwCEWntXacq4nmzzSe4tg7Iwrln4RzqvUVkZ5/tR4cdrKrLVPVWnD9JcTnijQLCDIaRjwwAFic5cVNXfNeJqi7BrakoBf4hIi0S2zxp6R24kcyDqloRW83rz7+817tEZNfkDSLSVkRO8TkmLVR1DU5S2wL4u4i0TDp3X+CG1GO89Smp6102p7waBYL5MIx8JHVFd+qK73T5NU4WejpwiIiMx6l+BgA9gP8BV9WvqrHzCC6I4SBghoi8gwsf0hk4Alfn5+px/iuAg4CTvfP/DzdFdRTwd297Mt2BCSIyGfgCNx11LG4UdGs96mE0QsxgGPnIGdQOs5H6Pi1UdbmIHITrBE8HforzJ3wJ3A3cq6rbOHpziaqqp466FPh/uLUczXCO6jHAbfU8/9ciciDOiX0Mbr3GDOCXwMtsazBmAg/jpqsG4yL4foxzeIep1IwmiJjIwTAMw0gH82EYhmEYaWEGwzAMw0gLMxiGYRhGWpjBMAzDMNLCDIZhGIaRFmYwDMMwjLQwg2EYhmGkhRkMwzAMIy3MYBiGYRhpYQbDMAzDSIv/D+D9bsArGTfLAAAAAElFTkSuQmCC\n",
"text/plain": [
"<Figure size 432x288 with 1 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"%matplotlib inline\n",
"import matplotlib.pyplot as plt\n",
"\n",
"plt.scatter(num_friends_good, daily_minutes_good)\n",
"plt.plot(num_friends_good, [alpha + beta*i for i in num_friends_good], 'b-')\n",
"plt.xlabel('# of friends', fontsize = 20)\n",
"plt.ylabel('minutes per day', fontsize = 20)\n",
"plt.title('simple linear regression model', fontsize = 20)\n",
"plt.show()"
]
},
{
"cell_type": "markdown",
"metadata": {
"slideshow": {
"slide_type": "subslide"
}
},
"source": [
"Of course, we need a better way to figure out how well we’ve fit the data than staring at the graph. \n",
"\n",
"A common measure is the coefficient of determination (or R-squared), which measures the fraction of the total variation in the dependent variable that is captured by the model."
]
},
{
"cell_type": "markdown",
"metadata": {
"ExecuteTime": {
"end_time": "2018-11-07T05:42:35.575216Z",
"start_time": "2018-11-07T05:42:35.570042Z"
},
"slideshow": {
"slide_type": "slide"
}
},
"source": [
"# Multiple Regression using Matrix Method\n",
"\n",
"Machine Learning in Action\n",
"\n",
"https://github.com/computational-class/machinelearninginaction/"
]
},
{
"cell_type": "markdown",
"metadata": {
"slideshow": {
"slide_type": "subslide"
}
},
"source": [
"$$ y_i = X_i^T w$$\n",
"\n",
"The constant could be represent by 1 in X\n",
"\n",
"The squared error could be written as: \n",
"\n",
"$$ \\sum_{i = 1}^m (y_i -X_i^T w)^2 $$"
]
},
{
"cell_type": "markdown",
"metadata": {
"slideshow": {
"slide_type": "subslide"
}
},
"source": [
"We can also write this in matrix notation as $(y-Xw)^T(y-Xw)$. \n",
"\n",
"If we take the derivative of this with respect to w, we’ll get $X^T(y-Xw)$. \n",
"\n",
"We can set this to zero and solve for w to get the following equation:\n",
"\n",
"$$\\hat w = (X^T X)^{-1}X^T y$$"
]
},
{
"cell_type": "code",
"execution_count": 16,
"metadata": {
"ExecuteTime": {
"end_time": "2019-06-09T02:31:35.132677Z",
"start_time": "2019-06-09T02:31:35.115627Z"
},
"slideshow": {
"slide_type": "subslide"
}
},
"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>x1</th>\n",
" <th>x2</th>\n",
" <th>y</th>\n",
" <th>x3</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>1.0</td>\n",
" <td>0.067732</td>\n",
" <td>3.176513</td>\n",
" <td>1.384291</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>1.0</td>\n",
" <td>0.427810</td>\n",
" <td>3.816464</td>\n",
" <td>1.354246</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>1.0</td>\n",
" <td>0.995731</td>\n",
" <td>4.550095</td>\n",
" <td>1.801504</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>1.0</td>\n",
" <td>0.738336</td>\n",
" <td>4.256571</td>\n",
" <td>1.374716</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>1.0</td>\n",
" <td>0.981083</td>\n",
" <td>4.560815</td>\n",
" <td>1.637240</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" x1 x2 y x3\n",
"0 1.0 0.067732 3.176513 1.384291\n",
"1 1.0 0.427810 3.816464 1.354246\n",
"2 1.0 0.995731 4.550095 1.801504\n",
"3 1.0 0.738336 4.256571 1.374716\n",
"4 1.0 0.981083 4.560815 1.637240"
]
},
"execution_count": 16,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# https://github.com/computational-class/machinelearninginaction/blob/master/Ch08/regression.py\n",
"import pandas as pd\n",
"import random\n",
"\n",
"dat = pd.read_csv('../data/ex0.txt', sep = '\\t', names = ['x1', 'x2', 'y'])\n",
"dat['x3'] = [yi*.3 + .5*random.random() for yi in dat['y']]\n",
"dat.head()"
]
},
{
"cell_type": "code",
"execution_count": 17,
"metadata": {
"ExecuteTime": {
"end_time": "2019-06-09T02:31:58.107318Z",
"start_time": "2019-06-09T02:31:58.098246Z"
},
"slideshow": {
"slide_type": "subslide"
}
},
"outputs": [],
"source": [
"from numpy import mat, linalg, corrcoef\n",
"\n",
"def standRegres(xArr,yArr):\n",
" xMat = mat(xArr); yMat = mat(yArr).T\n",
" xTx = xMat.T*xMat\n",
" if linalg.det(xTx) == 0.0:\n",
" print(\"This matrix is singular, cannot do inverse\")\n",
" return\n",
" ws = xTx.I * (xMat.T*yMat)\n",
" return ws"
]
},
{
"cell_type": "code",
"execution_count": 18,
"metadata": {
"ExecuteTime": {
"end_time": "2019-06-09T02:32:02.126263Z",
"start_time": "2019-06-09T02:32:02.105970Z"
},
"slideshow": {
"slide_type": "subslide"
}
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"[[1.0, 0.067732, 1.3842912829135907], [1.0, 0.42781, 1.3542457458385966]]\n",
"[[2.88936235]\n",
" [1.63851705]\n",
" [0.10238664]]\n"
]
}
],
"source": [
"xs = [[dat.x1[i], dat.x2[i], dat.x3[i]] for i in dat.index]\n",
"y = dat.y\n",
"print(xs[:2])\n",
"ws = standRegres(xs, y)\n",
"print(ws)"
]
},
{
"cell_type": "code",
"execution_count": 14,
"metadata": {
"ExecuteTime": {
"end_time": "2019-06-08T14:13:25.237796Z",
"start_time": "2019-06-08T14:13:25.230467Z"
},
"slideshow": {
"slide_type": "subslide"
}
},
"outputs": [],
"source": [
"xMat=mat(xs)\n",
"yMat=mat(y)\n",
"yHat = xMat*ws\n",
"\n",
"xCopy=xMat.copy()\n",
"xCopy.sort(0)\n",
"yHat=xCopy*ws"
]
},
{
"cell_type": "code",
"execution_count": 15,
"metadata": {
"ExecuteTime": {
"end_time": "2019-06-08T14:13:31.245040Z",
"start_time": "2019-06-08T14:13:31.046248Z"
},
"slideshow": {
"slide_type": "subslide"
}
},
"outputs": [
{
"data": {
"image/png": "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\n",
"text/plain": [
"<Figure size 432x288 with 1 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"fig = plt.figure()\n",
"ax = fig.add_subplot(111)\n",
"ax.scatter(xMat[:,1].flatten().A[0], yMat.T[:,0].flatten().A[0])\n",
"ax.plot(xCopy[:,1],yHat, 'r-')\n",
"plt.ylim(0, 5)\n",
"plt.show()"
]
},
{
"cell_type": "code",
"execution_count": 108,
"metadata": {
"ExecuteTime": {
"end_time": "2018-11-07T12:48:32.415464Z",
"start_time": "2018-11-07T12:48:32.409103Z"
},
"slideshow": {
"slide_type": "subslide"
}
},
"outputs": [
{
"data": {
"text/plain": [
"array([[ 1. , 0.98666465],\n",
" [ 0.98666465, 1. ]])"
]
},
"execution_count": 108,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"yHat = xMat*ws\n",
"corrcoef(yHat.T, yMat)"
]
},
{
"cell_type": "markdown",
"metadata": {
"slideshow": {
"slide_type": "slide"
}
},
"source": [
"# Doing Statistics with statsmodels\n",
"\n",
"http://www.statsmodels.org/stable/index.html\n",
"\n",
"statsmodels is a Python module that provides classes and functions for the estimation of many different statistical models, as well as for conducting statistical tests, and statistical data exploration."
]
},
{
"cell_type": "code",
"execution_count": 109,
"metadata": {
"ExecuteTime": {
"end_time": "2018-11-14T05:37:53.042411Z",
"start_time": "2018-11-14T05:37:51.646856Z"
},
"slideshow": {
"slide_type": "subslide"
}
},
"outputs": [],
"source": [
"import statsmodels.api as sm\n",
"import statsmodels.formula.api as smf"
]
},
{
"cell_type": "code",
"execution_count": 121,
"metadata": {
"ExecuteTime": {
"end_time": "2018-11-14T05:41:58.692475Z",
"start_time": "2018-11-14T05:41:58.679427Z"
},
"slideshow": {
"slide_type": "fragment"
}
},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>x1</th>\n",
" <th>x2</th>\n",
" <th>y</th>\n",
" <th>x3</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>1.0</td>\n",
" <td>0.067732</td>\n",
" <td>3.176513</td>\n",
" <td>0.885553</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>1.0</td>\n",
" <td>0.427810</td>\n",
" <td>3.816464</td>\n",
" <td>1.100010</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>1.0</td>\n",
" <td>0.995731</td>\n",
" <td>4.550095</td>\n",
" <td>1.323654</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>1.0</td>\n",
" <td>0.738336</td>\n",
" <td>4.256571</td>\n",
" <td>1.267457</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>1.0</td>\n",
" <td>0.981083</td>\n",
" <td>4.560815</td>\n",
" <td>1.300163</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" x1 x2 y x3\n",
"0 1.0 0.067732 3.176513 0.885553\n",
"1 1.0 0.427810 3.816464 1.100010\n",
"2 1.0 0.995731 4.550095 1.323654\n",
"3 1.0 0.738336 4.256571 1.267457\n",
"4 1.0 0.981083 4.560815 1.300163"
]
},
"execution_count": 121,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"dat = pd.read_csv('ex0.txt', sep = '\\t', names = ['x1', 'x2', 'y'])\n",
"dat['x3'] = [yi*.3 - .1*random.random() for yi in y]\n",
"dat.head()"
]
},
{
"cell_type": "code",
"execution_count": 123,
"metadata": {
"ExecuteTime": {
"end_time": "2018-11-14T05:42:03.424640Z",
"start_time": "2018-11-14T05:42:03.357545Z"
},
"slideshow": {
"slide_type": "subslide"
}
},
"outputs": [
{
"data": {
"text/html": [
"<table class=\"simpletable\">\n",
"<caption>OLS Regression Results</caption>\n",
"<tr>\n",
" <th>Dep. Variable:</th> <td>y</td> <th> R-squared: </th> <td> 0.986</td> \n",
"</tr>\n",
"<tr>\n",
" <th>Model:</th> <td>OLS</td> <th> Adj. R-squared: </th> <td> 0.986</td> \n",
"</tr>\n",
"<tr>\n",
" <th>Method:</th> <td>Least Squares</td> <th> F-statistic: </th> <td> 7167.</td> \n",
"</tr>\n",
"<tr>\n",
" <th>Date:</th> <td>Wed, 14 Nov 2018</td> <th> Prob (F-statistic):</th> <td>1.04e-184</td>\n",
"</tr>\n",
"<tr>\n",
" <th>Time:</th> <td>13:42:03</td> <th> Log-Likelihood: </th> <td> 284.06</td> \n",
"</tr>\n",
"<tr>\n",
" <th>No. Observations:</th> <td> 200</td> <th> AIC: </th> <td> -562.1</td> \n",
"</tr>\n",
"<tr>\n",
" <th>Df Residuals:</th> <td> 197</td> <th> BIC: </th> <td> -552.2</td> \n",
"</tr>\n",
"<tr>\n",
" <th>Df Model:</th> <td> 2</td> <th> </th> <td> </td> \n",
"</tr>\n",
"<tr>\n",
" <th>Covariance Type:</th> <td>nonrobust</td> <th> </th> <td> </td> \n",
"</tr>\n",
"</table>\n",
"<table class=\"simpletable\">\n",
"<tr>\n",
" <td></td> <th>coef</th> <th>std err</th> <th>t</th> <th>P>|t|</th> <th>[0.025</th> <th>0.975]</th> \n",
"</tr>\n",
"<tr>\n",
" <th>Intercept</th> <td> 1.7146</td> <td> 0.093</td> <td> 18.372</td> <td> 0.000</td> <td> 1.531</td> <td> 1.899</td>\n",
"</tr>\n",
"<tr>\n",
" <th>x2</th> <td> 0.9264</td> <td> 0.057</td> <td> 16.228</td> <td> 0.000</td> <td> 0.814</td> <td> 1.039</td>\n",
"</tr>\n",
"<tr>\n",
" <th>x3</th> <td> 1.5151</td> <td> 0.109</td> <td> 13.909</td> <td> 0.000</td> <td> 1.300</td> <td> 1.730</td>\n",
"</tr>\n",
"</table>\n",
"<table class=\"simpletable\">\n",
"<tr>\n",
" <th>Omnibus:</th> <td> 4.027</td> <th> Durbin-Watson: </th> <td> 1.852</td>\n",
"</tr>\n",
"<tr>\n",
" <th>Prob(Omnibus):</th> <td> 0.134</td> <th> Jarque-Bera (JB): </th> <td> 2.762</td>\n",
"</tr>\n",
"<tr>\n",
" <th>Skew:</th> <td>-0.112</td> <th> Prob(JB): </th> <td> 0.251</td>\n",
"</tr>\n",
"<tr>\n",
" <th>Kurtosis:</th> <td> 2.469</td> <th> Cond. No. </th> <td> 58.2</td>\n",
"</tr>\n",
"</table><br/><br/>Warnings:<br/>[1] Standard Errors assume that the covariance matrix of the errors is correctly specified."
],
"text/plain": [
"<class 'statsmodels.iolib.summary.Summary'>\n",
"\"\"\"\n",
" OLS Regression Results \n",
"==============================================================================\n",
"Dep. Variable: y R-squared: 0.986\n",
"Model: OLS Adj. R-squared: 0.986\n",
"Method: Least Squares F-statistic: 7167.\n",
"Date: Wed, 14 Nov 2018 Prob (F-statistic): 1.04e-184\n",
"Time: 13:42:03 Log-Likelihood: 284.06\n",
"No. Observations: 200 AIC: -562.1\n",
"Df Residuals: 197 BIC: -552.2\n",
"Df Model: 2 \n",
"Covariance Type: nonrobust \n",
"==============================================================================\n",
" coef std err t P>|t| [0.025 0.975]\n",
"------------------------------------------------------------------------------\n",
"Intercept 1.7146 0.093 18.372 0.000 1.531 1.899\n",
"x2 0.9264 0.057 16.228 0.000 0.814 1.039\n",
"x3 1.5151 0.109 13.909 0.000 1.300 1.730\n",
"==============================================================================\n",
"Omnibus: 4.027 Durbin-Watson: 1.852\n",
"Prob(Omnibus): 0.134 Jarque-Bera (JB): 2.762\n",
"Skew: -0.112 Prob(JB): 0.251\n",
"Kurtosis: 2.469 Cond. No. 58.2\n",
"==============================================================================\n",
"\n",
"Warnings:\n",
"[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n",
"\"\"\""
]
},
"execution_count": 123,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"results = smf.ols('y ~ x2 + x3', data=dat).fit()\n",
"\n",
"results.summary()"
]
},
{
"cell_type": "code",
"execution_count": 124,
"metadata": {
"ExecuteTime": {
"end_time": "2018-11-14T05:42:06.382425Z",
"start_time": "2018-11-14T05:42:05.862926Z"
},
"slideshow": {
"slide_type": "subslide"
}
},
"outputs": [
{
"data": {
"image/png": "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\n",
"text/plain": [
"<Figure size 864x576 with 3 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"fig = plt.figure(figsize=(12,8))\n",
"fig = sm.graphics.plot_partregress_grid(results, fig = fig)\n",
"plt.show()"
]
},
{
"cell_type": "code",
"execution_count": 135,
"metadata": {
"ExecuteTime": {
"end_time": "2018-11-14T05:58:41.909065Z",
"start_time": "2018-11-14T05:58:41.893565Z"
},
"slideshow": {
"slide_type": "slide"
}
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
" OLS Regression Results \n",
"==============================================================================\n",
"Dep. Variable: y R-squared: 0.329\n",
"Model: OLS Adj. R-squared: 0.326\n",
"Method: Least Squares F-statistic: 98.60\n",
"Date: Wed, 14 Nov 2018 Prob (F-statistic): 3.68e-19\n",
"Time: 13:58:41 Log-Likelihood: -711.76\n",
"No. Observations: 203 AIC: 1428.\n",
"Df Residuals: 201 BIC: 1434.\n",
"Df Model: 1 \n",
"Covariance Type: nonrobust \n",
"==============================================================================\n",
" coef std err t P>|t| [0.025 0.975]\n",
"------------------------------------------------------------------------------\n",
"x1 0.9039 0.091 9.930 0.000 0.724 1.083\n",
"const 22.9476 0.846 27.133 0.000 21.280 24.615\n",
"==============================================================================\n",
"Omnibus: 26.873 Durbin-Watson: 2.027\n",
"Prob(Omnibus): 0.000 Jarque-Bera (JB): 7.541\n",
"Skew: 0.004 Prob(JB): 0.0230\n",
"Kurtosis: 2.056 Cond. No. 13.9\n",
"==============================================================================\n",
"\n",
"Warnings:\n",
"[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n"
]
}
],
"source": [
"import numpy as np\n",
"X = np.array(num_friends_good)\n",
"X = sm.add_constant(X, prepend=False)\n",
"\n",
"mod = sm.OLS(daily_minutes_good, X)\n",
"res = mod.fit()\n",
"print(res.summary())"
]
},
{
"cell_type": "code",
"execution_count": 137,
"metadata": {
"ExecuteTime": {
"end_time": "2018-11-14T05:59:29.913876Z",
"start_time": "2018-11-14T05:59:29.553693Z"
},
"slideshow": {
"slide_type": "subslide"
}
},
"outputs": [
{
"data": {
"image/png": "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\n",
"text/plain": [
"<Figure size 432x576 with 2 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"fig = plt.figure(figsize=(6,8))\n",
"fig = sm.graphics.plot_partregress_grid(res, fig = fig)\n",
"plt.show()"
]
}
],
"metadata": {
"celltoolbar": "Slideshow",
"kernelspec": {
"display_name": "Python [conda env:anaconda]",
"language": "python",
"name": "conda-env-anaconda-py"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.5.4"
},
"latex_envs": {
"LaTeX_envs_menu_present": true,
"autoclose": false,
"autocomplete": true,
"bibliofile": "biblio.bib",
"cite_by": "apalike",
"current_citInitial": 1,
"eqLabelWithNumbers": true,
"eqNumInitial": 1,
"hotkeys": {
"equation": "Ctrl-E",
"itemize": "Ctrl-I"
},
"labels_anchors": false,
"latex_user_defs": false,
"report_style_numbering": false,
"user_envs_cfg": false
},
"toc": {
"base_numbering": 1,
"nav_menu": {},
"number_sections": false,
"sideBar": true,
"skip_h1_title": false,
"title_cell": "Table of Contents",
"title_sidebar": "Contents",
"toc_cell": false,
"toc_position": {},
"toc_section_display": true,
"toc_window_display": false
}
},
"nbformat": 4,
"nbformat_minor": 2
}
| mit |
mathewzilla/whiskfree | tf/.ipynb_checkpoints/3_class_simplemodels-checkpoint.ipynb | 1 | 642392 | {
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# 3 class discrimination of trialtype. \n",
"### Using sklean and skflow. Comparison to each of the 4 mice"
]
},
{
"cell_type": "code",
"execution_count": 163,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"import tensorflow as tf\n",
"import tensorflow.contrib.learn as skflow\n",
"import numpy as np\n",
"import matplotlib.pyplot as plt\n",
"%matplotlib inline\n",
"import pandas as pd\n",
"import seaborn as sns \n",
"import random\n",
"from scipy.signal import resample\n",
"from scipy.stats import zscore\n",
"from scipy import interp\n",
"from sklearn.linear_model import LogisticRegression\n",
"from sklearn.metrics import confusion_matrix\n",
"from sklearn.metrics import accuracy_score\n",
"from sklearn import metrics\n",
"from sklearn import cross_validation"
]
},
{
"cell_type": "code",
"execution_count": 612,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"# data loading function\n",
"def data_loader(mouse_name):\n",
" theta = pd.read_csv('~/work/whiskfree/data/theta_' + mouse_name + '.csv',header=None)\n",
" kappa = pd.read_csv('~/work/whiskfree/data/kappa_' + mouse_name + '.csv',header=None)\n",
" tt = pd.read_csv('~/work/whiskfree/data/trialtype_' + mouse_name + '.csv',header=None)\n",
" ch = pd.read_csv('~/work/whiskfree/data/choice_' + mouse_name + '.csv',header=None)\n",
"\n",
" return theta, kappa, tt, ch\n",
"\n",
"def data_parser(theta,kappa,tt,ch):\n",
" \n",
" theta_r = np.array([[resample(theta.values.squeeze()[i,950:1440],50)] for i in range(0,theta.shape[0])])\n",
" theta_r = zscore(theta_r.squeeze(),axis=None)\n",
"\n",
" kappa_r = np.array([[resample(kappa.values.squeeze()[i,950:1440],50)] for i in range(0,kappa.shape[0])])\n",
" kappa_r = zscore(kappa_r.squeeze(),axis=None)\n",
"\n",
" kappa_df = pd.DataFrame(kappa_r)\n",
" theta_df = pd.DataFrame(theta_r)\n",
"\n",
" both_df = pd.concat([theta_df,kappa_df],axis=1)\n",
" \n",
" clean1 = np.nan_to_num(tt) !=0\n",
" clean2 = np.nan_to_num(ch) !=0\n",
" clean = clean1&clean2\n",
" tt_c = tt[clean].values\n",
"\n",
" ch_c = ch[clean].values\n",
"\n",
" # tt_c = tt[tt.values !=0|3].values\n",
" both = both_df[clean]\n",
" # both_c = both[clean.squeeze(),:]\n",
" both_c = both.values\n",
" \n",
" # keeping one hot vector for now (incase we want it later)\n",
"# labs = np.eye(3)[tt_c.astype(int)-1]\n",
" # y[np.arange(3), a] = 1\n",
"# labs = labs.squeeze()\n",
" \n",
" return both_c, tt_c, ch_c, clean"
]
},
{
"cell_type": "code",
"execution_count": 466,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"image/png": "iVBORw0KGgoAAAANSUhEUgAAAYAAAAECCAYAAAD3vwBsAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nTkSuQmCC\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x10cd9a780>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"mouse_name = '32'\n",
"theta, kappa, tt, ch = data_loader(mouse_name)\n",
"fig, ax = plt.subplots(1,2,figsize=(20,5))\n",
"_ = ax[0].plot(theta[:100].T)\n",
"_ = ax[1].plot(kappa[:100].T)"
]
},
{
"cell_type": "code",
"execution_count": 613,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"image/png": "iVBORw0KGgoAAAANSUhEUgAAAXgAAAECCAYAAAD0JMwBAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz\nAAALEgAACxIB0t1+/AAAIABJREFUeJzsvWmUHNd15/mLLfe1MmtfUIUCqrByAwkuIimR1C7ZluRF\nnrF1PG5P2z7jmTmeMz3T3faxe8bTbXePZ+T22K22LLfVVluyLZFWUxQpLqLAFQQBYl8KhdqXrKzK\nfc+M9c2HKAKmuYgEQQGE4vcFqMyIFy9eRf3fjXvvu08SQuDh4eHhcf0hX+0OeHh4eHi8N3gC7+Hh\n4XGd4gm8h4eHx3WKJ/AeHh4e1ymewHt4eHhcp3gC7+Hh4XGdol7uiZOTkzLwFWAScIBfn56ePnel\nOubh4eHh8e54Nxb8TwBienr6buB3gN+/Ml3y8PDw8LgSXLbAT09PPwz86uaPo0D5SnTIw8PDw+PK\ncNkuGoDp6WlncnLyq8BngZ+5Ml3y8PDw8LgSSFeiVMHk5GQPcBjYOT093X7XDXp4eHh4vGveTZD1\nC8DQ9PT0HwAdwMYNtr4hQgghSdLlXs7Dw8Pjx5XLFs7LtuAnJyeDwH8G+nAnij+Ynp7+7lucIvL5\n+mVd63qjuzuKNxYu3lhcwhuLS3hjcYnu7uhlC/xlW/CbrpjPX+75Hh4eHh7vLd5CJw8PD4/rFE/g\nPTw8PK5TPIH38PDwuE7xBN7Dw8PjOsUTeA8PD4/rFE/gPTw8PK5TPIH38PDwuE7xBN7Dw8PjOsUT\neI8fGZXsM6yd+1McW7/aXfHw+LHAE3iPHwmNwjFq689h6SWMVvZqd8fD48cCT+A93nM69QVKK49d\n/NnSS1exNx4ePz54Au/xnmJ2CuQXvgUSJAYecD/Ti1e5Vx4ePx54Au/xniEci/z83yHsDl3DP0E4\ndTPgWfAeHj8q3tWOTh4eb0UtdxBLLxJJ30YkdSNCCGQl4FnwHh4/IjwL3uM9wTIq1NZfQFbDJAbu\nA0CSJFR/CksvIcSb7g3j4eFxhfAE3uM9obz6JEJYJAc/gqwELn6u+rtAONhG9Sr2zsPjxwNP4D2u\nOO3aHO3qefzhYULJva/5TgukAC/Q6uHxo+CyfPCTk5Mq8JfAKOAD/s309PQjV7BfHlcJ22wgKX5k\nWbus84VjU159HJBIDn2Cf7wPr+p3Bd7qFCG27d1218PD4y24XAv+F4HC9PT0vcAngD+9cl26dtko\ntSjXr99VmGanyNq5P6G0/FZb6741rcrUZmD1Vnyhvtd9r/m73Gt5mTQeHu85lyvw3wR+5x+0YV6Z\n7ly7lOs6/8dXj/BH3zzB5W5Ufi0jhKC0/B2EY2K0MpfdTqs6BUAkve8Nv79owXsuGg+P95zLctFM\nT0+3ACYnJ6PAt4DfvpKduhb5+2fn0E2b1XyTxfU6Y/2xNz32qaVnCKlBPjB4+4+wh++ORuEIenMF\nAEsvIxwLSX5nj4dwLDq1WVR/F1qg+w2PkRUfihb1fPAeHj8CLjsPfnJychj4e+BPp6en/+7tnNPd\nHb3cy11VZlcqvHhmnXBApdmxOD5XZP8Ng2947Pn8HP917jH8qp9P7v0gPuWNfdnXylh02iYnXr5A\ndv4sqjaMKYXpT8wSDXUIRvvfUVuV/DmEY5Lq30tPz5tPgKVID43yPKkuN7vmWhmLawFvLC7hjcW7\n53KDrL3AE8BvTE9PH3i75+Xz9cu53EUc4XCqcI4j68fY37ePG7t3v6v23g5CCP7soZMA/OpP7uY/\nffcczxxd5Sfv3IKqyK879qtHvwmAbukcnDnB7tSO17XZ3R1912NxJSgVmnzvoTPUym1g7OLnhV6J\n/u2LhLsi76i94vIJ9z++rRfvz7HaIKuvDdrKcUCQzSwzODL+tsdCODaVte8jyRqJgfvfUd8utiFs\nKms/IBifJBAZuaw23iuulefiWsAbi0u8m4nuci34fwkkgN+ZnJz8XUAAn5ienn5PIpCOcHhm9UWe\nWXmRYscNznUs/Uci8McuFJheqXDjeIq9W1PcvquPp15Z4fRckZsnXuuGOJ4/zUJtmd5QNxutPKcL\nU28o8D+MtmXz0MIG+3viTMTDV+Q+LKOGrAYvCu3ChQJPf3cK07AZ27JKd3+Yg3ODyIUG1VqUXCFD\nf2w7batNMpD4oe0L4dCuTiOrEXyhIfeaZp31819GUaP0TPwKDz27yOn5Ir/5Cbc9vbGCEGNv1eyl\n9h2L/MK36NRmAAh33YAWSL+tcx1bx2it4Y+M0iydoZ57CdusX3MC7+FxpblcH/xvAr95hfvypjyf\nOcRDM4+gyRp3D9zOsdypi0L/XmLZDt86MIsiS/zc/W5K3117XIF/8cz6awTedCwenn0MRVL4tb2/\nxP9z9D9wunCOz0985nWpgj+MH6yVOFdpIuCKCLzZKZA9/+cE4rvpGfspzhzL8PyTM6iqzM23LNGX\nXqKR/mlOHNpgjwJyO0A+t8QB40HOFKf413f9FiEtRMNsElZDSJLEyWKdlF9jKOK6WfTmCo7VIpLa\nhyRJm0Hb7+JYLQyjzZcefJHjCzYA5XYCH1BefZRwWEEO3cSJl5dRFJlEKsTSbJF22+RDn5hE0xQc\n26Cw8Hd06guoviSWUaZeeIWuoY+/jXvPk5//JpZepGvLZ6jlXgTwatJ7/FjwvqhFc77kWm2/tf83\n6Ql1s9rIslxfxREOsvTerdV6/lSWXKXNA7cM0Z9yhXakN8JQd5iTswUabZNI0LWIn189SKFT4r7h\nu+kN97A7tZMjG8dYaWQYiQ697Wvm2wYv5SoArDY7l9VvxzQpPPhNai+/xPA/++eslJ7BJyzKhQvE\n+00OPTNPIKTx4U9EEI0lTusmz78wgyDG0HAXhcUK7VqLE8YqNibT5TkqepUHZ77DP93zBWr2EI+v\nFhgJB/j1XcMAtCvTAAQTkwghaJVPu9a2fwt/czDGXMEm5Fdo6Tb5RoBXIxjVwhRqfAcvHZh/3X1s\n29HN6LYk+blvoDeXCcYmSI1+juzUf6BZPEmi/35kxfem49CqTlNc/DbCMQAu1qMHLn7m4XE9c82v\nZBVCMF9dJOlP0BNyLeZ0sAtHOJQ7791yd9txePzlJVRF5lN3bbn4uSRJ3LWnH9sRHJ7aAKBltvne\n4tME1QAfH32AhXqbvugeAE4Xpt7Rdb+3UsAREFEV6qZNzbDe0flGPsfKv/03VJ5+CqfRYPr4QXz2\nAgBBtc3pV6YxDZub9g9j6c8hhOClpiC3GiIeUdmx3XV7yJaMWXEDpQfXDvPQjLuO7aWNAo+vFgAo\n6W52rBCCVvU8kuxn3VH4X5/7HQ7PPQySxjeO7WSuEGeiu8R/e0cNgGz5Uppps7LMycPLF38eGkty\n1/3jAGSWKzRLJ11xj+8gvfVnkRUfkdQtCEenWT518Tyn06Fy4GlqLx/CqlSo5w9TmP87EA6p0c8R\niE38gyqWkmfBe/xYcE0LvBCCXCtPw2wynhi9+Hkq4C6WeS/dNEfO58hXOty9t49ExP+a7+7Y3Ysk\nwYun1wE4kT9Dy2rzwPAHUaQAX53O8NyGn6D/dk79I4Evdyo4zhsX2pqpNjlfbTIWDXJnr+unfidW\nfPP0KZZ/71+hLy0Svulm8r44zYib075UdTNisovn8QdUto63Ucwy06ZFcWYfOCqxoRypbjew2m4H\niTZ6kIGp0jSqrOBTR1npDBJUZNIBjYZlYzoOZieHbVQIxrbxvcUD6LbBeb1NO3gfF1YbTA7H+cJd\nFZqnzwBgtS5Z67alM33m0g5PErD7lgEURSK77Ao1yCSHP4EkKQBE0rcAMo38EYQQNE4cZ/F3f4vc\n1/8L61/5M+b/5f9CafF7SI5K78QvE07uuehvV7QoihZBeALv8WPANeuiefq7UxQ3Ggx+3BXD8fjo\nxe/SQVfgC+0SE8nxK35tIQSPvbSMJMHHb399IC4R8bN7tIszCyVKtQ5ni+cBuKVnL6dKdSwhUCUJ\nfDdQtlJkGiW6AkH+fua7HMweYSK1lV+c+DypYPJim7YQPLZSQAI+NdJN03Qt99Vmh13JS9ksh6c2\n+JvvzzDSG6EnEaI7EWD/rl5iPon1v/wKwrLo/eVfwd57Gye+9S1+IrFAS9pKvPdG6HybeLRC75Z9\n5HOP4gPONnrRKzEk2SYfOkZlM5um3gjTJdksFXuRUxs8MPJpXip0IYTFF7YPcrTQoNAxqegWvop7\n/+1AL+cWXma3T6XgyJxd7waWiLQsfNZuBo98Bf/ozXRrlwS+UExi6AJJgnRvhNXFMoZu0zsYR28s\nYHbyhJJ7ULVLmQSKFiWU2Emrcpa1r/8RzWdOgaKQ/MSnUEJhmrVTCNXAOJxHu9F962vX3GvaZhPF\nl8BxLs/95eHxfuKatOAt02ZuKkcx3+Tkiuvb3foGAv9eWfCn54us5hvs39lLTzL0hseMbi50Wis1\nOF+aIRXooifUzdFCDQn4H3eP0O3XUdVB/uPZY/xfh77IwewRor4IF4rz/MGRf8/J/JmL7R0r1Nho\nG+xLxxgI+RkMu8HLTPOSpWlaDn/1vfNUmwan50s8fWyVv/3BLN9+bp7awRew63WSH/kY8Q/cw8PP\nz3L3jjUcGxKpD/Dlx/LYtkwyUWV80sJnlJg3bWbOTgISobBAUi0eX3oIVTNoNEKkQx2MvOtjz7Ra\nICm0O8+jSTVCm16Wsm7QKJ1EkjWeKy1zo0/l0+EAY6rg4Ok1JEAUW6x89WFU4TAglxhLV2lYbuB5\nedUtZzA0mmRidx9CwNz5HAPDcUa3rAHwdLXAfHXpNeMfSbkrZQ3/OsHtE2z5V79H90//LF2f+CTa\nTWkQYJ+roa9lMFpr6I15t5IlDggTYXs+eI/rn2tS4NczNWzbVZCF2hIBJcBA5FJdk1ddNIX2e7Ma\n8tGXXDH55B1b3vSYVMx121woLtCxO+xO7WCjbbDa1JmIh+gJ+vjCtgFarR9QLz9L3ajzybGP8K/v\n+i1+/bYvYDkWf376a3xv4fsIITi0UUGW4MOD7lL+kKrQ5ddYbXYulkb4++fmaBtuJson7hjhd/+7\nW5GAXLlF+YnHkVSVxAMfJl9p0y6fJBnScU5XefapacImVKoRotEmmbXHAchrW+m0XLdHs64yGuph\nzrTo+Du02gFSwTai7rqKFipzyAhMa5H5/CrTL7l+81ZtDtuooMS2cyh3in0BN+gZ0yOUGiZxQEGi\nJiIsR/q5sT+PLMGK4aAbGhu5FOFwk737Bhnf2Y0kwYWzG+S0s/R2F8k3NZ4rzvJC5tBrxr958CxO\nQUcZj9D/m7+Bf8AN2xqtdYzWGqrVBU2b9soF8gsPApAY+AiS4se2WghhIRz78h8SD4/3AdekwK8u\nupa5HLZpqXWGQ0OvyZZJ+OPIkkyxfeUt+AsrFWZWq9wwnmK4580X+qTiroU9V3czfHanJjlWcIOI\nt6Rd6z7sC9EzE2by5H3sT3yeT419BFVWuX/rXfzvt/5PxHxRnlx+htVmh0Klxh0bS0Q1V3BbTYOB\noI+27VDSTeYyVZ447JYSiPk7FCodRvtixCM+CoUaZj5H7K4PoMYTPPrSEvf0LyIcgXW8wvaXH2ab\n1aFciSFLEHVK5G0HvXzLa+7pNs2931qwAkhEVQuEgmaGaBqrDAQBbOaOFxF1N8Cq1dzFTUfaJj0y\npDcXf5WLvQAMSq6lvBzbhvzpz7J1uI7tSJyra2TWehBCZstwlr4BQTjiZ2AkQW6tTrN5EkmCbHYM\nVVbJNtcv9rOzvETx23/P2lyIB09Nkll3J5tquU29cNwd+8Re0CQa8lFso0K874OEEpNEum4C4Qq7\n43h+eI/rm2tU4MvIssTQna6VHKjGX/O9Iit0BZIU3gMXzRObGR1vZb0DpGKuwG9YS6iyynhiK8eL\ndUKqzM6Em1L56KEXSBaGkIRMJXspF960TdYaWQRg2Ab/8eSfc8cPvsrEt/+a5vkpChsN/suXXsI3\n46ZLLlRb/MWj54j6XUH6hX3ncDorCCFIhh3KbRtHklm7+8M8tZhndfYM8S4bsaJDy6YR6MVWQ7TX\nXb++IklIkTGOTf3DLCRBtFVHWAqdkLuC0Oe4IRqp049AJ9l8nm2yn9achtqxidAkYi5Tlrp4cv00\nt25a77IW40IuDTjcuvo8AbNGPdjL9vxxYl0Os4UkuXqEldU+JMlhsD+H2XInry2TblwiWR7AMP1k\nFnro8/WRbeZwhINjGKx/5ctg27yo3c6Z9W7+/cN5LswW+Lu/OEg1dwJZjfDQcwq1z92ICBiEU7cQ\n67sX4OK+sIAXaPW47rnmBL7TNsmvN+gbjNGJld3P5n2Y/yhdMB3oom400K+gL7VY7XBitsBoX5SJ\n4bdevRlz2mzX52jLZSYS4yzUTZqWzU2pGKosU6xVyb5sI3DdK3pBx7Rtvjv/BL/+yG/x1XN/Q91w\nhTSYWWR82Z2sFk+9yNGDSzi2oLnkCvALh6e5Z/AYbVMlFW7RE2nhp0irtkxIyuIg09h3N98u6RzI\nlflItxv0NLSddCSVxdSNIAThxYWL/Y/676RSd8fOp8qAxKHlfnAU9KDbr04rSMRnIuwBAEx7lTvk\nBJKt0BsPslOaRUJwpOPDh8WEpiHJGhvcQbYWIZEo09PJ0LF1ZCRqhpvRc3Kth2BzC41mmHi6gM9n\nsVq4wEsbFTLRBZAcNta7adsTOI5MT2cY0zEptIsUHvoWRnYN576PM1OEgUSDagu++fBZenuKqIrJ\n2nof23vO0ZOuMb3RxR9/J8Tj/+L3MdbXXxOsdbxceI/rnGtO4DNLrqgPjSaZry0hIaNVYlw4m3vN\ncalXA61X0E3zzIkMQsD9t7x+YZIQgsdfXubIebcf1Qf/lp9eeZGBnMHu1A5e2XTP7Nt0z3znkSOo\npp/grhaOIvBVTR6afZLvLT6NaZk8MHIvv7jjZ9GUbXzwVBiCCsquKI0L08xP5wFoVXW0hs7Hok+B\nLGE5CrdOun7qrlCLpaVTxAOuFXp4x34sIVAkh95RG9ERfKMxyVJ0nKYvSZe5QXdzAyEEQsBTx5uv\n3hkfyzwLwNlsGhSbTrABQL4eJNGVB7kHgKyl0xdqgb/F3g92sUOeRxcaU501dvv9aJLgjHo7Xzvv\nLv4a6ylSjGm8mvaubfFjOYL5fJLEuhu8llWTtiMw6gt86+gyz5XixFMV6o0IG7jWfCvnvhFl58+y\ncuQIhz76Ob4S2cG9W5f51dtP8Jl9GcKmw/Cgm245PR1jaCBHsxng3MspinXBkeRdHDxwEkm5lPLq\n5cJfPzSOH6N1Yfpqd+Oa45oT+NVFV+B7RiKs1DMMhQdQUTl7LPOaOuzpK5wLb1oOz51cIxxQ2b+z\n53XfP3dyjW8emOWrj03RqjdpnnYX2eyZbbMlto0LlSYDIT/9IT9nzizTWVHRo1U+/qF9tCNttJbF\n8wsvAdBZ3krm5AjffaLFtmyIvvVVfJ8eR7uvm5ivjRAOW7a5wdZb8kcJxx3OzrhuKq23hRCCdKzG\nXDZHrXcEX9LPTN0GIRiVVpH9EivSNoytSTYGXZdEj1jCPxBAkiQkCRYzrk+716qxs75IyOnQsVSQ\nHGzVwFIM8vUQpeEz2IZNRImyatnYskNk2wUy+nnCUodzdpKO0+J2v4blyBxsDdLJtd377N3DD+68\nneSgIBKoEYjbrDY1djoqGu4kUDIVMpZNQjVxZjMEHUFf0nVNKQ+/wED1PE7efUyt7z3N45/+Rc6P\n7cTINblp0J1sh7USXaEO6VSVQitFMGCgKA7ruTRbOjX2WU26kTi8JAESAtdd5rlorg+EZZH98pfI\n/+03rnZXrjmuSYH3+RVa4Qq2sNneNcbYRJpivkkue6m6XOof5MJfCV6ZzlFvmdxzwwC+zUCnIwRn\nF0rMZ6p8/Sk3mNoxbA58/wTCcF/vty8brOV0tkgr3Jz006h1eOHJWRzZZtd9aXqjPbQiRZq+Jvg6\n2PUkvtUYx2fy5GuC/a+cwAmpyD1uvn9gMEg8WcJ3Uw8Ihz3d8+iGxEyzm26/4EzrKFVHEAxLHI3c\nSiEySGgkiq1bRBcb7JTmADgoTaI2TEwzQKyzQTm0irLVtYSFgLbpitwd+eNIEuyqzQMyTrUbJDBD\nTXx6CISEI4r0EMdBsGLZDPU0CbfcWMXRdpE+RSGuwCn5RoyOg1kziMah7OulM3wLIc1mLLEIQG15\nDHnTbSUQmK0gedP13d+rnWO4+BThkJujblp+duYPcf+ZB9lzQSdvRWlG4+yJhOhlg1jAoCmC5Na7\nGBlyrffDC910p91nIl/oIh/egq65b1UBNJ45O40h3LH2gqzXB0ZuA2FZ2M3G1e7KNcc1JfC1Spta\npcPgSJIXppaxNoYZj48ystUV81K+efHY9BV20fzg2CoS8KFbLtV5f/LwCv/v353gjx88hWU7/Nwu\nE1WGZ2fqCGB6ix/VEWizj/Ex5QVGOkd44uEzCEOmNrbIfTtup2PptMI5lrpXAZByfVRJ8HODOe4P\nrZOoFCjf3Qu4oqOMhgj4V/hBucZkzxRaQuHkmQSWpDLsrLLRzrHoxHiSD2P4QiActKgPudKhu5hn\nUNogZ3RRkePElt0Hfn1onkJMII+FMAyFlUqUpuFDcSy2m+vk75xkorUACB4IaezxqfTGbSQk/J0w\nDiVCdXdCyJg+RjWbQTlH3opStKp8IBjGEjLnnO0YRVecb7YvsEuaATlKLZkm1ePGEzbyCUopNxZg\naR2UToR60Q1Y3xjOcqvUJrQp8Cd37qA2eTt+u8Ndpxqc27sfgGjN4oZ+13p/3tnP2kY3w4Pr6JbM\nsUwX6XQJy5YplmJYagCf0ElrdTQknn96EWvzTdCz4K8PjIwb23Ha3uK1f8w1JfCvumcGtyQ4fMTC\nXNpNSh0gFHEtvFbD/YMsdgyiPjcIWui8+1z4pfU6c5kae8dTRLILZL/8JRb/6IvI3/gztrTWqLdN\nbjem2Dl4iD2pdQqOjwvpMZ7dF8WJqvSk3Zo0duUMhWyNajLL/tu3U+5U+OKxL1Fp+7HSGbAUbpov\ngSTROnqeybMHsVSV2JhGy/BzZnkUQiqRzhpO6yhjWzcQtuC5yoTbvnYBWU5xhI/RJEQwN4di2Sh+\nBRoW/UOzSBKcN8eQqzqh9RZmSGF+rMxAJIgUUCisaByYdVfnjrcznNu7n+ZAjNkJky1969zeV+WT\noQAT3a5F7G9HEGoZq+la/yv4kS13MoqcXOHG2TajquCcmKDhKJB1J2EpFuIe+RX8okK1O0WwV1Cp\nRqgDfssNdGrBOrKjYNdshOmg9PuJ94C/476pSXaQ8E99EltSqCd6KfQOMhkPMT2XY2dvESFF0csR\nuhJVfD43CN8VbhMNd1jKp+kk3Ymjr7XIXXvCCCGItP0YtvvYO95ip+sCfW1T4Dvt9/12mhvlFmcX\nr1xc8ZoUeDXpw+y4KXqzSx3Cm7Vg6k2DJ1YLfPH0EgeyTQJK4Iq4aA4cd63rB5ItMn/8RepHDmOc\nPUVDDrAUGqDPKPHR+6tIssT+ba7wHeyaxAhomPcNI6sSLSOMLBl0D2YpbZ+hK5Dk/37lT8gUa1i1\nFLK/Q6LYRyQCIKgF4oQbNXK3biWqyhy5sJelqREKxSQDIZs7s0dQEirLcz6apkZIMlkd6xDUbsKW\nfNwnHyKkbmCp7jiJsMpIbwlTKMyoo8TPlZEE1LdE6Wn52bH54B9ZSbJQSjKeKrOva5n53b2cLgc4\nsyPAbUOrF8cknS4Ti9UJtSMogSrVWgxJKGyYFqLs5sArKy3uLzsIZF6xdiBZDlbdAAmyfeMIICVV\n6POXkWXI5VNUkhnCtRQtycEfctsxURElA6nLh6TKFKp+bFVC66h0wlUE0kXrfVgzkI15/KrN/HwS\nMd9iy/DmZKQ6fHhiEUOoHIjfS/6mNLZPxrYEiZE+Iu0sQSREyzUYbNuz+K4HjMzmc+s4F12n71f+\n5vsz/PG3TuFcoYnqmhF4IQSZpTLhqJ/j2UvVBY9fyBOK+LACCi+FBM9mywhgpaGTDnZRbJfe1azd\naJscXa+yLWEQ+PZfAWD//D/li2M/z2N9dyMBEztbKGELSfIzEGsyJApsOD3sloaJD0s4mTalJ9zA\nYs/WeQYSPfynM3+N6ZjE1u9B7XaX3EfqaV66I4c2NEPCcLNu4lslLEumvplVksslifQF8CdVhC14\naOUGBKD2ClrhAKo6QkBvMyEtIMfiF2vNd6c7xDXBopVAdCTCLRszpNLsC3G/vhttNMTSepgT+hh+\n1eJzN0yT3iORtQ4xH1pHlWFnvEO55eexmWFkSXDbzWfptgMQaFKU40hKAsuqIiQQtuCvbwqh7+vl\ntJjEkIPEFst0HIEvpFBLdFPRNfqlGlsk18LayHdhawayUKgIiYbh3nODEJ2OjSS799KYNRAhFa0D\ni0un0INBlsZ2IOtF5ueyF90zmWwPCatGV7JGxQrjCBiK1znpTGJpGsgS9eEwlUAveqfFtupRAKRN\n40HY7ct+bjyuHfTMpU3infb7+3daqnXQVBn5He4h8WZcMwK/sVaj07YYGk1yZqlAWi+zzckxtVQm\noxus7++h7pe5oSvCQMhPoWPQFUhhOCZ18/KDK48dXyW6J4W5q58OEj3//a/x1wsKhuLDEbC1T+JD\n44tIaoKukZ8EYH/cLWVgZZPYQqJ2qIVZERw/t5XihUk4OMRA9k5+Y8+vUihZyIkckbpCM1zBqqZQ\n0qsMmBmaA0lSSZOpja1Im6vmN/JdKD0Bmk2b1SU/Tct1M4hUFk0dA1llqLWGJEFH6YPNgOFEyi3h\nO92Ikph16+FUxqNuemVviJYW4FtndgISPeEmYZ9FogtkXUGKVtihqSiqxNRKkmNLAxxomwQCBneN\nr7tB14SDIifYttZG7vLRrJuoiQC+pMoJewe+Tpu+7Co2EqGAQHIcfMs1UlKFESlLW/dRavkINd1s\noAqCxbrr9nH0CNXN+3daNiPzC9jYSA4srK0zvXsfQlFIr5xk9lyVbekytUaUHTttRjeDq0tikEoj\niKxKnLR2IpsOsm7TGAxTD3Xx0vNPkGwXSbTXaTvudW3r/S0GHuCYBmZu49LP7dZV7M07QwhB/chh\n5v/FP6N0/f0+AAAgAElEQVT4qFuOu94yiYXeeB/ny+FdCfzk5OTtk5OTb3tP1rdiZjPPfWwiRWHd\n5vPZ7/PTC08S1Ws8uVhAaDJ9iw0+v7WPoXAABwhrbn2ay3XTmJbNK3k3JU8Phjj/hV/jZdFHtug+\nJAPpEJ/ddRJZgmbgPkTWQNiCnaMVguEiZ4wGx9pJynsmOddzD2srQ6xle/A3IsTWu/jLc23CkyDJ\nGrvn2hQsDePCrZjZrZza6Ue/2V3Ov5Jx78MMqbQ7IVqtAHELnsxsxQbG4lUGIrMEVXdXqT3FE1St\nAHWpC9o1hGGzPZTFciTmj/USLHSwfBKR5QYIQSiwzrdPT9CwA+zsLVDV3UlDkSEs3Idpr9/9d2NB\nwXY0XmmnyZbTxKNtulBQQnU0KcLtRRtJkZiXHD4UUjnHdgzJz67Th1HtLJIsoQ4n2Tp7Bt9CjW6p\nTFDSyee6aEaLxCq9SJKDrHaomm5apr8dobGZWVM3ZWQEqaK7srUidzO982b87RaTxzOMpovIMgxs\nvY38epPBgQ1adoA5aYR2W3CWCSxFI7pUJ7JaQWgK+jbIpnfRCYQZrE5hWZtZUp4F/77HyGbdtLBN\n7PdJoNUs5Fn7//6I7Je/xLKeIDu1hCME9ZZJNPzmm9i8Uy5b4CcnJ/834CuA/4cd+8NwHIe58zkC\nQY2Kr87OYoao1UYSDneXTpIxDHyGg2/BdWv0hdwBUJR3l0nzwpl1tG4/wWadLqPNCSXMoYX8xe9/\n5g6IaDUOL/eTqSVZfPH7iLxOIaWw7YYT/NqeDLeFStRNd0HO+OhZ7r7nFZKpKorpIMsQ6RsnGv48\nDSONkXVLGzulbs5vD5Iesii24jhl99fQ6XKHMl9M0hfWWJVUgprJF24/zefjfnxqNz3ZZZJzyyy0\nhwAJ214i3i6QUmrMFZL0We7YmGGNYNUkvVykU20zW+hiOFjAp9jUOj7aloojBCV/nW5ZZlhTKFqC\nYGUzSNoZZKPj5uL3O0H86jqjuTo9wc3YiCLoUX2cdHYgmxY7zxxhrREivrsLNRXCZ+gYOYMI7mS5\nkU9hBHRkR0WWBEJICCDgb+JvR1hz5xyaIZm8L85g2X1LUuVJ9GCIbdMnqThhbhrYQAgJxZfGcRw0\n1WbWHqZIFzFZ54S9A9mwkWpLqOUNJMuhMNjP/PY9vHTvp0i017FsV+Bt6/0hBlcDu9GgcuAHiDfZ\nu+Ba4dUMGiXuvhk6nWt/0tZXlln83d+mefoU0o4bmeq9m0PmBPl8E0cIYqFrQOCBWeCzV6ITmaUK\n7ZbJ+M5uDl6Y5/byWRxJRuvpZXd9nli1QLrtIBxBp23SF3SF0MItjnU5FrwQgqfnNxCaxo7pk3xm\n2yACyG3WYf/A3j56lRMIZJ6bG2amsIR56iwrZZ2TpsVnoz5iksyZWoCljRSqz2B8W4FsoJdYwM0C\naZ85g+1UcHSVo8p+/IrJT+66QH/QwbxwF88vDHHwwqRbUhdBJ+ne11ouxePdnyO61c9P7p5FlkCW\nYLu8xPjKOcRKi3XhLsZS1QVGNTf3fXkjRRiJalhBMdw/zNBCm1eW3M0+7p9YYakURxU2eT2OLElo\nEuzyuaLdjkxyPjoKgFyPUZTcTKV+fEhygV1nFpH7XSVOyxLTbKdDgMRSBb+hU5iYINDj+tXnt+3m\nxNYJOpaEaUkUigmSHfdc21FoC/deI0oTxdGYVQIIIdBkmwuDuwiZm/VwdPfNIpnJY41EGIg3WXUG\nmT2XJdHruqVOLKSwUTgf24Up+YguNyilFzC0OpHVJkgSvnqb1S3beeazP/0PLHhP4N+MyrMHyH39\nazRPHr/aXXlL9M0Aa3Dcfbt9P7hoGqdOIgyD9Od+huDP/BIApuTjmUfdEiPXhItmenr628A720/u\nTZg56/rQtu/qxTx5gbRZRbnxFrp/7ueRgJuPPEeg4V6q1TDoC7ozXHPTWr2c1ayn5orIcQnZttjf\nm2Rbb4phWaWdbaIoEp/ZH2SmpfG37Y/S8Yep5p+hHpKZG/Hz8XAAW0j858M3cGDuHmTLh9qVR1Mk\n8k4S/7TrFw6GCkhOiMqpAsJWuXM0yy3DOT40vky9HuP5+RGMDT82ghMIMktVzKBCRumjJCUI9UxA\n2hVZR0jslmfw2w5P93+KDV83AXRSUpmtgSy2I9HOp3AQZLHRmhYgELZMM5+my66SXlqn3AkSQqdV\ncsV2UJW5wa+iC0E+v4uKullBMydRdFyrqFeVaUttetc2kPoDFGybMV+Ak85OZNtGX6zxnZH7kbf1\nXrT49GCYs7d+jL84uZ8/fWEfJcUgasZg0xWjbRqGmuSKbLsdQRcQkyV694wT3BR4tWUh6zY0BeN7\n3GPPlPs5dq7DYFeFTDXC/KKKtVrgtLQD2bTx5wp0uhPogQbRlQY4DraqITsWc7Uk5qsC7y10elPM\nvPsmq6+u/pAjry7GZopk4KLAX/uTtl113cKhPXuplt03Dp/VorTRYBCJ6BW04H+kOzp1d0df95lp\n2izMFIkngwxuD3PjkmuN3vhPfgFtcJBT33yQ0YXzrHTdBARQFYXh/gRdUz4qpqsSNbv6urY7hsXv\nf/UwN0/28NkPbXvddZ/8zkms/gjjc2e58Zd+Ci0WpXyhjLAEsYkkz+RzHHXuBQ3CY0XSc2tkPt3D\n/eEAlhDkzSSr1RijqisSzZg7yaQLK4RqFYjAxIbCUV8bq2ES0Ezu2NzAYjxdYXdvnkCohrwwQR2B\nI0s4NZOKX0XZ3GREERbPcRuGJYjIFuPyCo+IW1gMhAjmzpJIZlGFQ5/PYLGQQDF9NFQTX1NBQiYa\nb9Bs+0kbGmO1ZZSlAtHhJiNGiVLLjdLv9/sIyTInOibPHVwF4QMkGm2FpuljzUiT8New24L61hAR\nTSajm6DuoCWCOGcL7Nk4yGRjmYen01R3bifUqNKKxBFKkMCNI+wzjvHkoTSJph+/30DX/aTNBqta\nBBs3VTLQjlBw2gypErfeEaH0SBOEQO3YBAodKnt62JNcJJvxMXrkJKvjW5BlmCnFUAMydCUwJJXE\nfIVOfxS/tBUjcBzFcPAXyug9KdRCjsaygdHnCjzCesNn8mpyrfQn19isNFrYuGp9ejvXXVrPoiUS\npMe3UABCqnPNjOGbUWy7btDe8SHOrboT1K6NFzg/8Sn6mwZhpCt2D1dC4N92Pk8+X3/dZ3Pncxi6\nxe6b+3n6kUcYbBdZTQ8xEUpybj7H0ds+yMce/QbD088z2/dhspkK8VSQHr/G+WqTuL+HtWrudW0/\n+Mwsxy/kOTWTZ/6FBQqhZYrdi6S7o9i2TEfbBoywP6xR0SVWz2aZXaqgRjUCQyGONiNEnRp1O4Qv\nGYDhIB+IBtCF6z9O+xooqkTEFghZYjnhwxaCUW2dKq7ox8sycXuZJgnu3LJGQLMRTQslrBL2W1Sr\nEWJABJA1cHQo6xbRhA/VsfiUdoBH7fs46OwnkllhaHCNjOHO7saGoBLdYPzV4OhGmupoFCttE3/F\nfYC2jq3w6NmtDCEhB/uQioKfzT6NXzjk5n2IiSj9qvsSN1UJUibAuLVOcXQLykACPebjEfEAv+D7\nDlob8nuj9AMVx2FZ7ERfr2Ov5ZlsLCMjSKgGVWDi3HGO3nA34ewK1tgQZ/x7+PmbnuHc0b1kZQM5\nVWDbYo1VbZK67T4+/naEjJVjSJXIlKeRtQCSYqG2FXyVDmPj7hqJ8okmq2IXo5vumXOBOl23x5FV\nleB8jchqg7V7+kmtTrEedsdBK66h96QwpSjCKlBvBunoGj6/+YbP5NWiuzt6zfSnteFa8PWFxavS\np7czFk6njZ7LEdq5i4ZrJ1DLV9CukTF8M5q5AsgyFV1iPeNa8zG9QP+Ij7kpg+VjGXL3jF5KgX4X\nYn8l0iTfVhJ6u7Hxhp/PnHOzZ7bv6kU64Bbjsu/8EACnSw2yQ2OUekYYbWTQOgWaDXchw6uB1ph/\niIpexbTNi22u5ho8/rKbS28LyDQNVtNTVHwFZqsLrNQ3aKeGSecy7PrQvQgh+Jun3Voz6ckkQpLZ\nIc0y8dKzbFmZRVHD7BjswxEC03GYd7rxSRYJxSEoJIgLbH83U1aUlxJ38fB/8ysICVq+GFLbIKiZ\n3DGWxRIyUtidU3d0lwiXezF8bSoBA0t3h7Hpk7HCGn1Snl4K7NNfQZIk9Hiag4sDmLZ7vl3p5+cD\nvezyqQghqBTS1EajtOJJtP4QpgotySJr+sCoUgv0kouM0mNUiJs1ts8XMFs22uZDtL4yhqTKGB++\nhcCOHtSIhtU0EZLMEoN8+ILOtv+6jr3QRCdOMS9TPlvh5toMMoJz/TtY3rqDWCnP6NlT1M4V2Btc\nYKu0SosQkS6ZG3ZfoNIOsdw/x9k7FvClF8haQUAQ6ETYsNzfbbWeRbZMzLAP2XAIB5rklG6eWttJ\n0Yoz3duHlgpyyBhHpO5FViOMt+ZIL9QpAb5Kno+UDxOw6ggEXRWBr1aHhJ/tGtTLSS7MjLoLY97n\nKx/fC4QQmCX3jdTY2MAxzR9yxtVBX3PfiH2Dg8gB1+X4XuXBX8nnxK5WUWIxJFmmUmrjUxw0x0A2\nGswjGNyeviju75Z3JfDT09NL09PTd72dYzeW3JK0DbNJx3ItXL1jsjxXJJkO4a+vMbCRYyXQzZ4P\n3InpOExVGiT9Kur+DwLQ1VyltSnwvZuB1rBvAIEg23InEEcI/urx8zgCRpGQJYlaRKHjaxIr9fJP\nQr9B39qtIEmkGwso0SgnZgtMLZWJ+WSI+UhR4i6Okp7N0J9ZBKAu92MIONBR2NjcmDq1uQFHM6qh\nqdt5QfoJLoitCEXBCipUAykywR7uH5nDL1vM20MIIWibCqOpCkHNppJeJRe/lMcf6PITn6kiP2tx\neH4vhRcqjCxewAyHOLQ0iCrbDPXnwIFsNka/IrPR8lGLx0FxH4r6eJyWz+Zoxg2uTtbc7e7m0tsB\nWO12rf5W3UKWJKaNYaqlHhJDCqaq4F/LkXthDf+cu1H1ohhkZ9NBdkA/WcUQQ1TPFtGEzc2NGTr+\nIC/e9lGEorD35CE2tARWuc3u3gKDwq1aOdUZZWgwx83bltEy47QDAmXrNGsDRULBNiuNGOfP70YI\nkOwW5cQAnViQTspPZnyEl7XbmOu5ibOf+im0/UM8IT7ICXk/ihxjV/s4IxuuoVCWYeWsTikQJ110\nMH1t1E4YdTZLz6kSCdN95FvtAAIJ4Vyb4nU1cdothL7py7ZtzI3113xvWfbr9me4Gry6gtU/MIQc\ndN2a9nsQZK0+/yxz//P/gFl692VRhBBY1QpqPIFtO9QqbTb3DsKo1SgDN+x/fbnyy+VHttBpcf0c\nv3foD/nnz/+f/OErf4IQgoWZIrYt2L6zm7VvfA2Al/pvoi8VYqbawnAEe5NRUjfuxkGiu52luVmP\n5lKqpJuimKm7gc1nT6wxt1ajV1Po1xT2TaTZKOk49SSRag/npnJI3Qlk22Y+uYph2nzjyQsApPvC\nIEnslOeYmQrS3yky0HF9ZCtOD+VqiDl7F1ncDJbYpjYUU3FkOUCkmeXj8rOM6aexgioSChHFYN9I\nnrbj41RzHEmSUGUHWYL+3gKVdAZdqRJAcINiM5kziC03ELZMYT7ObXMnSefX0PNtdEvlhv489w+5\nf3Dncim+19I5WPDT6N3MSqnVcfwKxb44F3Jd9HaK+NQCqq1TUXop+uKs9LvHSlIQgLViCGQJ30gX\nKuBrNhCmww36HF3VHBnRS2mki42UipzpUM8kELbg1vYyAbPD1K5bULrD2B0L32qOyeYKHwpfIKDZ\n+PM6OIKZzijNVoC7x1cY1H388sNFtKYPu3uFqtOiJmRaxX7mi3HCRoRTO+/FCqo0B91FScraBsZ8\nBX21QX97hf3ySeTSK9z7zIPcHZkik+tCSNDb28Rx4Ovrt3Eh/wAlRafg+AmWQwRKOkZaQ0iCju5H\nODLC2/TjdVhF13qXNstgvFrr5VWefmSKb/7lKwDYrSbFRx+5Klb+ayz44KsW/JUNsjqGQeHbD+G0\n2+irK+++vXYLYZqo8Tj1agchIBZ1DS675qaBXxN58O8Ys05VrxHRwqy3clT0KtkV1//U31iApVWm\nIluQt40gSRKnSq4fbW9XhP7BNBv+Lro7Bdo116+aDvhQJImO7YpUppGl2tB58Jk5Aj6FPtNheKyL\nB24dBsDOjdDV6kXPZ6ilukmVM+TVOg8+O0WxrtMDNPtCyDhsk5axT1dBhthdGmFaZO1evl+5Eb9/\nL3UzREXXkNpBGhJICT+ybfLBR7/OFmOFLf4Snc1Xuk+NzCP7JJZyXdRDSYQAy/AhBPT05wiWuhFa\nk12yhN/WEArUt4fp6iojOTKtQJpUPktz2R2PVFeBrV01QprFcjXOqYbCTKxIJx1ACIvYmRqybqGO\nRJH8KmOskS6aqKKJKik8PnAzK2k3UyYYcdB1jdZygGh/GEnzkVxdoWtYQnFstp+ZYnh+GgeFwvA4\np7a5Y11bdf+Yd9en3aqaO25GVmU62TI/6Nrn/t6GXR95ZqkHp6pjRPwcXd0FwC1bSqyFb+b2w4P8\nzGM651thtE1L+qkLWwgHOzT6o7QlaKcCKI0OseIakdIcjZkCnwwdZKS1gP8lg1RfAb0ToN2IYAQl\nCssGAhkfEj0E6WoniCMRQsLuddi4oRtHleh0fNiO5G368QaYZddSDW6fBC7lmoO7ZmV5vkSt0sEy\nbWovvEDx2w9dlXTKVy1438AgcsB9Nq90Hnz1uWcvCq/TvPy3A2N9nfbsDFbFDV6riQSVktteYrMo\nnmjWkSSIBK6BNMl3SlSW+Xcf+G3uH74HgKX6Kvn1Oj7Zpv3Ud7BlmQOpfewdS2M5DtOVFkm/ykDI\nT8CnkosPouCgbLizqCJJ9AR9VAyQkMk0sjxzYo22bnHbcBINqA4tY4U2kEN17HIfgUAcLe7+QfdG\nBY4e4MDRHCoQ7gkhRX2MSBlWNmS2VlYQH+0hENcYqK7QUYIsZYNYbQupYTK7PgBItNN+JFli57GD\ndJd16kWdQblEs+5eJxaRcbId/IfX0CU/BZFgJdNHqRynO1HDGVhhpNKNLGTMoEL2rj7iW5pIm6GN\nbHScou3HrBrEwg5GvLTZ/zogYRf7iXVuRJIUrOY5lDakV3NIikxsexx5LI4swA76UZBobomj+D8D\ncQ0tJPHKyd1QiTNsCHBsErT5SPgwd7XOUvEPkW+55ZOzaj++8TC6rFDsBBh2CqTrOVZGt9EOBRGO\nRWNZJ++PYysK8QGwLJlCJ0y8WQZJIqN30277GR8okEnvpBLeyYGuj9JR/NzcWGKyscR6Pcbsaj9K\nTaDbNsgSfdU8C5UE6/UU29JlNMVhbj3CQmAI//Yoaxvugrf1pk1BS7Cns8jNWPQjIQNtBBF7kTtv\nOAOShBlUsW0V3VC9VMk3wNr0v4f37gUu5ZoDlAstrM3sNb1jYZXdY+366wObbatNuVN5z/qpZzKo\nXSmUYBDJ5wNFuaI+eMc0KT/x2MWf7VbzLY5+aza+9lVW/vDfYmRfXZiVoFpy+5rodg0uqdUkGtSQ\n5Svjf4cfcS0ax6iwJeZa1AuVZUr5JpOd89iVCq/0b6emRbhzcgtztTa647A7EbkYbOgMjALgLy5d\nDHj0BX1YQpAKDpNpZHnlfA5VkQnWdardazzXeIavnP4aSt8CCIn5WpNyfzcIwe6JUcT8XmwBO2MB\nCjF31uwT8yzOZgjsTxAcj2Dbgr4pNwDrT2jUZyrUK23yM67wOQNh7JbJvuMvuvdl2ZxbTXLHqFvz\nvLGuYjySpWdtBcm2mfv/2XvvGMvS88zvd/LN+datHLordE4zPYkTOSSHmRSDREpeUYS0ToCBhQH/\nYa9tGDYMy5CxK2kFa1feVSQpaUUuJY5ItjiRE3s6h+pQXTnXzfmee7L/OLeqZ6QhRQ4b44WXL9Do\n7lN17j3hO895v+d9vud1htnYzLGZTyMIcNSLEusM9pwfI3iySI4inaIEnsdOdB+vDPhZcXA8DjW/\non5waBsEF68witqzMAjle7WNWgmrYRLIhUnV/CyhHvapjqw3RHM0jX0wzcLiKLVqDA8IF7sM5PM8\nMXwZDIf7ijdZSxxC7GoEHZ01b5BDxgjXRvdjigqna1cBuHDiIJIrEZytMGF7HEDhtYlfZKuUo1hK\n0smGGdD9h9xMBVjZyiDLDg8GXkLRt9mUQwQlCyU4wAcqV0h7sD43SWS5y+COgdwyieTbmJaA6Uoc\nz/nZ5fi1eSZSeUQvwOV5v0G6Y7f40tYP2GfauKi07BZXev2bskKbrFhDcg3snv1011B/vpr1XWIX\n4LWxcaRIFLNHhYDvGbUbbd3CrvsZqdP+h+D3F3Pf5v8499tY7r3n651WC6deQxvyn0NBEBADgXsK\n8I3XX8WuVtFG/fHldt57Bm8VC+A4tC77Mx05Ht/L4JP9/loXudu+p/QMvM8AbxkVRqN+AWGxvIJm\nNsluXEJKJHkteBw1bJCJh7hZ84uOB5ORvX3lfVO4CMTbOxhdf8D0h3wuORkco9kU2Cy1OTiaoFip\nkB+7jYCA6VpIqW0UWaBo2ORzQ2TbLZx6FLOZJgKcPj1EaCCE4hm81FjgQRnk+5J0HAlJEhio+7OG\n+FALcq/QXzNxe2oWyfFoLtSwRXAEuKDJ9EU7TPX7FEVjUwHLQ3IcHn/xbxj59nnCpQJzpo3rwUEv\njGi72AEJJ+c/JIlGiaDRAkEAQUJrSAQ0F3JxsrMWngfjmSpivIRthNAVFdspkigk8fBYDRfR8/5n\nWapKOxygUPOzLjumIDguzWSOO4tjoLgUT6bxgMiKjusJrL4cQHVs2moCx7VJtquYqMzVJtmJThN0\nukxWt6jHU+jJHJmrZbJFizQiQQEcQeHa7DRLc1m6fVG61RCSbtNNB1hqxfBciM/AsuAPv1FHwVSi\nbKePsU8QcYBu2EXt2OQulOg0AqgBHVVy2N9Xxm3Y2NkIh8fy/JvXTqC5Co5j8uXN73P9sU+wHZsm\n6lWZLF30VyfjUY4nWXMHcEQNR/N18Kah0mj9vAvQ3w+rl5UrqTTq0BBWsYBr+MnD2wG+0TKwa758\n9d0Avtgp07Y797Rv8m7s1gXUwbsNesRg8J5RNJ5tU/n+dxEUhfRnPwe8+zn+RJ/lONg1P8nR524B\nIMcT1HoZfHLQtwTRTP2e2hTA+wjwC6UEzVaRkBKkL5hhs73JdPEtBNeh+MijWAQY6BdwPY+b1TZh\nWWIsEtjbv78/ybaWJmGUaJX9rGF3Rasm53AqvnHXUEhlZ/Q2lmjwC5OfQBIkBNEjMVDDFGXslk24\nJfH17/uF1WSywJnCHIIq0zEXeXxDJzwYxDJcDNvBcz1qdpt4tQiBASICxCt3C0pyzUDdKqE6Hhs5\nhQmyTKQayMUagufQCib3rvLE0i3SxW2O5H+ILSyxvJkhX8ggeNAetUAKoWIyeOM2QfPug5RGIJKs\nIQgCkgOO45JRXJSI/+Do+S62sYLWCdCWLBzFZCTjX6NibpjF8OieHFIyXALdKjfXDiHgIQk6RipA\nazhMtxPguVunia+vUg+kQRDpSiZuz3ytkwnRNRLcX7uF6Hlcnz6O1IijNSz0tMadkMB10ebU8Ru4\nrkitk8RSRdymTLDUxZNFKLVwltuIWQ2hL8CQWSHmAXiUwvvRrCa3cNjan6RyIIHouODI3B/XeWCg\ngCK7rDfThB6Icnt5CMVRERGYqN9mMXMf6iZ4AkztW2SmtUrA61LCo+lGed09BZ53F+BNlXrz5wD/\n98Mul0EQkJNJH0A9zzf14p0A3+yYexm8+y7t8vSeW2dRL937Y9x9CWWye9ukYPCeZfCNs29il8vE\nH38SdcBXo7nvkaKx6/U9QzS76r8QpXiCelUnEtNQAipCOEzQ7RK9hzYF8D4C/IvzY1SrvpRxNDbM\n2FqTbGcDZf8Mb8r+G+zAWIL1Vpe27XAwEX6HJ3J/OsxqqB8Rj9Ytv3v6bgbvEsOp9iOKUOuuUE9t\ncKoSZfwH19m34t+UdvIiiDZGRWfxTpVqxyIrehSHbxOQqpw8/zJf+uZ3OfxqHevZHeYv1IlttDD+\naBVD20//1hqeJzO8eOod5xWsdzjQ8o2xtieGeKKvjdFwcM9sE7BbmKa2240PR5C4nnsM0XN45q1t\nFq6OsboxSDDQ5dGhm3SFKAMU4HodSTDx8GjhEQPETBfV0HFkGf2tCoLtkUUFAYyyjlpsIiBQd2Ss\nlSPkL4kMvLFNTe2jO5giNO5TO2KzytBcla6hsV+9TXXK56/zERlLAHdb4UzmA9S0DAD10SWqlQ4q\nJtVoHNWRONH0Kavi8D7iSw08IJ9UqXccjoxV6c9VmQreAlEhe72MAARK/oNnZ1O0bvu0yAOTS2h9\nINIBBPA8ju98n0lzHTkdxs15pFI1QECoJnh8ZgXPgyvZk1Q7MovVFAM9yW0lkKWaiSB3HRrDKm8F\nDmGmohyvLeAgsB5MUSeGuVXfA3jDUn6ewb9L2NWKr9OW5T0KxNjcwDRsqqW7NEWzY+5lpu+W3XZ2\nAb5z7wHeqfsvGikW29smBoK43e49MUirvfg8SBLJZz6G1KM332sG/24vIy8cpd00SKR8eSehCGG7\ne09tCuB9BPitRpRy70QnlBxPXGzhiCIDX/0qK1sGCC4PTY1yo9p74GomL17a2OPbB9Ih1oK+ra6x\n4AN8RJYIyxKVuofXiZFJVEgsneHX/7rMY2cWcc6e56NvNDi4GcMLmMhDC3Q3WhQtlyAe7rGLHMg3\n+dy3f8DxS68T1S3EyTCOLDBxtYp1pgBdl9HlOZKlPIk7dTRDwxMFbFXEkQWChsXB1gquLHDkQBjR\ndvD+fA1sj5DZxJY0nAd9WWUxPEIhup8rE+OETIt0dwfPFZEHDXTZH6iDxja40AwFqQElPAQEHjpX\n58t//C+YnJ9FudLA/NYW07aEElOxmxbags/D1wWHRP8y+xGQdZfkzSaJAzFa4z7XHqx5GNUg2UyF\ncP+/X/IAACAASURBVGeLen8Gs9qlervKmuciIuCGBsnH/GPuhusUojuMsI0uBZGSGkHbwJIVbCeM\n2rSoCB7VJf/eHh32OfLRtcuE9Dxq2zd6z22vgmOzMznAHzwYoOa4HMo0EOUOrhBBFB0QBJZTx0gd\njiCIAoFCHjvig3HX0NjazPkNwqUlZl+P4gkiIVnGkixuHyxid3N0Ay02ct9hLvoyFyYETjT8l1HZ\n8lDcDrWdFWzFH1OWqdL+GQpn/38Mz3Wxq1XkpP/i14Z8StXc2qS44xdSraB/TzpNfU8v//fBz/M8\n9F59o6j/5Ppx1/O4slDCdn48SNuNnholFt/bJgaD4Hl45s9eOLeKBdT+AZRUylfoCMJ75uB3s/b4\nE0+C6ENu2/Yp3njKV/84wTBB1yAWkH7mY397vK8c/O1e16LBl28Q7rrcmhnBSaRp1mSkSIPheI4r\nxQY4Hn/73Dxf+8Ed/vTv5nBdj3hYpRjpx0HEXV0A/MLKQEijuNVGch2eWXyRE/MNVFci/uQHufbU\nPlwBnnh1haGCA4KHbbgogot86E2O3l7ho2+UcSWZrQcOoP36GJWDEW7sCyD0Fq6ZqorkOvSvrBPZ\n7mAFJEQX9KyKHZVwTYWU1aH6wUOkgx1qz5bB9hByGpVxnzZqpP0XUzHqZ0OFvhzbkX2Ug8NInsk5\nZ4BZw1881XfR5+i21DhFPJKtDQTPoS4PUcwNcf34Q6CJeGWTExfeYlToTfkQsPGQplMc7OQQEQlT\nQvA81q4M+uZjkoNsKyTiDSYPLHF1+iEAWssNEkMB5EcHEF2DBFCKZPFw6YaaeIkCQ11fe186leGV\nJz/LpfseJ7LawQOcSANFdImpBuPBItValG5L4WD+NZyeGshJqwjmBp6aJBL7J/yt8SSzzUMkK0M4\neMQP1UFwCTweZ37kGMF2k31vXORWK9O71y4LS6M0dZVJcY2VYI6o5yCiYASbDK0exZEsdvZvIKsT\nKMIIcyMaMafJkLmNWTcp5J9Fmz6Prfrad9NQ6Br/8dvLvp/hNJt4to2S8gF+l+M2Njf36Jlu2qdO\nu/W7oO7+PYC3XAvH81/uPw3AX50v8bvfvMbLF3+85nxXuviODD7YA8vOz3ZPXcukbUkYUT/JEUQR\nMRh67xl8r2it9OUQtJ4l+C1fhBFP+sdsaWEEIC7d24L0+wbwxwbzzG5nad6ahXOXKMVlrh2Lcm2p\nCAik+yx++9mbtFyXbknn0SMDjOYi/PDKFn/w7A0c1yOVirIVyCCWt/ckS1PxEN18m08WXmOo3OHO\niEb0f/hfCXzxF/jhYIcfnh5Ec22eebWDujQGkoVy8FU+dHWdh2Y7NKNxXv745xm736bdsqhvdDh2\nRwfVp4c8oHh8H4XIPgCCpp856H1hrJB/DBsPPMDQlE7tYpfgThMPMD7UTzjhZxKr4hgeUAv0Izkm\nM1tFro4/gS1pDNQX+fD6q5TkPtJehfjsOh5wx4khmzU+mX+FhJ6noya48vjTXHzoaZpaBD7Qh2wY\nfO7St/lw8S2yrTVM2WZQb2I3IvRJGxzKv0rlUBJsyF0oIDgSsmxx+tQsV1uDrE/MkMpvIVS6DOsO\noiZD1EVCQLWDGIEOTieKENARzC2eEV9B1U1WZg6xMnAMtW3TwiQWauPaCh8ZKiIIsJHPUYtlidtN\nXM+/hq1WCqcyj9lawTMlulYfa1f6wZUwJz1msweYemCRy7HD4Ho8+dx/4Gh5nvjOOoLgMj66iWGq\nvHkhzOqCRsUOMxrwayHhVgoEAT0dQo0/Qcx9jKjwNNr2J1mI9/FYcRYAa/EE1tY4jmzgib6KxjZ/\nDvBvj106QU75tKkUDiMlEphbGxS2/Ax+F+DN5t1r5/w9Dr7ztm5ZPw1Fs9NTluyUf3y2bPcAXo6/\nk6KBn10Lb1brXBz+GG+5B/a2SeHQe5ZJ7l3TZAqc3kvvlr9KfJeiMRT/msa8e7vw7n1zk/zA+Abz\n63HWf+/3kIDNdIjs2jpri69wuNFkpFBjJRSBwWk+HrP4wFAY82COr5+psHH5Bt/YWeGIa6GLGoLn\nUfjG11DSGTRD4rGVOxxsrbKZVbgy08eji3NcPn+TfZUuW6WjvJVI8GDtJl/Z+C7ltED6BYN426Ee\nT/K9z3yFYNDme9ZjRDeucd9sESIKymf6uXipzLFbHbJmiRvxh1HsDiICluJiJDQGV+skMzIHT27Q\n3bZQz/lZ7uVj0yhigmy0RosIG8Usw/EYphQloa8ztLPBjZyC0jWJuGUG7yywvu8gQ/iFrFIkguAJ\nfG77ZVTPphrweTnb8Kdvpcwgm/tGWU1keOq5b3JffQ7qc1RqOS5bzxAQdabWXkXo2jQTKsJ0nNSd\nOpLkcOTgAqpqczs8gACcuvgKhfg0gUqOQLtFcaKf9GwdyZNoijYiHgiwpas8LW5SXbnJvBIiUIwC\nHqq+SrEwwSEERvqLuC5sb2aJqnN05QiKINDAI4LH8PwUi4EGcbNA2vLli42xCJ2xICeFW9yIHEYn\nyAOv/wCv1sVB5PHtV1k//ThT+9dY3ezHbc7wYqNNSIaQGWDXCklwPU7cPIt406ERi1Cf6GM12c+t\nwBE+W32R/eYGiwyTXBtnMNTBUQN0DY2EoKMbNkHtfTVW/Y82dj1odjN48Gma9o1Z8lt1xKCMFfGv\nlaXfBSOn3cbzvD1Zs/42+Wm5W6W5MEf+d3+H4f/2vyMwPvGO7yx2yhT0IofTByg3/P2qzR9PsziN\nOoKqImh3hRi7GfzPWmhdvLGDIYcxXA/X9RBFATEUxt725aJvP893HJPVplW+SKzvAwjiXarF2i2s\nhsN4pomgKFQLTQhnSPQomo4UIAaE73GPgvdtVNdCGb6w/SJyj7M7vtSAJYAznAQowNrnH0KwbYyX\n/4jfOqhSD0v0xWwmLYvcpsX4lrlnXdk86xuTiVKIB50OlWiQZx8P85GzeQrP/zFhOczy1KO0wwFC\nusvJhkjc1on3PM/0QIjvfP43cBQVw/OoiXE4OMj81NP0iRU6rkb9YZnxzT8hcruOOqoT7+bZiU6R\n0tfZrmeIuCqnTs7i6C7N75aJeh6rY1Nce/iL4Hk8KF4BQHBldqK+llakgeyYaG0DVxK58fgD9D+7\nzOMv/DWWoiB6UEoofCb/CmmrwUriMAtikkmg23BgAIp9Q6zLM3SGQxQ/dYw3zqYZ6GwjxscBOLz+\nQwJdA1eAD535C/L7pjAnyowSZaC/yLwRB22IYLXE0PoiA0kVgRyJtSrbU4MkhSqiJ9LoBvHULgKw\navkZ1fjIJpXrUxi65yt9AhMMezLhSIt4rE2xEsO0XezRBLW2P8jbcZFG4grDq8eZbqcBj65ss923\nRP+oiMujXPSOATC2eJPx2zc5N/pBlqorPFK9jjK4iKI4BGnRlpIkUMghgAeyZHP08C02b5qM12Z7\ngw1Yg9OqxtePfZFWUeNjO6/yb0e+xOHqTZzOBE0tjFFX0WSLfLXDeP/dTPA/5bB7fivy2wBeHRzi\ndtWg07aQBiO4PQdSp9tTkwkCOA6eYSD0TL861l2Q9fDYvHoWsdNBX5j/BwD/7cXvcq14g9987H+m\n0vCBvfaPAXyz4ReC3wa09wrgZ2/vyjoFvnb1W7iqzRFaxEwTu1Zl9X/7X4je/wDZL/3yO76/UXiT\nZuENlGCOUHxmb7tdrYAo7tUTlVyONmEEAaJx/3q1RP/vwD1uI/m+AfxF7zi/EDnLTW0U+k/QEFdo\nBjYQ1/dhRTq01QzVhIPdeZbvPRoGDzRdYG1AZG3Az2AjpQB9s33c1yiScypIRpuY06GmRPibp6JY\niod+4CBLJ+L8tVknEbrAxy402b9l4ohQC4vE237zh+9+7qs4ssLI1jwHLr5EtGVz5eQDFKYOsC3m\nUByDgNHm0v2P8uSLf8tY8yr57H5wYKR6B2shwun7biHYNrVv1ogaHQqZQV766C8i2m1cSeIt7QTD\n4hai5VEJ+Dy8IKm01QSCJUDaYTq+xWtPfoonXvhrFMvPiA5uVIEq6+EBFtP30etHTbxm0ADmD57E\nUjUOCgsU3QidYI6zwRwnEUklqkQwYBE6AZGx/CpjeV/l4yoi9k2VbjzC2NAc7e0OApCz+tHtDk4x\nhD25jqWaaEYY01Wwrz6OdOgcfYVtHCdEcgLsXuasqzFUp8Xa2CZHx7cBiZs1ja6qs2LvIxP0ZyTJ\n+G0WcptkSREoDTE4ukA3u0bcdbls2Dwlt7klPolk6zz68t8y3zeGI6a5lYoxwyaDWZN6PULbTuLi\noiHi4heEp2fm6c/VSL+5iQdsPXIAxZYI7NSJrm3w4dJ5ZvuOMtaqcDLe4L7lOa4a/dQ0CRDwUNgp\n/xzgd2OXL/aisD3/Z/RNfB5zaIQ3w4fIzFax4iqiLOAJ4Fg9wMr2YRXyOO32nqvjrkQyIAXoOl12\niqsMcpc7f3uU9QoeHrVuncpeBv+jM1nP87AbDQJjY+/Yvgvwa8VF5hc3qRl16kaDptUiJAdJaHH/\nTyBOSkuQDCQIySE6tk7balPt1rg0P4dYv2v2dXXjNt1wg4zXJAb863N/wHRIZ+yF50CA7C/dBXmz\n46/4dcx3ruq1q1XkRAK36Z+7ks7QacSIaB5ir+japCf5vseU4fsG8A2iLJ04wPORCtaWTlAYoen1\nwUAYMb2N2rcB+m0QQGr18+Hhp3ngvv3cWC1yZXOBRfMKrXSR4ESIW/mnuBFS+PBnD/Evv3Ge2HiN\nVuQCotjP1WGXRmWBxzdqHL+jozgem9kgrx6K8fFzPhf40oc/j2xZfOav/h+S1SK2KLGSy5IY8viw\n+izbRhL5T24xlzjI5VNDHE3nGCgvcrvvEUTXIiZVeODkHIpgUv5mnUirxnJmnB9+7pdRuzoxYZMV\n/Rbp6DPYQRlZtzHEfgJmh7aaphDxB+ZMbpmdQD/Lk9Mcvv4qmUIZD7iaGWFVGqfZH2GgLSLjgmQj\n6gqyXsUK+gZrx8TbLNtR4ggYPaoim63RnXcJCfC1T6RQzCz7dsbJ3JpjoFsmvVlnavMaUzev4fTm\nQ8OlBeTWHRxJ4eROnrJ8kM34MSb0AiXPJbUO999pY9KhVO7DcWT6lQ0mmpcJbJQZVjOMtWy6koBp\nFQgHbiJ0w6h2H8O1m3QjdxjcMIhbK6j2MkrLYyDQQNAEUqZD25vjk9Im4ZCNG4VGxCbYXOeg3UKc\niuDOt+gsQ7qzTlkWMFyZYcdAdg2q50WMtyKM57uQ0xjPNfB0B0+zsQsiw6tzjH5pjOvxoyTOFdFc\ni3Rpm+1B31nTcEM0/xG+9z+l2KVodpwNAp1ldirLdLMDqGs+zdCOyCQ0FVcR2fVpU4eGegDfQkn7\n3P0uBz8aHeJObZFip8ggd7nzt0fN8OtadbNBRTeJH0lTLb4T4B3X4czqi1wv3SRmy3zIcdgRO1xd\nPENQCRKUAzSrs+wDXpp/gRtecG/fgKSx5fiy438shhaPkQQSnW1qoQG+sv9XGNmXpLj0p7B2iXw7\nz62nEqiWR7ZyntHv7XDk9Ec4lJrG7PgJjWPfBXjPdbHrNQLjE9i9Tk5ONIPdDhBV7q6nqbo+wIud\neyvbfV+Jx6XBE6QL36E0eRULCPyI33MiO5ypfZ0zl0BAQA5JBEMKqfnjJMt+VV/sWPz+pd9HOF2j\n1ZslqfoW4/M6993qoFkehCXkB5KMCTB0vgy6w+X7H6cwOoJWb3F5+EMISQlHlMhlK8wE5vnm7AFu\nbyVxJg4DsH9ZYG74NJPd62BLJO1Nop9KISgG+X9fJ1GvstE/ySuf/AJaV2fo/DIVN0aqP8aR6R+w\nHnwYpW1jhjVEWtQjfQiOhOC5DITzvMaDBO0OF/bZfKQA66l+ziSfQJO6HGv7t0cDdNEjaCmIHQuC\nMLw2T3yiyUBEYBPYFYslA2XC9SbrOQUrkEF3dc41+vD6s4wn6+zUQmTbZcY7O0y11+gza/QX7yoW\nbAmiwhyb8WP0uTYf2X4eeivVPWBxex+C6jIx/xah3kCemLtbRHts719NoGczu/fjxb2f7orgDu1t\n6exte6i6Aqz4/ymCBSQpkmT5R4yYXuQNzG9v/YPNzp+vMRJrEG34ILV//gZlaZgmA1hWgJ2exWzH\n0jmXv8QjA6dRJZXtdp715iYlvUxJr+B6Hvvio+xPTJALZVlprHOrPMdcdYGAHGA6sZ+p5H5Go0NI\n4r2Vu92ruFKc5bXNsyiiQlAOEFKC7I9PcDh9AFVSfDpBktg2a9xod3mwXScYyaA28nhAPSQxrsoY\niohr+NmnNjBI+/Kldyhpdjn40dgwd2qLVIM9NVVP3rgblmvTsvz9Cs0aTkQhkgthiMIe153vFPmT\nG3/BanMdURBp1fw3y4ZX44XVF/c+a1/bYB9wMDzGUyc+TSaQJq5FUSUV27WpG02qRo2aUafarVE1\n6nQsnZASJKKECDgh5i/YhGSTgeYitdAAihUgGUhgJweoAr9UG+eV4jzFqT42+5psCnnenP0zhpUA\nv9KrTdTbebrtApqkEuo44DjIyeSe0Vg77CdomufPGiRRoig4NEIitl4ibHcJyj8KHX+6eN8APucV\n2AwO89GLGt8f6SfrlDnQqJJuWNQElY4moQcEJM9DNV00y8WURdYzIdYzAdLlI8RaKfRgg05shfTO\nUQ7fidDX3qS/1iXUdVEcv3DnCuCmNfLxLJE3q0S7bVxR5OaxB1k/dQQLFU9zWT3yMl1BY8D6MHJZ\n53dfux/HFYm6HYbaBdYjQyQFjWIrhZu9HzwXqc+kctEmsLhNwrLZGNrHix/7AsFOh0df+Da3Ih+k\nKULMnKFqPsdofIt8KYOriGyOjGKHFLRLFgk9z0YtiRHTOLB6hYbqS5ou9R8EU0ROlqA4DkBIkShZ\nMAKECgJWwuHopdcpZxNkYr4xWQKw8Cgu6ISB9YEYmvQonbleG8IDSbLDMrrbjzgX4dWNHK+mT/Bf\nr30bzTNRLYO/+MgI1QgcuvI4guBQDg9ROjWJulolVi5zbmqatpdkMLKB9GgKJRrG3e5CSkUMSty8\nOUpFbHF0bR3zUB8bG4Nk2mvcmikhKAPkJz9G+kKV0nEFzZPxVop4ah03cImTl2E6bSONBllva2w0\nhjkVX0KWXGZvxzlUXGQ5NoMnBXzDMFHBFjWMEBxdfQNHkLgR3YcpqZiJKFY2QUhxOXX+h2iGTrhZ\nRwAcQAKinSpNdQDT0Nip+wD/2tZZ/mbx+yxUl+nYHeaqC/9gHJ/PXwL8xGM3IxQFEddzuVXxV0er\nosJwdJCR6DBj0WGOZQ/fswf2vYbruXxn8QzPrb38D3720vprBCSNY9nDKMkq5ZEkamuJG6aNvnGG\nhnSIvsYAVljGk0XKnU1CsoeNjBFQeKN1kwPA87fP0OAGruey1vDpiltl/5osjGg4ooAbLyBd+2O6\ndhdZlJGFuy/CFxbPI2oPA6BmAvybK3/DSKDLW4XrlB2LB/vv44vTn8aeX2L7u7/FyX0Pc/TkY3Rs\nnY6tk47W4ZU/42hkP5nU9DvOURZl0sEk6d7s993i4huruO4yE1oJyem99Hv25FLIX+wUL7T50HyT\n/s/+T4iCy8U/+D9ZStho98fYTVsWyzf51po/TgYrDl8E5twCC1abzY8mkfV50pygWr/D//jGG/6X\nH4E/OpIBFomf/b/43z/wz+9J04/3DeCPSnfIu32sHjnBV186A0A1lWVx6jRjy3OMb27tFVA9oJLp\nR1fDPLxUoj9/gFIkRaq1yvT6OTxZQjLnCFrvlC05ggCqiGC6iGWDgfIGjiAyd/AkV089SibaJGB3\nsbsWoeUCjv4wpq5x2zKAQYKiw4Rq8imeRx6RyI90iYkdCrU4czen6G8scHjxNQD0YJg70ye4+NAH\nCW7rnDj3GlvJLrgCoudSPpij7ST54Pht/jb/BIGaiRlTejdNINteYy58EoCx29cRuv65LOMPFHdw\nidi6QkMbIKRplD0bbFB2dI40XiOX32SnkiQ75hBR6rStJGU8dmoi48DKvkdoXTPxjDDaUJPQ0Ajr\nXoRQRyez02W1N3j+LvMAX9x6gblRlULGILd2AEdUEFUHzxB4efhjfObav6YeCVIKHEHTPZIjOvFR\nCdcNoYyG8FyPpdURriZV9P4aD9+X5HLpMKV6klxzCU8U0LwR9HAUcg0+njjHf7A+gXcig1Frgz3L\naL1EwwgjfWiQcaGJ6nmEhAhvVGZ4fivNSGWLlFng/PCnAegGm2yNLLJ/Zx1lxeXygYeYP3EcK5r0\ni35Awq1Rj++Q++4VJM/FFkF2wRJlop0qqGB2NfLMc2alxdnt8wBcLl4D4EByiuPZw2SCaTLBNK7n\nslRfYaG2zE67wGhsmEOpaaaTk5iuyXx1kbnqIsv1VVYa6yzV/dpHcD7AY0MP8+Two8S1979faMts\n80c3vsHt6jzZYJp/evRXSWpxOnaXhtnkeukmF/NXOLdzCaZ9wD3cAysP8JoaouthRRVwPYrGOoNS\nGkUQqQUkVqwSB4DN4jKz8Xc2B9ls+7RFOyQxOxUEHCjdfNfjLDU6BHqrjAVBYL6h8LR3lXhIJTj6\ni9yXOw5As9XL+JU4GamfY4kggiDQNVdY470VWR3H5cblTRRVYqi7RqWnZmm3/dmCGPbljN1qGUcW\neanp8Jn9A8wMHmHg9VeJf/BhjK6fEAwEojwaP45ud5FLi0CZRa/MFU1HkmT2G/6K1iwep3OnMB2L\ni7eLHGmsEI5FOfjgQ/eso9N7AviZmRkB+L+B40AX+I25ubmlH7dP1ioRljosTB7l2MJZNg4c4uzE\nYziCzOyJR5CMMtrWBkooRCvVj6NEkWybvp11BteXOLH5fZKVAqLngQGWojI/dYzN3CiGazOQabN/\noE5U6rLjZNipx6hVA+TTw7hRjYe5RNyp8+flJ2jc3KHkRJFEl0jQpj9WIyObHEy2GBveQZJ8BcEw\nJUxLYmNtAPAYG9tgJzlOhRSrUwfJD4wyPXsFvZijHkhw4/gWQ5c8YoLA6mYdb+QJdL6HHZEQTY/o\nWhs34N84I6eymR6nr7BGvLhOyPA4N9aHacYREwXQunjh8wT1p3AMicDRCN7lDqYrcMPRuA+QKx6M\nQSDm0i5DA49iZoxXUkm2biWxLchmSnx4Js+LHEL0HJJzO+CoiLhInstiaIjF0BC3JloInkCy6bt9\nTo6tcufOPvqXNqmpOW4NPUhAj1LP1FguDTM+ur27KA9rWefm4hjC6DyfHejSkpJszOcIYBLvFpla\n91ju8zMnb8RkkxTduokSVgkkIxw8C6rtcuXwKarNIT4dPcugUKTc1ihfSNMviMyPHOHUynnk8Dbb\nKZfCwDXwPI690cARYOm++7HCPnhmKfOQeIV+scB1KQe5LLl8kVtjaaY2KzioxFolSPjSU3n8Ks8u\nXX3HeN0fn+C/OflP9/6/1dqhqJd5aOB+Hhl8YG97sVPm67f/CkVUeGTwAb488zkEQaDQKfHc6svc\nrs7TNjv8YPUlXlx7hePZIxzLHOJQeoaQEnovj99PFXOVBf701l9SM+ocSR/kK4e+REjx+emQEiIT\nTLEvPsan932UhfVZFv/dv2J0/AjPT/pAHVYHGfSOUGcTOyCjNkyeOvgwK8sb2OikctP86v1PUHjz\nX/HpgSf4/ENPIgkSzy6d4Xz+Mv/96X/GH774OxTCLl+5rCBulzjwm79NUAlguw7fn3uNH+x8H4CE\nO4wZ6Cl0TIdgaIrzlx1SEYNHTx/fO6fdVaxXTYGVuU3GIwE+OJRmZLdt33vQwRd3mrSbJodODMBr\nVUKa/5zudpDbzeCdTpPgV8fQ2heBT6IN+wVZS99GkDREOUjUtfnygc8DUM0/R5ElPnbic9x/5S0C\nV+bY+vQJ5igy4MKHDn+JYk3n7Pfe5Kn1RaKfkBHrF4EP/dTn8G7xXjP4zwLa3NzcIzMzMw8C/6K3\n7UfGW5eOcOjBec5znOc//suUSaJi8qBwkW36WFGH6Uz4BZpIu8Zjr32D7OIaYm9hgCOKVLL9dAZT\n2AMxtvvHWHLGcIL+KWwDl4C7khOQ4jZTwgpTtRXmZseZM8YwvDKeCNGDCsFcggc7s2TbDYYGikiS\ni65rrKzmWEsPIc56qKEu7VYEPVTnbPUJQGCqdoGPzX2da8cf5vDVs7w+8UuUI2myRFCdFoIcxV2v\nI41PsFrMESiYVI4nSV2tInU9wuEOl048gejYnNr8ISHDoxaReHP4IciDlPad8t48rnHgho5sxBGi\nCYRAk7AJa0euc7kdJdjJMcwKUg9oO6LDjeYgMIgm2zw0sc0T+9dxBZe0tcb2skaxqtAP9IldMlqD\nG3qWM30PcaDyCkPaMIOhBoV2mv7+EneWJjC6Ma4NfggcaMZK7AzMoi4/zeWrByhOxjjMLQbHPazg\ni3wqHUGVgvyF8SjpRhtbaaO4Btmywu14DFyP9WA/G944WtyhdC6PqHc5sVZGVwUyM6tkzCpnuyan\nNJWI4nDg0DylisJssMjxNTi68gJXD6WQGGQ0HyBbe4v5EY22sozsHiapF4jtrHCum8RujNKaGsF9\nxOXgzRZbI0M0Yl0OzcvEdL/Y5xgSshnkg0PP8IPi3zAeG0UWJeZrS9wsz3EoPcNb2xf5xty3sF2b\nsdgIvzj9GcaiI7yxfY5vzj+L6fgg8NbORQbCOTLBFDfKc7je3eX2qUACz/O4WLjKxcJVREFkPDbK\neGyEkegQ47FRsty77N5yLL6zdIYX119FFEQ+te8ZPjL2FKLw7msbBUFg2AjgbZpETiTRPT/z7joG\nSo+mcDURyXBJOgEKEjQAO5JCi/p2t2rXIRvyVx7bPYvgqBAgWdbJRwMEI0nUxhYBR0BURVRJJN9o\nIQoxRDGOroMS922y2ysNYtNJ1sJD1JcbNGo6+ZbB9aUyj/UMzvRgmLSmsNLq8odzmzwckpjhvWXw\nu/0bkukwTr1OIJVGFIW7AN/zoxElB1EVGLLvYDku2vAIaCKu0CYQ3ofnWhjtDTzPRRDEPZuCUT9+\noQAAIABJREFUVP8oVuFF7ECQVq9DuNrqOc52/O9woiHoE1G09E99/D8q3ivAPwqcAZibm3trZmbm\n/n9sB7XYZWb5GhfHjlAmSZoqTwuvozkdjql3aIlB5u1R+m8tkHxrEUyXthKjHB2m/5ROdNIjrIh0\nOi5rG0Emy2tk61WWS0OMHdvGS4iU7QR1N0LKqTEqb5PzSty8uY9lY5ihwQIBzWS0E2JloB8rZPJg\n9wWG4k3EpEfXVLloDKI+3+TG4aMoWzZhU4euDYJHsBPHFSxET2ErNMkYsxy76mvxLbWJbGWR5mco\npfLEG1FCpoxTN9i+M47gwqCcZ+tAjvTNKt2EjB6IcOqtl+i/soYjwvc+0IexnkLCZmxeYTMFSB61\nhE4mD1K7Rj1aJ15Mo3XDnDshMnmzn9FyiXIlQTCoM2RuULKz9I2LHB/OE9Vsbi3t45XgCu3w3xHZ\nOMREx0MPTfCJ4UWSr1+js/+DLJHjYucZ9pdr7HQiBDWDc8Y0dhSUikOUEhfkFJ3UFnKwQ8lsENzp\no5ROspYd5vPi83x2oEGFMN+xH8PpaohOi3qyQjERoK/WJdyp4TRTSPEgruVSvVImIDb45fA5NMvh\n7NEwnmSxspGjMFjmQtXjSH2Atf4FCmH/gVgY0ZhZNXj09TECzHFw3S/yXpkK0zIuodYG2b5ukw2N\nM9y20Y+k6SR1Wp0VLp1IMXB7jNnJLfavWEgdByQX1xRQrSzdjoKHx2hsmEcGTvOb53+Hb80/y63K\nHV5cf5WgHOBgaorrpVv81oXfYygywGZrm6Ac4CuHvkRCi/Ha5ltcKc6y3c4zEhnksaGHmU5O8t3l\n5zifv4SAwKm+Y/SFMtyuLLBcX2WpvrL3jPyG9WVOxk++x0fybuh2l3956ffZbG3TF8rwa4e+vNeH\n4cfFbicnM6bRcf36guGaqB0bEXAUESsss/rmOprn35NuIPquZly7RdbGyhbxhg/29aRGFr/QKvUk\njflmlUDkIWRplJp1hYAmoSGgb7VJTsZpjYSJrTW5fmGVyw2LS3eKnEz6Sp9uMMwXx/tQJZE/nNvk\njuG8Z4Bv9QA+FBJxdR0tFiMUUPc4eLF3jkgChWKSeKxFobFNbmgYsdcuUw0PYXfLgIdrt5GU6NtW\nsSax6zWkeJxWw0D1TGj6L6pm27+W8ngEQRII9FbN34t4rwAfA95eDrdnZmbEubm5H+kQ1FYTuM/t\n8LHHv8PWzBSn7OssLjWIF0xMUyVs7HCocQevauGqEvp9YzCZoEOYNwJV+u0QhpElq9rsm1xDEVy8\noR326+uIkoeKiaTelUF1nAB6U+X44Xk07a4caQw4vqvmUKBqRbhqzbDRTpPbWudA9zatgRBDr/uq\nAVP0p9FxfR1TitNVRATXpqmmiJoVVvtSlDJNMvks5WaULSHFYP8SQVsmdg10W8QI12jdCdM6oWHf\nn8KMBsDscPD6OQBeORWhpJ3ENV1O1ud5pniJF9JRZqeCdFoxXNHBbn+fdihLnDS59XHWpm9Q6Ftj\n80IEBwmxu8Xn51+nHgnx3cf/c8qvn4NKmrSyzcmkzmv3CYxFLzO9HuVqaAJDixD+yhCDhctIC2kq\njSnm6z3/EcGFCyEs2wVBwCOD4IC3cghrcxITkQ4u7nwJwUzy74OPcCSywlX3EK4oMb62RJcQdbXN\nzcQJXP0OcnuF+nKGQJ+B3lhEShWQ3Am815s4ksj16SCy6VAoplDTQfRgi3Ixx07Ol84KBLk2aTGz\nanDfmt8PtBoL8dqpJBspARETN3KFeOAQfRGV6mQCPRPAaL4Egkdm9QBKJ0FXliinmvTXQZRcXFNC\nFtOsNbZAguHIAMPRQT4w9CCvbZ5lp1MgF+rjvzj2FXKhLPPVRf5q/jtstraZSuzjVw/9EqmATz9N\nJydpmi3aVodcKLvHo/7a4S/xyOBp/nLu21wqXCOuRvnC9Gc4mJxms73NenOT7y0/x9evfZvJB6eI\nqnf7ILyXuJS/ymZrm/tzJ/iVA19AlX4yh8JdDbwREtG7PUM218Lu2qiAIDjY4SD51QLZlAcIdJUQ\nUtg/3reraDq2jijIvHbuKomWP62uRaQewDcg568LqXbrqLEjCIKAJ4URVYmYIuM5HtFSG7flYcRU\nbl8rUO35tpg1v2Cuh8LEVJlMQCWlKRS7JoIsvyeKpt30s+iAYNMB5ioOjYyNbLt4nocU8nGgI8c4\nf+kooyNbTGRvM7T/aaRRX8OmhYb3WkA6VssH+EoFBAEpHMZttVAHh2k1ukREC880cQ1jL4NXhxXA\nRpUGfurj/1HxXgG+Ae+YT/5YcAdAEKiGB8i9dJO+m4vYRYPxt+3hAoggToXRPpAmFJaAJmmaQLA3\n5Snc/X3Pw0BFVFwEF2rdKHUtiiFoRJ0WSbtBKt6gY6jc3MyxI/UjuwKjmzuIKZWEXIKFKpWixNpH\nBujEorQiSRZnjhPe6iC4gOch4DJWuUYhOo6uxnCVPNeFNO3UMY42l7iTPIUjFvDwaEoi7vAsO/2b\n4ArozW060Qqe2HvxdETaUpSAfT+5OwFmTz4CnTmuTbl4132QsKMq0pEsI2KSWWqYqW1WQwu0Yx1S\nPdI7p4fZshRKA4vYy8Ogwfj2MpYELz3zRQ7euMjJxQ3AVzLES3DxUIY74yqnbvg629aagJm0uBEG\n5UiZ33j2Di/En+Z2qB/J1Ak4XRTPQcDFQ8ATBCxBoqgGcS2ZBoApwHyNJrBNBvDvT4kg4MHWNEUZ\nzo70FA1lA7NsAFkssnSBfzv8aeJiF7fQphldQd53E9kK4YYbbE5cYtf1zUOnrY5QCWpoQpvLpx9n\ncfoYnigSNpfpNM5ha8uEHpikrAwCoNZmaUhbhOsZIqVhKtk1EDwaPYdKUbCxnQCKF6NibYMER0Ym\nyaai/Frs8yw2lhiND/FfPfBP9njrbPYED04eZbW2yXhieG+hym78KJolmz3Bg/uP8J255/jWje/x\n72a/xv1Dx/lnD/86D0vHiEWD/OGlv+QHWy/wX57+z971M7pWl69d/TYfmXyc0cTQu/4OwPUb/ore\nr57+AtnwTz7db+r+jMgLC+h6z7/cs3DNHu8peb6rYkSlXusCAnYwQm60j0VAtLpks/75m54BqCQK\nW4Sb/v7tqH+twoJFOhvFsl10T0cT/BeErMYQRIFs1OfSQ2sNpDo4IRHTdKHuzwq8dhNPkjDVABMD\nCQKyRDqssdUxkEIhBNPYO46fNJxeG8K+qMgKsGNINE2HuAeRcAA1nGMZaEs+mLdaIaTuEtnsZ1kf\njeLh0JcdpSbVaBUhEnKIZ6OsNmqoySQJP8lHzuZw8h69UySm2PS73+HhsRBqn8c54zDlosY/PxL5\n/1RF8zrwSeCbMzMzDwHXf5Kd7gyeIudu4OUNxKwKY2Euh6ZI93XZl2ggqgKCIFCth7lzYQI91uR4\nYo14zEHQBLqGy6oe4mJnBHGrxsnZBSQxwErqNIaSoB2WKcs2/XUPFYENwablCUx4ApogYgILxKAC\nMO4biT2VxogFUBomVlgGUSC1UAJPQvRsRlrXWcmdAFukkl1ja3wWtx3jmqVxOwQCl3Eli+LQIoje\n3QsqerTjZQLtGIqTxbIqdIIehJp0ui/idu/n2pHj1Kw55PoAza4IeDz5WAklEkXW03h6DaNvA09y\nidQzZPUkngBNMUd2c5rt8RtshQYQbJdMe4fnnzhC4/8l782DJLuuM7/ffXvuS+1rV3V1VXej924s\nxEICJAiSADUUJXEbO2hR0kgxmpFDljT2jD0x/sd2eEahsbVaIyvksGSJIjWiuIoEYZICCGIhgF7Q\nW3VXd3XtS1ZmVu4v3379x8veAFIiKA5l2ieiIqoy33t589V93z33O+d8Jz+M32xwZvS9uDmdzaPj\nNESD5sYq+sQ1Lj8+Aleg3bC49FIV58EsR0o+3XQWJTHECb/DvtrXGWrWiRQVL2HQ0SP6LegCXxhP\nstWvc98LJ6jqwwzVzrM+OEolVWRwex3dc9nKTiGFQipaor/hoxKhRwGaDFFlhGNatBMZzE6HbaPI\nRnIQuZWArX4QEWGqi9LM4QuJqIyBYoK5i7EzwTfvuY/S7A0i3aa41MAb1DEy0yj5LJ3u5+h2n0Vu\nvovONiT3fBuRkBy+kqZDxE5xAxTYzcT/JS1yCbBIKSlWowoCgemmKZdjoPvX9/4aQgg69YAOd1cn\nZihQrb518am3DzzC/vv388krn+G1jdf5nW/9MR8/+BGOZ48zkXuev7nxIvcVTzGZHX/Tud9cf4ln\nFr/Jxe0F/tV9v/wdc+1bXpuLpQWms5NgG5TtN/dK/W7W3IgDq/Woc6smwZcBogfwQU/KNjOVw74Q\ne53NUKWya6Mkkzi1xq1713I6IAwGdjbRRdzEYlNscyKrsbtWItrXYmW7hWrqt4BMs2IapOnE1xbN\neJeguFHcjLrXyS1sNggSKQxVoVWzaQFmz4eKDBOv3bk1ju/Vdiux47N0dRkBdFQLV8afv7pSJZkz\nkYCtxAtHy05heefZ2thCZiVR3WPn8iphwehdbwc3GsWr7mJOTFK6EcfVWmp8vtVjG7aW5rHY4chI\nCqGHLNqTuE5Audy6dV/e6mJ1p32/AP9Z4In9+/e/0Pv7Z/6uEyTgBlm0j06iRhFd3eCTpw+xvpmF\nTchZDieGKjidFGGlyDawVs3zLBOopoJuqfieJOzelNMc4spIrPegADNI8p2AZE8/vUZEPlIYR6Iq\nEUINUT0X022jhy7V1ASqCJlev4i27LExOUMtO8Tcq2fpBoOAJBiQ3LDuRfUibHUXzTwLUkdJxwG6\nQILm62iehR4paIGJnaojpCDUPaYvP0iyncebC1lcGEdqGm6qjXHwFVb3nSa3uQMDkmhtGhBk/BZ1\nx6Iv7TKslhG+jjR8Uo0++hdOUsrZzGRcnKZJsTuB4VnIsEDK3+HVwybN/An6z+7SlHmaRyZpTmWY\nSBl8eHSYp40lrgZwrdBhX2DjqimuHy2CDBippykf3otcUZmsXWSw2cBTLb4293auHZpnZP5tfPyd\n17CEy8HkfWytfZWFBzJMnAEmZpj1L/ETr/017UyOaiGHxRztTJmBZIJhaqhBwOTKAp5hUR4cpbBz\ng1Tp9jbaFyob1iDzuUkuDQwTtLNE7bv1SmCYVYCuj7hYIJur0lfawr/us/JgAyN9koT1KF3nb3CK\nL6Jo+3FSEGxPMe8N4A5eI8zViewUrhoDlhHYOORQpUlkNsh2Z/jV33mJn37fAe4/OPQDS1V7ow0m\nB/jnx36O//XMf+Db26eZyIzxzolH+MSJD/M/PPtb/MdrX+BXT/7imz7/zE6c6bPZ2eabGy/xzolH\n3nTtszsXkEhODh59y+MKdncRhoEb3a6m9GSE4kVEmsDvZan4qduw0e2Bt5pK3ep4JKXEDrqoIkW2\nWSOcPUBCabAbOSh7kreyYJa2GqjW7UwiNRFfa73rYagKSm/jIEIYmoDtNUFORqjdDu3iABn99jjS\nerz4RKaF+A4NwP8u67Q9EkmdF16+yiOAVczj9wDebntsqBJdUegq8W7Dd3XCQKFdPQNqhCy5uMo6\n5uAkEPcHDtttZBCgFQqEvSpWt5cmm0rGY/c6cWFeIekQSIWmlmVMBv+waZJXr16VwC++tbNi4F2s\n9dOf7fDJl++hk+2nMJXEa3h0yjrPr1gcJc4Tr48nyArwOgFuy8dp+CgqZMyQfNCmm89hJ9LIMFY7\nrIYuuY0GQsuQDza4x7pENu2S7XpENR9Z8cC7zQktFY5yo+8kyTWXzRNj1AaGmLpyAa+WAxVsJImq\ngRQ+N6ZW6PRdQ6g6atfA7RTpV7aJ9JBiuQ/cGVKtIgKFet8GWidLbfQ6qU6BltLl2PPf4Pzwk+wp\n7LJSz+IvHsGcOU+9fwU8nY6dBSTZyTZLwTgTss5Lvg1GgCIFg2tzpKSG1kpipWxcTEAhUx/CM20W\nphdJDsyQXkigt30qR4v4/Q56+E0WK6tc3L4Nps3Q4drRc6SqwzRkktnCINMP1PjmN8dRlYjR1iKR\nUDi75wluTE4Q3Rgjf9hkW3MY8S+zVeuV9SdvMDY6xMbmEMuH3sfZB3+ctvcCA9d0imVoabAkZ9hz\n/0m2Ols8IarMLlf5ljrLMQsOejfiHGtAlyF5v8ljO2d5uBXwyccHsWWWw4s2hxa7OKbKRnKE9fQA\nm1mVoDVAo5TmEjCmCTLru1Qm/gK19U5S2XvoyMsouTVS5keJxrrYxXUCfQWkgpayqMgYwJJei6Y+\nQhDoiERIVBum64b84RcvkzA1juyN6Y0oknz9zDo7u11+7KE95NLmXTO7XO9iaMqbXv/bTFd1fv7I\nx/l3r/02f3X9S4ymhnlk/wmODRzm9fJFTpfOce/w7YBrw21xvb7EWHqEmlPnSzee4eTgUXLm3Ro6\nNxeBE28R4GUU4W1vYQyPEIS3Ad6XOqobInWBVHvNVyyVm3sHJ+rRXak0Xq9PqtuTBTB7z5s7OUJe\n1NkJJTKj3dKjWSxVUNO3G3aodzS7SKgCwtsxNSPnwZrBmAxQwoCOlSSj3z7+JtiHpoXqxl2dhPKd\nM4be9N2lpNNyiXQFpTe20T3DXFqO37fbHlcijyMIbHE7PmJ3E+i1uH9DtO3ihusk748r4EO/fZdM\n8M0qVkdJAiGZTOzp+14ZVLC0kKrMIRWFPvcHJ1fwQyt0ChUfLTI4vXyMjbxB4niG6U6FdGuVdKdO\nc7Qfu5FF2/HIalt84pUXSfW2l6EQdPQkGa8TV6qqKkKBraEJXj/xCL5u8sRXPo3V7eCqSazwtrZI\n+IZxREmdzuwwN0b2E2wolJliN9nPxNIC4WYG1DibIikU7GSd5X3niCwb4etY18ZxalNoCN5X3uD6\n4FHO3HcFvAsUG/vI7+TJVUfoIujbjj3QcrbBXw4cgB2oZ8vo2U385cMkjQ06k1XQAvTpi4SNIs1p\nl0uEXG7YdKVkWFUYD1NcnL5IcekoxW6WRjN+oPv7dvF8hXMTr2GnPBqBxpi5w8C9cxTHlnhx83ki\nGVEw85wcPMqB4izzq1/jxeY2rXSdVjr2KK7Vm/yfkYE6cpVDQ/somdMk8zpvO7DEpreXxtQYm8C3\nuxEf1C7zj/SzbGsJOv4We/d22doeYnLd5uSDU9x4+QgbZRs/3UEb2cW51Mf1agt13xnO7QuZXYYP\nrX8NLQypmRlybotaTmXp+CQLwXtY3bb5H39qmvsvvcS2vcJeN41lBvTZTWaar/fmAtzoy3Nu7AAr\nWYWVZJuM4jJQcVGjr+AHAjejEmglWs6fx//0O2KMkazSTQkcQyFt1yAHkacghInTSKAqAkUR/N5f\nXeDXPnacbMrgj/56nuvr8QP64qUtPvjIXt51aoyFtQZfeXmFi0vxgzw1nOHYvn7mJvKkEzoJU8Uy\nNDqOT7Pj0bZ9Jocy9PUUBAtWnp8//F/wW2f/gD+69KfMjU/yk/t+jEvVK3x5+eucGjp+y5M7V449\n8wdH7kNTVD519bN89vqX+cShj936bg23yfX6EntzUxSs/Ft6Pv1yGel56KOjhNHWrdcVP4MSSsJE\nDJYq0FZd7p+9wdVre/F73PVNKdzI8+hGsUOR7MbJDWIS8pHCVhjRzmokd2IQXa6WUbLxOGUYohgx\nYO9JW6wpgptS0AAtV9JEMtRr0dhNpO7y4G/+HhgmKnEu/M3c9b/LPDcgCCJqQchhI0aMRLGAv9yj\nm1oOV1yfYzKiq96mS3Y6RTKZ9RiTdlw8dw1VjxeA0G8R9KQxbmbQANjSALpkCkk8IJDxMUJAmzjw\nXOy8Wa/n+7UfGsBvTl9kcvEkSdcjN5Ymt9ygNV2g1j+MEoZMXFgkXXHQI5eoEPDM+z6Ib5oU6w2K\nSp0MTdayaZbCEepqnpTiMqQ2mOqukF/eplPI0UllUcMAs2aDAl5Ox6j5CKCaGmF3cJDGQIErh+4j\nMAyMvMfga2VGTm+BNIiUeIsoEDQKW6zvex1JRLC9h+JGP+9efo0/mzjIwdYSdW2EhbkKkRribU9R\n2bOAFMOMrBwiCUg7S6gEdEZvEM4/AEjc4hqJbgJNOjQzCkoE0rPQBjbQBjYI4mJVVCl4KKFzUo+1\n71/1XTYOvsRxNUe62U+rncZJdaiKAnbKw+gm8a0uG3texklcpbxRpWDm+dj+n+BQ34FbIHFPIkvl\n3B+w4IdYdpZks4CbatJKNYiGVnmeVTgCulCIOhLJn6MGCv3JYVKpYS4YR1CaC/RRpyZDlrQ2e46m\nWXy9wZUvX6O8aeNabZ788CFMZYL/aecsYXkCqsO0hwNEn41W9egOjHH0X/0r/KjLqztf5/mX2wSl\nLvmhLr9f+TS7uRpKocDSRBarO8XEuQHc4Sv42gKBplAuaATaDW62J+4C3d5UVkOJ6UZIFEIVTE+S\ndCDbhOFyhOaavHJIp5VUyHaqcb2EG6KIATptlcmhFB98ZC+/+1cX+M3/+DphJPH8iHsPDDI7nuML\n31riz79+jS+8sESnxwnPjedQVYWFtTrL2387PWDoCv/48VnecWwUIQQz+Sk+PPfjfOrqX/H7r/wJ\n//TQz3K47yDnyhfY6pQYTccP/Zmd1xEITgweIWtkeGHzFV4tneHh0fuZLcRpdTfpmVODx/62IXxH\n8zbjgLwyNogn12+9brpx8F9o8RxKC0lTakxObnP12l5CNwbEm6mSkd2hq8UgnOk4oEAqWaLg6OAH\nNNIKxetNgjBip10jr0yiEBF0fJRsvPC9e6TA59UVbisWQbMTd+41enK6TjJFxrgT4HuN1A0DkzhV\n8nsF+Jspkj4wkYw/s5npEKZ3od3PjaVd+tebdIwcjpYkXngEm50B9rEGQkVTC7gb6wjFAqHGAH+H\nB29fiat37aAXx+jLUFUFkXrbGXXUAoSQrv/getj+0AC+k+3lrppVOs5rNCcCosBDaThINNrjCmJM\nJTAjIuFwM267XUyhKgUUpYimjaKpwwih0wAaDEJiForAHb2wDafF3oVL7Lt2idZUgQsHH6Y22ofU\ntDuOsTl5+lnKwSyeluSmToJEUh69zs74NUSk4l4/gbLbx4eW/4rXCrE01tX0HryJNM3iabCLCN0H\nxaU9UCRckSiAQKGZ3UFk6girg9BdiBSa1+9FWCFmvkywOUOwsQ8lV0GfWEIYTYLyOEFpitGj12i7\nNsWUwwOywKtKma9S41CUxTRctrUUa6lvo0cmTySeYvpInj+a/zPK3SqPjj/EB/a+D+sN+idmeg8P\nZodYqG7iJJuc0vK0zt9PKAXFQwr5wyHXajfiHGYBQkZ0A4eSvc1W580iXk/bLhh/AffB5UjAWEyX\nXb/0PBkjTWrKxsnvIAODmp3kz94+RUFEZCemuLz1DIZq0G4pCMPB2HcOJ18iclXyZo6EZiJwCI01\n1h662hOkiimQBAqiOsa47Gdc5Hh9R6XUASSIZJu2k0JEOumESseDehCxCVwFpApafYl2cpuxHhes\nuiFqNIaUsOMvsxCW+JmnTvFHX7pC0tL42acOcnyuiBM6PHDP2/jLZxd5Zb7Eidl+nnrbHgYHVCzV\nxPcFl5Z3WS216LoBXTfA9SOSlkY2aeDrghfPbfLHT1/l/GKVTzx5gEzS4JHRB7hUvcKFncu8UPg2\nJwaPcK58gbPlC4ymh6m7DRbry8zkp8ibMaXx0bmf4DdO/y5/sfC5WwHXOxeBt2ruRo9e6c9gB7Hn\nnBEC0409VqH35B8IaaDRUtNAdCvDRrkjF76ViIOkmY5DeKCAhcNIbhq6l6knVMJmk41yh0jroig5\n0rqk5t321p/+o9MkgagHpALwuio+EqMnIWBbKfrvpGh6YO/qJhneWi78zRTJQEZYXheE4BvON2BK\ng4vvYLdqIyRUMuMgFPoKIdWaSsPuNbJXTbTRHO2t1wjrdVQt3aNobnvwYa84y3YiVFWQ6suy22eA\ngDBUUNWIXrkA9rWF79pU5K3aDw3gAyS+7pBsF/C0V0EaZOsWmeo91Pu38aw2gRGgSpVsRyfftImI\nqBTBtjoQruP551EiGK4EFFqgkMJJF2hkUvgqGH58fqswycVD01w6fA+KsACdtOigS5u6GCRT2+LR\nL32RK8OPxeDem0i5ySUW+lYo6zbSSyDnDxO5fbyn8hIWAedzswgiDhUdlqbPQyRwlmYx959G+jrt\nhUF2gJHeatHoVaSqfVughBy9UuA9+0K+nVnhm06SYCsOrprVDN1GXP6uyYAHdy8wIssMHHqQxLED\nvHP500z5Jp9pubyeXUXdHSFKbaJFCR4vfIj9I5PkDIN/eeqX8aV3y+t7owkhODj6CGP1v2QnjDg5\nUEV5QuPa9QIPP7SPfDHJ+6Yef9N5YRRS7lYpdyu0vA4tr8XTy1/Hi+ItuO4lUAIVkQoYyw4jkTTc\nFm0RoRbKt65T7v1QOX/X9bWh27/7MqDuNm5JyGqKRtbIcLKn0viFxWfoyggFncsrBS6HMf+SS+vY\nfpfALkCvTWDLvjtz92bCpV7P0E4q6JEDQvY8+GH6cZmuDrL+tYC1uWf4tY+/j4FMkldLr/I/f/4q\noQNWn2BmeoRPnBhnpXGDT115js6LAZqhcGx2H49NP8i9+/ey0lrj3M5FrlSv4wqLriywKY4ydP8A\n2kKbs9cqXFp+kZFiiqFigkz6fsR2xCfXrmA2Z3CVBzhtXuL900/cETi97ZlP5yZ5aOQ+Xtx6lec3\nXubYwCEWG8vM5ve+iZf/XszbiL32bsagW43vVFYRhH4cBBVGTNH0RzYrIsuuzKEoEYF3m6KBGOC3\nZAzC6XYX/egAEhgfOAXbl6lrELYarJVaqEmBEBpFU7LjhTGvH0UI/7awr5fUMLoBkavgEd3y4G09\n8QaKJgb7rhbPh6j7Zj357Y0G3/q/r9M3mOKdT91ux3ezivXR8rdRwzIilaQrHUgIJBC5IQrQsuKY\nzFC/pFoDtRvfExn6GOP74PRruOtrqGYar7t1V3csb2sLJRFXsaazFlomG2cSAuVqgeG0+BsWAAAg\nAElEQVTBKmFoY3XbjK9c/4cNsn4/5l56kE66Rr42wv7XnyLp2fgkAEG+OokR2OytvwiaRyOxl6o6\nhqcoJK/tUrQXSSQrbA0p7AwLdvJpSoWIUHNB3CFudDPGFdyIuY6eCcAROiFZTN9Cs0OePXEIo+OQ\nsi2IVNyxG7wwfAWAgV2DsfM6Lw+oDKvzDCbqfOrQR+iWNQ7377A2fI1Q80noDzOYm2dHC/BX5wg6\nguZwwPC2RqQGHFte4YVCErV/k9lVhXddXKI7f4EXHz+It/wgRBr9QZN/Gpxm1RhhIzXIO2YLDB35\nx+jDI7f+yen2fdzjtxieOc7vn/8T/P5NhJ+gffk+PuuW+Sy3QTRpauTSy+TTJpmkjqrEQWshQNcU\nLC3FB/NZAjxCZZamuZ+JkzpLlQ5+qYUfRCiKwNJVTCP+sXQVy8gynSog0iAlHC+eYql1nevNGyzU\nruNHAf/i1K/cpdbndst8/uxvYQo4te+jXN1M8q3z26yW66CEoISoQuET77uHfWN5BAqSqOe9KGSM\nFJZq3TXZx9NjfGrhs1TEDQqDmxxNP8DhwmGSapZmx2Oz0mZxs8nV1RrhHfiuKIL+nMXekQxXluo0\nDQuBg6r66K6CbvRToEQNSaalop2e4unFF4mISDSKDMqesPEqVM5FbJrbGG6GojzCzd5HO/PwJ4kX\nCFMufuTFu6BoAKWboZsu0h1x6AQRuY7NXCLC8QWtcputUovYh5yK7xsBUGDlVY2F2Q3O7JxHIDg+\ncLdn/oGZJzlbvsCXlp7B6VWOnvw+6BmIPXjFsrC1ALvXeSijCNpB/FCFPX58OKyBlqUms3F2WhDh\nhRFq8maxU5sSvWYfhsDoU1iRoxzpdTSrRxGRFrCysYvWy8ZJqAZeu44FKP7NLgWx7+4bAjVSUbwQ\nHzB6OvMd1boryKorCpaqYGsxcRd2b1MfvhfyyjeXOP9avIjVqh0ee3L/rXl1E+BTfhs8G68v3iUJ\nJUVkKCi9Rayrx6/nEwqZrIndjc+T0scYjwXEvI111P1psCP8WqX3PvjlHawjx+naPsWBFGoqhRiI\n722p1MfwYJW06lColVHM7z1Y/3fZDw3gpZekpvnkAd0DjwQCQaTE3rOvJZkffgIkKHdEz7XECLXU\nKMsJhWQAg4sRw733pZBEho/UfEAhJkcUXNPHNW1cqwlCondVzG4CNdTwzC4d00aTglS7j0gJ2Nx7\njnp/r99ioFJSk5TeUUerrlLa2sufTWUJywqqHrGT38JNtkj6s5xY2Obb07sQmATlcbJ7lwj6r7Ge\nGqIvNcZPnfhvaSx9lovOMtH+fjr7Psw3Njw6N7IoCvzM++Z45Pg48EH+tuLk4sSTAAwA/9WpX+Cl\nzVd575534d5rcHm5RrXh0Oi4NDoejbZHve2y9bc0sdgenWYi3+Ir84OE8jsr+32vpqn9JM1hTEPl\n31++eitIqSgCVRGcGD6IF5r8+dcCFKVFxkgx1WdSazl0ugF9eYu/eanBsyIOLMme+x3JOHsljCLC\nSBKEEUEo8YMIP7yfsLCEHFvklfA5Xmk8R+QkiRr9hM0iUatIUk8wNZzhyEw/h/f2MdqXvPVA/9L/\nsUBztwDUURWb0DXIlDwawDYSIaCgwEi9QAqBl1LpDiUJLBWj6WM0PYyOAilBlIEwrRL5CqIhMZpZ\nlO7t+RsiKQvYDD3CSq98HwAFpSfzPIlABa4hiQAzVSfo5AmdDP/+zy6jzJXZPzKNJi02ym0ySYNM\nUidjpHn/9Hv4y2tf4PmNbwMwqOyhUu+i6ypBGHLxxi7nF6tkkwb3HRzk0tIu5XqX//yJOZK+zeZv\n/y9YcwfwtrexpqcJvQbdHsCnFQXH1wFJq0fRDHhl0PawSx5Dc1CciI4f3KJozj57lZUHYuoiMRJT\nhBeiOY4pCQyhUo8kIq2xs1ZCm47fb/oCvxXvBlX37l1XpAmChIZVCzF05RZF01Gsuzx4iFMl2zc9\n+F41q+v4fOaPz9CodckVEpiWxs5Wi1bDYVtEXNxt488vAipmaEMY0unha9J6lFBXUHsA7/Sq2tM6\n5IpJWssuQaCgaRGiL/5cd30d83AsCxHs7qJms2xcjjNtXjP7oAtaeZ313/wqyjELGUg6dnwf0tik\n6lo8+X9A9kMD+Izp0rJvp0QJBDsj19iZuIZhpxldOUy6VSQSkoZps6O6hAIGA4Ocl6TY6VXWiQhH\niVAigSpVVM9Ace8sxZboHZ00SaD/TeNI3REDc602q/vO4CZvpyUJLYREF+/qfUSteEsWOrF3IvQ6\njWSdpJ3nsdUmJWWNQDc4UJhk56Fz7Lq75M0cH3nsCY4NHAbgKf2jrH3tW8yX4HI3ASQQRpdf/tAJ\njkyOvuX7GAtUxbm2JGCk7zsHkvwgot31iSJ5CzT9MML1Qzw/ousG/OxMgO0EOF6ApiromoKuKoRS\n4nohrhfieCGOH+J6MZ98s6/kzc+w3fgarh/i+sEtUI4iCCPJ0tYI8Sm1u8YnAFUVVOoOZSmR8pbK\nb/y+EKiqQBXxYqFrCpoqsAwdXTOx9GOojUM4yVVcs0TT2iK0VtGGVgEYTY0wmBmlrW9woZ1i3lbZ\nsctsdUqEKYlXie9bQlzAVx6mcK1BCdAUiSYlu6HCLpBDMtBv8PaZM4yIMotykivRXjbDUTQ7QKm7\nRGsthIgwcgqpEYESSVo1Db8ZgRPRkhFRAMnxNFpGJ3RCwkqDoBuyG2js3pEtshco2HkCAZcl+K4K\n8w9wcb3Kf/nM87eOM/U40oOQMJemJusQ6Pz6n8xzK6D0Bnvu9dtxlJbt8/HOK7hra7hrccMXfXAQ\nGTRu6dBYAtRAAUK8tI7p2LjODmbSZVfmmNDbCAeWVmpM6T2w3t6lYgMKJHIGUaSwLodp+SFFM82u\n04CMRqNURb8nppKqXf9Wf1fNeQOtpigEhgI1yCRuB1nbmGSNu+Ero2t01NiDv0nRrC/XaNS6zN4z\nyGNP7ufcK2vsbLX48uVNdo0NjgTzrG9kwRpBD2KnaEd1MfSTaNooobx9z3wR44zwfZKZeBWw7QTZ\nbIdQsxGGib26CuFs3FawXqdTHGL91TPsB0YvXaY0NoGydo1uYx7zsWnCqo9m+ISRQlp0UKOBWB7k\nR42Df/xgg8+f68NDYCDYyJYpddPI8w/jRCoNqaILj1BqRG4MhBD3BRJAkgiHXmqsvJnfGt2ZSXXL\nLCQpIAmEQtKRYCMIVI9EfoeU4WKGBq18Cc/qVSNuT+BsT8U31zchUlGyFaz+Dey1OURgENh5wssP\nos+c5csHStzMv7tSu4YiFB4df5h/tPe9t5o7lHZtfvvTV6i1MmgqqPk6YabEg8f6vi9wfyumawqF\nzA9uq/f3MdkD8DCSSCnRVAVFuV2l91arDu+2B4A4TrDcXGOhtsi1+iI3Gsu3tMjvNEUokLbQotgb\nS9jb6DMVysuDzCBoRNCPgsw1uR6maLQV/BWHV7fHUcQIRmBiRg7j0ZuDzthRLGsKFO7gCAdRCDUF\ntVEhVDV2RscJJjNkrzewnRWq/RWksUuhmae4chBk/Izst1osTp0nDCz8jRnMpINrWySSAVqxQms9\njrUkd2eQ46+jaoJHj4+ytNlidaeNZaioiqDjBAgRUwWDhQTDhSS7ly7T3ngZc88UCHCXl+mcfx3t\nxH5sCbpixPvhIP4/+RmLXKNChE2RBlsMYBjxd/zihS9zYiPPASBthAR6ACFYaZWADCBo+gH9Vp5t\np4GTU7FaXVxtBKREdWrsHWzRJg5432lVv0UhE5NgShhghA4SgS01/vQrV0lZWu9Hpyt8NCOe81ev\nbdFMrlFZjHnwUhTxp19boF6zMYEbWw3eM3WBwuvXWOHtKFGA0etBGBkjJKwHiKIuQdDldu85BS10\n+dbSFkuJLINAxc6TzXZ4+fxFLDXL0PYWz11v8HgqAj+gmkgxuXQVgEoipnEWjhzHNnLcq64jdxzc\nIMD1NNKmTWgOM/mv/82PHgf/0KmjfO7cFktEaAh2mz2NDNVHqAEqIWhg6jaJxAadoV1QQxL1DEp7\nAMfLkTZsSHTwTZsoEkShRhSpIHuejIgAge8bNPwkDT9BGEUgIoQSoTg57Oo4NsSzfWcClACRaCE7\nN/OGYyZ4eEBQzq8gC2Wsvi1kqKDvzNFenyJavxcj8xKe0mZvbg+PTz7K/sK+u7r2lHZt/t0nz1Bv\ne3zosRmeuHeCSAScKb3OYwfux23+4LZh/283IeIYwE1Q/09hqqIyk59iJj/FkzyOHwU03AYd36bj\n23iRz2Cin4FkP7/x2u+hJGLlxHTXZ01rsUE/4yhkrDY7hW3ySYefNAyuLE0SOAlwEwQixFMDQt0j\nROJIgdPbUUohMcw2emjGpXqpBoZewxcuRneCbHUEWknUFoysxJo9vuEi0z5FJ0TYRYbXDhApIWt7\nz5GrDVOojjO5Ncvq3GsouQr+2izmQAM5UMJevt3ssLubxRyDUHg00q9Tbg2iapIn3hvy8lmbzpaO\nDHXmJnIsrDWQUvJYM6YN6m9/P6O7K7jLy4SdDgls7EgiVAsFB8WXhJog0lSSnRapvEuOJlsMIqxe\nSqG5wYricgDYM5UmUnwIIaGpSKsPbGh4Af2Jfqiv0MhpJFe6NJUcQvg8br6Cv9/iK9GBHsDHtC1A\nJ/DJ9JqAqGGAHnbxVJOgG/Hy2u34m1EwyczmGddjgF/utnjmuevsDaCI4PmFMua+HLmRBCOrLcyd\nJgMDJewzTew9aUIZ8vTQgzxZegmhpMgFq9jeSwjiYrOEV6dr5DHCLv3rdcr3ATtQ6eQZkRXG+x2W\n94wxeqVMKvCJKvFioXsRCbdLpKhcGjhGNoRymGB0ZhRYhx2XmeolOof7GRBNvO0WC02dIz8g/++H\nBvBz+09iaJ+Ptx5Jj+TwAjJV5zEtzQ2jw2rgYvoSXxN0BQwqCnlVcD1hAyUU6AWiQJFxwzRVgPqG\nzzEUHU3RCGSIH/pkjQwNr8mDI/fzsbnHef16hW98a4H5nThAQqQhO71cX6PL5IzHrzzxFIoe8Mlv\nCV69vo4+dQlFC0lOrSOFRmdtHGX+MG97h8NPH/kIunL3bdzetfn1Hrh/7F37eM/9kzdHx4Oj95E1\n05T5+3it//+ySEYIxFvyanRFu9WJ6Y02kRlhOV3B0XTS3ZDXZQt37hXSlUnyu6NYWzGHfLF3fDtb\noTq0TCu/893YD+D2/LzTDC8iHAzYmdphYE3DchIEahrVs7DsDPndUfK78dMskWzkt+nkK/imQ7E2\nQrYxyOCFt1M+9CLG1BXCej9UBggro8QBqwDpJiDUQfO5tLWJ3x1EHVrla6V5GIXEKMh2gbWNI7z7\n1BxfO73OF/of5Lg5wPmzXX5VruILlauPfoSty2U6/hC6YrJudjH8iNCMd8yZ5i7ahELWaYAFsve6\nGugkxmrwOiyVq8jZmNI0hWArimnZjXqX1Q0VFKinVPRUAUVJIMIOo+Yul6OZ+FpuGO92gpha9HyJ\n3wvwFpIaVuRgq2m0UPIvf+4IIjRpOwGfKlcJAb9H15qhz4ef3M/2cys4ts/kO0dpR5KcriEEDIYe\n82ctvjD5YY4oJi0k5k12yF3nQ0qTblry1d5LCb9F18ijRT6m5zInW6xj0LLTVGWecbVEMh/vqE4+\n93XkvpgCHCnHlGHh7e/gVH6Ua5dKNCoO+vYyjENUdslUS2yrMwzQJKk7fOaL8xw5MPjdJ9pbsB8a\nwKuqTiHfpVTJYu17HjSf/pUTfGVniAODFR7ZN89LdMgrgocTFs1ugevtiJ+e3ctCa4Pr7R2mc9Mc\nHjpBy28zkhzC1AzWmpt0PYe+VAEBbHZKPDhyL32JIrZn898/+7uIxiS2vZ8/XrzCE/dN8F//3CPY\njs/TLy7xpVfWsTTBR945zVme5nrjBr93cYWSvYMvA1KzGu+ffg9BFPLVlW+gji0wKobYXM2zeynP\n37ib5NImCVPjxmaDS8u73NhsIiVvAPf/b9v3whmGUciuU2fbLrHVKVGyy3hXXOqdZpziKEMUIVCE\nipQSN3TpBl3c0EMgUBUVTaiMpkd4//QTHCjOfl9jTWpJ6gUXO5Em3akT9G2iAvVIw3BVTLHC1oCB\nIK5mdpNt8pUx9izcx+boNRwpINQQ6Tqa8NAiiR5ILDci6cS0YbWgYSdUPEMBbJA2idZJOpkmpYnz\nPWpRYDhJivUJcs4w7cELNFPxzsJNNan0r5KvTDDoZJBX7yeYPE0tXyHqZEGJODwiOXlolk9e/DpC\n82MarBRnq0StPP7iUQ7doyMTNa5xAzn7PNe8Kk81S3w1c4JX8oeQbsAzNZ2LUz9Fd10H4sUmBC4B\np5B4vQDrxPYNlOOQq+yCBa7e06EJdbp9cSaXsG2k7IGsgEUn9qhfuFqiuRZhHoBFvx9ZjAORedFL\ne+xRsqob4ad01IYXpygGGl7Pi1NDUEMfz0iA4/MbF36Djx/8CFNDhwnLVQYsna4R76INz6Wmg91y\n8TI6nUjy6FCetzW2+KzwcBzBOWecSIvHJ5IKqd14LCk66EYOHYF+U4rhpicpJbr0yJu7rDNE5FhU\nKDBOiT2lS4SA7AREpV6apt+rCXjwbdivxHTwv/kn99NcPkMUgtz1qB5/jLqaBdZIJBxmv4NT8v3a\nDw3gAfoGLEoViDo5Bt0i6ztDKGrElZ1+luoP8BMnqyhans+dDbDrsRe1uNBmdnaC6fERtne7vHLh\nVdxmGqEtkcy6JLMubbmL72lIz0K6Fp93S6hejshJEHhxL5KXe6mEL1zc5tT+AfYMZfjSK+tkkjq/\n8JMzlOUSajn2SNbbmwgEA4k+DhbmMFWToWSOXzr+8wwkiqS0DL/9l+e5uLTLldX6Xd9REYKZ0RyP\nnRjlocM/OF3nfwhzApdKt3rrbz/yWW9vstpcZ6W1Tttr40V+rD0iJSk9SVpPkdSTqL3OQRJJN3Bo\nuM1esdKbTREKCc1CExoRtwO5lmoymOjH1MwYvGSIF3rcaCzzO+f+kP2Fffz4zJPfUzOLO63ltelm\nI/xUCrNVi7OsKnvZs7WPbt915tY3GXFzPHsqgxJZHFxqM7JRYcc4xtFGm9rIIjuDKRJtn4gQ11Cx\nLYVOUuXm3Ur4krllh8CcZmPPILnNJsn2MInOIMP2OobUmD/1FHhfYXvkCtvEKbqFbkTRj7iR0ShN\nLJDZHUaJFPpbeerdUXJai0aqijF7hrFlDd09hza6i/QM3IWTSDuPktthMlzhne0Cb3/4FwC4UJ7n\nfz/7aXasS9j3w4cch89tZHH9kFcLh1Dx0UavoxZLgKTYKLC7FuuqRLoEKRm2t4Eh8rUdGACnR0mq\ngU5NkUS6itFqg2egC4kqBP2tGtcy0xgFi4/1TfNZ5zVKgUGhJ7Y1rsbPZUfGAE8k8dMaVqO3Hwp0\n/CAi1ASKGy80nmYhnZBISi5UrnK+HhdSlOw6CSNeXHTPZX6zzoiEMKXzeEpl7jN/TOnSRdJT76as\njVOij2zggJZEFE3S6/GuvpiJFy4pQQvj34Ob2TlCJaEESL1GwsoT2Ba+7MFoRtL3wZ+kM38JZ/16\n3OnJE6iGRt35OvXKNFYiibNxHk226dZVlAiM9QXaDzwKwKF7NkAucTO29Pe1H16hUxjEgkEYBJsz\nrLcLCKPLu56QnL/aZGdxkE+9fBsQ+wYCiukk15aTLJxXWDgPcLe+RqvKdyU6QiKE2UUrtnlodi+P\nHthPuxvyuedvcPpqmdNXy1gmDB27yv+28MVb5w0nB8kYGepOvVfc89Jd1xUIBpMDTBwa58f2TzBi\nTGF3Je2uz/hAmoN78iQt/Y3D+ZGxjm9zsTLPufJF5nev4kfBdzxOV3QKZo60kcZQ4slvBzYtv03J\nLiPviH6bqkHezDGaGiZv5RhODjKcGmI4OcDesVHaNf8t0S+rzXW+cONp5ncX+PXXfodj/Yd4/973\nMJb+3hbUilMFoeD11BH754+RTCtxzWRukAsD/dSGDpDpiWttH4pI5OYRywFONM3u3IOomoonJf3t\nMg/tvoCfSvDt9FHKoQFCkHE1Dq1/lYV3H8fSx8nulgEPIRV2Cx/AndRQRQJZ+BCFxss0jA2Gmnne\n++w1cq0GF2csvv5AltKeK8zNz9A1sljL+2keeIWwO4ha2OG5yUH6qgEiKfHW5pCdHAjQhlbYyVZw\nnlvEnn+E5MF7ODJwkH829FF+88pXaQ9s8pnmFoEf0wh9bGGfvEhCD3F6RWJOOX2rrMRXQxACPRvf\nj3SlhjIb0dESGPiooU4tjJCGgt7tEgQuc4n42GOFq5z1DxIIhcwn/wI+qOBbHeh1hJoSG1TtBB0j\nBvhIF0g1nguCWCk2sF3ChIbajmtbPTWBLgVuoHO+ukwisR9VySLDy3h6XNKuey7ZWrxIeCmNK/NX\nGb90ke3iKHJIhSpEWoL+IEYQP2GS7RVoJXv56a5nIGRPAqG3mHmqDrpHItkilXKoVC32hfOcD3yu\n3pOmap3n+HvnmPnDa2QHTZRdD09EfLHawOqaCKWK/ZlnSHxglE49rrrtpFO06aVgJmtEzR8cfvzQ\nAP4Xv/jfUXdd4F1E7QIoAcbcaV4ot6EIppnFX92PsGy0oRVkNmAz9LByGqI6jVvPkc/BqZlxpsdS\nqJHJxbVtTi8t4/mCPX39nBrfz2ghRzFncK7xMs9vnqHtd3hVnuXKYpqZ3BTJQ236igr17TRy5Aab\nYYf9hX2cHDzKob4Dd4k0dQOHzfY2NadGzW1Qc+tstrdZa21Sss8AZ0hoCR4de5B3HXmESEqW2jfY\n2N4klCFFq0DRymNpFjt2hZK9Q7lbJZkwEYFKQrVI6SmyZoackSWt387VlgBS9hQXY7BUejy0QOl5\nuhFRjxqJ34tBKpIhEZJIRqhCQREKilCJZEQkQ0IZe8maoqErGn7kc7l6lfOVyyw2lm/1Eh1JDTGb\n34sqbne6H0kNsyc7znBy8DvqkcfD/t5TvJJ6go74zovId7PJ7Di/dPyfsFBb5AuLT/N65RLnK5c5\nNXSMe4r7SRspMnoaVVFxAhcndAiikLyZpWgV2LErKFLD6xWU3DtYZqE8S6B7VKcO3p2vSSxut7jn\nMNmgSW65RbLcRR8KqStZKplBvpj5ILJHzquqRJERdlLlzJPv5W3KOUw7wKvruDkDs+mQ2ejQnB6J\nsxxVjaj4CNkowk4rfO5jTzA9f5n+rQ1STpVG3xblvknydZeUapK4ch8V1SVlOESFHXZKkwRLBzF9\nnVAIdFyUXBWJ4K8fyZL59B+yceinWPUtBjbmGfWGKPWVMYbXyDLLbtmhc2geRQ35YMriJcdjOYjo\npCXFqgIBuEEMxrLQozMaHlq3S0tL00cNNdDxgK5qYIUuA0bIU5nErfs3E1VY8Ae4OPxODO8VolQL\n0WvkUhQNvrU+QmMyjRARgaXdapAjEFhIgo5HkNAxWj6umsRVLVSrTeQmCLQ6nncOyzMIvHmCrMXZ\nuRS22qazvUaKDF2rzE62xPIHJ+kkHfKVEuPVEdKDK9RTu+xq4GRDnntHhxfDImZOxWx1CXyf2sEX\nkUrI8Pp+0g04f++rnDFD8CuMqUMUyPN/lSO6SRcUsEKP5yqv8fwH+jjYkeTSOUpbEauuxn6pYIQ2\n3skcCaDZsPCzKuf3Zik7yzwjHFqRJFm2+YUftTTJIAp4ePIY37hsI90E5swFfuWh/yz2DBQNPwr4\nwuLT9CUKGMo9LNQXyZs5njr0BMf7D1NzGxSt/F1f+r6pOT7+8EMEUYil3Z0SON7/BO/f+25WWmt8\ne+sMp0vnOFe+iECQLWTYPxJwuP8R7h8+cavl2hstoVnM5Ke4WWF40yIZUbYrnNm5wLPr3+LplW/w\n1ZW/uctr/VE0gWAqO8HR/kMcGzjEUOr7C/T8p9JRf6PNFWb4tVP/jEvVK3zpxld5rXSO10rnvqdz\nLRK4vUIZ3XbRfYvOQJsp1lmRo+hdmyCZ4KS4TLusofRrFEYbrC6PMr21ziMTZ2kGJotMscQkSeGw\nnxuMim0oO7y4e5jjBzcpiBZT6U1WDw3zPKfou7RON+rjvu3TBEMWm+ow5ahAFABKhFQFNw4d5sah\nwxCW0epfodm/RXmkw+jKYYbdFO70OVqpJjJU0YZWCZsFnF4WmCJMhDAQMsLXAz57SlK7UiMIU8AY\nQpVkq6N4AyukxSbtmVWE4THWGGAw4zGphSwHEUq+QnJLQiCwXYkiA8K+GLRlIyAMIiL9NkUD0MRg\nLO3y0ZyGpQi6jk7C8hlqrrNezeJqCZKKSV3YdNIGRuRhaR7zpX6SkwlUNyJIKChunZvpEwmg0XHx\nsgFJ4OkHRlgf3yDU1m4lWHj+VTxBXMnuvsw37413JuPXW0CGpvoCfuDgJVXGfZ2E3u5dW7A1UIov\nIhV0ReIqCh0p8YMwFqJPeSiRiuYmiJQQw0kysuugTmhEiZjSGfDSDBclJwzJgWP/DZ/881/neqbB\npZwGRDAGiV7z7s0hnz9ISYp1m9qkTTTdB6yBt8bZ3vfR+pM/ennw/+ED/5Za1eaFa79OFKg8fHCa\nueK+u475F/f+8+96/p0l8Heapmhoynf+GkKIW4VBPzX7Y7T9/4e99wzS7DzP9K6Tv5xT5zjdPbln\nBpgAzAwAIhMkBYgURcleSZTk3bW8ZVva8taWa10ru1xel3+4rN1al7fsZcmWRHElJokgCZLIYWYw\nARO7p3tC5/R1fzl/J/rH6ZkBCBIDAhDA0Pef6Tn9nvSe0/f7nCfcT/22ZfdhIAoiaX+KJwce5uHe\nY5xcPcvptbcIqn56Al10B7tQRJlCq0i+VaRltkj6EqR9KVLeBLG4n+X1HC2zRc1oUG5XqOhVavo7\nfdSCsCm1dMuqdxzXasdBRNwMSIpuMMpxfZIOzqbVLrnWPDa2bWM5FqIgIgki0uZ8mbaJuemCGY4M\nsDO+nbD2wbvHfBIQBIFdie3siI9yozTDRjNPTa9T1WtYjoVH9uCRNCRRotQuUwHzdooAACAASURB\nVGiVqLQrtKsOhuQuyPJmkc3ewRVGlTUu2GPMTmRIBReoNT2IusPR1FkEP1QjforFIJPX+xkZmGdc\nnMYzXSJZWSV+j4AgCawJYY5ol/EIKjcaPSTNAr3da/ym/l3sgIMUlJCkWykbl9EdmUItRN6KshJL\nUy97iDTKDCiLJMN+lFiFH1waYLFrmu7Zcbpv7qW8/RV8PokbThNt+CKxSyGylowRyuNrR7HULAoJ\nav4c0thFRs4kCSsNjKE+pI1elrwV1kwdKVTEKibJrQ2Ri8xhqm6xjyBZqL4CNOI0GyIes4kQVnBs\nm6YuUXdW8Mnu36+06aeuqTLKYxk0SeRHeYVKxU8wlWW/nMNuzDC97xLaZjC14bHoEkuU6n7yDS+K\npKDqOk3vHN5SHeh1NeV7TiOni9RzXUTYQ9kfQm3rOKU4Ri1Eb22RHYUVECXyQYhV3JRVSxKY9yew\nRQevMobq7UaSUpiNJvvf+BYTPlDWe+goRQhbPrIHOvnN7/xbTE0h/ntprp+JUfOHWFnsRdMrtNUA\nomIzOH2EPSs/oqtPZC4icXUe7nEihDQFW7D4X//qWY5eLnKgUmPht3pRBJMFReKmvtlPVhSRm0Fy\nWg2n7cduhLDrIZyWn0/1rbEjVuPV2oPvagX5QfGhCH50dPQZ4AvT09P/2d3GarKKJLZJJAQKrTyP\n9v7ehzn1zwxZlG8r8X2UUCWVB7rv44Hu+973PslAEKnpufvALbxviILISHSYkejw3QcDf37qG7RF\nt1hJabZQVIPh8DpO02KvPMnI7pv4AgZ2UceYrKH/ZQ2nbrE7loNOHz6fiKCrlPQQlhOkENdQ6zmC\nwQaZdAPSKu22wuqFBNdrfRw5dJ5wqI4cEWg2FZZW0nR1ZNG9bvwiEyuQocBObrrqqJsCN7dK4ccz\nZcTL4wi2iS2p9L61n3vL3+f7e3zc6NWojJ1B0Vwf8q1SIdMqIDaCEKwwc6SNP3yUrpMimqFgTx7C\n7p1AAhxLotbysh7sYaY1BLyBo6uYmz5p01+kVn+JF1WHh6oiVb9GwliiFnR77d6y4OUOiVYkxEKt\nxulCgIBaR12L4+8osN61hqmAbW0WEzlV+sQyq9kYoiq6janbFnVtlnC7H4BCepZiRwGfLWP73EDq\nkfNQ9I6zrIYZz77BYG2Wil/j2ugOTo0tEF55mKdf/w5+u8XykIrhkxk6HWZ9LEhH7RI3Rvfy0iOf\np+vVZQKiiq4mkGoGD736TTRTR4m4xGotm9Q63EpbRWjTJoRSqzKvhVmPjdPx3Cq9B13DYKMcxs7X\neDZ7gFZR56upz2InBTjtPgdBtkiL7tjGag+V1R66tAJ9q0t0tHIU0zKvaP28cClBYs8Ej377P+I8\nfN8na8GPjo7+H8BjwPv7Jt7Eb4x8jprR+MCf/1vYwkeBqDfExmY1qtxsM9g3jfW9FYw514IVZYGW\nJMAtbRRFQIzIKLkq5KoYm4KYHk0k7ZnDTPnxaQJ1K4g/4laOSpJJreHFBP7dqb1EfG0Csk6mKWEb\nIaZuxDiSOMPf7f4SYaHKM/KPyOW95GMdCDWD8tUgpXKIo8fO0dW5zo3Zbuo1PzgONS3GdXUvn379\nLM/dF+JaP8RKNtniDjztIClPntW+myiig14PIviqKNUQquGSdocAxVABHJCDdZqWh7P2ALYmIRjn\nsde6UQzX7SmOnsMRba5YMOPAwZDA0dfnee5pAUQHyXSNFbnLx9esp3DkJh7pFP3XdyFbKmc9r6H7\ndHrWTBYzLuXYVpHtwixvru3EK7tLkmzVaIrrqK3tGEqLjd5pROAfRVR+pPQBLXQpQEvyMepZZXhb\ngeJsiq/u7iNkbceYSrFWVSgpARb79yAYbl69IfqJXivTszFDuFLg7KGHsX0ioi4TKhZBkOlZmnGv\nISzRqsB8p4Bd12hKJhv4SQJzWpglHPDEmSfOm5NuGunCeoQfrkcAHUESkPwKsiahqg6GIWA2TJSG\ne9+RlM6Xhq8w4d1FcW4nO84+T6i5xsMHw7x4PsPfXtnBM6EN7mhdfjh8GAv+DdzerP/kZ9lpd2LH\n3QdtYQv/wIj7oyw7Jm3VQyCXJ/JaFttwEFIapiYgt2ycmonQ4UHeFUIc8CEoIktZlcIZH95ak07v\nInKtjb9cQyxXMa+D7Mli9Ps45RlkTh8jbsnkRBNJtKnrMvmGl3ngeHONVHONM6U0w6XXWdy3n68E\nfwMrIoENsdkC/lKTsZGb4JEQBBjZv8Bbr23nlkb6YmQX+XSKHXNnWc5AISySqi4RnYvx1Oo5qhcU\nvvGojSKFqAvgWVzFloK0vTW0RoB4JUHLVAkXOlmOqdibwXRZ6qLd8CPrGqakYzUCmAvbeWT/eU40\n27x8WKV/uYXjtEERUCwVxxappfrwzLZoBxR61nqRN6Wc46sDrA5dpTT0B3Qt/BXL8RaCNY3R1PCv\nrPNUpsSCILIhr+A4Aqrupe0rYQoOPY7G6lyEYF8TG4emEuK+XWdp94b5/6x/wraxWYyJNvGWTVCI\nMOk43Aj2sJHeTWqpjWQ6xGMlqlUf1xOHGFCuESrnKaXiBJfqGJIX2WryfM8IjyxeQwjKSHNlmsUu\nptBoAXE8JAE7pDBQMYgoLdKDKxSbKu35TnyCSLxbQQz46VoqcWD+e3ilJqGAztdHkmTHthG/3A1l\nODa6QNrX4PlahGp/nGf7/5DM8izH+hw6pA3+8kyK78bv54mPywc/Ojr6+8Afc6d+2AG+PD09/bej\no6MPfOgr2MIWPgGkgwkMoUE9ECJWWAdNJLujj7/b9lksucG+2AIjSgURG91RWamHmFyKs1EP4hkU\nSEoijl9ES+k0bY3Yygo989fpnruBb6rGQS6xW77Bur+fRrIDrWMQKREgYTU4fPFVhpddqYBhgBwc\nmjrJlZ0HKB4YRG/5YE1A8Bpc02VeO9fHY2MrdAZyXI0XaeVj6F4RtWnRNuNohwb4YnKNv6+3yPfU\nqKbrXH1Lo3dV5/PPl3hjr871Xg+N6Azr/Tkc0S3G0lp+RFMhUE4Q3aYhmDaCA5LYSXDkDZTzD2Go\nLcyNHqxalM5Wgt+5Ocs3Y1HmuhpI9W9jykeRWhLhVpi1a72EK7cSl0M0QgUkXSVS6CTbcw1Lu0Er\n82t4al+jqdWYm2+wxzePdKSDHdJrvBiQ2Kh7ABHd1wQH1Cv3sFoRCDTWMHQfTTmAenGFE/H7MD0K\nV0Pb6GYVxXZQgCA2Z0Pb6VhqAiKCYLF751UsU+XkmT3MFkfovniN+SG3Z60tytiGyWDR1fgsdKf4\n1tQYa7iFkyFVJO2RoWISSHroUYoc3HMFVTHQUbgmiCzMd5FpyQTNPFZNpiR2ES1eoNi0WTzSoGcm\nh1LuwpHadGlr5C2bbO05ktKn0Eqw1jXAt0oGx0s3+J171zl7MfYOUb8Pg7sS/PT09FeAr3wkZ9vC\nFn5OEA9GQCoysecIfRs3iB8QeUF6AlX1ACGmyDD1dt0rL9ADt6I4t7pmVh0Hv1in1B1nOZWhtfth\nuqprDM9O0j9zlb7yJH3lSfT518h29JBZXUQxdHKJDJfH70MxdfzVMsNTl9g1cY7vW3F0NYQPk6mm\nRH3OLeL64aTM7x28wmD/EiumzMDQPFZb5crVYSYub8cXy/DZoWvM+aq87ug8f/hO0w/BEhEcgWp0\nHbnlI1ROUA8W0T11HG+d2e2n8Ps+R/rMOqJuM78nT1O0kSwFtZVnML3G9Y0eXrrRy+OldToLx5F7\nrpHrmKEtFvFbUbqnDuCYAs24iiffdrXceyco1sN0ze7hwddTFL0nmRh6ArF2EPrP8v2Ug9CfoM4u\nLptjaL42sWodMGiqDeLZHrzNMC0F9GUfPqdCU/bSXrAxVtpIfSZ6pYWncaf3U5cms9wbwH/dbRiT\nDZa4Qp2iXiKw8yKzl/axZnTjXd4AFNqaiKdWoY3Gi8kDnJ3ajo2IP6SwrWKi+myckkkTh87VLIcO\nX0aWTEpOEASB4W0L1Kt+yEVu66ashLax9NQImvAKsaVOwis7MSUdffsFNAluFL0YWp6ik+OZk+co\n+WKcePgp5rpHeUw5wWpn4BdPbAxc5cAtuNiaizv4JOYioMvYWov5ke1kR4cwDQFd8XDvq98ntb5M\nJZakGE3iCAI1R6Ug+LFUDb/iEFJsFNsitJGlUE7QlsLE6suMbZygIftxsLi04xEuPX6MRH2RwZsT\nZFbm6Vm46Z48LJMe1fGnpnlh5ShL/SPM94/y1N/9vzxy4yW+9/TvsupJIRVbPKJeYUlaYV6yWLTT\nDCSyDCTcsNdSMUg1UkIqxKAQ5XxuH2MHb/BEWOR8tU7FcBAsk67pnQiOSEvWaUom0Wqc69tPMnbu\nUTZ6p8il51GXJlEarsxBx1SY5WFXVyWo2zwcq/LX1SIz+Sh/Lj5AJwLpxVGMtIIluamCNW8JM54h\ntGKCILA+2mbdVyXjb6Ett8kGt/OWPYo5pwNxRP0A6rbzfLfVQqm38IVAEyx8WTerqxGo0rG4h/WO\nm5hqmZ7Z3dQ0N/J8PXEv+8++yNe1LyFOl9GQsJQmqiUQbGs8NvE8WWUHbbyIqw2y8jZEn8QrqykM\nZ7NeoSqxH2i1LWY8CdKCQFIvcaB4lcHmMll1nDoZnJKA5tVZjkk8OXwZVTG5cHmERN8CEcVi9rqf\nHaNTnDk/Tqul4THKKJZO9apGxeqjszKC7LToL7xC52sV9AgE9BR7Uy1U8xW0ukSg2mD/914if7Qf\n4hCIaySTwY8kk+ZjJfgPJwv7y4MPL5H7y4NPai5sx8aSJTSrRU0OggJjp9/gphDhRKibYL1B38Ya\nQ40lUnrppx7HEiTOdz5Gwd/FnLmLsY03qWpxnIqX4TNX2bP2I2wRFAt0Cap+hVjZwHqjgPZGkf60\nh2xwNy3B4VLPcQ7MvsAj3/0af/8b/wWBDj/q2Tafu3SN+dFeTu8Z4kRkP33GMs5lmC2HmfVJjIo2\noeYqFa2DqYkB5u4bhqBbBRqaKSO13bzvoVCJM4FVVEPGCJYxEiscU3ycancSWerElE3qoTzhQprB\naTdWtjg0TH+8wm/VTnM+Nsir17vYmX0Tv9Ngqhwhrru6KfmOWbrmI8i6SmHIQz5yDmxoaI/ijZVp\nr4X5Qv48CW0Je38cf7fE9+eHuBGax1DPUyrcxDBhcOMotlKASJoVLYFxrY1t+ZnVS+yoL2AHBlkL\nDZOtSsTPXiGuJbBkH0evf4+KJ8FE5gHsRhAj5CWoF3l49QVYBUOQuEfxgl9GFwWoK1zteJi0bfD5\n+a/TlAOsB9wFTpX89C1e52oyiYBNUr/EwXYW7ZrNbCWEuVZnrlShd0kk1KrTvthkj+8VzvoeQZcC\ntGUBiiIKITSjxv6VH+I1KgiSgJ13SDNLevHOOxSiQEftJvbfnEEf8uLp8bC+XkHarKT+MAaQ8FH5\net4HnC1Sc7FF8HfwSc7Fv/mrP0GMP0M5nqFn4i2uJc+iewV2TzXYNunjSniI9VQcb/gG3aUV4lWd\nYN31XzuCwHLSw2IySKY8RLvRjeMo7Fx7mbVQH3nfAHMjp8CuIzQ0thfWOXSlgi3Cy/cEkEyJ3VM6\n8UaTy5kH2Qj0I9oGBxe/g9+oYIki6+lucqkucqkO5gfG3lFh61utEZsss3wgzujaJAdfeY4zPZ+l\npsVIM420L4hdbLE+24vgOFiiQsRYo2voLM+1uokPZ/mMz4tXghMXdlPMRlnpu0IxucS2iQdQm27e\ndmkkRKPHiyf3A754+TrWio5Yc1P+JgIDXMkcI4GIJbmSBeWBIMvx57DFMkk5iu79AqErs0RXBRRb\n5765byBis9HdzaVdh5nxRag5LyN6ymy/cA+SkWZ8+YeITo2/TT1EQQ0jODZPrJ9kb/UmFSXKuZ7P\nINk6I7nTTGQeIFZbQKgvMpvsI6h2gSUgOEDAoKt+Fe9KjpBZJ2BXUTbFvxzgXNeTlD0p/HqRuhb7\n8dcDHJu9qy+QaCzf9V2yBYH1zhGuefejOC181JGVKu2kl4F0jky8giMKCE2LclljpRChQ8ihxlvc\nxKZnRUe60kSttnBUiaF/+x+QZdf+TiaDH9hf87Fa8FvYws8TTEVBKZ0jWfEz0X0NgP41mbWUQqJS\n5ckbJ1m2Fb4/FOJMjwfR9mBLgOPgaTu0PK6I9VL3VTzNAtsu7eVq6n5sUaLprZCySnzuMnwnvJtT\n2wRWEwK/9kqZh0/XODEe4Guf8fPYSYux5ZN4jRqp2gyGUmXDJxFs2nSsLtCx6srNZof6sfrD+K6s\n8NIDz1DuSGLLEoF6g71vvoItSchCExybipHhmH2Wi3PbsAWZ3tJF1iLDlOQUw2/BM4+skw56sWxY\nycYpZiPofhklU6BThLBRo7lZkLTHM80ZfSeDCyGs6w0EHFYPbMd3bYmd1VlWmtvBm0KyFNY7blDr\n7oNKDUGFz2ktvnZznYfOPstGcIzFyA7O99zPtuwEyaUlHl76OsdlhbVMN1JR5WpHmnBzjYIgsK1Z\n4XeXvsdL9z7BztVJeqo32YjHeP7hL9Bxtoohe5lIHwNgqHgRyycwefyzOKsG4ZuuwdBMBTg18CSR\n6RJiaZHFkbfo8u7n8+pFTEmgfsIHDYG6FkMzauT742A5dDU3SCcLRMJV8p4h3qw9ilZvIJkmsmUg\nmSa2KNEyb1DP+ii3MnRqMXbsnOGxHrd1ou0IzDudmI6NYfm5uNjB7v4blPQQ0Y4qjdQ6b7QNrrZM\nts2ZXNVslrsf5Q8GLuGVfjbpjvfClgX/CWDLgr+DT3Iu/vU3/xVq1WCt02HfVJ3zYz7++eoo0d3j\n/E/r3+RTp/JsW2zTUmSeO+JnoVNlaKnNgckGmYLJywe7KO/vRbeabJSy/NoPVKZSDwEg7M3xh48/\njSzKfGV6mfPTb2IHThFfUXj65BqBtkVNkwm0TSpekVN7fEwPeHGEKNb0MEJmDo+/Rl/zKPeefIFE\nPouV9OB9Mknjus6P4o+Q7ern+AvfZvDGBBfHjpIzhwk116l4U8TqSxT83YSsPFUh6Oq36yoDhQv0\nNiYxHk/wo2tjSFYnHlumOe4hNbOG39tkLdtBRMkheQTGmmeQrudRLIOGL8CrDz/NWmcf/nqZz3/9\n/yKr9DKZPs6a0iS392UQBcBmXJU52PJTfr5CJpflVGY/tdAetyePYxNpZonaK8Sby4TKBd7qfJyS\nrwPMCqsejUOh1xg+t4yw2X85m+7m+Sd/E0PzIBUbdL7ltoAUnTy9wdOcG/8SDS3AMfMMc690AAIr\nsRLG3kEEwUO7dRHduoLjNBlXZR73e5itylRuJuk5fYns3kFeOfAZAFLkGGSBt2qD6IEIiq2z7+QL\npFYXaN3n4ebyIeb3bsNSRKrL11DnHLa1g4SZ4N7iRXLJDC8eeIZ6IPCO981Li8R8jtp6ntXeq3jr\nYbqW0gh6kkx1hoZ/CuvBI9hagN/e8xDqpjLmh7HgpT/90z/9oPv+rPjTRuMntUT41YPfr7E1Fy4+\nybl48cbrfPFHS9wzWSfUFLg8GuS3n/7vuCrlOZ2/gJHci1brIV5eYvt8kx3XDXbNNvE3bSxRpn+5\nxhNP/SHHxx4h+4NJds5PsRoPYkX9/P7nn0SRFUptg2cXNvD6FBqtazTDJjf63BTGSMPi0rCXFx/e\nxXJcxhEMnKtHaFdDRLUhWu0aza4Yc9uPE6iViS+uUL9u4IQ0djjXSEzN0ndtClsQmBo/QHCtQsWT\nRBINGkoUHAdZaaATZNfoTdZzMcq+JP3rF7BnDcrR7WD5aSQ83DM0QcSuUpzyMNY8zVD1IpnZq8i5\nGoaosRDdyfnUPgIFgz7nJgvpYWLpNl1XLpNuz3Fa66OlikiBIrLl8EzIi36xTXxmlUVPitNHH8FK\nBvBttJCEPKnqGueOfIrLR+5jWWti6UOE2jnaapSR7gK9u8pIYRVxsY4d82A8keAe/03mnS6a3gBG\nqYWvZVNNVJncdxhLiXBEPE90vUhuNYwAHL/2I7xreTb6BpA8XajyLtC7WK7XSckNhv0OSmMN33yZ\n73/qaSzJQ4eQZZ0ES3RiqR70Upug9SxlY4Hx6SLh+Ro3Ry08M0kaGQ9qOIkXGcGjEDMKTA5v58y+\nxzA0Dd9qg+BKjYy0TsJbpECUUiSC7Y+SudlJdD2JYAeQHJOqliDshcsDh6mZNvelUyibevt+v/Y/\nftB3fIvgPwFsEfwdfJJz8dLsm4xPbiA6sJjRWO6N8tjAA3zj+nfINfP8s6O/x/hjxwnu20dj+ipy\nvcFqzy7+OnyYBW8HO6sz1KeuErn/GM5ffxXJcTj1yGd5et/Y7cDYifUSM9UmjfYZtGab7Vc/RSK0\nnx/RxeVMhGuj0FbzgI5djdNe6WXQp/Kvfucg+ZtVNtayNNQG8z3D2J4gPQszKGs1nIUmoVweRxSw\nRImhm5MA5AK9yNE8dstPvcOL1dJQLJ1949eo171Ua0Gy3RGsVpCcNoCtAZkW41OvEzw7S2fhOr56\nGXSbYleG85ndPD/WgWntQMDLTU+NY84UU6ldLAvdxIpFEvklxqwVLlr7sX0N7m06DBWbyK+v0ZA8\nfK3rUSS5jTMQR6mYSE0NQc6xsmuEwFKV0HIQHJFoY4WaFiOeWaMvVqH5Zh65YPDtxAF+uDrOjWqC\nJzPnucEA7ZQHX7ZJbaAHOxBih3CNnc4V3nprDw3vAj31k4RbBv35RXZcOUNTF1iPZtCCEVR1kIuT\nCjsSWeIJlfnxPVyXRhkWZ/m0/Drz1w3W9QrtQp7aXJZG8gZC0MeBmIpzs058rcSMMkaoaFLrVHGi\nAVpJL2u9fZRjHVh2CaMxQWZKwGw26b34IgcunKZjdZL5eBo9GqPe5UO2qxyd+DYt06DQ1c+Ne7ej\ntZp86rt/Td8jj9/Ootki+F8wbBH8HXySc/H63FlGprJINkz3qbQyaXalR/n69b9nMNzHY32uu0UO\nh4kcf5DoI48y8MgD+GMRnp/TCVk6yfxms+pinsnu7Szs3ktH1EdvyIftOHxjLotumdRbL/O5bY/y\n9Kfu595dPRzfO8jVOYnVGynEZpiwX6V6YwhFa/DlB3aT7grxzdLfYItFHrctPp2cINZpkduzm9e6\nj7DW2U8h1GZy30NM7TnEtoUJfLUSi7GdNByJtDBNOxJDKkIrpRIWKnR25Jlf7MQ2vZS8XXj1Cvcs\nPsuOqXMIa66EgbQjhLQnzKmdx3l9/1NQDVGLT2LJBqFajIrpZT6WRpAU9EgA70IFrdMiupSl385z\nqXEYa8XH6MUrWKLM1zofpZoKYmROY0lr2MkdBFYaNKUkasUkuNxEcix2hS6wpndjCgpn6l6aRZue\nK7MU1TBvdd1HbzTP/EYIzWmzJ55lXuil1unDDKh02Ws8LJ3k9ZkUej7F/PA0sbF+ZuSjVIUAvmae\nkeVpCnKQqhBEiHgQvSF8L94g2Sryg+ijGILCUfsEp9tV5qqg1Fq00+cR/WUERad89R7eKOzCkk1G\nS+vEmxvkPIOohTyeIqQWl0i0lqjn2xScl7DkBZaLUerBKjf3rWKYAqPTJXZOXqbdClGOhalnYswP\njTHh6cDeHgNRwLf0InUhz/g9j97OovkwBP/RSJZtYQu/gFBFlfZmO7pcRCagBDi5egYHh/s6Dr5j\nrCDLSD5Xhvb+3R10x338ILGfdiSDvrwEwJuhLnAsns8W+d8uzvI/n5+h2DZx7AVUEe7rvQdVc/Ma\nAl6FP/ntfewKejELKdYvbke0FKTRc0QHJK4XZyi2S+weGGH/8S+yYIwREuv0aLPcY1xkoX+Ei0f+\ncxZ7d+AJ1DEe6kQ7EkSyCmhtH+WRBuJqGwfIDyf4of8Bznu2o0Xa2I6G6ZGwe3R87Rr2poe3/nAC\ncZuf7ESVt3xr2KUmLX0Dpe2h0DGLIFp0CjC75MVY2sxX7wyypqS5uW0XmVKW/3LlW3wx+wIC8I3M\ng6z7Y8gDbyD6q7SzDqXFCqVtYURbwFtoY0sChXaN2QUvDTVCrLlCsyGiTuSRHIcTkV3kWgLKSIp7\ntuU4Xx0m3l5hRJgBSSJkVXhceY03Cwr1hX5MuQ2SwX5tjWCtQk4bQnIkHASOTb6KGpXAMlHDGu3u\nbs4VhqnJQRrrJaaXAlwqK9SSy0SqMWRdQ/TVCBZT9KCiCjYnwnv5XuoIXqtMvL6Et+LFn22SXlvA\nnlxDud7Amt+LYwuowxexuy/hOBKn5GP8beenMASZI1ee5Qv/6f9k1/kT1P1BQjsSOIpIbLII2V4u\n9KY+skrWLYLfwq8sNFGlrbrslo/I1M0GJ1fP4pE87E/vfc99nzzchyVI/LDjOHi8TPl7KXsF2u0L\nSJvpjBFVJuN1qDTPsD+9F6/8TgVRURT57END7ESgUxbp9lUQZIOzuQu8uXYOgCOdB0kG4xw88HlO\n2/tdrZCMj8ef/Ss8jTpi3aBYDGG/tI51osCK5S4gwuIogqFhYKPWDHSfyiQjLAx300h6MMYldl95\ng28/8wc0vQEcwPvcBsa3VpnzRmiLy+SuT7G67XWq0QKWoiOEyiiOSEqyCOeaCIbNykAfkmHz+gOf\nodGbwNdqIVsWk/clyN43hbb7ZQSlhSocw+s/TmOxTs42KQ2GyA2HECwHQ4uwERgAIFFf4su+V9lX\nvYYTkJG3JcGBmws+lnr3Ed6bZikX47h4liPCaZ5WX6CUD7I6sQPZ1KhEsxyLmczSIj20AILAZPoY\ns9HdeNpNjrz6HF1LM4RKeSb2HubC4YdwbIfq9RavXB+icnMPOLDRP4ngCAi2yM5GmuFAlfDwy3ju\neYGIdwFBcPBqhdvP0kuJwoHtrHdF6HDimEsjCLIBjog+fYDfGs2SOJjjz3sfZ12N4DFbtK/nKJ3J\n4rTaNFsnoZQj0giTnNhxO0Xyw2IrTXILv7JQJZV8WMajq1T8IrXWBrZicLTrMJqkvue+h3Zm+Mvv\nTzFpeNjzpT/m71+bJ+TkaRlv8et94+yMu3qA/8+Vv8S2S9zf+ZN7bA6NRnt3qAAAIABJREFUJUm+\n4sVTbiHpKcqiypur56gbdRKeGEPhfgA8skRX91H+fGGA8a44e1Jf5Tf+6t/xw+QhHimdR2k3ubl/\nHyVdI9RyoOqKKnhqC6iXwRHA6T7FwGgPRGWS2QnC5SKHzrxIoFElF42RKLqEtSB0AouowxdwRAef\nFKZurRPauEhJeZCd3ja1mp9mvkg7E+fSvvtxJAnneBLpfBs6PWTXHiAcuIbuqePRj+F0DEAAWuEK\n5esV7KEQelEngk0EAb/ZoC17iTWW8U24hVnivgif2XmZ2dI9NJZt/AkLIxpipLvARjbA3sxNmk2V\n09M9eFquNIMRzrPXI+EAYk+e5WtlasSpq1ESzQUG5q7Ss3iNQjTF957+PQRVor5YJZUs09hQGIs3\nKDajLPncLB2tEUAcnOJE20ByBDQTDi0sIzngFyqErRUsQcXwSOx6a5Kngzd5VniCxUo/jiVj1yI4\nzSBzuTqPbptj2p7iL5RH6a4VWPZ1MFLTKea/gx4qsqY9xlAb/IJ/y4LfwhY+LDySxo+OhHj23h0g\nCOxIbmMg1MvDPcfvuq8oCuxOB3GAb725ii1IhDabOkzmp7EdmxulWS5tTNDhTzMQ6v0pxxEZP+Tq\nzdxz7yDjqd0U2yV02+Bwxz3v0CQ5lArz60O9PNGTJPngA0i2xZPZEyhGm9TvfJkH//E/Y3w4Sm6z\ns1gbhz35c4yVTrIr+yr3X1qk2J5iVLhGXvPSlmW6NmVyr/d1U48GKGsaasvEMRQE2URzRHo3O1+F\n7DWU4Dq1WgAQsFvzADT9AbAcJq1hlAeTNDNBSl0Jem/uY3jiKN2FdZrLVcymSTRsIIoC1RsV2vkW\nuqAjI9D2xFGcJj7TJfeGqPGytAtFcjjct4xpi+hn8oy/eBoaUG8EmV/s4Mz5nSym3XuwJIOxrh1M\nNrxYwKmmwcqogN8oIIervLTzAWxBQLRtTvZ3ULpaQC+1qc9WGDGW2WGqPDSwxNNpA8FQkW0J3Vdj\nyjDZochYgkPUTiA5sNQzyLWePYwvPM+hmWdZGcow59vNi/qvkdNVQIDaAL50JwFN5+R8F42Whx5F\nQtx5jnrEYX94ktbh5ymEiiTRKFZFqjh4Rfm2//3DYsuC38KvLDyKhiUJNPyunsqhrn13dc28HYd3\nZnhrtULLcFXJYq0gFUnlzbVznM1eoGa4Hboe6H7v5g0793Xi9akcONSLf9a47Z45mNn/jnGiILAn\n5mbnqLv3IIVC2K0WHf/0jwjsGQfgsfv2cu7qa7QtlQ3JRI930LXmFnH5jh0n0ijwPTvPUf9xCttr\ndFxepSb7ifccx/vYb/Fnf3sBj9MmkG1T7y4wKHjoEStcBVqqAPYU4DbfaKgzSPYYougDCa6GdhNq\nicyoSZJjMdrzNxENjU8fClKdXmfqhEpoe5TAMFSmi/g0B71Rxye5rqtoc5UrgQF21Wa5NniIA/sO\nU6+usquzyhsLMjk7SKOnFyHgRSm+yZWJbuIJm8f33s/LrYvYksk/vvcPkcRHyDdKiMsnKDgW9Sd2\nIzgO95ttTulPYSGwHKzTWmwCYBs2VtZi+44ZjJxFqBseF+Kk/XVOmE2uGwY+0e0LsH/0Eb77zEG6\nBvoZddZ5qe3H27aJbY/RimYonV8j54AqCqR2xgmXdPYeGeWrL8/yvendXMtJBMdfpLXrEpdlEduG\nEUUiU+xiwYHlHj/BjgC2bf/iadFsYQs/T/Bs+sR1j9vkI6SF3mv4u9DdEyEFrAAqkE4ECcS3c279\nImE1yH0dB9mT3MGu+Pb3PI4gCAyNJVFUmW3RQXoCnSS8ceLen1A+vwlRUej97/8HEEWUWPz29kzM\nRyoT59JyBSyJjuP3w9+4BB+95yBDhkh05U0GD9+P0zvEzOyfseTZidWQ8YUDtCUNU9R4YMLkhDVI\n57ZVQqIbUG1pIgPVNS6ECmh6mIa/hM/aQBT7CCsyEU3hZG0XAE/E43Q88DK1coOuwd/l11v/kR/U\nexmaOE3qeIi32gG6w1VefzOJX3RQBIGwWOYHI49yMrfGPYf2sG/nMK3qbyLKAR7Sazx7Yo70YIyx\n8W58wQBXJqYZGO3i3pFRMrEYPsWHKruutVQgzkpjFdvxAZD2aRxIdvDNV/PYOPgcPxXAiZ6C7F4W\n9BQPLr6BfbOO9I/2sTeZB2Bc2c719UssmTaioPFk/17uyZjENIWz2Q0mh9yvtqNym99+eJT/23Cw\nLq6yrTvM0x0JkvuChGNeXpvc4Npm+9f96x5OdzQJmzJf3PU5Ytkf8pWrvQjAY/u8ZNuFn4+WfVvY\nwi8yfKrbsUjXXIIPqoH3Gv4uxJI+OmWJrGkRBNKdIQ6PfYEnBx4h7UsiCj/7H6koiPzLg//t+xqr\nJJI/cfvxvZ3cXK4w3B1m+MHd3Py7vwbHxjsywpiiwvitpjshlv74X7Pw9SnC5RY+j0sHiYiXm7V+\ntpnncSQv3s1GIC1VILlhMnfvGbo83WA4eKQSFn1sj/jRZIn5mptuuS3sJxF5GtMoo2gR4qk9PPHa\nd7BmKiR//b8i7nuOWi1OQ9LIOQZj7SIdepbjezv41mstulLul4onOAjAIwci/OD0As+9ucBD+7ro\nGczw1Bc1uvrcZuNjiW3vmAPHcVisLhNURzCBDp+G3nA1dEQEWjUVRAshuArCLja0KNb5MqLXi5z5\nNMbKXwBwZPgpvp27gWE36A6OIokSGZ87HzW9dvt8Fb2KKAi0NptrJyNeRnamb/9+73CcxfUaIa/M\n4TfXSUUthogz8thh5q0EC4UFdvRHmWq+xnxlEce5/xer6fYWtvDzBq/qWl/O5ud36GckeFEU6eoM\nYy0UEYFURxCPrNEhp++67z8kDu9Is5prcN+uDKKq0vlP/wjHshCVdweOPZKI6ZWoV9p4N1M4Qz6V\n675OZK1Bh7OAd5No2mEvnqkmmmyxYa8BMBAIMZU3GY24c/cKRfyyRNqrIgpJFK+7CIUzxyi3X8Wi\nQqhnB36xD2OtRFWeR5Z0hgqnkNtNYn057rWbHNufxmnfuc6QX2XftgSnr66Tq7RIRbz0Dv70L5xC\nq0jDbDIcVShaEjuiAcqzrka8g0O16hAKCliCCIJDy5apSx7SO3ZyeVnmysQIh7Yn6PdEGY3t5Uru\nJMe7DrzjHFXjDsGX9QqO4zC35spu/Dg5x4Pu12JMsRAaTYYaAKtYjTpnZ9yxR3d38J1SkbAW+sj0\n4LeCrFv4lYVfu5O2KDoSHulnb4Se7gwhIyAikOr42Vw8/1BQZIkvfmqY7pRLuv7dewiM7/uJYzVJ\nxPLIOLaD1Ta5RSteVcIxNAwcgqLbIrAS1qBl021Ay3LZ19tSyJ1YhYpOX9CDX5bYEwsg/hhBiZIH\np2wgBgJIfj+qN0VltYglSHjVNjWthtBs8Z+u/g1XjFe4sPHuVs8hn7tANVt3F+NarLoKkAOhJP9y\nfJCd0QCVoutz1wHLEhjtSPOnR/4FPsV97pVDjxL/zK9x/nqOS6spxra7YmZ/uPMz/Df7/ogjHe90\ntVU3LXhJkKi0q8ysVCjXXQt+vdh4x9hbcZpb1y6F3HelMTPDiStreDWJvcMxyu0KUS1y1/t7v9gi\n+C38ysKneW//7BW8H8hqSne6f6iyLBJL+j6ya/u4oIquBQ9Q27TiG22THf0xBNuD4YAsCIQUL3mf\nm52TLN4h2NqmETu7WsUjSfyLvf081ftu15Fj2xgbG6ip1O1tG0vr7u+8Leoel4oejd0LQKH5bg3+\nW18Yzfb7J/ieYNftbeVNgm9t/r8zESDujdGVdAvYlnt2YyYzTC+UGOgIEg26LjxFUhiJ9r/rHLcI\nPuNPUdWrTC24qZUBr0x281y3MLvq9gDb0EUMQSLy0MMAXL0yT7Ha5t6xFA27hoNDzLNF8FvYwoeG\n/20E75f8H+gY6S6X4JMdH00Hno8brovGJc5qyfXDN1om9+3O4NGCmJv52GHZQ0M0aaoC8dIdgjXa\n7uKwUXYJTRHFd1nvAGYhD5aFkrzjvsplXUI0fTq637XOx71uwVOlVXvXMW7FCN4XwddWAOgJvJvg\nb1FvR9xdkLf3RQGYXixx6WYe23EY3/aT4xtvR9Wo4ZE0Ep4YpmNRrLtHTkS8FKttWrp7nZZtMzFb\nQFMkbARWgl2Ej7mpuG8tuZb+vdvTFFruohbzRO967veLX7w3cgtb+IjgUbTbPwd+Rv/7Lfj8Kk98\nfhfHHxv5qC7rY4UmiZgel6Qrm4HWRttk37Ykv//pfeib9TahzeyUYkgi9DaC19suhWyU3mmx/jj0\ndddaV95mwRdKbhpp09fE9LuLrbYZCK20303wtyz4xvu04KNahIDqLty27VAu3bLg3ZvqiLu/G+1x\nLebVfIO3rm0AsG9b4q7nqOk1AmrgdvZVueEePxN17yVbcP9/c7lCvWUylnG3r6S3IUeiiIkEE+0A\nAa/CWG+EYtsl+OhHaMF/oCDr6OhoCPhLIAQowD+fnp4+9ZFd1Ra28DHg7dWqYe2Dt0UbeB9k8PMK\nTRKxNi34SqmJT5Np6xamZePgxdwkw5DojimGZAbeRvDNxi2Cb/FeMHM5AJSEO1eObVOqGxABXa7i\nBF1rWtnMwqm0390jwPc+Cb7crlDRq+xJ7Ly9rV5tY1sO4ZiXVqEOOGRiLuH2Zdxnb1o2l2bypCJe\nuhLv/UVnOzZVo06fJ0ZYdfevNt24RMfmvtlig75MkAs33Hu/L9rm4oLDotdd5LLdu6jXPBzrDSCJ\nIoWW+0Xz8+Ci+RPg+enp6QeBLwP//iO7oi1s4WOCJt2x4CPen48A6ccNTbxjwVdLLfweV4O82TZp\nWWDiLoJByaWKYkxBq90h2EZ9U8un3MS2f3p5vVV3rXUp6JKhsbFBRXSDm7ZSR9jcbler+GTve1rw\nd3PR3PG/d97edss9k+kK0wQU0QLRvV6fRyHode/bMGzGtyXuGo9pmi1sxyaoBghtGge1phtg7dlM\n8VzLu+6XC9dzqIpIb2udlF5ksa1gmDZXva77aLfHvdefJxfN/w78h82fFe64tbawhV8YvN2Cj/l/\nNQlelUQQBUSfTKXcwuu5YyXXTAsTdxEMCrcIXkZs2uA4iAjUG252iGk5lGrtn3wSwKpvZpxsKnLq\nqytUZfdnQW3fzioxy2UCqv8nW/Dv0wf/XgHWcNKPCWiiTX2z0higJ33HRfd+3DO3AqxB1U9o04Jv\nti00Rbpt/a8VG2QLDdYKDXb2xzDmZuhpZjFsmFkpc7mi4LVadBVcyYdbFnxUC9/1/O8Xd3XRjI6O\n/j7wx7h9aoXNf788PT19bnR0NAP8BfBff2RXtIUtfEyQRAlJkLAc62euYv1lgWfTMhf9CvWNJhHF\nteYbLZO6YWHhAWooWHhEhULQRgBEGwRZpNY0bh9ro9QkFvrJqaZ2Y9OC998i+FWqsg+f7OCINnLE\ndUtY5TIBJcBGM4/t2O8oFrvtg79LmuRG061C7fDfCejeInhTdo/nAap6ncgmmfalg0zOFZElkeHu\nuxPsbYJXAoRV991ptW18Ho14yIMsiazlG7fdM3uHYrRenqE/NsI54NkTc1TbNuP1RfRZt6ag2Crh\nk723K6w/CtyV4Kenp78CfOXHt4+Oju4Gvorrf3/9I7uiLWzhY4RH1qgbjZ+5yOmXBdomwTubfnh5\n0zPRaJnULRMTL1DDtnXCiod1S8cU2cyXFzCtO26Z9VKT0d6f7F645aIR/e48t1eWqcq9RP0iVcAb\nioIkYVbKBJUeHMehYTRvB0nh/bto7ljXd+Iqt3Lgq6b7xeF1xHdUog5uprtGAirS+8iGulXkFHib\ni0Y3IOaXEUWBdNRLttjg4o0cArDTb5BvNhnpCUMOJuZca323v0V7YQFL1ym0S6S8H20854MGWXcA\nfwN8cXp6+vL73e9WG7MtbM3F2/FJzoVP8VA3GvSm0yTDn/wz+bjnwt5MgxSCrrvKq24SvSajNwQs\n0Q1ECphEFIVsW6AUlLBE3AbaQDLqZaPYpKHbP/X6s4brvkn3pRFlmZmNPIY4RDCqUAXSsThqJIxT\nq5AIRSEHStAhGbpzvHBk0x3kvPc8Ne0GmqzRnbmj0VOvtlE1mcbmguSzZNDM28e536/x7791hd5M\n6H09A6HsLjKd8QSDnR3gCFiGQCToIZkM0tsRYjlXZ3qxxEhvlEgtSx4Y3LedrgsSyxs1wgGVfZ0d\nrM+eh8IiuqWTCSU+0nfgg0oV/C+ABvzZ6OioAJSmp6efudtOGxvv9qv9KiKZDG7NxSY+6bmQBTe4\nZtZENvRP9pl8UnOhiSJtRUQCmlU3i2VtvUpRMhBF1+J2LBOf49LFWlwBQcDeNKT7UgE2ik3mV8o/\n9fpbxTKC5iFfbOI4Dtm1AmRAUtyDOG0JIRBCX11BNt1nspjdQGu/88tKlkTK1fZ7zlOxWSEg+2+P\ncRyHQq5OJO5jZskNZPoQWMrm2Ai4Y0zLXa2aLeN9PYOVgusGcloShXwDvxSiiYAiCmxsVIlu5vU7\nDuzoj7Jx4QX3PKluhjrrLG/U2DecQIy4XwsLZ8+BD/ziu9+BD0P4H4jgp6enn/7AZ9zCFn6OEFD8\nqJKKT/HeffAvKTRJQPdIeAGr7VrJjZZJXbVQZNd1ITo2wU2TfS3pErBju/76nnSQ89dz75kLb9Xr\nt/3vdrNB2b71peD68AOKDzkcpj0/R3Azc+ftLpRb8GnSe6ZJOo5DVa+9I8Bar7YxTZtw1Mu5tQqa\nJCBbAtXqneuVJRFZEmnp1nvM1B3UbruB3AUoIETIcScQnI7deZ/GhxO0X1hEUBTUri4O22XOX9/g\ngfEuPKKrp9O6fAUOfrRVrLAlNraFX3F8afQZ6kbzAyk//rJAlURamugS/GYAs9YyaEoOSSWIbTtI\nOIQ2c+JXEy7BY7r0EfarxEOe9yR4u1FHSbr531a9TlVy895F1U0t9Ct+pJAb3AxuptRX35blcgte\nTX5PH3zTbGE51juUQW9n0ES9VGdzeFURmg61+juv16NKt6tP74bqjxG8V3AXQm2zBWQm5t5fPOSh\nO+lnplRCjkQRxP+/vTePluM8zzt/tVfvd+u7YSVBogBSJCWSokhql0xLliwvsSIvkhJbsZOxncTj\nOXYSTU6ck5mJJyf28TnxzEkcO5GXjNfYlrxKlhdZlChaFCVqIUU2QBI77n5vr9Xdtc4fX1V19719\nAQgECIj3+53DA95Gd3V1AXjq7ed7v+dVOXZokv/0z0XOTRwXyR07Tve5Z7l1X4XJV11bgd+7f6sl\nEmC+MMeRicM3+jRuKLam0tMVNE3BS8Kymkl3TMkq4wMaUFRCNGCzktSFicCXcgbVCZum69MfUwHH\nQUDU66GmFXzHpaUnuT2G6BUvJBU8QKErbiRt7xsX+HTxs2QMCXxy4ylP2PT6YbbO4CafNbsOpkbf\nv7IKvuW3UVAoJJ/DQryfaojX768WmS7bvOU1ixBFhM1G1ik0jKIozP7AB4lUhTd/scWkenWRGbsh\nBV4i2eOYqkoAFCs2/Y4Q9lYi8JN2mSCOMRSwiJgc6jBRPVHJF/MG1QlhSaSZNMOErhDx1KIJO+2s\nBz7UhYgX9DxaIvB2N/kW4Y/Po/GDCD+Ixn6W7ZU1DDpo7JJFDOQtcd697uiNwjK1sTeo8e/ToWDk\n0VRhU5nJYBFFE9ctZ+n83I89zLsfOkzQbEIco0+O7zCyFhc5f+9Byp0I69NPXNH7XylS4CWSPU7a\nC29aOlFSHaf97RUjhx+DoYCpwIQ2JBl+sss1bw4EfoxNE6WbnLIKvkNLF8/3tSamZmJoBnpi0Ziu\n6Lhp72LRwO6tkuMEvp5kwph5IeypT+53R8XcNrRvyIMfzi/SIvF5Is3b8dxgS7RE6pXd7Zev3DNJ\nK6/S+au/wVtevqJzuBKkwEske5y0F141VIhjNFXJFjKLpk4A6CjkFIUJbWjZri92uRZzgwr+/Fdr\n9C9eHDl+WsGr+bSC79DSC+R0cON2ZnOkFo2e5NHsZtHAFQi8MbA6mltddEOF5HMWkliCYNvGW8vU\nCKM466jZjTAK6QTuyHtokbgWkTLmG0xDCLw2xqJJWQ9bPPXgAgQBq7/zm5d8/28EKfASyR7HzARe\nQ0ERPney2FrQNUJUTAUsBSb1QX4PngXE5G09E/izX/gKGx/9g5HjZzEFySYnYdHkmchpdAKXoiEE\nPl1kjVttbN0amZiUcrnAscyDTzY5xbFIkaxM5rLqvJBU8ngqYTSo2K1kF2/PC1n+yK9w8Zf+89j3\nSL9ZDH9LiEMjOeROgQ/qojVzN4vGjwKaXouOc4DCq1+Dt+0G+VKQXTQSyR7HTnx1NdnGnzc1mn2x\nh7Vo6Lgo2Q7XSTMPJKPvvDyqEaAqCtUJsb2+bpTwt06NHD/KdrEKIe+3OvS0Ocp5g3roUUgq4bSC\nDxoNylZxbAWfv0wFv7190e14BH5EZTKfvaZYMGkCum/S9jtUkpgKO1l87XY9Wk98njiKiIMARR+V\nyXE2UOSLm0M/3nlTyiyaifECv5WEjE3mJln80fcSR5f+BvGNICt4iWSPk1o0JAJvmRpeUu2WDY0o\nGboNMG0OMntCP4+iC885bxvktJi6USRsNkaOnyVJJh58K7FgcskkqUJSwau2jWJZhI06ZatE2+8Q\nx6MJlZfLo2luE99m1iJp000+UzFngh6jBVYm1iC6aADay6vEQQBRhL+2uuM9xnXqBL64dl2aO56f\nVfCXEfgpexJF01ANY+zzrgYp8BLJHsfMBF6U6bauEUUxcRhRMnQUdVDBFqxy1qfuBzlivY+fbGmd\n0nwaehG/0RgR5oHAC0HsJIuoRjKmrzDkZevlCkFTVPBhHNILR3Pmr8SDV1Cym0annfTZl6zsNbap\noVuDCj5F05P2zAtL2WPjFjzTbxbDOTnpzaMTN3Y8P6inFfx4D36znwr8te2BBynwEsmeJ+2iSRch\nraSSN2Ih/oo6iFW2jBJOp8D8agCxBrpHsy+q1omgTaDqtDEzWwYGFk1awbtJh45uixtK6sEDaJUK\nYbNJOamOW9tsmssJfNtvUzDy2ca1tNc9XzDpJa/JWTpGTkULTFr9Flu9Or/x9d/l0xcfFcdYWcuO\nN07gx1Xwbi8AJaLljxP4Omq+gGqaO34PGBr0ce1y4FOkwEskexwz8eAjTQhuWtHnY/Grpg4WVm2z\nzHuMV/H2T4uKVdE9GkmGT6WzCQgfPmgMhC5Lkky6aDp9IfBqkkMzUsFXKhDHTCRRBttbJfP2ZRZZ\nvXaWzw6jAp9W2TlTw8rpQMznLjzJv/u7/8jnl79IrIpjtlaHBH5lUM0PvweMjnns9AJ0I6IbdvFD\nf+T5QX1r1+odhjx4WcFLJJJrjZUIe5RYNHrycyrrxlA+uWWWsA4cwk2mYSmGT7PfJOr3KbeEMG73\n4bd78G4/WUTUhRAWhiv4pJOmksx63Z5Hc6kumiAKcIPuiPB2E4smXzSzqj9n6+TyJqv7TnKi+Tx5\nPc8Hjr+PexeOA9BcX0ctFkFRdrFodi6ydns+qXXeHAqti/p9Ite9MoG3rr3Ayy4aiWSPY2ticTFU\nk5T3xD43k18tfSDAqmZj3rIPVxOir+geda+Jv7rCRGJdiAq+nr0mcjugaSiWJXLeA3HgWPUgFIFv\nKWknTbGX+OG+O3KuOUuc6ziLJmtfHDreSAWfCrypUyzYtIxVVFR+5sGfwtZtwo0LPEGNsBdhHDhA\ntL6BfwUWTRzHdHoBpQmFPtDwWkznRIjY5RZYQVg0JaOIqV27xdUUWcFLJHscM6nYw+TXOAkV05MW\ncXOowlb1HPrkJN10nq3u0ey38JaXmfBF5VrXizssGq1QQFEUYs+jm9SVoSY6XIYr+FTgc7vEFQw8\n+J07Tse1L7odD91QMUw9s2hsS8coQD/fJh8XswlKRUt8Jk/VWa0omPPzhO0WYbu97X06aIpGLnmd\n50eEUZzdfIZHAaY3ut0q+DiO2ezXr4s9A1LgJZI9T7rImlbwJMOztcRJsfVBRaxqORRFoZtEDSi6\nT8Nr4i0vUQiFYLt6jnBI4KNOJ5vFGnba9BJPP1B2CrxWFP651R8fOHapRdaxAt/2yCfZ7Nkiq6lR\nN4WdZHuDz2YlbZKeavBlfRV1VqRfeiujVXzLa1Myi9lg7k4vzZ9JeuGHtshmHTS7bHLqBC5BFFzT\nOazDSIGXSPY4VrLI6iVqkE55Ign0Kg1Vl2oi7N2kRVAxPBr9Jt7yMkYcYqjQVc2sgo/jmNDtDCVJ\ndugnw849JQkaG/6GkBf/b3hpBT8q8LqmYhrq2D747QIfRTFd1yNfFDeUrhegqQqGrrIUi8HcpjsQ\n+LQP3ld0LhRDztrCHvKWRxdaW3571AbKvP1E4MNBHs1gk9P4Ct31xU0ud53mEUiBl0j2OGnXTLJX\nhzgR9vRXVRu096mJ9941k5hcLaTptfBWllF0nWLepKdZ2SJr1OtBFA0lSXboJm2XfTroioalDbp0\ntETg9UTAx8UV7BYZvN0b73V94pisgu/2w+wbwJneGbTAQG0PLSAnUQWeZtCeyvPF+Lz4eciH74ce\nXuiNLOSmN5t0AbgfDir48DIefC8Qff4aKvV+g3q/ke0ruBZc7UzWPGLg9iTQB/5hrVbb2U8kkUhu\nelRFwVQVfETFFydhW1GyGKqoYvGvH8coSX95N6m6i3FAo9/EX17CmJ2lkDNYa1pZBR+NyaHpaRaa\nEuNGomc9tTpgUMHT7aFP67vGFbRcf8fj2yt4tz1YYAVh69imxlp3nWbQpNycB08jjEI0VcMyEqsq\nX2KhssiZ3mmAkYXWcR00qUVTsA2It1Xw9TRobLzAp+f82NITPLYkooKn7Sn+3UP/cuS6XC1XW8H/\nCPBkrVZ7M/CbwL98yWcikUhuGJam0kv0JBV4P7FJ1GRubS+Osx2qXc1GjwKmOn3afgff62HOLVCw\nDfqqiZcIfLgthybsdOipJjldwQ06Iz3wAGpOPC/ouBSNwtjI4HyKzqbsAAAgAElEQVRSwW+PMdgh\n8J1BiyRAzwvIWTq1recBKDamk92sworR2+Kc/VyRCatC2wLFtkZ64Tv+YEBJSlrBF3Pim8iowNdB\nUdDLg4iHYVa66+K1RoH7Zu/hvtl7eOO+B6+JuMPVz2RNh20DHAS2rsnZSCSSG4KpqnhxSB4gqdzT\nThUlad/rRjF+5GNqJi4G+bBPaaMLMzlcW2Vmfp5CcjNwuz6R7++IKYgSgS+aKq2gx/5ifuQ8VNsG\nRSHsdCgZBVbcNbaTs3TCKMYLosxWgYFFUzS2CXzBJIrjZJqTxnObNQAK7jRRHNL221SsEqyIFMfA\nzDNhqaAoUJ3BX1ohjiIUVaUTJAI/tPCcCnzJNsGFXji6yKqVKyja4DyH2eiKzWG3T9zKh171/rHP\neSlcVuAdx/kQ8JNADCjJrz9Uq9W+6DjOXwF3AY9c8zOTSCQvG7am0vQDdEPFDyMUbZAJrwxV8N2g\nh6mZdGKNqbBHqS2e08mpmPPzFJtCUnqaSdhqih54yBZZg3aHnjbFjKnSgh0VvKKqqLkcQadD0Zzm\nXPsiXuhhDq0DDHfSDAt822tjqgZ2EmncHRL4vhcSA7alcXLrBSatCXLk6Pse3cQHDy6eR4ty+LrJ\nRNIy6U+X0c9dwF9fx5ydxc0q+MGiaGrRlPM2bA48+DiOCep1zMXBAPDtbPWv3y5WuAKBr9VqHwE+\nssvvfYvjOA7wZ8BtlztWtVq63FP2DPJaDJDXYsCNuhZF2+CC28eydLpBhJHMJ61WS/Q6PZZr0Ish\nX9Yo2Tn8WCEf9Sm6ospv51Rmjx2h+rwQ/K5mUVIDVEX8/uT8DNVqidXAJ1JUikWDNWCmVNnxmc8U\niwQdl+nSBGyCWYJqYfCcqSR73s5bI6/tBC6VXDl7LE7aPRf3T2AmnTSGHdMJXF574B46z+kEroKZ\nV6hWS2ysLWPG+wn1Iger0/A8hHNldCDXrTNVPYLaEPbV/PTU4H2SLqRDi1NwHtAiqtUSfqtF7PsU\n5qq7/rl2I3EDPFxdvC5/9le7yPph4HytVvsfQAe4omXftbXW5Z+0B6hWS/JaJMhrMeBGXgslEcNY\nU1D8CNPSaHU81tZaxJFOSy3wvL+Bs7ZJMxZVcy7sUUpiBxpFDdcqo8QbAHRVi/XTF/FWxM+dUIW1\nFusbTWABJUlu1EJz52e2bML1NYxIVO1nllegPKjglcR7v7DcwE47f+KYRq/JvtJidryNNSGefT9g\ndUlU6Y3+FhTgcO4wz5kt1Fjl4soWB80WrRdPY1oLuH6E2hfvt2ar7AfWa6cIDx1lJWl7DF118D5b\noqqPk3Xfptthba1F/4LowglzxV3/XLdcEdSWD3f/s38pwn+1i6z/HfgBx3E+hVhk/aGrPgOJRHLD\nSYd+oKmoQYxtabjJQqai6pwt38MzXkA36GXzWvNhL6vgG1M5tGKRgp1aNKKTJp3mlFo0HVfYJulg\nqOHFyhQ1nyfsdilq4ve2L7RmFs1QL3wv7BHE4WjCY2LR5PJGtsmpFQpL5OjkbVhJHr3b7hH5Ht7y\nEpau0PdCJpKNR+uJtqYLrekia37Iokk9+LJtoipqtsh6uUlOAG4g+uCn7aldn/NSuNpF1lXg267x\nuUgkkhtE2gsf6QpKGJOzdOJYjK/LWXq2nb8X9Ai6iXCGfQqhDnFMY2ownxXEZqew2SDsCEFM++A7\nvQBMUMxkfN44gc8J8SwnY/C2t0qOCxwbv4u1j50z0DSVbtIRtOmvcbi4SMUqYed1wKfT6eOdPw9x\njGVqbHghFauEgsJyTrwu7YVPNyYNn3en56MAOdvA1qzMg7/cJidxPcVz83Kjk0QiuV6kcQWBqqAw\nJKJJdWonm5G6QS/rQc+HfcxiiVInYisvmuoKdiLwSQUfbeuicbPOHHGM4rZFVhhsdipGosLevtlp\nXOBYmhu/PYcma5FMs2s0n3fdInpCimXxmdxWgFt7Thy7kCOMYohVimaBzaiNPjWdCXzaRZMfCmBz\n+wF5W0dVFCzNGqrgLz2qrxf0COMkwngosfNaIgVeIpEMdrMmzc9Fc7RKTgWoF/ZpJwKfC3uo+TyT\nrZCWHtAL+hSSCr6nWoTDFk0i2m4S+BVryaSlXSwagFyytbazLVEybyU3kaHAsVYS0ZsKfOCHeP0w\n2+R0aku0QM4WJ7l75g7x3IqomvudEPfZr4tjTwhPpueFTFoV6v2mCB1r1Am7XVzfRUHJOnVA3ART\n28jSzEEFX7900NhmEhOsoqKp49soXypS4CUSCVYSNNZLsoILSS6Lm7QADls0re6ggtcKRSZa4iaw\n1l0fePC6TdCoE3Y6qLkciqoS+R7dOI0mFoue4y0a8Vg+KdC3Z8KnFbzbH+xmHY4paPSbfO7ck0RK\nmPTARzx58WkAXn/gNdkmoomKeB+/6dM9eQJzcZFcXnxO4cNP4Ec+yuyMeN7KMp2gS97IZROjQFg0\n6TcXa9iiuUwFv9ETPfCmdv1S26XASySSEYsGoJQOoE7EPK3ghxdZc1EPNZfntrd/FwCr7lrmwffM\nPEGzQeS6Q5ucXHpJDk2gpl727haNleh3a9dF1uEKfuDB//mpv+R3T/8BJ+9+lLXCeZ5c+TJbST/+\noYn57DUTE+K88qubxJ5H/tgd2Mk3l54fMmGJ3af+pKjq/bU1XN+lMGTPBGGE50fZpClLM/GjgDAK\nCep1FF3PFpi3s5GM6rO062PPgBR4iUTCwKKJk6lOBVMIdTOxY1IPvhf2aCWdMPmwT9R1mZ/aD8Cq\nu4FpaBi6Ss/IEdbrhJ12JnBhp0MvOY6ndFBQxnrPqUWj931URb2iqU7DHvxaV7RmBkaPz/CX/I9n\nfw8lFDcW2xpUy+V8gVANmFxfFcc9fjyLDB7upHETr95bW8X1XfJjYgoygdeTpMzIS0b1Te4aO5Du\nYs3r12eBFaTASyQSRBYNQJQN3hai1ExaDQcWzcCDLxgKUbfLbK4KwGpXxAoUbJ2uahIHAbHnbcuC\nT5MkR4djD5NW8LHbo2QUsuo8JRXT0UXWgQe/1auTU3Lc9rU3cYt5K1EcsZgTN6H8kMCbmkFg9phs\nrIKikHOOYSc7Y3tekAl8s5ikRK6uEMThSMfLIGgsreCTG6HXJWw0LtkiuZ4IfNGfoD8m/vhaIAVe\nIpFkFk2cTHVK++JTgR+2aJquRzFnoOdyRK7LlD2BqqisuiI4q5AzsqlNwEgWfCrwvbg11n+HgQcf\ndl1KZonmti4a2xxTwfttFBTyWo7Nfp08Rax+ge/b/738Hw99mKoprJnhCl5RFDA6VLrrmAcOoeUL\nYyv4zbxYl+ivrYjPp4+r4AcePEBvax3iGK2ye4vkRncTYjAfv4VPfuyZXZ/3UpACL5FIMNW0gleS\nn0creEM1UBWVXtij3vaoFE2xIcl10VSNam6a1SQYrGgb9GKNCHEMbZtFoyskUcHjvenUoolcl5JZ\nxAu9kYRGVVWwTW1Hm2TByNMNewRRgB2KY+eLJtO5SXpJ907OHO1WmfTWUInQbrkdGEx16vkhE7YQ\n+K2og1YuE6yLG9iIRZOcQ2HIgwfoXUgGiiws7HrNN/t11FBHiVTOn95i+UJj1+deLVLgJRJJZtHE\nya86IkyxmfjtiqKQ02w6/T7dfkClYKLmckRdlziOmc3P4AZd2n5nqFVSiN1A4IVFkzPE3NeKOX4L\nvrZN4IEdNs3w0I84jml6TYpmkc1k4dL0xDeOLAt+aJrTMNW2EO1w8VZgMNVpuILf6jcwqrPEW1so\nUUxB32nR5K1Ri8ZPYgqs/fvHfsZe0KMbdDG8wbG+9LkzY5/7UpACL5FIBhZN4r2HQUQpZ2QVPICt\nW7hdYVdUCpYQ4jgm7vcGPrw71CqZVLMjFo1mkqzfMl+YHXsuaQUfdt0semCcD58K/LK7Sjfosb+4\nwFbSW651bVRNwUrPJZnmtH3Bs1rfIELFLYtK2zaS53shlmaS03M0+k2MmSpEMUU32mWRNbVoxGcO\nL4poA2v/gbGfMe2Bz3XKaHbMwv4KZ17YZG352mYRSYGXSCSDqILEovG9kHLBzCp4EAut3V4i8EUz\nixQI3S7VvOgVX3XXsgq+mwzX1oajglUTzRB2yVx+F4G3c6Ao2yr4UeHLWXqWlXNi6wUAjk4cYTOJ\n31Vck3zBzAS96wVZdZ4SttuUGy0adpWmK4Q68+D9JAXTqiQVvPh8lXa4LaZgu0WTbIBaWkUxTYzq\n+M+Y9sBbvSLFRZX7Xn8IgC89fnbs868WKfASiQRNUTBUJbNoUoHv9kP8QIidrdn0u8kmoYI58Mq7\nLnOZwK+PBI7BQODdTpdYUUEXorhbBa+oKlo+R+i6lBMbZ3tcQX4oKycV+Nsnj2QVfNw0MnsGRMdN\nzhrdUOSeqKEAW/kFGg3Rl5/eBHpJdk3FKtMNuihTIgys0g5H2hrTwLPckAevhjHq2ibWvv0o6niJ\nTXvgzX6OyX0W+w9PUp0v8WJtjc31nVOsrhYp8BKJBBALrdsreIBmJ93sZBH74rFy0UTLDbzy2Xxq\n0Qw2O3UTgVeTjU6djtjh6evi1/Q149ALBSLXzYZb77BoErHu9DxO1l9gwqpQzU1n1ofetTOBH57m\nNEw3yZ/Zyi3QaomdtekAkb43qOABekmsQbmzvYIfmscKWLrFZCtECSPMXfx3gM1ussu1n2PuUBFF\nUbjvYVHFP3UNq3gp8BKJBBALrakH7/sh5Xwi8O6gFz72hWhPFKyRdsaKWcZUDVa765nY9SxRuWdJ\nkkkKZV9tM2lNZH71OLR8nqjrUk4EvjlmkRXgbH2Vju9ydPIIiqKw1aujKxpaYGZBY4NpTqMVfO/F\nF4hUhc1CCbeZfMahLhqASiLw7aL4TJV2eMmNTrZmMVNP7J5d/HeANVdsxgo1n1Iyr/bw7dNMVQuc\nfn5919d9o1y/EASJRPJNxULeIpfMsfaGKvhGZ1jgRcWatkmCqOAVRWE2X2XVXSM/m1TBebHVX002\nOnX6ARhiF+tu9kyKXigQdbsUk57znXk0QrpOrotq9+jEEQA2+1uU9LLYJVtIh20LsR7e5BT5Hr2z\nZ/DmJunbPnrbJ47jkS4aGFTwdTuioirCg9d3tknmh8LGrkTgV5rrKJFKt1jP9hgoisK7//5dWY79\ntUBW8BKJBIDvPzLPP3nVQRQlsWjSCj7d7KTZxJ6o4CsFCy1ZZI26wr+u5mfwIp9YExaMZxdBVbMK\nfjDEO2B+lwXWFD15TSEQgtvcscgqHj+9JbpVjk4ewQ99Wl6boiJ8+6xFMhHh4Qq+f/YshCH+/jk8\nq0sUQq/rY2Y7WcW5pr3wdb9Ft2RRbocj8Qqdno9laOjpDmDNYmYrEfh9u1s0W14ds5+jU9kgN+Tp\nF8s2swvlS16bbwQp8BKJBABVUdA1FcPU8L0gq+Bb2ywaXRMCm7UzuiLOdy4nFlqbkegQCQ8cYeEf\n/yiqKWIL3DBpUdR95gq7++8wsHWUfp+Cnt8ROJa2JV5orDFtTzKdm2KrLzYK5aJkk9M2gR/24Hun\nXgQgPrCIb4obVLvZR9dUdE3NumjSXvh6v0GrZJDvxyjeoMJud/1szQFEBT9dD+gXbbTiIJt+mK7f\nw1P6GF4Ot1C/blnw8BIF3nGcY47j1B3H2d1Mk0gk31QYhpYssgrhGlg0FrFvkc+pKIqStUmmFXxa\n7f7hqY8CYqNT6f7XAsKnTzc+Kbq/a4uk2/H4xB8+TZA8N0wWWne2SabtjDG3Twp7Ju2gsQJxXqkH\nn05zGq7gey+Kzhv18AF8UyywtpviV9vUBhV8JvBNGgUhl96a2LEbxzEt16eUHwi80fUpdSPaM+N3\n6QKcXRXfOnRFBzUeyZa/1ly1wDuOUwJ+Huhdu9ORSCQ3GsPURhdZU4FXLfBN7MRRGN5xCqAkctIJ\nW6BErLcHopxuchIv9Hf14E+fXOfUiXVWN73s2GWzSMd3CaNBPHA69CMOjCH/XQi80U8EvjA6zWmk\ngn/xRdRiEWt2LqvgW01hLVmGRj+5KeT1HIZqUO/Xs0yaT/zqo/zp736FpaUmfhBRHBJ4loX4N6ZG\nEyLjOGaju8nZ5nk+98JT4noZMbZmjQ1cu1a8lEXWXwY+DPzRNToXiURyE2CYGu1Wf6hNUohtHFiA\nimULodtu0QRRsunHyNHXfVaaPR678Hlev+91IocmqcpzljYyHHuYZl2I7WYrZA7RY5+2Srb9DpUk\noz2tmmPf5GhWwYvWQ8UV75NV8KlFk1TwQbOJv75G/lV3g27jW2kFLwTetjTqLfH/iqIwYZXZ6jWY\ndQ8Az2C4Dc6e2uKFU8KKsjWVMIzQNJXwopgctTU1MDWiOOKXvvprPLPx3MhnDQruiP9+PbiswDuO\n8yHgJ4F46OGzwG/XarWvOY4zPuxYIpF8U2IYGoEfoSoKeUvPZrCGnqiAjWRgdtomGXWFwNcTD9wN\nukwVC2w2u/zOiY9y39yrkxwaYUXMlcq7ZqQ360JsN5sRc4ibR7k6aJVMBb5gi3Ox4iKTtkhsTC0a\nWiaWraPryezWNGgsEfjUnskdOYKi20MefGLRGMKi+dLjIhtG79u0wg009zjwDHfdnudVb7mLj3/y\nBDS6nDu5zi//3KPYOZ3jq19mCtiYGFT1nz7/OZ7ZeI6DpX2Ue1VWT7ncfnAfX1Iep6TvbuVcCy4r\n8LVa7SPAR4YfcxznBPCPHMf5YWAe+CTwlssdq1odHy60F5HXYoC8FgNuhmtRKNlAg4lKnsmyTcv1\nqFZLmKeFGNnFiGq1RBzHvKDrqH6farVEvbaVHcOyY+ItgzCKCO0eBSXMLJrDs/O7fs5u8m3BT6r9\nvBoxPzEN50HLh9n7Pr76BXGu6kR2rPYzopUyamhMTuSyx9Wkw2V+tkS1WsJdEUFgc6++E392msDo\ngxLT7wZUqyXyOYMwinn80y+iouDeGsMMXFyswznIeS3uePAwYdHiiV99gv0LFQ4XTOqbXbT6KqGi\nsFVRqVZLLLVW+eMXP07JLPDhN/44v/4LX8DoBfzwP3k7H/rzv6WUK1zXP/OrsmhqtdrR9P8dxzkF\nPHIlr1tbu7ZBOt+sVKsleS0S5LUYcLNcizgWX9aXLtYp2DoX19osrzTY2BC2RaT0svNUczm8Zpu1\ntRbntpawNUsM5g4bQBECgxMXz3Lo/DI91URRAqbNyV0/58aa6JZJF1kbq5uoB0UQ2LnVVSyvwG89\n9wc8t3US1EfQgkJ2rJXWOgW9gN8Fa17PHl/fEt8w+l2PtbUWm08/K36eWqDXCEAB7JCtzQ5ray3a\nW6KiP/bqRW4/WuXTW5v8XeMi5249SfgVk87FZdbWWiytiuPf9eoF3nTPIj3X49QX/isdu0In6LOy\n2uA/felX8UKfDxx7H1/93ArtZp9Xv+4Am60mcRxjxMZl/8xfyg3gWrj7MSBtGonkFYKRLEb6vtjs\nFAMt1ydplgF90Feh5vOEXbEAutbdYK4wy62VQ3SiJBMmNFjrrhNsbdLTLFTNZ26XiIJ+L6DfC1g8\nUCE0hJ0TJUM/AJ6vn+L/euIXeG7rJHdMO0wVc/TFPYc4jtns1akYwsLJFwedKWmuTN7SiaOI3ulT\nGPPzaIVCNoowsj3ctkev69NObKJX3b+Pg7dO8ZpDx8WBFAgnS/jra8RRlFlXpaRNUm1uosUhbX2a\nqK3xqXOf5cXGaV4zezevnrmLp/7uLJqucs8DB+gGiR10HVsk4RoIfK1Wu7VWq127rVcSieSGkgm8\nF1JOFjObHS+LCo71bvZcNZcn6nbZ6G0SxiHz+VleM3s3ip5IQmCw5q4TbNXFGD89YG6XDppWEvg1\nVS0wfUDcBPxWJ4sreG7zJF7o8Q+Ofy8/dveHqBQsWq7YgdrxXfzIp6gmm5yKw0FjSViapeMtLxF1\nu+RuEQuzmqphqDqh1SeO4fOPnoIoEqee1K3O1O0YqjA74skJYt8nbDZoJdELpaTbqH/+HABtcwqt\nY/PHL36CklHk+45+NyeeFtX7HfcskC+YmcBf70VWudFJIpGMYBhDAl8Y5NG0ktCx0BgIvJbLE3se\nK03R2z2br/Ka6l2gi+cSmKx1N/C2NumrJhg+M/bU2PdNF1jLEzn23y5sme5mI4sMbnpNZnLTvG7h\nPhRFoZQ3CcKInhey2Rf+fz4Wzy0MC7w32OiULrDatx7Jft/W7KwX/utPXcyik9O4AkPVs523aeiY\nv7aeVfBpm2Tz7z4HwLl9PZpTK6io/OCd34+l2Dz52GlUTeHVDx4U55QJ/E1ewUskklcWhjVG4Dte\nsuEpJlAGuTBqXgje2pZoD5zLV5m0J6gWhVViU2Ktu0G73gZFwbZUNHU01TElbZEsVWwOHlsEoN9o\nZxZNEIccKO3Lnp9aIy3Xyzpo7GSTU2HIoun2B9Ocei+KHaz2rbdmv2/pFj1jsFN2cVGce9oLDzCT\nmxafMy+qe399NRs+XsobuCdqdL78FM19Uzx95zJ63+b9h7+XY1O389TjZ2k3+9zzwAGKJXFe3UB8\nVinwEonkZSWr4P2QSrbZyafR7qMaAb1oyINPWiU3t5YBMn/9thkhxGpk0+zWcTviNWnS5DiajUEF\nf/DWaQLVIOh0sDQTXRHndHBY4PNplIKfxQSnI/CGLZpeP8Q2NRRFoXfqBRTTHMmJyWkWPUPctAxT\n4+Atk+J1/mBjVc4Qxz2liVZQb2WFVtdDUxVyhsra7/0OAH9+Z0yeEke+/jCdMxqNLZenPn+WQsni\nvocODc5JVvASieRGkHrwnhdQGrJoGh0P3QrohQOBT3ezNhprKCjZZKc750SF3OvH5LsR3aQrppzb\nXdAGFo2NYepEhgX9Lv1egKGJG8PB0kCYS4W0gvezCl7tigp5pIL3xLCPOAjoX7yItX8/ij5oILR1\nm6a9iaar3PvQQQqpp+4NBL4fiNXc04UeaBpbn/wEza02xZxB+8kv0D99ihOHc/T3zXDv7N1ogcn5\nk3Ue+6vnicKYh992JLuuAN3kGuY0KfASieRlxDR3WjRr9S49L8S0IvqhRxQLqyLNo+k0N5i2J7PF\nyPmyqIK9PhTdKNvFWi3tnpTYrHexczpmsiFJzeXRI4/l841sm+X+4mL2/FJuEIaWxhTQGt3FCmKR\nNWfpeKurEIaYi4NvASAydgKzx/f/0/u496FD2NsSJQE6vmi1dHMaF77nDRDHNFs98rHH+h/+TyJN\n5bN35/n2W99BqZCjU16nue5x5oVNFg9OcOTYaOdQ108E3pCLrBKJ5GUkrTQDb2DRnFsVFoadxBT0\nkoo2S0xsuyPdMWnC4qy1SLEbZuP7jkwPBHqYOI5pNXqUJwaCZ5aK6JHPhTObeJHwu4d3wKZxBc3E\ng9cUjaCpYOcMtHSIeBzT6wfkTA1v6YI47sLoOaRzVAPEe9jmYPB2ihu4GIqOruo8Vtlk/n/9Kfqa\nibG+RLCxwVNHbUpz+3jd/L1YmkVjaik5X3jjI7fv2LmbVfDSopFIJC8nupFaNCGWqWEZGmvJ5p9c\nTghVatOYiZdd3Rrtb0+99qIyQb4T84UJ0Uu+MD1+a36n1ScKY8oTA8GzJ0oowNKZFcJYiO3w6L5h\nD36rt8WkVcFt+yMdNL2haU5ekhNj7dtewdvJZ0rCxrYN3gbo+F0KZoHjU0dZ6qywPJl066gh/ZzB\nF+7M8923vRtVUbE0k+bkClZZ5f7XH2aquvMzZ33w0qKRSCQvJ2ZSwfqJwJXyRhZEVcgl+S6pQB08\nSKzA7GYwMmPVNFR0TcHzFFbax7hoV3ntwQJ3HL50i2RpqILXismw7o1G9thwbHDao1/vdGl4Labt\naXwvJF8a3uQ0mObkXUwr+FGBT33wdOFz+1QnEBV8wciLFlDgt57+EwCCOxb59W+rcMucw/EpscHf\n0iwiPeDYd9nc/4bDYz9v2kWTlxaNRCJ5ORne6ARQKQwq4mIh8acTi0a1c3iTJWa3Aubsmex5iqJQ\nyBksbbicjo4x4bd4/9sHvefbyTpoKoOKNh3qjdtHicQ3h+HBH2kFv94SVf2sKSyikR74oWlO/YsX\nUSwbfWr0JpNaNFkFn3rwyQ0ujEK6QY+8nuPu6p0cLO1nqSE8/1O9F+nZGt9127syGyadNdsPd9//\nKSt4iURyQ9gu8OUhgU+HgKQVKEC9msfyY6bdUTkp2gaeH6EQ856VR7Gmds9USXvghz34NI5Yjzy0\nZNj38Og+y9QwdZV6RwjzlCJuMPkxm5xsQ8VfWcZcWNjhh6cDN9Kb1qCCD5LPmrR4Gnlyus2/fO0/\n5/23/QAAr5q/lQ8c//sj/flWcrx+csMYRzfoZXbO9UQKvEQiGWG7wKeVMsBEYbTaBbg4IQTTWFod\nOU4hWWh9fedpysoWG/0tdqM11CKZkrZg6qFHORRdOa1tw7dLeYNOVwhx+pzhFsl6S1TR+ahPHARY\n2zpoYMiD32bRpPZOJxAdNPmhYdueJ6TzwQN38eDC/SPHS78RXKqC7wU9bM3aNTb5WiEFXiKRjKBp\nKqqqjK3gJ0qjYhhGIadLQsj6Z86MHOedrzvIux88yMOrX6WdV1nrru/6ns1GF0WBYnkgzsMV/HQs\n7JedAm/i9RU0RcfqJoO6hyr45U1h6UwH4nXm4s4untw2i2b74G03aZEsGAOBT+fUlnI7N24NLJrd\nK3g36F73HBqQAi+RSMaQju2DgQdvGRqVpO89tS02elssTwgZ6Z05PXKMV982w3e+pooaRbTzGmvd\njV3fr1nvUSzbqOpAktJdskbkUemLqIDtAl/MG8SRyoK9SDfJyhlOklzeFOI82RGj9MYJvLXNotk+\neDvtgR9eEM2SJPM7LZasgg8uXcFf7xZJkAIvkUjGYJjajgq+UjCzRcG02l111/BMFW+qRP/smSxL\nPiWoC1umnVNZc8dX8IEf4ra9EXsGhiyayMPulVAVdYfA64Y4x6qxQKctBHW0gnfRVIXihuigsRbG\nWDTZZxrs0LVNLeuicZP1hsKQRdPqDnJotnO5Cj6KI3phX7UZl4EAABQSSURBVAq8RCK5MQiBT7zt\nRMQqRTMTpdSiObEl0hnV/fuIXBd/bW3kOMFWIvB5ldVdKvjWUAbNMKlFo8ZdwrZKySiOtEkCxEk2\nfUWfxU3mqOaGLKXlDZfqRI5w6QKKaaJPT+94/1xWwQ8E3jK0LEd+UMEPBL6dWDSFsRbNznWKYXov\nU1QwSIGXSCRjGFvBF61sQbIb9OgGXR67+HlKZpHZo6I/vH/29Mhxgi0xmDqulHat4JtjFlhhyINX\nunSaHiWzQNMfreD7ihD8ojJFp+2Ryw92sbZcj04vYH4qh7e0hLmwiKLulLxxglwumDQ6HlEc005a\nMwvbLJq8paNrO49nqDoKyq6LrC9XVDBIgZdIJGMwDI0wjAnDiPmpPN/x+sO844EDg5bCsM9jF5+g\nF/Z5y/43kD90i3j89OmR46QVvDE5Rb3fwA/9He81rkUSoKuJvBtTCYiimJxawAu9EeF0Ef3oVlTE\n7XgjHTSp/161IQ6Csf47DHfRDAR+pmIThDGNtsdKR3QHVXODPv+W6421Z0DsAbB1a1eL5uWa5gRX\nOZMVwHGc88CJ5MfHa7Xav742pySRSG40WR6NH2LZBt/1RpEOGUaiqm97HT517rOYmskb9z2I5Qvv\nfXsnTSrwhek54u46671NFgpzI88ZxASPCt5S2EAFKm4Du9jGiEUf/YuN0xyfOkocx9TDNWCWTidK\ndrGO2jMAM7H41VoYL/CpZz7swU8nG642Gj2W3FVszWbCqgCIqr4bMDuZ33mw7JjWTVHBX5XAO45z\nBPhirVb7zmt8PhKJ5CYgiyvwhMCniBF3Bi82ThMT85b9rxftgwYY1Vl6Z04Tx3HW3+0nFk1ldh+c\neYZVd32HwNeTSnt7BX+hu8L60RyvOdHhgXN/TOPsO3hqEv723Gc5PnWUpteirzQB2ExuEuMq+Mme\nuMlsT5FMURVVDAvfVsEDrNY7rLprHCodyD6T2wuI4jgLVBuHpZnZ4ux2Xq5hH3D1Fs19wH7Hcf7G\ncZw/dRzn6LU8KYlEcmPRt212GsbWLWJiFBTeeuCN2ePWocNEbodgfeC1B/UttGKJxYkDAJxqjFb4\ncRyzcqFJecLG3iaYF9pLPHpfkeAd70GNA2Y+/id827MaT68/y6q7xvn2RUhmvzaSBdZ8YbSDBqCS\nTJvaTeBBVNzDHvx0WYjv2Y1NojhiYSgpM+uB38WiEcczvzkqeMdxPgT8JCKRWUl+/XHgZ2u12h84\njvN64P8DHrieJyqRSF4+jKFEye3kdJuW1+be2buZyQ1yXexDh2k/+QS9s6cxqlXiOCbY2sKcneO2\niVvQFY3ntk6OHGtzrUO/F3D4tp3dLRfaSxiawYF3vIuPfR3u3fgMR59awm/Y/O2+x5iwKyiGENGm\n6zENFEqjAp+3dIzlc/iGgTEzs+M9snPXbTpDOTepRXNhswkVRr51tLu798CnWJqFl+Tmq8poHZ0u\n2g7vjL1eXFbga7XaR4CPDD/mOE4OCJLff8xxnIUrebNqdfcsir2GvBYD5LUYcLNci4nEX87nzB3n\nVLILrLrrvPeeb6M6lC9j3H2M9T8AbW2JarVE0O4Q9/vk56vsn5/GqR7h66snsUpQtsXrzpwUrZNH\n75gfeZ8gCll2VzlU2cctt1bpFau8cOB93Lf619z5/Auc+MNPcuY77gc1xNBV3H7INLCwOEG1WiIM\nI9bqXW7dVyE4uUR+/35m5yq7ft6SnWezt5mdQyHZsbvV7kEFnMVbst97fll08izMFnf98yrl8lCH\n8qRFzhit1DdPic98x/5bqFau75/31S6y/gywCfyc4zj3AOeu5EVra63LP2kPUK2W5LVIkNdiwM10\nLdKi89yZTUqTowL1yP63cs/UOuVwauR8w8k50DSW/+Zvsd/yrfgbQsiivPhcR4q38szqCR57/svc\nP/dqAE48uwJAoWKNHKtntgiigDl7lvX1NqWKzVqjz9w/+9/4+n/8txx9cZ34o5+n8MZZ4pxBqysq\n+SAKWVtrsbLpEoQxUyZEnoc6O3/Ja6vFOn4UsLSyhZ5MpSrYOluNLuyDfDD4s7mwnMQXh9Gux1RC\n8Q3owsoGFWt0itUL62fRVR29l2PNu/yf90u56V+tB/8fgDc5jvMp4OeBH7zqM5BIJDcd1Xkx0GJt\neacA3TVzB28/+KYdj2v5AlPvfBfB5iYbf/yxrAdenxQ2zrGp2wF4blPYNHEcs3SugZ03mJgaXWA9\nUz8PwGJRmAPliRxeP8RXDA7/1Ie5WDVxzvR55Kk+pbyZRfumi6xLif8+1RdibB08eMnPu33oBwib\npudqWOqggwYuHVOQMggcG22VDKOQpc4Ki4U5NFUb99JrylVV8LVarQG85xqfi0QiuUmYnCmgagpr\ny+3LP3mIqXe/h9YTn2frrz5J5Ash1CdFyuOB0j4KRp7nNk8SxzHtZp9Oq88tR2d2pCqeqYtogX2J\nwFeSDptmvcfc4jTL3/82zN/4Sw4/s8qXH+gQxhASk0vijNMWyeLpr4OiUHrgwUuet60N8miKhggt\nmy7bnF1pM2uORgy3LxFTkGLp4zPhV9w1gihgX3F8y+a1Rm50kkgkO9A0lelqkY21NmEYXfHrVNNk\n9gP/AKKIxqf+GgB9Qgi8qqg4k7ex1a+z4q6xdE5sUlrYv9MbP9sYFfhyYhOlm6LeftsjPPWOY2CZ\n6OdEXIKRM7KwsrSDpnThJIVX3YWR3GR2Y3tkMECuID53hdG2zkslSabsFhl8sb008rmuN1LgJRLJ\nWKrzRaIwZmvd/YZeV7jzVZRe91D2c2rRwKhNs3Re2CcLB3YK/On6eSasShbRm/bIN5PZsNX8ND/+\nyE+z8KEfIeeJ8zOsQZW9vOmiEDPpNym/YaedtB17TFyBaiUDQKLRCVBXZtGMDxw7LwVeIpHcDMzM\nicW9cT785ai+7/sGWTJD1fOxSbFl5rmtEyydb6AbKjNzxZHXtr0OW93GiAiWhyyaYUr3vZb8/Lw4\n342niSNRdS9vdKgELlaxQPGeV1/2fO0xgWOBLjZRqf7o+bVcH1NXs+Hc49itgr+QCPx+KfASieRG\nki60rq984wKvVyos/tOfYO4HP4SWGyygTucmmc3P8PzqWbbWXeb3VUYy4AEudnZWuems1kZ95+7Q\nTlksoHrrq2z8yR/h9gKars9Uv075wYdR9MsvNW6PQQZwVdEFFPSskee2u7vn0KRkFXwwWsFfaF9k\n0poYSaa8nkiBl0gkY5mqFlBVhbWVb2yhNSV/1KEyxh45NnkUvZEsZC7meHzpST5z4fEsS/5CexmA\nfYX57DWarlIsW5kHn+L1A5ob4rF+eZrNP/kjXnj078T5+w3Kb7y8PQPjK/hGLM6j3Rldg2i5PsXc\npWepjkuobHltGl6LfcX53V52zbnqsDGJRPLKRtc1JmfybKy0iaJoR6V9tRybup1aS7RQ/tHmx2g/\nK6IN/NDnbQffJCIIgH2l0U6T8kSOi2fr+H6Y7bQ9fXIdLRI3BuW+h1E3v8bJv/hbqD7MXNkaO4N1\nHNs9+DAKWfdWULWQjcZA9PteiBdEl63g7TEWzYXMf395OmhAVvASieQSVOdKBEFEfWN8cNbVcHTy\nCKX2NLESMbNQ4D23vpOyWeKjL/w5J7de4GJ7CV3Vmc2NRgvMLog1ga89eT577IXn1rIqtauaTPzw\nj/F8Toj6obtuv+Jz2t5Fs9pdJyIkl4/ZaA4E/kpyaGC4TXJQwV94mRdYQQq8RCK5BNX5ZKH1Knz4\n3VB8jZxbYWauyE+/7sd55+G38Y9e9QEA/vvTv8nFzgoHygs7NgLd+9BB8gWTJz97mvqmS78XcPbU\nJrPTwu55+vQm//ZvtniueIiy4nHsTfdf8TlN21MoKDx64XEutpdZ6ogdthMlg24/xO2JzpkT55PW\nzuQ9d2PcIuvLvcAKUuAlEsklmLnEjtar5bmvLhNHcPT4QOhum7iF77ntPbT8NkEUcHBip7Vi2QZv\neOR2wjDm0x+vcfrkOlEY4xyvYuoqjbaHril8z5tv5Wd/4lsoVoo7jrEb07lJ3n/svXR8l1986pf5\n6tozAFQnxGLoemLTPPmcGEl4n1O95PFSgd/q1bPHLrSXMFSDan730LNrjfTgJRLJrkzPFlEUWP8G\nd7TuRhzHPPPUBTRd5djdo4uNb97/MKebZ/nCylPcMnlg7OtvdWY4fPs0p09usJn05x85PseHpvN0\nuj4P37WAZVxdBMBDi6/Fj3x+98TH+MLKUwDsn5rgy3TYaPSoTuR4+tQG+6uFy1bwU/YEi4V5vrr+\nDM9unuDoxBGWOyvsKy7uSJe8nsgKXiKR7IphaExM51lfbWddLi+Fc6e2aNZ73H58dkf+u6IovP/Y\ne/mHd3wfb7v19WNfrygKb/zWo5iWRq/rMz1bYHI6zwPH53jrvfuvWtxT3rT/Yf7ebd8OiIXS/dMT\ngKjgv/z8OkEYc78ze6lDAGLX7gfveB+qovKbz/4+p5vnCOLwZfXfQVbwEonkMlTnSmytu9Q3u0xO\nv7T+7We+JCII7rx3fCeJoRk8MH8vtm7RYvzAjGLJ4sG3HOHRvzjB7XfOjX3OS+HtB99EwcijKioz\nkejh32j2eO6smAx1/7HLCzzAwdJ+3nHobXz89F/xa1//bQD2laTASySSm4iZ+SInnllhfaX1kgS+\n1ehx5oUNZhdKzC6UL/+CS3DnaxaZXSgxPXtpq+RqeXBBLNA2OuImc2GtTe1cg8WZAoszV/6e7zz8\nNr66/szQAuvL1yIJ0qKRSCSXIe2kefpLF7l4tn7VVs0zX75IHAtxvlbnda1683ejnDcwdJVnTm8R\nhBH3X2ZxdTu6qvPB49+b+e6LhZdvkxPICl4ikVyG2YUSs4slls83+KPf+jKVyRzH7p7ntuOzOwZl\n70YYRDz7lSUsW+e241dmcdwMKIrCdNnO0ilfe4X2zDAHSot88Pj7qPca5I0ru17XCinwEonkkui6\nxt/74L1cPFvnua8u80Jtjc9/+hSf//Qp5veVuf3OOY7fvYCmj1bT509vceKZFTbXOmytdwiCiHse\nOID+EhdCX25mKkLgF6bz35A9M8wD8/de47O6Mq5K4B3HUYFfAO4DTOBnarXaX1zLE5NIJDcPiqKw\n79Ak+w5N8oZHbuPF2jonv77ChTN1li80efYrSzzynXcwMZUnjmO++NgZvvDZ0wComsLkdJ65xTL3\nPXzpyUo3I+kA7vud2R2DSW52rraC/yCg12q1NzqOswi8D5ACL5HsASzb4Pg9Cxy/Z4FOq88Tj57i\nua8t8/u/9kUeftsRTp1c5+wLmxTLFm//9uPM7Sujad+8y313HJ7iqRNrPHzXy+ufXwuuVuDfATzt\nOM6fJj//s2t0PhKJ5JuIQsnire8+xv5bJvn0J07w6U+cAODALZO8/T3HyV1iKMY3C689NntV3vvN\nwGUF3nGcDwE/CQwvna8B3Vqt9u2O47wJ+DXgzdflDCUSyU3P7XfMMbtQ5rG/fp7ZhRL3PnQIVf3m\nsjNeiVxW4Gu12keAjww/5jjObwN/mvz+o47jHL0+pyeRSL5ZqEzmeNd777rRpyEZ4motms8C7wI+\n6jjOPcCZK3iNUq2WrvLtXnnIazFAXosB8loMkNfipXO1Av8rwH9xHOfx5Of/5Rqdj0QikUiuEcq1\nCBCSSCQSyc3HN2/vkkQikUguiRR4iUQieYUiBV4ikUheoUiBl0gkklco1zVszHEcBfjPwD1AD/jh\nWq324vV8z5sJx3F0xB6Cw4jMnn8PfB2xMSwCnq7Vaj9+o87vRuA4zizwJPAtQMgevRaO4/wr4DsQ\n/wb/X+Ax9uC1SDTivwEO4u/Dj7AH/144jvM64D/UarW3Oo5zhDGf33GcHwH+MeAD/75Wq/3Z5Y57\nvSv47wKsWq32MPBhREDZXuIDwHqtVnsT8E7EP+RfAP73Wq32ZkB1HOc7b+QJvpwkN7xfAtzkoT15\nLRzHeTPwUPLv4q3AEfbotQC+FSjUarU3AP8n8LPssWvhOM5PI1rPreShHZ/fcZw5RCTMQwgt+b8d\nxzHGHnCI6y3wbwA+AVCr1T4P3H+d3+9m4/eAf5P8vwYEwL21Wu0zyWMfR1Sye4WfB/4LcBFQ2LvX\nIs1y+hjwx8l/e/Va9IBKUslXENXpXrsWzwPfPfTzfds+/yPAA8Bna7VaUKvVmsBJ4O7LHfh6C3wZ\naAz9HCRRw3uCWq3m1mq1juM4JeB/Av8aIWwpLcRf6lc8juP8ILBaq9X+ksE1GP67sGeuBTCDiNp+\nL/CjwG+yd6/FZ4Ec8BzwX4FfZI/9G6nVah9FFH8p2z9/GSgxqqVtruC6XG+xbSJOLHu/Wq0WXef3\nvKlwHOcA8DfAr9dqtd9B+GopJaB+Q07s5eeHgEccx/kUYk3mN4Dh+Wd76VpsAH+RVGMnSKrYod/f\nS9fiXwCP1Wo1h8Hfi+EIyr10LVLGaUQTIfTbH78k11vgH0Nk1uA4zoPA167z+91UJL7ZXwD/olar\n/Xry8FNJAifAtwGfGfviVxi1Wu3NtVrtrbVa7a3AlxEzBT6+F68Fomp9J0AyT6EA/HXizcPeuhZF\nBpVpHbHo/NQevRYpXxrz7+ILwBscxzEdx6kAx4CnL3eg6z2y76OIqu2x5Ocfus7vd7PxYWAC+DeO\n4/wMInL5J4D/J1kgeRb4/Rt4fjeanwJ+Za9di1qt9meO47zRcZwnEF/HfxQ4Dfy3vXYtgJ8DftVx\nnM8g9OhfAV9kb16LlB3/Lmq1Wuw4zi8iigMFsQjrXe5AMotGIpFIXqHsmQVPiUQi2WtIgZdIJJJX\nKFLgJRKJ5BWKFHiJRCJ5hSIFXiKRSF6hSIGXSCSSVyhS4CUSieQVihR4iUQieYXy/wPvPQhMfDxX\n9AAAAABJRU5ErkJggg==\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x15d17fe10>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"both_c, tt_c, ch_c, clean = data_parser(theta,kappa,tt,ch)\n",
"_ = plt.plot(both_c[:100].T)"
]
},
{
"cell_type": "code",
"execution_count": 614,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Desired training/test set sizes: 1640 410\n",
"training set shape: (1640, 100)\n",
"test set shape: (410, 100)\n",
"1D train label shape: (1640,)\n",
"1D test label shape: (410,)\n"
]
}
],
"source": [
"# Let's use 20% of the data for testing and 80% for training\n",
"trainsize = int(len(both_c) * 0.8)\n",
"testsize = len(both_c) - trainsize\n",
"print('Desired training/test set sizes:',trainsize, testsize)\n",
"\n",
"subset = random.sample(range(len(both_c)),trainsize)\n",
"fullrange = range(0,len(both_c))\n",
"toexclude = np.delete(fullrange,subset)\n",
"traindata = both_c[subset,:]\n",
"# trainlabs = labs[subset,:]\n",
"testdata = np.delete(both_c,subset,axis=0)\n",
"# testlabs = np.delete(labs,subset,axis=0)\n",
"\n",
"# non one-hot style labels\n",
"trainlabs1D = tt_c[subset].squeeze() # Change this to ch_c to classify choice instead\n",
"testlabs1D = np.delete(tt_c,subset)\n",
"\n",
"print('training set shape:',traindata.shape)\n",
"print('test set shape:',testdata.shape)\n",
"# print('training labels shape:',trainlabs.shape)\n",
"# print('test labels shape:',testlabs.shape)\n",
"print('1D train label shape:', trainlabs1D.shape)\n",
"print('1D test label shape:', testlabs1D.shape)"
]
},
{
"cell_type": "code",
"execution_count": 615,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"# Define models\n",
"lr = LogisticRegression()\n",
"NN = skflow.TensorFlowDNNClassifier(hidden_units=[100], n_classes=3,batch_size=128, steps=1000, optimizer = 'Adam',learning_rate=0.001,verbose=0)\n"
]
},
{
"cell_type": "code",
"execution_count": 622,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"image/png": "iVBORw0KGgoAAAANSUhEUgAAA9QAAAGUCAYAAADUE2rBAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nxgAAAABJRU5ErkJggg==\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x125735e80>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"# ROC mouse + 2 MODELS of all trials with binarised labels\n",
"fpr = dict()\n",
"tpr = dict()\n",
"roc_auc = dict()\n",
"n_classes = 3\n",
"\n",
"\n",
"trialtypes = ['Anterior Pole','Posterior Pole','No Go'] # 32-34\n",
"# trialtypes = ['Posterior Pole','Anterior Pole','No Go'] # 36\n",
"\n",
"# Change the model/labels here \n",
"preds = cross_validation.cross_val_predict(lr, both_c, ch_c.squeeze()-1, cv=5)\n",
"preds_NN = cross_validation.cross_val_predict(NN, both_c, ch_c.squeeze()-1, cv=5)\n",
"\n",
"with plt.style.context('fivethirtyeight'):\n",
" fig, ax = plt.subplots(1,3,figsize=(15,6))\n",
"\n",
" # MOUSE\n",
" mouse_choice = ch[clean.squeeze()].values\n",
" n_classes = 3\n",
" for i in range(0,3):\n",
" these_trials = tt_c == i+1\n",
" binary_trials = np.zeros_like(tt_c.squeeze()) \n",
" binary_trials[these_trials.squeeze()] = 1\n",
"\n",
" wrong = mouse_choice != i+1\n",
" binary_preds = np.ones_like(mouse_choice)\n",
" binary_preds[wrong] = 0\n",
" fpr[i], tpr[i], thresholds = metrics.roc_curve(binary_trials,binary_preds)\n",
" roc_auc[i] = metrics.auc(fpr[i], tpr[i])\n",
" ax[0].plot(fpr[i], tpr[i], lw=1, label='ROC ' + trialtypes[i] +' (area = %0.2f)' % (roc_auc[i]))\n",
"\n",
"\n",
" # Compute macro-average ROC following sklearn docs\n",
"\n",
" # First aggregate all false positive rates\n",
" all_fpr = np.unique(np.concatenate([fpr[i] for i in range(n_classes)]))\n",
" # Then interpolate all ROC curves at this points\n",
" mean_tpr = np.zeros_like(all_fpr)\n",
" for i in range(n_classes):\n",
" mean_tpr += interp(all_fpr, fpr[i], tpr[i])\n",
" # Finally average it and compute AUC\n",
" mean_tpr /= n_classes\n",
" fpr[\"macro\"] = all_fpr\n",
" tpr[\"macro\"] = mean_tpr\n",
" roc_auc[\"macro\"] = metrics.auc(fpr[\"macro\"], tpr[\"macro\"])\n",
" ax[0].plot(fpr[\"macro\"], tpr[\"macro\"],\n",
" label='macro-average ROC curve (area = {0:0.2f})'''.format(roc_auc[\"macro\"]),linewidth=2)\n",
"\n",
" ax[0].plot([0, 1], [0, 1], '--', color=(0.6, 0.6, 0.6), label='Chance')\n",
" ax[0].legend(loc=4)\n",
" ax[0].set_title('Mouse ' + mouse_name)\n",
" ax[0].set_xlim([-0.2,1.1])\n",
" ax[0].set_ylim([-0.2,1.1])\n",
"\n",
" # Logistic Regression\n",
" for i in range(0,3):\n",
" these_trials = tt_c == i+1\n",
" binary_trials = np.zeros_like(tt_c.squeeze()) \n",
" binary_trials[these_trials.squeeze()] = 1\n",
"\n",
" wrong = preds != i\n",
" binary_preds = np.ones_like(preds)\n",
" binary_preds[wrong] = 0\n",
" fpr[i], tpr[i], thresholds = metrics.roc_curve(binary_trials,binary_preds)\n",
" roc_auc[i] = metrics.auc(fpr[i], tpr[i])\n",
" ax[1].plot(fpr[i], tpr[i], lw=1, label='ROC ' + trialtypes[i] +' (area = %0.2f)' % (roc_auc[i]))\n",
"\n",
"\n",
" # Compute macro-average ROC following sklearn docs\n",
"\n",
" # First aggregate all false positive rates\n",
" all_fpr = np.unique(np.concatenate([fpr[i] for i in range(n_classes)]))\n",
" # Then interpolate all ROC curves at this points\n",
" mean_tpr = np.zeros_like(all_fpr)\n",
" for i in range(n_classes):\n",
" mean_tpr += interp(all_fpr, fpr[i], tpr[i])\n",
" # Finally average it and compute AUC\n",
" mean_tpr /= n_classes\n",
" fpr[\"macro\"] = all_fpr\n",
" tpr[\"macro\"] = mean_tpr\n",
" roc_auc[\"macro\"] = metrics.auc(fpr[\"macro\"], tpr[\"macro\"])\n",
" ax[1].plot(fpr[\"macro\"], tpr[\"macro\"],\n",
" label='macro-average ROC curve (area = {0:0.2f})'''.format(roc_auc[\"macro\"]),linewidth=2)\n",
"\n",
" ax[1].plot([0, 1], [0, 1], '--', color=(0.6, 0.6, 0.6), label='Chance')\n",
" ax[1].legend(loc=4)\n",
" ax[1].set_title('Logistic Regression')\n",
" ax[1].set_xlim([-0.2,1.1])\n",
" ax[1].set_ylim([-0.2,1.1])\n",
" \n",
" \n",
" # Neural Network\n",
" for i in range(0,3):\n",
" these_trials = tt_c == i+1\n",
" binary_trials = np.zeros_like(tt_c.squeeze()) \n",
" binary_trials[these_trials.squeeze()] = 1\n",
"\n",
" wrong = preds_NN != i\n",
" binary_preds = np.ones_like(preds)\n",
" binary_preds[wrong] = 0\n",
" fpr[i], tpr[i], thresholds = metrics.roc_curve(binary_trials,binary_preds)\n",
" roc_auc[i] = metrics.auc(fpr[i], tpr[i])\n",
" ax[2].plot(fpr[i], tpr[i], lw=1, label='ROC ' + trialtypes[i] +' (area = %0.2f)' % (roc_auc[i]))\n",
"\n",
"\n",
" # Compute macro-average ROC following sklearn docs\n",
"\n",
" # First aggregate all false positive rates\n",
" all_fpr = np.unique(np.concatenate([fpr[i] for i in range(n_classes)]))\n",
" # Then interpolate all ROC curves at this points\n",
" mean_tpr = np.zeros_like(all_fpr)\n",
" for i in range(n_classes):\n",
" mean_tpr += interp(all_fpr, fpr[i], tpr[i])\n",
" # Finally average it and compute AUC\n",
" mean_tpr /= n_classes\n",
" fpr[\"macro\"] = all_fpr\n",
" tpr[\"macro\"] = mean_tpr\n",
" roc_auc[\"macro\"] = metrics.auc(fpr[\"macro\"], tpr[\"macro\"])\n",
" ax[2].plot(fpr[\"macro\"], tpr[\"macro\"],\n",
" label='macro-average ROC curve (area = {0:0.2f})'''.format(roc_auc[\"macro\"]),linewidth=2)\n",
"\n",
" ax[2].plot([0, 1], [0, 1], '--', color=(0.6, 0.6, 0.6), label='Chance')\n",
" ax[2].legend(loc=4)\n",
" ax[2].set_title('Neural Network')\n",
" ax[2].set_xlim([-0.2,1.1])\n",
" ax[2].set_ylim([-0.2,1.1])\n",
" \n",
"# plt.savefig('figs/ROC_allthree_trailtype_preds_'+ mouse_name +'.png')"
]
},
{
"cell_type": "code",
"execution_count": 617,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"image/png": "iVBORw0KGgoAAAANSUhEUgAAA9QAAAFZCAYAAAB5ZWerAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\ngFpERERERERkEDSgFhERERERERmE/wG6BN335dE4kgAAAABJRU5ErkJggg==\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x13bb80048>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"# Softmax probability version\n",
"# TO DO: change this to utilise all data in cross val by setting up custom function.\n",
"\n",
"fpr = dict()\n",
"tpr = dict()\n",
"roc_auc = dict()\n",
"n_classes = 3\n",
"\n",
"probs = lr.fit(traindata,trainlabs1D-1).predict_proba(testdata)\n",
"probs_NN = NN.fit(traindata,trainlabs1D-1).predict_proba(testdata)\n",
"\n",
"with plt.style.context('fivethirtyeight'):\n",
" fig, ax = plt.subplots(1,3, figsize=(15,5))\n",
"\n",
" # MOUSE\n",
" mouse_choice = ch[clean.squeeze()].values\n",
" n_classes = 3\n",
" for i in range(0,3):\n",
" these_trials = tt_c == i+1\n",
" binary_trials = np.zeros_like(tt_c.squeeze()) \n",
" binary_trials[these_trials.squeeze()] = 1\n",
"\n",
" wrong = mouse_choice != i+1\n",
" binary_preds = np.ones_like(mouse_choice)\n",
" binary_preds[wrong] = 0\n",
" fpr[i], tpr[i], thresholds = metrics.roc_curve(binary_trials,binary_preds)\n",
" roc_auc[i] = metrics.auc(fpr[i], tpr[i])\n",
" ax[0].plot(fpr[i], tpr[i], lw=1, label='ROC ' + trialtypes[i] +' (area = %0.2f)' % (roc_auc[i]))\n",
"\n",
"\n",
" # Compute macro-average ROC following sklearn docs\n",
"\n",
" # First aggregate all false positive rates\n",
" all_fpr = np.unique(np.concatenate([fpr[i] for i in range(n_classes)]))\n",
" # Then interpolate all ROC curves at this points\n",
" mean_tpr = np.zeros_like(all_fpr)\n",
" for i in range(n_classes):\n",
" mean_tpr += interp(all_fpr, fpr[i], tpr[i])\n",
" # Finally average it and compute AUC\n",
" mean_tpr /= n_classes\n",
" fpr[\"macro\"] = all_fpr\n",
" tpr[\"macro\"] = mean_tpr\n",
" roc_auc[\"macro\"] = metrics.auc(fpr[\"macro\"], tpr[\"macro\"])\n",
" ax[0].plot(fpr[\"macro\"], tpr[\"macro\"],\n",
" label='macro-average ROC curve (area = {0:0.2f})'''.format(roc_auc[\"macro\"]),linewidth=2)\n",
" ax[0].plot([0, 1], [0, 1], '--', color=(0.6, 0.6, 0.6), label='Chance')\n",
" ax[0].legend(loc=4)\n",
" ax[0].set_title('Mouse ' + mouse_name)\n",
" ax[0].set_xlim([-0.2,1.1])\n",
" ax[0].set_ylim([-0.2,1.1])\n",
" \n",
" # Logistic Regression\n",
" for i in range(0,3):\n",
" these_trials = testlabs1D == i+1\n",
" binary_trials = np.zeros_like(testlabs1D.squeeze())\n",
" binary_trials[these_trials.squeeze()] = 1\n",
"\n",
" fpr[i], tpr[i], thresholds = metrics.roc_curve(binary_trials,probs[:,i])\n",
" roc_auc[i] = metrics.auc(fpr[i], tpr[i])\n",
" ax[1].plot(fpr[i], tpr[i], lw=1, label='ROC ' + trialtypes[i] +' (area = %0.2f)' % (roc_auc[i]))\n",
"\n",
"\n",
" # Compute macro-average ROC following sklearn docs\n",
" # First aggregate all false positive rates\n",
" all_fpr = np.unique(np.concatenate([fpr[i] for i in range(n_classes)]))\n",
" # Then interpolate all ROC curves at this points\n",
" mean_tpr = np.zeros_like(all_fpr)\n",
" for i in range(n_classes):\n",
" mean_tpr += interp(all_fpr, fpr[i], tpr[i])\n",
" # Finally average it and compute AUC\n",
" mean_tpr /= n_classes\n",
" fpr[\"macro\"] = all_fpr\n",
" tpr[\"macro\"] = mean_tpr\n",
" roc_auc[\"macro\"] = metrics.auc(fpr[\"macro\"], tpr[\"macro\"])\n",
" ax[1].plot(fpr[\"macro\"], tpr[\"macro\"],label='macro-average ROC curve (area = {0:0.2f})'''.format(roc_auc[\"macro\"]),linewidth=2)\n",
" ax[1].plot([0, 1], [0, 1], '--', color=(0.6, 0.6, 0.6), label='Chance')\n",
" ax[1].legend(loc=4)\n",
" ax[1].set_title('Logistic Regression')\n",
" ax[1].set_xlim([-0.2,1.1])\n",
" ax[1].set_ylim([-0.2,1.1])\n",
"\n",
" # Neural Network\n",
" for i in range(0,3):\n",
" these_trials = testlabs1D == i+1\n",
" binary_trials = np.zeros_like(testlabs1D.squeeze())\n",
" binary_trials[these_trials.squeeze()] = 1\n",
"\n",
" fpr[i], tpr[i], thresholds = metrics.roc_curve(binary_trials,probs_NN[:,i])\n",
" roc_auc[i] = metrics.auc(fpr[i], tpr[i])\n",
" ax[2].plot(fpr[i], tpr[i], lw=1, label='ROC ' + trialtypes[i] +' (area = %0.2f)' % (roc_auc[i]))\n",
"\n",
"\n",
" # Compute macro-average ROC following sklearn docs\n",
" # First aggregate all false positive rates\n",
" all_fpr = np.unique(np.concatenate([fpr[i] for i in range(n_classes)]))\n",
" # Then interpolate all ROC curves at this points\n",
" mean_tpr = np.zeros_like(all_fpr)\n",
" for i in range(n_classes):\n",
" mean_tpr += interp(all_fpr, fpr[i], tpr[i])\n",
" # Finally average it and compute AUC\n",
" mean_tpr /= n_classes\n",
" fpr[\"macro\"] = all_fpr\n",
" tpr[\"macro\"] = mean_tpr\n",
" roc_auc[\"macro\"] = metrics.auc(fpr[\"macro\"], tpr[\"macro\"])\n",
" ax[2].plot(fpr[\"macro\"], tpr[\"macro\"],label='macro-average ROC curve (area = {0:0.2f})'''.format(roc_auc[\"macro\"]),linewidth=2)\n",
" ax[2].plot([0, 1], [0, 1], '--', color=(0.6, 0.6, 0.6), label='Chance')\n",
" ax[2].legend(loc=4)\n",
" ax[2].set_title('Neural Network')\n",
" ax[2].set_xlim([-0.2,1.1])\n",
" ax[2].set_ylim([-0.2,1.1])\n",
"\n",
"# plt.savefig('figs/ROC_both_trialtype_3probs_'+ mouse_name +'.png')"
]
},
{
"cell_type": "code",
"execution_count": 623,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"image/png": "iVBORw0KGgoAAAANSUhEUgAAA4QAAAEwCAYAAADvir9xAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nAElFTkSuQmCC\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x163bd10f0>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"# TRIALTYPE\n",
"# # Confusion matrices. Mouse vs model\n",
"# mouse_choice = ch[clean.squeeze()].values\n",
"# cm_m = confusion_matrix(tt_c,mouse_choice)\n",
"\n",
"# # Confusion matrices\n",
"# cm_lr = confusion_matrix(tt_c,preds+1)\n",
"# cm_NN = confusion_matrix(tt_c,preds_NN+1)\n",
"\n",
"# CHOICE\n",
"# Confusion matrices. Mouse vs model\n",
"# mouse_choice = ch_c #[clean.squeeze()].values\n",
"label = ch_c\n",
"cm_m = confusion_matrix(label,ch_c)\n",
"\n",
"# Confusion matrices\n",
"cm_lr = confusion_matrix(label,preds+1)\n",
"cm_NN = confusion_matrix(label,preds_NN+1)\n",
"\n",
"\n",
"with sns.axes_style(\"white\"):\n",
" fig,ax = plt.subplots(1,3,figsize=(15,6))\n",
"\n",
"\n",
" ax[0].imshow(cm_m,interpolation='none',cmap=\"Greys\")\n",
" ax[0].set_title('Mouse ' + mouse_name + '. ' + str(int(100 * accuracy_score(label,ch_c))) + '%')\n",
" ax[0].set_ylabel('True label')\n",
" ax[0].set_xlabel('Predicted label')\n",
" tick_marks = np.arange(len(trialtypes))\n",
" ax[0].set_xticks(tick_marks, trialtypes)\n",
" ax[0].set_yticks(tick_marks, trialtypes)\n",
" \n",
" for i in range(0,3):\n",
" for j in range(0,3):\n",
" ax[0].text(j, i, cm_m[i,j], va='center', ha='center',bbox=dict(facecolor='white',edgecolor='white', alpha=0.5))\n",
"\n",
" ax[1].imshow(cm_lr,interpolation='none',cmap=\"Greys\")\n",
" ax[1].set_title('Logistic Regression' + '. ' + str(int(100 * accuracy_score(label,preds+1))) + '%')\n",
" ax[1].set_ylabel('True label')\n",
" ax[1].set_xlabel('Predicted label')\n",
" \n",
" for i in range(0,3):\n",
" for j in range(0,3):\n",
" ax[1].text(j, i, cm_lr[i,j], va='center', ha='center',bbox=dict(facecolor='white',edgecolor='white', alpha=0.5))\n",
"\n",
" ax[2].imshow(cm_NN,interpolation='none',cmap=\"Greys\")\n",
" ax[2].set_title('Neural Network' + '. ' + str(int(100 * accuracy_score(label,preds_NN+1))) + '%')\n",
" ax[2].set_ylabel('True label')\n",
" ax[2].set_xlabel('Predicted label')\n",
" \n",
" for i in range(0,3):\n",
" for j in range(0,3):\n",
" ax[2].text(j, i, cm_NN[i,j], va='center', ha='center',bbox=dict(facecolor='white',edgecolor='white', alpha=0.5))\n",
"\n",
" \n",
"# plt.savefig('figs/Cmatrix_lr_trialtype_choice_'+ mouse_name +'.png')"
]
},
{
"cell_type": "code",
"execution_count": 624,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"<matplotlib.text.Text at 0x162eb5160>"
]
},
"execution_count": 624,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"image/png": "iVBORw0KGgoAAAANSUhEUgAAAxwAAAD8CAYAAAD9q6IXAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\niIiIYf4PG/R2wzG6lqEAAAAASUVORK5CYII=\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x15b217550>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"# preds = cross_validation.cross_val_predict(lr, both_c, tt_c.squeeze()-1, cv=5)\n",
"# plt.hist(preds)\n",
"# x = tt_c[~np.isnan(tt_c)]\n",
"# x.shape\n",
"# plt.hist(np.nan_to_num(tt))\n",
"with plt.style.context('fivethirtyeight'):\n",
" fig, ax = plt.subplots(1,3, figsize=(12,3))\n",
" ax[0].hist(tt_c)\n",
"# ax[0].hist(tt[clean.squeeze()].values) # when predicting choice\n",
" ax[0].set_title('Trialtype')\n",
"# ax[0].set_xticks([1,2,3],trialtypes)\n",
" ax[0].set_xlim([0.5,3.5])\n",
" \n",
" ax[1].hist(ch_c)\n",
" ax[1].set_title('Choice')\n",
" ax[1].set_xlim([0.5,3.5])\n",
" \n",
" ax[2].hist(preds_NN+1)\n",
" ax[2].set_title('NN choice')\n",
" ax[2].set_xlim([0.5,3.5])\n",
" \n",
"\n",
"plt.suptitle('Mouse ' + mouse_name, x=0.5,y=1.1,fontsize=15)\n",
"# plt.savefig('figs/choice_number_'+ mouse_name +'.png')"
]
},
{
"cell_type": "code",
"execution_count": 625,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
" precision recall f1-score support\n",
"\n",
" Anterior Pole 1.00 1.00 1.00 746\n",
"Posterior Pole 1.00 1.00 1.00 650\n",
" No Go 1.00 1.00 1.00 654\n",
"\n",
" avg / total 1.00 1.00 1.00 2050\n",
"\n",
"Weighted f1_score: 1.0\n",
" precision recall f1-score support\n",
"\n",
" Anterior Pole 0.63 0.53 0.58 746\n",
"Posterior Pole 0.50 0.44 0.47 650\n",
" No Go 0.47 0.60 0.53 654\n",
"\n",
" avg / total 0.54 0.53 0.53 2050\n",
"\n",
"Weighted f1_score: 0.527602929973\n"
]
}
],
"source": [
"# print('Mouse '+ mouse_name + '. '+ accuracy_score(tt_c,mouse_choice) + '%')\n",
"# int(100 *accuracy_score(tt_c,mouse_choice))\n",
"# print('Mouse ' + mouse_name + '. ' + str(int(100 * accuracy_score(tt_c,mouse_choice))) + '%')\n",
"trialtypes = ['Anterior Pole','Posterior Pole','No Go']\n",
"\n",
"print(metrics.classification_report(label,ch_c,target_names=trialtypes))\n",
"print('Weighted f1_score: ',metrics.f1_score(label,mouse_choice,average='weighted'))\n",
"print(metrics.classification_report(label,preds_NN+1,target_names=trialtypes))\n",
"print('Weighted f1_score: ',metrics.f1_score(label,preds_NN+1,average='weighted'))"
]
},
{
"cell_type": "code",
"execution_count": 580,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"(array([ 746., 0., 0., 0., 0., 650., 0., 0., 0., 654.]),\n",
" array([ 1. , 1.2, 1.4, 1.6, 1.8, 2. , 2.2, 2.4, 2.6, 2.8, 3. ]),\n",
" <a list of 10 Patch objects>)"
]
},
"execution_count": 580,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"image/png": "iVBORw0KGgoAAAANSUhEUgAAAXwAAAECCAYAAAD9z2x7AAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nl6RCGPiSVIj/C5588X7efH00AAAAAElFTkSuQmCC\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x15d194f98>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"clean1 = np.nan_to_num(tt) !=0\n",
"clean2 = np.nan_to_num(ch) !=0\n",
"tt_c2 = tt[clean1&clean2].values\n",
"ch_c2 = ch[clean1&clean2].values\n",
"fig,ax = plt.subplots(1,2)\n",
"ax[0].hist(tt_c2)\n",
"ax[1].hist(ch_c2)"
]
},
{
"cell_type": "code",
"execution_count": 605,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
" theta_r = np.array([[resample(theta.values.squeeze()[i,950:1440],50)] for i in range(0,theta.shape[0])])\n",
" theta_r = zscore(theta_r.squeeze(),axis=None)\n",
"\n",
" kappa_r = np.array([[resample(kappa.values.squeeze()[i,950:1440],50)] for i in range(0,kappa.shape[0])])\n",
" kappa_r = zscore(kappa_r.squeeze(),axis=None)\n",
"\n",
" kappa_df = pd.DataFrame(kappa_r)\n",
" theta_df = pd.DataFrame(theta_r)\n",
"\n",
" both_df = pd.concat([theta_df,kappa_df],axis=1)"
]
},
{
"cell_type": "code",
"execution_count": 610,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"(2050, 100)"
]
},
"execution_count": 610,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"clean1 = np.nan_to_num(tt) !=0\n",
"clean2 = np.nan_to_num(ch) !=0\n",
"clean = clean1&clean2\n",
"\n",
"# tt_c = tt[tt.values !=0|3].values\n",
"both = both_df[clean]\n",
"# both_c = both[clean.squeeze(),:]\n",
"both_c = both.values\n",
"both_c.shape"
]
},
{
"cell_type": "code",
"execution_count": 646,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"/Users/mathew/miniconda/envs/py35/lib/python3.5/site-packages/sklearn/utils/validation.py:515: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
" y = column_or_1d(y, warn=True)\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"explained variance ratio (first two components): [ 0.55555146 0.08664667]\n"
]
},
{
"data": {
"text/plain": [
"<matplotlib.text.Text at 0x165f1e1d0>"
]
},
"execution_count": 646,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"image/png": "iVBORw0KGgoAAAANSUhEUgAAAXoAAAEKCAYAAAAcgp5RAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz\nAAALEgAACxIB0t1+/AAAIABJREFUeJzsnXd4VFX6xz83nUBIAwIBmYR2VTCoiA0Ru6JUdW37211W\nZKVZQAXBLgoYRawEF0FcdwWsMAaCiCAgIkQgBgQukCbJhACZ9J7M/f1xZiYzkwRSBjLA+TwPTzK3\nnnsmfM+573nP9yi6riORSCSScxev1i6ARCKRSE4vUuglEonkHEcKvUQikZzjSKGXSCSScxwp9BKJ\nRHKOI4VeIpFIznF8WrsAEsmZQlVVA5ACJFs3Kdaf72ma9omqqv7Ac8Aw63Zv4H+apsW6XOcx4F3g\nak3Tdpz+kkskLUMKveR8o1TTtMttH1RVjQT2qqq6E3gD0BACXqmqaiiwRlXVtpqmveRwjUeB/wJT\ngAfPYNklkmYhQzeS8xpN00zAIWAUcCEwRdO0Suu+POD/gM2241VVvQEIBaYBI1VV7XqmyyyRNBUp\n9JLzGlVVrwF6AjqwXdM0p6nimqalaJr2o8Om8cB/NU07CvwITD5jhZVImokM3UjONwJVVd2FiM/7\nAMeBvwIXcYqOj6qqEcBowBb6+QyIU1X1VU3Tyk5fkSWSliGFXnK+4RSjt6GqagHwhKqqimOvXlXV\ngcBjmqb9HRgHWIDvVFUF0TAEAf8AFp6JwkskzUGGbiTnG0p9GzVN+xU4ALxtzb6x9eDfB1JUVfVC\nCP2jmqb1sP6LAuYAT56RkkskzUQKveR842R2rfcg/k/sVFV1N/AD8IWmaa8gUi4V4HOXc+YDEaqq\nDj0dhZVI3IEibYolEonk3KZFMXpVVa8C5mqadqOqqpcC8cBB6+44TdO+bGkBJRKJRNIymi30qqo+\nA/wNKLZuGgDM0zRtvjsKJpFIJBL30JIY/WFEqpmNAcBdqqpuUlX1Y1VV27asaBKJRCJxB80Wek3T\nvgWqHTZtB57RNG0IkAq83LKiSSQSicQduDOPfqWmaQXW378F3jvVCbqu64pSb7abRCKRSBqmScLp\nTqFfq6rqY5qm/QbcDOw81QmKonD8eJEbi+AeOnYM8rhyyTI1DlmmxuOJ5ZJlahwdOwY16Xh3Cv14\n4ENVVSuBo8C/3HhtiUQikTSTFgm9pmkZwLXW338HrnNHoSQSiUTiPuTMWIlEIjnHkUIvkUgk5zhS\n6CUSieQcRwq9RCKRnONIoZdIJJJzHCn0EonEo/nf/z5l5Mg7qKqqOulxRuO31NTUNOqa27dv47vv\nVja5LLt372T48Nt4/PHxPP74eMaPf5ivv17R4PFHj2bz6KP/bPJ93I1cYUoikbSY0tJStn62FIBB\nfxtDYGCg2669bt1abrnldtav/56hQ4c1eNxnn33C0KHD8Pb2PuU1r7rqmmaXZ8CAgbz88usAVFVV\n8dBD93DHHXfRtm27eo/3hNn/UuglEkmLKC0t5fv7R/Pw9m0ALIlfxR1frKRNmzYtvvbu3Tvp1q0b\no0bdw6uvvsDQocN47LFH6d27D6mpKZSWljJr1lwSE7eTm5vLSy/NZPbsN/noow9JTk7CYqnh/vv/\nyg033Mxjjz1KaGgYRUWF3HzzbWRmHmH8+MksW/ZfNmxYh4+PD/37X8748ZNZsuTf7N2bTFlZGbGx\ncwkK6mgvk+MaHiUlJXh7e+Pt7cPBgwd455238Pb2xs/Pn+nTn6vzLIsWxeHt7U3Xrt145pmZjWqU\n3IEM3UgkkhaxbcXnPLx9Gz6InuPD27fxy/L/ueXa8fErGTZsFBdc0B1fX1/27dsLwMUX9+OddxZw\nxRVXsn799wwbNpLw8A68+uocfv31F7KzTXz44SLefXchn366mOJi4aZ+2213MH/+h3h7e6MoCqmp\nh/nppx/56KOlxMUtITPzT3755WcAoqKiiYtbTI8ePZzKtGvXbzz++HieeGICs2a9yJQp0wgICCA2\ndjZPPfUs77//EaNH38N7773tdF5s7OvMnv0W77//ER06dCQh4Tu31FFjkD16iUTSIhQvLywOn2us\n21pKUVER27b9Ql5ePl99tYKSkhK+/voLFEWhTx8VgE6dIsjLM1vP0NF1ndTUwxw4sJ/HHx+PruvU\n1NSQnW0C4IILDE73yMhIp2/ffnhZyxsTcylpaSkAdO/ufKwNx9CNI7m5J+jZsxcA/ftfzsKFH9r3\n5eXlkZuby4svPgtARUUFAwde1cyaaTpS6CUSSYsY9MBfWWz8ljFbNgGw9PobGP7AX1t83e+/X82w\nYSOZOPFxACoqyvnLX0YSEhJCfeaNXl5eWCw1dO8exYABV/DMMzOpqanhs88+oWvXbkDdeLnBEMWK\nFZ9jsVhQFIWkpN0MHXoXhw4dRFGa1lh16NCBlJTD9OzZi927d3LBBd3t+0JCQujUKYK5c+cRGNiW\nzZt/on379k2skeYjhV4ikbQIf39/Riz7mu++EtknI+69Hz8/vxZfd/VqIy+88KrDfQIYMuQmVq9e\nVe/xMTGX8swzT/LeewvZvXsnkyaNo6ysjOuvv4HAwMB6B0V79OjFjTfezPjxD6PrOv37X8bgwTdw\n6NDBeu5wcqZNe57582PRdR0fHx+effYF+z5FUXjiiak8/fQT6LqFtm3b8fzzr57kau6ltRcH1z3N\n/hM815ZUlunUyDI1Hk8slyxT4+jYMahJqTxyMFYikUjOcaTQSyQSyTmOFHqJRCI5x5FCL5FIJOc4\nUuglEonkHEcKvUQikZzjyDx6iUTicezevZMXX5xBdLSwH6isrOTWW2/nnnvub9J1Nm/+ib59+xEe\n3uGUxx46dJCtWzczZswjTbrH0aPZ/OMfD6CqFwFQVVXJZZddwb/+NbHBc0aOvJ1Vq75v0n1aghR6\niUTSYkpLS/ls9VIA/naXe9wrm+oSWR9ffrmMqKiZjRL63r370Lt3n2aVNTq6J++9t9D+efz4h0lN\nPUyPHr0aOOPMOlpKoZdIJC2itLSU++eOZntH4V4ZP3cVX8xouXtlU1wiQ0JCefHFZykpKaG8vJx/\n/Wsi1dVVHDp0kNdee4kFCz5m5cqvWL9+HYqicMstt3HPPfcze/YrFBTkU1hYyIMP/o0ff1zHK6/M\nZt26BL78chl+fv706tWDxx57hh9+WMvq1UZ0XWfs2Ee5/PIr6i1reXk5VVVV+PsHcPRoNnPmvEpN\nTQ2KovDkk8/Y/XAAUlIO8+67bwHQvn0wM2e+SGBg2xbVW31IoZdIJC1ixdrPhchbHXe3d9zG8oT/\n8c+7mxYCccXmEqkoCj4+vk4ukTNmvEjPnr34+edNvPfe24wd+ygFBQXMm/c+eXlmjhz5k2uuuY4+\nfVSeeWYmmZlH2LBhPXFxi9F1nSlTJjFw4NUADBhwJffd9yC7d+9EURQKCwtYsuTfLF26jICAABYv\n/pBVq74hMDCQoKD2zJnzVp2ypqen8vjj4wHw9vbmvvsepGvXbjz//HTuu+8hBg0azKFDB5kz51U+\n/vg/9vNiY19n5syXMBiiiI9fxX//++lJQz7NRQq9RCJpEV5eXuDopKJjd4NsCU1xiYyO7sGIEaN5\n+eWZVFfX8Je/iFi+rtscLVM4ejSbJ56YgK7rFBcXkZV1BKjrUmkyZREd3ZOAgAAArrjiCtav/4mL\nL+7boKOla+jGRkZGGv37XwaI0NDx4zl19s+bNxeA6upqunW7oNH10xSk0EskkhbxwNC/YpzzLVtC\nhXvl9Xk38MCjLXevbIj6XCJti5DExr5Dbu4JJkwYyzXXXGd1tLTQvbuBHj168tZb7wGwYsX/6Nmz\nNxs3/linUerSJZL09FQqKsrx9w9gx44ddifKhhqwhjzDoqJ6kJS0i+uuu55DhzTCwsJtZwDQvXsU\nzz//Cp06RZCUtIvCwkI31FBdpNBLJJIW4e/vz7KZX/PV98K98t7b3eNe2RD1uUSGh3dgyZJ/s3Hj\nenRd55FHJgDQr18Mr7/+Em+//SGXXz6QCRPGUllZSd++/ejQoWO91w8ODuHhh//F5MmP4u3tTc+e\n0YwZM5716xvOkmloucBJk57gjTdeY/ny/1JTU82MGS/azgDgqaeeZdasF6mpqcHLy8vJ8dKdSPfK\nevBQtzpZpkYgy9R4PLFcskyNQ7pXSiQSicQJKfQSiURyjiOFXiKRSM5xpNBLJBLJOY4UeolEIjnH\nkUIvkUgk5zgyj17SaArMZrZMn0pwRjoFBgOjl3wM+LZ2sSTnILt372TGjKf47LMv6NixEwALF36A\nwRDF0KHDGnWN9eu/59tvv0JRFLy8vOjVqzcTJz6Bj8/5J3uyRy9pNFumT2XMqm+4O2kXY1Z9S8KE\nCa1dJImHUFpaykcfreWjj9ZSWlrqlmv6+voxe/YrzTp327afiY9fxZtvvsMHH/yb995biKJ4sXZt\nvFvKdrZx/jVtkmYTnJFuN1dVgHZpaa1ZHImHUFpayv33x7N9+z8AiI//lC++GN5i90rhDqnz9ddf\ncM899zntW7bsv2zYsA4fHx/697+c8eMnO+3/+usvmDTpSScnyMcem2L/3dGdslu3C5g27Tm8vb1b\nVF5PpkU9elVVr1JVdaP1956qqm5RVXWTqqofuqd4Ek+iwGCwe1fpQHF0dGsWR+IhrFjxs1XkfQAf\ntm//B8uXb2nxdRVF4amnZvDll8vIysq0b09NPcxPP/3IRx8tJS5uCUeOZLBt289O52Znm+jatRsA\ne/fu4bHHHmXChLG8/PJzdnfK99//Nx9+uIh27dqxatU3LS6vJ9NsoVdV9RlgEeBv3fQ2MFPTtCGA\nl6qqI91QPokHMTh2PktH3s03l17O0pF3MzQurrWLJPEAvLwUwOKwpca6reW0b9+exx6bymuvvWQ3\nDsvISKdv3352g7H+/S8jLS3V6byIiM5kZ5sA6NfvEt5//yNmzHgBszm3jjtl//6X1zn/XKMlPfrD\nwGiHzwM0TbM14wnALS24tsQDCQ4NY9iipQxe9xPDFi0lJCystYsk8QAeeOB6Bg9eCpQBZVx//ac8\n8MD1brv+oEGD6d7dwJo13wFgMESxb98fWCwWdF0nKWm33V3Sxt1338eHH75LSUmxfduuXb+hKIqT\nOyVAUtLOOuefazQ7Rq9p2reqqjqaMzs24UVAcLNLJZE0EnOemelLppJRno7B38CSZ2Um0JnG39+f\nZcvu4auv1gJw7733uN298oknnmLXrt8A6NGjFzfeeDPjxz+MruvExFzK4ME3OB1/3XXXU1NTw4wZ\nT6MoCiUlJURH92DatOcIDg5h7NhH7e6UXbt2Y8KEx91aXk+jRe6VVqFfpmnataqqHtE07QLr9hHA\nLZqmnar2WtU6U3L2c/9L9/OF8oXoZuhwn34fK15Z0drFkkhON02Kjbkz62aXqqrXa5q2GRgKbGjM\nSZ5m/wkea0sqy1QPBwsOQ6j1gwJpxWmtXiZXPKGe6sMTyyXL1Dg6dgxq0vHuzKN/GnhVVdWtiHfn\nr9x4bYmkXgz+BhxTgaLbykwgicSVFvXoNU3LAK61/n4IuMENZZJIGk3sI/NhsSJi9AFRxE2Po6a6\ntUslkXgWcsKU5KwmNCSMRU8ttX8OC/W812yJpLWRFggSiURyjiOFXuI2Csxm4seNYcttNxA/7h8U\n5Jlbu0gSiQQZupG4EZvpmQLoSbtYisKwRUtbu1gSyXmP7NFL3Iar6VlwRnorlkYikdiQQi9xG66m\nZwWGqFYsjUQisSFDNxK3MTh2PktRrAuTRDE49u0WXc/V3iD2kfmEhkh/HYmkqUihl7gNm+mZu5i+\nZCqrAr6BNpCk74LFilMqpUQiaRxS6CWnBXf0xjPK08G2doVi/SyRSJqMjNFLTgu23nhS6C5WtfmW\naYunNvkarvYGhoAot5ZRIjlfkD16SbPJzc1n3DgjGRntMRgKiI29idDQEMA9vXFXe4PYsS2L+Usk\n5ytS6CXNZuLEBFat+hugkJSkA5+xaJFYi8bgbxBxdat9cHN64672BhKJpHlIoZc0m7S0duCQOZ+R\n0d6+T/bGJRLPQQq9pNlERxeRmKhj67YbDIX2fbI3LpF4DlLoJc0mLu5OKio+s8boC4mNvbG1iySR\nSOpBCr2k2YSFhdhj8hKJxHOR6ZUSiURyjiOFXiKRSM5xZOhGctqQXjUSiWcghf4spsBsZsv0qVYT\nMQODY+cTHOo5Qiq9aiQSz0AK/VnMmV7ow7VhGb3kY8C3weNdZ8f+vn8DBXlmj2qMJJLzASn0ZzGn\ne6EP19DLzXsrmbxmtb1hWT7Bl1s++LjB811nxw7U8tkybapcdUoiOcNIoT+LKTAY0JN22XTU7Qt9\nuIZejptDeMy6TwHapaWd9PzYR+Zz/O8bqfLLJzoH4jT4qU26W8sokUhOjRT6sxh3L/ThimvoxdRJ\nNCi2hqU4Ovqk54eGhPFIxI2MWfVtoxojTx9zkEjOVqTQn0XUl8VysjBIS7NeXEMvF/e8jqUj/ewN\ny+i4OKpqTn6NpjRGcnFxieT0IIX+LKKpWSwtzXqpY0w24W2nhiIkLIjjx4tOeo2mrDolFxeXSE4P\nUujPIprq8d7Q8Y3t6bsakxWYzSwf8yDLzVsxdYJLLxrCG2PfdVtu/Okec5BIzlek0J9FNNXjvaHj\nG+rpm/PMPLlwMr+afoZSuCb6Wt6ZvMAu5FumT2VLymp+eQBQIF1fhb7Y26kxaEm46HSPOXgKciKZ\n5Ewjhf4soqke7w0dX19P35xn5qbpgzD1z4JgQIeEPWvwWzzVLuTBGemkR+BoQV/nrcK1EUl8Zgcb\n39zaKCFz9+LinoqcSCY500ihbyWa06trqsd7Q8fX19OfvmQqpoAsJxHH31nICwwGog7swsGCvs5b\nRZ1MnTZZTHNoLCRy0XPJmUcKfSvRmr26+nr69394N1TinD9Z4Szkg2PnUzylkqzvbTH6G3jD5a3C\ntRGhquVCdq6FOtyxzKJE0hSk0DcRd4lOa/bq6uvpG/wNJPXeBb8DOviW+nLLxbc5hYeCQ8N4YOky\nHrB+7tixbtZN7CPzSXxmB6Y2WVCswOHhpG8dwrj0b5wWD28K51qoQy6zKDnTSKFvIu4SHU/r1dnF\nx1ArPs1pwEJDwtj45lamLZ7Kpm+CyD/0CfkorFqlU1G5GP+YhCY3kudaqEMusyg500ihbyLuEh1P\n69U1RnzM5nymT99oXTqwgCVLRgLeTscUmM1snT6Vv2ek8/vxv5LvEPT/dXch+VfWNpKm8Tt41hxR\nZxas61tTF70zSScZF2gM51r4RyJpClLom4i7euJnY69u6pPfs2btWEAhKUln7NhP+fe/73E6xnF2\n63K6kc4U7JUVlOw82Fudxd1JWXVmwbq+NQ2tHsbI8rtb1Ciea+EfiaQpSKFvIq3VE8815zJu3r9a\ntUe679dCHJU6eVNenWMcZ7cuxEhWyD8pjRqFwVBIZfdcEhx65tE5tis5z4J1fWvKrjGx7tmfWlT2\ncy3844h8W5GcCin0TaS1euIT3514xnqkBWYz66ZMomTbVsKBqmsGcfM7H9LZkkS6Q1pORE0S8ePG\nOJmQOc5uDQUeGVLEsEU3A5CXPwS/xX5klKfjuyeHBVoWUHcW7OkYv/C0MRF3It9WJKfC7UKvqupO\noMD6MU3TtLHuvsf5SFpJGgRZP5zmHumW6VMJSVjNBKy6mLCa2b8ncW/xMXQKqCSGXiRjqPiBMavK\nnEzI6pvdautxpuSlYt50LeF+D9KlWx6fD/2OrtlZdWbBzrj3JRJn7yDP10xoZRgzn3+xxc/U3Dex\ns6G3fC6/rUjcg1uFXlVVfwBN025y53UlEB0YTaKeeEZ6pMEZ6fjiHE6/xJTFCGAqRuZhpCuQrwTU\nMSGrb3bruHljRI/z5xGgfYAJhT17dH6/MIlOt2dh8NcZpNQeP+frV8UMXQXK9Cxmf/XqSXuojoPE\nffqUMmvW4DppnM19Ezsbesvn8tuKxD24u0ffH2irqur3iHSM5zRN2+7me5yXxE2Jo+KN6tM6NmDz\ng9fTU8kBhlE776nEeowCqNZ9czt0QM/MPKUJmb3HmR+DY/NhKgzDFLqrjoA2tYc6ffpGVq36G7ZB\n4oqKz1i0aHSTn/+kZW9kWVoDT8vgknge7hb6UuBNTdMWq6raG0hQVbWPpmkWN9/nvCMs9PSNDdh6\nxL9vOsHA/GoWWpMiX/L1JaptW7IC2jD5aDYgBP1ASAgnhtzEQ/NiWfrUtFOakNl7nCHJYHIYjQ1J\nFge4CKhrD7ULXRg3b0yD4ZOMjPY4NiDicwvqI8/MlAWT2Ja6lZKyEhiER/eWz8YMLsmZxd1CfxA4\nDKBp2iFVVXOBLkBWQyd07BjU0K5WxRPL1dIy5efmkjBxIu3S0iiKjubOuDhCwsKYPDne3iNOZwoK\no1iBkcv69qW8Tx96HTzIQh9vLuzYkfLevRlnPQ/gnyu/tl93///9haLoaK6e/Toz/vMcaSVpRAdG\nM2/8m/gv9WWtuo7CglFQEwPeyTDMKAqmQ5/gXvbnW/Lsx0x4x5e04jSi20ZT2aaSlf614RP///qy\n4pUV9ufq06eUJIdE+z59ylpUV5M/eISEI6vhcqAc2AOhfqHc2udW4qbHERba9Gt74t8TeGa5ZJnc\nj7uF/p9ADDBJVdVIxPBh9slOONXCFa1BfVP7m0J9A3i6TosG9ZpbJsf4te+xH0gwfUE4oCcmsrSi\nmmGLlnLwYBsce8RpxKBjZN+x48xMSrKHZj645HJ+rLiL2Js32ydMmY8XsOKmQVxiyqIIGJqYyO0n\ntpI4OAuCIFFPpOKjahY9tZTbXr+BpP5GwAhlELIvhKgePTAERDFrbKzD8/nyweTaRcdve/0GCLAX\nj4P5h53qYtaswVRUfGaN0Zcxa9Z1Lfr+DhYcBn9rlbQBYsCQF80Hkz+mprrpf7Mt/Xs6XXhiuWSZ\nGkdTGx53C/1iYImqqpsR2vDw+Ri2qW8AD2iVQT3H+DWMZCKZrMDolLtuMBQ49YjxPcBbbUMILi5y\nGmxdvi2UX/JrY+ETJiznjoqvmGnKsjcGywGzn7leK2OnkEwADLn4JrsP/rTFDTeCpxpsDA0Nscfk\n3fGf0uBvIKlil5PBmyeGbCSSxuJWodc0rRr4uzuv6Uk0NtWuwQG8Zg7qmfPM/Gve3/kp/ad6FwQ5\nGa7x6zRiAKPT4Gls7E3AZw69/q8Iz4f/4WxmacJ5MDUtrR1KWSoPMII0YogmmREYCSkPAb2sjkjO\nuPfFetMmT5XZEvvIfCo/rGRb6lYIhMrICvLyzYSGhDXKluGkdetyfmzsTbX32yXud03X64gdLwc4\nJWcvcsJUE2hsql29PVBdb3YKnLjvSriIehcEaYgCsxnfYz8AI7HfONLMN50ur81xdxG60dVbCTeJ\n8+8C3goJoWdUD7K6dKN8dylgBIqBoURHF7Pg52tJ5ANAIRGd/Yzirz5ldCwcYF+pqjK6nLx8M3O+\nnlVv2uSpMltCQ8LwC/An//J8UCBBX43fYn8WPbW0TsbNhAnL+eCDYY2vW5fzQWTsfPrcskZfQyLx\ndKTQN4HGpto1mO7WzAk7m/ZtgHZABdCbOguCNMSW6VNJMH3DRDJJI4bqzrm0v6gXY3dGQXo41zy5\njsrKGn788VFsQvdn51/5O79jRuFhhrK+ZCA+qRfgtX8f+RXPYWswIiPnEBc3kRtu8AVTbS/fhxh6\nn1jDNr8o8i+0CbNomBqqv/oaRte3p9SSlHrPrfPGktau0fU6fclU1v16hdP5Lc3YkUg8ESn0TaCx\nE1MaSndr7oQdW08WHdhjLYv13g2FkwrMZvRNGwgHViAGQK8sfZAff6wA+gLHSEjwA6pwFLrU0oF8\nMNTEK+ujya26VOyuqkZMkVgG3AmE4FcawNY7biMgdyBwD7YC9iKZAkMUKQWHRP6VH1ABKZ0O0bN9\nr3rrr76Gcdpi57enSK0rdLDfBt89ORTkmeuML0RHF9epw/rqyP52Fl4NObXnGwyFTf6OJBJPRwp9\nE3DnxJTmxvu9qr24PXqo/d5T4yazJjjeLohVcVUsnfE5W6ZPxT8/3ynG/kdZH+BZ65bPgQcR4l17\nlOKVy49+95Bb9TeHM5cBD1E73PoAEfmbGJyYSAKJ3NbZQm5pLzqV/crFbTZxrGQgh7RDEIH9LcSs\nmfl6qqi/lLxUzJuvJdX3RsYdEAuSuDaC9ucuAw5Dno+Z0A1tMNSU0ScXFmhZGKdNZcbMl0jUJpOX\n143QkExmz3m6Tt1WVleREBTvFHKzX3+YERhFmxNX088nk9EpW4kft9LJNlkiOduRQt8E3DkxpVnx\n/hKFzgfHk/3nrUxL/4nY2JvYl/qzyPcGUGBfys8AtEs5zK3AEiAPobflNY5hjiDr73cixLsMCOCa\na9rVCYc4muwEks8wRrEAIwmIpuK5zjsoMBwT9sRVcN+R9VQ8QG07sRfCu4bb62/cuG/Ze+BvdisE\nW1wcICUthXvmDMNUfhT2jICSGIhMpmyYkbJ+cOtyWKGJ0gRnpDPnm1cw3f+NNe4PMz89QUVFtVPd\nhhwIEeMb1sexNQBJ+i4IBP5ipN+WnWzfkIXZpDBhr4FXN31N/yHhzV4VSyLxJKTQtxJNjfebqo+Q\ntXogpgPCK8Y2cBiZA+kO3fbIY+K8g+Zc7rVeIxJ4VwULm0Cz+cMXihMIAR5A4TX6e+/i6O7OpJba\n9tmU2pauqDOMtdZQkBg2sGXvONoTp0fg3E74Qo92vWqf3aUhSUkNtM98PbBnP+WDy+DLEZC1UhyX\nr0P1MPDxIT4rhvtJZgFGCgxRdeoxrTiNquoap22UUidVMnbs205vZ6OPHUIhi4kM50tWQr5C+irn\nRqip2N4sTNVHiPTu5pGGaJLzAyn0rURT4/0dOwZx2Y8rMbkMHD4ceS1dl68hPQKicmBwz0EAdA8L\nY7kpixxgBvBOBHC1EeY9DTVDED34/4ib48dwfiWgxocvji5AmI8uo63XMW61bESnGhP70X0OsKBa\niLwOaKGhnLj+RgbHvs2WaVPs9sRROZDo2PiUd3UKc7nG1c1VG9lr7YHTybrZxReHP/tD+euUUsAX\nrCHe5xbPULRZAAAgAElEQVSGVAbQxWJ0qsfottFUlFc5bbumxyD8yv3toj7j7peY9swmMjL+Zk+p\n3HrgCfS9e6zpp+4ZnLW/tSmAnuiRhmiS8wMp9K1EU+P9ueZcjpU7p0oaDIVcPWMOn90dhfnPbhCa\nybSXHgWgumcv/m/vHj61Hh2dA4ltgI6BcHSE/bpe/JsLfVbzSfUa7uB569EhwEMEW15kEd8xieEA\n6B068Pllw+manUV6WAQrDkSTt64bYYmxvLF0it2eeHBkF0oKFUwWk3i2WOf1Zx3z9g2GQrQOm2sT\ndyqxvmg4++J46RFYUMAaMCqtVkhI0Bk6VGFkjJ+9HuOmx3HiRJFT3c7464vM+fpVcX1d55WXd5CQ\nULtSFnzGW1Z7Zd9NR8QbhBsGZ88GQzTJ+YEU+laiqfH+ie9OxDTiC/DKhPwYItubiY19lmnTNpJo\nEnnsKWU6r83+jEWLou2+8Nkb16MXFvK6pvDL28PJKq1Gd4hjWMiloroUCwrZnMAxxlFKNSqPYmY2\nEApHdcIq43jQ8CPL13jxW1XtfYeNuY9b/08n9s1vGBwSxgMne3aHmawAl46bCd2st+0NXhu88O31\nA0r+g0QHj6JPn0oqKwNJSNARAaPaHnfmkRCm+EFwBhQYdLx057o1m/O5adRcTIUXQ0g1ScO+JWT3\ndbj22m32yoPy8nniycX8ursQgpKp7J5LXv6QZoVcpH2wxFOQQn+WYF945D6RKtkp73K8dAu/bzqB\nq2jZs06i0uny8GCeW76TzUevJLNoJSIsMxeRYlkCjCebIm7Gh0ymA28govoHyedRoDtisPZBQGHv\nj0cZwzfE2Xv/4r6l5Rey6vBrVH5Y2eTJRmFdwjDtzRKpmJVwcd++bHh1q9MxeXn5+Pl9xqZNR8nP\nrzVQDjBvZPjeb5nEcNKS+vJR/xf5z4/T0bEwfclUNn0TRP6hT8TxJh0YBe2ScWzQHHvtoaEh+Mck\niEXMFUjQwW+xX7NCLo7jK5E+FxA79u2zYiETybmHFPqzBNeFRwK3p7Liy0Fcmj/AaQFug6HQJaMH\n/gz3pvioLfYcAvQDazgGwJ9w/IkBtgHT7dcSAm8A2lqP1OnB7yIURDKJjiOcnZPhEoRtQBMw55kx\nZ5vh0tpL9SzvXec421tAXl4+06bVhn1Gp2xlkmk4X2AduM3UmTbtM7hwlaiDEucGifwYLJe8T6R3\nW8L9bqRHj1JiY290upe7Qi6O4ys2/x37IiwevJCJ5NxDCv1ZQtyUOIpfLeX3Pd9zbWYVC7V8Qsln\nEVn4MYo0YvANOUJs7CzufW+ek1AFhtfQl2Qy7MLsnFVTE7aZlEqgeCDO6TIir6YtRnz4FS/CKQdy\ngTiM6Iziq6AY9OBkuNAIe8Di0zQPu+lLpmK6MAv2Ar5i4HbGzBca9J93DfvEj1tJ2t6+TuVetw78\n/wiCu6nrga8kU3hpAYVXL2Bg+dHGW1i4icY0Ip7c6/fkskkaRgr9WUJYaBj3pvnx6I9VjHDY3hkx\n81XHyNIhdxMaGkLA/lynWaTROUKYf+A58rgGMANzgE4QtIvCvxqhDfjOi6SqZjgivLMGKKYNTzGE\nbNawCFDYyVQGe93LazHpjO4TwKbSRRwbkANlCsQPp9Q0gHHjvqmTf96QQGSUp4tVxC8Rx3XKi2DO\n17Ocer0VcVX4+/nVO/u3tLKSCt8DUFUr5mVlvpQd/gTi8+wTotpmXIYe/AelfzWCrsCXw1mXe4V9\nwlZoaIi9jNrxA/gdCqQ6/XaU/Ev4U0llefJDDH3ngxZPompMI+LJyxd6ctkkDSOF/iwiOCOdYpwz\n3PdZ9/0a0ZXE4huY2mchlYVPE7l9I2EXfUeWNpztRTGMIJlofieP1xEhmcniKkU6LAqBHn+gXraQ\ntN9CKSEMeApQKEPnB2YgxD8EKCCz7ZXM4m/0oZTwiH0cU3IgfjjsW0k1CqvqyT9vSCDqEz7XXu+v\npp+Fb47LuQlTJ7EldTVeV0C3vaPIKbmLKksQMBRQCDEPIeTAZgbWGFlYYmR8N/hSAf43HDJXUmYt\n6+ZtD7Iufgavff2qKONxBX4fD0UfAgq70emVMIp2flPrrIfbVBqTbeXJ2TqeXDZJw0ihP4vIiuzM\nhjKYEaZQmDyU8pL+KIRzIZspzqnmUE44tkHT0pIn4LfJ5PEBeShkoOPffhTRhaPI5g7KHUM05dfA\nvtcxB42iP3/wCyL10La/imsRPfyHgDUUFU0jKUmkJgZEGmHc3jp576mpgU5lb0gg6vW5+XiKk/hT\n6nRp+7nLzVv5xTYD9zYj4e/7kWv+AtuJvb1SmPRDBf8oE6cu1GDDFl9yLc5lzfPpzcRn76LqkghR\nxt+HQ9EdTsesbhdDevZ7fPn6g2ST3eywRWOyrTw5W8eTyyZpGCn0ZxFrVS/WDAS+HA4l92MT9d1M\nxVd5DnTnlaKy7SOc4nNF8S2kBxzBp/wwjnbD4qfCUWLwvuB1yOwJ+nBqlbaENj4nUPutJDW1nMLC\n2muW1/Qm8veuHPc+QJXDu0Zu1UbgdnvZGxKI+oTP1X8+QA8Q87vaiHOPpeeQl2/G1Al7yIj8GIq9\n9xPs/zhllf3w03cRYjrKqzzFapJZiJFQIKhDW3Lz665dm+trJnwPMBgoirHWiUOqafdkEocWwp7V\nEHN6wxaevNi3J5dN0jBS6D2M+hbCCA0NIT83V/jYXI6192zzqgFQsHiFQ80BHMVJJ8Xhcx5YstDL\n21DFAGpFfiEwAdCxXJDMkYLhoL+AcwrmHVwS+CTx6/5JzKBpFBb+034PfJMJ7xqOd/kPHAkaJcoW\nkkzY4Ayn56pPIBqK27v6z6NDwJY2lHcqgyowXZjFtMVTuajHINLjvWGfyLipQKfCasBWyef8YG0I\nU9FZzXP0iThO6R9eUBwMQZOgbTj4JMMtRo7/qmAOycJ3I+heyVQzFRHiagtBa0WsX0EsMSiq/LSF\nLTx5sW9PLpukYaTQexgNLYSRMHEiA7V80i/DmklyIY6iHhiwmaKqgVC5HJEtU0xbvClhGaJR2ItY\nzvdBanvqy4Fw8H0DopNh2Br4+HnE6OgEIAG88vEJ/yf7Squ4ovdH5PubQB0ler0hyaAaCdjflcuz\nCjjygBEUI+hwQZHz4h/1CcTJUg1dQz1KOHBh7bkZeemsmPQN25Z/Tb5TppDNgM25ISzlGpJyiiDH\n4fnbjIIrjbATiobqtdUSZCR4y8P0KexHdlAymROMwvxMR7jDgQxbSM4qpNB7GK6GXzavlXZpaSzU\nQFkOB8OMpLatpqLkD6q8wvEN2cyg9kaSTsDRytrFQap5ls78zIk24VSXdwLdWfzs+fFVs8FnlBCz\nNrbJRMLsjC6jqA6G4n3xFKMAT0GHUfCv10CH8B98mVQYxrc1WcIr3x8ogt3KTm57/YbmLblIXddO\nfesI2NpbNC7DjBgCoggNCWPIVeHWwV9HA7Z8RMOm4xyecnl+RYUDI8AUA3niugQC7SF0gJEdG1eS\nWARDtkBZR/AvCuDKqKsoyiuSYQvJWYUUeg/D1fDLYCikwGzm98xMhiEsenVgGWt4V13DjhEKFauH\nszb/eXy8k6HPMMi4ESqqqOBSjmLhyrLPqcGHnYzGOWdnLzARUPA+cD2XfG+ki6+R9aGjqFJiwDcZ\n7jHCV86TjkJTYui1wWg1UbsNS0c/THl7xAsDwB442i+bo0p205dctGIL9aQWHybzy4vIO7YM2+zW\nSMtkYo3TxHFW35yU1ECOla7nWPERKNgL5a87POdcxBvKaufnLy2GHAeHTEbBX4xQAeF6BDrZ3H0F\nlN0mDqnQy9m7YQc7Pt4vveolZxVS6D0MV8Ov2Ngb2TLtSSKys+1LhMSq4B0BmgX4bjgcEGJVjQ6d\nR0NlEfAyNkE7wB9cGz0HjnSEaj9Ez7YI6IjouevUWLrQZ9sIlmOk6w3fkX2DEZIRURzXSUfBh5hk\nvgS//hcxcFYsAHETdoAu1oPFl3qzZMA5n76LVxeGVtwlslhcesi2UE/8uDHEHetNosMF22R0Y+sz\nUxgcO5/Q0DAWLRrNuHljhAPmHuDXy52WN4Rg4GMgDB+fFzB0785x8/cU1VyM7ljQzP54L/DCv6Q/\n1e1MxN78J8cC14Gi2w8pb1/GihsHcVGnCAoMBvrNfJHXv5klJxBJPBop9B5GaGgIb7xxo31A9skn\n1nLlLz/Sw7r/KxWSHRf1eNvFzvfgTaA7K20hXVjbC6joAqaHHO621PrPH7iT7/z30/Xq7ygr0UWW\nS29gD3h3MWLJGYXuGwNhyeQNM/Ktcjcr566wT+3/78KtTLOuC3u0JJujera9jJFekfY7utozjCy/\nm3VP/dRgfQRnpBNNtZPdwoCyXxmzykhcZSVt/PwJzkhn9wUpMMBa5u3OXjZigpgIaVVX6wQWj+f2\niG/44UQ1tWtw5UFxKTWF/6SUYn4vfQL9xzEEXudHpV5RG9bPgZmmLBRTFnrSLq4q2kHi4Cw5gUji\n0Uih90CcBmTRaUc8L2NEB15wXNSjHCh3dpxEL7HudBAwsmHjW+CVYv0cat2fDLxg/1wWlUzZjbpI\nplkHfX3hz1Iw3wx0MoJqtJcxIy/dqcyOg63LxzzIluWrHTzy9drzmjDhxpxnZm5YDlXX7KLb3lF0\nKIohgGTiMIrQ/batTMgXQ7GxZfDn5Yhr/9UIn4+CKhXyS6DiMhzXu91zrDt7cp+Cmm7ADPAKh7Zp\n9glStoHqP/1iSPzZyIBqL6rDLYTlwHO/iWZjogppEbCvJlt8D23qPo9ceETiKUih90BSUrwRGTHt\ngCIOoQJCgsJyIFVHiMtvQFgQ5NRm2kCU9ecyhLl7NvA6VDvGq/sBReB9HGoWAl2Aw5AyAOZFQmAQ\nVT4ahzsaCW6LELEK6qzS1BBdTdnEafCwBdZHwBoSSJjzIO9M+LBJE26mL5kqesvWCVEhq35gTVKZ\nvZkKp1Z0j0dA2GqI9IP8SujQ3ciegyOoqViJo3jDA0AJ1LxZu90yF6p64jpQXdI+mdATCn33/R9/\nlvbEUL2TTIxMUOFL+1uVRYSLYuo+j1x4ROIpSKH3QMzmDMS6UEKI/iCZ+xnBAowM0BRS3h5OfrWK\nTjFYIgALYqZPMELg/4KYyVqNEHWbgBUgAuhWarpRu1i4DtXLoegxKPoIGEtxYSRtrzOCbrKHcQIr\nfbn0RFtuDisn32x2vp7tLgYDE8p2YeyL8LBRLCToq/Fb7N+kCTd1UiwH9WaloRfBGekUGKKoqqxg\nQupqB9GFzsvhQ01k/0/wi6HASbyrrPXTC2dRj4DKgzi1ZEFradPPyISfRrCtcCmgkIVODaM4EmF0\nTt6phqDEYAb1uo7YSbXPI+0CJJ6CFHoPwTZRKjUlkLycQBzFuYowvmAsCUTSnTTyiu4HTAjveNuM\nouegzR8Myatmi3c2lpouiK83l1oBW4PNw0ZsS8FZ8NoBa7FbFRcNp+xgKUP75LItdSuVegm3Hqpi\niZZPKGtYPmECt3zwsfNz5Jn5qkcV63Uv8LfUGZRtyoQb195/j3a9nLxmCvLMzH1mKyj59nv4R0Cx\nBgWKglLpEtYiA/H2ko7zzOA/wdITlJfBvz0YNsNII1GrYIvL0oKafwzX5xjJcoyW+ULhZQX4lfs7\nhWakXYDEU5BC7yE8OeV71ibYPGbmIISpAIjD1usuYjj7mIGY9BSPk4pWhYHel83e4eicAO4FfgAO\ng33SVKXzOXTDWQiLEbn1tccoxTGc2DOf/AEFsHo4q4pi6N8umY3FRj488gOxLrny05dMZU37ePGX\n1YRwT32cqvcfHBpG/wtvJF3/1n6P3jnCkWeBnx/tK4LIxzGsVQMcBWY5PPMLiLeSR4SrZbkOpZsJ\n2wB3abCZZI46PERez2SohIFferE32kJZAPYXhNTiw/WW33HhEYmkNZBC7yH8uruQWoGNQsSTsxA2\nBLXCq3Ox9XMRTipaUw41r1vTBfOAN4E+CIEXg5DwufM5eAGzEb3cg4gQUFvEoiTimCj9IFRnweoR\ndquBTHRuDBpF5m1GUPKcsk3s4YrewD7w2upF+/D2XNP1OmLHN03oGtP7f/6elzDN2MFxPZsIk4W5\n1nkGeeEduNT0B39yqfVIC+0polDEkuz1CRdYn99h25G7yM+8mCUcoyPh+PpNpCo0CMI1uCaeVf+d\ngE/FJShaMkxcaPfgCdifW2/5HRcekUhaAyn0noLT8nYWRL80njqCTpb1553UDrj6IbrPBcDXwJ/A\n1Yhe7ERqnSeHAi8CAxFR7BGIxbYfwjYNy35d/2x8QzZzf8lPJOVAYpFzCCOH/qAYbR9Zl5rAuLf+\nQRe9M0k6Qvwuhs5aFzp1jsDPp24sv7kUmM1smT6V4Ix09h/LYbspy14780JDCb/+RobOfJGvR34E\nR2stD7z4GTiOaAjXIhq1I4j0Usc6DsKizyaHZeTwJ1ReByUJ8A8jxE2Aig+pRoEanTYLFPoNiiM6\nB+70lRk1Es9ECn0r4mhgFkAbMau1+HJotwvflK1U1UQDj+BsMPYgiv8k9KpLwBICjLJeLQVYCRwD\nnqQ2hXI5ojH4GIJ2QdE04BMgwLqvCFiEGMCtRjQKd4KymKpBRmYdh1uzIbIwGZODGLZrc5A8B20s\ns5Sxqs23DK0exsjyu8koT+dYRg6m/lmYlCyS9F2Yxu/gWbOYaDQ4dn6jZpc6irrtvC3TpzJm1Tco\niEi7Y/9c7dWLq61x/OoON8HR2r0V9ESsrrWQ2kHo4aJuWI4YrPXF5mcv3oa6i3ovHgrvA2XO2TlK\nzSXs2CxqenakmYI8s5w1K/E4pNC3IrX58gVAKV5eRwkO20REzRoCap4niXGIHndHFH4BDOhBb6J3\nTAFTMJSPR4jO52BfrFsH5iEW+L4T0WvVwX8/TIiD+CzQ+kDNyw7HL0MIvC8i/XAZ1ORCbyjzB+NN\ncIXxJ8L2P0iJ0p8OoZnM+XQS/96i892+7yizlNnj1Nk1JtY9+xMAt71+AyYlSzysAlRncXeSmGi0\nFKVRi3g4irrtvOCMdBSEo80fOAaaoDg6GhCNaObhbcA91PbozcA0sK0vay+YLyKOvxenLCSKEG9H\n1sHpsuGIbKjaFq7Sdy89roGCUoj0y+KrsYPoEvQy2dkd7O6jHTvajNYkktZBCn0rkppiy65JAIZi\nsawl78Q/KD7Rkzb4I7xZ7gJWoTNHHFukQ9F/gJHUhm7ApV8LDLPu14AI8MmFT58R/i41US7HByHy\n7RVEo1MJ3X6HQ4jxyz1QfpWBDd8vApwnAoVWhlF2aVa9A66uWSfRObV3DM5IP2nd2O6xtyKBtSrE\naRAG+KQcYr/ZjBEh8g9iNxNmT2RXJsXFUVUjGtG88ufteyNZS2lgBpQGI0JdjqEaP0Qi/O+I3v4x\nRCisArjGpa6uAOai0BH/oAOUj1lAWri4jHkvsH8AST+LQfWkJB1T4mTW7ZlFfWmoEsmZQgp9KxJg\n3ojocdrSGkVCeBWjqbJO7vFmBjqRWOx92ASE+lpDLGzGdZEM20IiUAhe7aHtXihaBCVLEMHzY4jM\nngsRyzeVimPpAnwJQbuhSwL2cUsdzL+b7eV2mgh0IUT+3pVOhog6mTGOWTO+e3JYoInevQ4UGKJO\nWjf2e1wNKVcBy8UKUdrhw1xZXkYxMN5aaw9az6nuFEFIWBjHjxdZXT9D7XvL/BPJnxwH8aPguAq5\nO8FiADIRYyJ7AUcjNNvkqmdxfCsQqZkGaJtMzRXvidP/QNSVH3VW2sIURkI9aagSyZlECv0ZxDXe\nPD74ENGmUfzAxdZFu11z2hVquBZffsOCDvwXEU7oisgJ/xSCNAhsB8c00KMRanMnQpTaQ9cEKFAR\nwpWDCD3YRMwmXs8hQhrB4PUyTIijTWobypQye3HCu4bbn8NpIlAb6GSIYN1zP9V5XsesmYI8M8Zp\nU+2TnQbHnjwDx3Wy0c7oNiwoCmOWw8CrrSeP9fP+Yzl8d+WVnIjsRpcuQ51cQEuDkmvrtygCLJWI\nwehtiPGNAGoHv++01z/0A6+nwdJL1A+PAMHolVuour6qtgp/RiQxKc4+O9Ek0y4t+6TPKpGcbqTQ\nn0Fc482zI7uyECOf8B3PMoUq52gzwotmG1X0RMTga8AWwkEX28bECS+AeeOh6ElEH3czsAEIgMy7\nQD+ECEFU4NiICBTEslUJ1u3Clya0KowyvTYk06NdL/tzNGciUHBoWJMW1na9R7/+Q7noaDqKKcte\n6rbAjoA25ClwSIdHTFlEmbLQSeRQxHb+9P+RwIreRJPM4WAju1aNBO1bh/qbg2j45uE8kWwZkIaY\nbHYALDGIgWoHQ7iqAfD2eBizUNR/W+Ai4DIjIZ88SO/j4r4LMLIu+r5GP7dEcjqQQn8GsQ0igpCU\n7mFhfGCxsOfoQKp4HpHT3hkxANgJMcjqKOwf4SzQPeGbEXC1ESxBCJG3TQ5qB8wSk4DsvXYvagcZ\nHUM9h7APQlqG0Xnlcb7933hmf/WqsBOmC5XelfaFRGb+5SX46vROBKpvstTWA1PQk3bZS70joA1P\nlZehIAJZP1H7PtMnx8RUry95GAsA/0uBMV6DRVqkvf46W392cKnXImCS9S4HESGgP3A2hMuHoutg\nSRU8vVgkRFnfQC64bz8TMqsIzsjEaLib0dZxA4mktZBCfwYpMBichKq6Z28i//yT75ym2T9MrQB/\ngrMAHcdZoI9CTQxgBIMG+2ZQmx8egBhYDbEeb0AMus5FzIh9CbgUAtZDTSRU1d6nc+DtRBt62MMu\n9iX/FKsV71fKaZ8IVN9kqcGx8+1ZNwWGKNSUQ4Tu3cMynBdInIdIlOxgsdhrC6Cd5YSDLbHNAkK3\n7nXcngtswTluPxxhOdERoegihENJKmwELqu9jKtVQ0iYnDAlaV3cKvSqqirAAqA/wl/xEU3TUt15\nj7OVArOZqsoqPgkJ4QTgd/mVVBcXkVdYQBTJJLIG0Vusda0UGTOOAlSOCDVcjPC6eQgKYsVYYtfv\nQHsNat6iVpjEQtm1nuyRiLzwMsSkqeFQnSOWD6yqvY/BUOhU9paYczW0AHhzcA3/xI/7B/rePXUC\nUbaco/9Qmx1fCXQgmXy7HUQRXLAd8idBSU+wPANEIywSplLbc3e8cgSicXittlBKiPi6koEQ8D3m\nxdXH93BX4kWUXxROj7Y9WfLsx8isG0lr4u4e/SjAX9O0a1VVvQp4m9oZPec1W6ZPZVxCfO3w50/r\naWex8DTwJUa+5mYsFCGWvHMc4ZuBsDLIQIRz/kSIhnWAtaIn/HEnZBugpj91QxBGxIDjNOu2NdRm\nmVwH1SFQNJuAoGe4sOd1dO5ygkqDkdtef9cuzC0x53JeaMS9C3MMjp3PW5s2Qn4+w6ittWzgARV2\nR8ClOdBbE44MgT3XgL+PyIwJSYa8ti4e9IuBKITIQ91sJn9Eg/k5orEohIAkYRB6GIiBKt3Ce6sO\nkR0CmLPYk53Mw3MtLHr6M7c8s0TSHNwt9NchYgdomrZdVdUr3Hz9sxbX+PzVFpEwGQrcg8KjHEXk\naDsedQkiKJFg3ZcEgflQegciXj8dLAoc7GI9zrbYoE2YTlDbiw+17ncMcryBSFIMoXfU1axbd7M1\nTPNdbZhmsdIka2FXmvo20JQ3gODQMDoMuZFRq75lOeJ9xx9IUGGlNanokA6jlsNUTWFywXDwi7Ev\nMO4d9zw1TvXtg3N4zGYZYZsNOxKUnbXRHqqgLBLmvwWhm6GjEbrAsVCcUlM3797c6PqSSE4H7hb6\n9ojAsI1qVVW9NE2zuPk+Zx2u8fkS608dmMhwRA77b8BonIU6GCHOOpAEkScg83Eo7wF8hsgO6WQ9\n505EsKIMEdqJQIRw/mc93zXIcTG2NWN79CjFnGdmU/oGkT1iPaSp1sKuuL4N+O7JYcttNzRog9DU\nN4DBsfNZkLiDS0xZlCDCNEldvECx2J8hKwImasMpObESKADTGki9Hq+qw9Q4DbD6AeMQ4xjh1vp6\nCvEdLLf+DMK5sVwGVQ/BsamwdBI8EYdXANQ4VnNgs6pOInEb7hb6QsT/BBunFHlPnR7u7nKNXvIx\nyyf40i4tjR179zK2rIzvELkwicQghPkCRGTZJvJmcIwpUw2HV1rPeplasZlr/RmC3cKAYmCy9e53\nIYYodXAMciiboH0i3brm89ZbT3PjyLfIN02GPaLHSxvoE9zrpHVxqnpa8uzHTHjHl7TiNLyTs4nf\nkEk4wgbhjZ2J9O3ShaLoaO6MiyMkLAxT9RHn+UbVR+q9R35uLgkTJ9IuLY2uA6/gGFcQYTJRHB3N\npdEVpOur7I/pnQOJtlAXWcBTUK5QhY4XT2PhEoS52WPWOpxA7fiGjUIImgRFru6XQbW/V9xB2Kd5\nRBnWsEsvtN9/SNSQ8+bv/FQ4fm+O37tt37L77693X2vjqd9fY3G30G9FKMlXqqpejVhk7aR4YjZC\nc7JJ6jPfcu6t+tpnR+o/beLO+z6mhD5UU8QxOiJS91IQPckLEZHm3ogB2TaIVEBbJk0PnMUmitpB\nXA2REVKB6IFi/WlGBJPfQMT8d4H+PErbhymM3sjgO6s5emiBuF6+DkWjCLl0M7PmxTZYF6eqJ1ud\n3G+tk3ZHKwgn017qizMzGZ6ZiZ6YyNKKaoYtWkqHmk5O0SfLzkwOH8yo0/OPH/ev2jkJwOzIroR1\nikAHHr95Cllzf7NbF3+uwfUUIxpBZx9/S+dAOHYALOHWespHjGNk4BwGO2yN53+Ks/vlH9Su7lVC\nGTGEDswlInEPhW0LCK0M4635b50zf+ctxel7c/jeT7WvNfFEm+mmNjzuFvpvgVtVVd1q/fxPN1/f\nY6nPfKuhP9LXnkxgP58g4uwOq0QxF2dTrSUIcb8Y7JP+14D3PqhxFCGd2uyaYoToZAH/RsScI6z/\nQLhgFiGi2aHo9KPwipUUbnXJJa+JYcjFPnhZIH7cmJM0YI2vk9mRXZ2k07aMuUKt983Qg5CXAjUR\nwjXdmQ0AACAASURBVBtngZaNcdrUOnXpOuZxiSmLESbxpjD7561O1sWzfX1RAnpAkW2A2qEUYcnQ\nvhoODkD04k2IcI0FnBYtsXnWj0LkGLxCbXbTbETjewdl4WP4scMPop3uC2V6FjOXzuSDydICAep+\nb46eRyfbJ2kZbhV6TdN0xHvveUdT/kjNed0QPULbIiK2szq6fM6jrvBngRIKzEQMEqZa9y+0Hn8B\nYgDRMUXwOUQefQYiNBGImOlphKITIqQfmCx68tZ7RbY3Ezv2bbY80/gG7FR10j0sjKUDr7L7yE80\n1fW+6WrKZqYGwzXn67hS35iH7T7d8swoQC4KExnO7qoYir2zrUcKv30fnyKqlT+huhpuXyNOPliJ\nGKBQEKEcx7p/ldrwWD1rznrlg/9M6GoUo8K+QKkC8cOJz72Mit+/ITb2JkJDQxpVd+cqrt+bo+eR\n6779x3LgJOM5ksYjJ0y5iZP9AbsSFppJSllfRDjFsY+b6vA5DxH/dRSUbKAXVFtT+yi17nsNEZIY\nj+iVDqQ2RVBBLEKyDyFcL1m3W3ukRcMh3gRRRjq3nUjntrdjMBQSG/ssoSEhJ23A8nNziR/3rwZ7\n+/VNELM1EjF5ZlbW431TYDDgl7TLqVbqq0vHyVOujUZmaBh6WRYTGc4XNkvi8jwiI+fQqdPFHDuW\njsk0QdTRQR3KRsFAI0rIGvSdU6xvS1E49+hLEW9ckcB+l+/tOFhmQJkOWSYIMooI257hsG8lpSis\nWqUDn7Fo0eg6z3I+4TrpzdHzaHDsfJb7++J/8LD9Ow01Nc3WWlI/UujdxMn+gF3j9y+8+zAj/rIK\nIcbzEDnxxYhu4DL+v70zj4+qvPf/ewJJCCFkQyCAJATM0VahShUpIIqWFgUBa6HeWysu9Lr13oqW\nFChaSwVMXfq7v1a8l6rxuqGtLDEQqxWkkesSl7Bz2AySDPtkAoQsk+TcP55zZs6czCSTyWRmMjzv\n14sXmZkzZ57zzJnP8zzf57sIs8EuvIN2DBON2eNjGcJ+/1uEv4kDYZs/iAj8mYVhOxYCZRPFr5uT\nocm84zkSphVxuauSl399vdd1tTWAldx/f5uz/bb6xF/umwkFz/L3xkae+ngLmUDT2PFM8pEAzfx+\n86DRkDeCKQ8vZOnMm/jKbo44Tqd//2/x3nvXM3ky2O2egTDhzOVM31mEo8HGB7Z4hB3+AN6rogN4\nZvjGSktBeD3d4+lL50hoLoJ4iKu6XM86Kl4TGTXPb9rKeZSansFtb74p7OGTryXdlNdImnE6hxT6\nEOHvBq5xOHhz0jiy7Xb+wDQayr9N/TsvcgEDOcF4hMAbnjAvIWztLyNC72vw5JyvxONGif7/IOAO\nxEwzDvgZXm5//AWR6dLwu9cgrRR6APZ7PccO2gZJcKTe3qr9bYl1n6+/btNc1dFEZsZ7Zr38Roff\nY3xOfFwja+7+OUMzMkh07BPFvnVxPn58F5Mnw/HjO4GJGCJ+zehMdnw0m70NCt7eTL9F9H0SIm2E\nZ9AQZrNjiGjajxADaqrw0e8BJEPL4K+gxn/EcbTTvoNBF352B1bIkvaRQt/FlObP49/sdi7nXir5\nIeCAlqEIYT6LCMpZhbid9yAiWesR4pCGsCn/RX9sZJe0mf63IezvTlq7/SUjZvPPivfaFsAs4TZJ\n8Qxsh0aiZeuulH4iXtsSa/vgLGadhooBkHMMJgzK6kxXhQTzKuNGtjNlUCau/t/n+PFd2O0LsNtt\nwHTSe/2KDFsmGemVuFy57G64GbFvYe7DBMSs3VcwmgNvG/4TMKAM0ovE4upbwPAi4k7fSmrzeK7+\nTgoFBT8IY090no44GISatiYYko4jhb6LST1UwQNMoxJzqP0biI3RexFh94MRIr9cP+YPeERlA8IL\npBjhxmd1o9QQaREG4i1EZxAeN/+OmG0+D9oQ+O+nYPg/YWoRvXZsYFz2JE42XEG2rWMRrwDvKnGs\n1bMulGlQe9rGT4LqpdBhXmVkAr/u/wnfXvVrrr7agVnER9Qn8RkL0eqgz4nHEGYaq5j3w7NPYg5G\nS0IMrt6rK1sjaImIrM+Ge+iY1VRfupqE+ltIT5/dxVcfWiLpBRPMalDiHyn0XUxNdjZfl38bb1Fo\nQtjjn0a4T2p4pz9IwxMo1ag/b7gFov//NWJgyEOYeJwIm/1ARBWkBv24EjwZGe+FBhvsmgfMoO7i\nIpL79uH1h/8W1LVVNVV5XdauAx+1GfXaGQI1I5wZNgytrMxryZ+fvwmnMxGziB/hFFfxG4axDZqN\nDJ9WMU9EbHqbg9EMF1jroHCKXjWXMfzcp3zzRT1ne56hpUeL20GnI4ngooVYMZ9E0gQVLUih72Im\nFDzLirICsJtFwYWwr/dFbJw69aONY24FVujH7MfjFrgCj7ngLN4bswv0f8bjpxGDwF79PGYbs2cD\ntjMCNKz3MMq0MvdHXqk6uUX9ss1lfrA/ukDNCDeuWEFhQ5PXkv8/Z38FXIuxGuph+4RK7QkqSaeM\nauK1pXiL+f8g+taJWF39FugNtmqIzwRXPmg9Ed5O/RGD9VzqWlK5MD2HjS/OZu5Td7AuaY27bzqS\nCC5aiBXzSSRNUNGCFPouxBC1OX1Vzh69lQMtN9BIGqLbb0PMxFcgZo+N+t+9EcFODQhxsiE2Z6v0\n1wyxtrpeXmR5nI6wzxtJexfhNQNN3gYELkC+ko2teGgFDU82cai+gt6fHuR51en+dH/L/GB/dIGa\nEdIyWi/5s7NrKC/35Azqk3KCmtOG1827uLSFuAsT2jaD9gvEjD0RsZeiAOWgPQmNZvObEaS2CjFI\nwPvvN1Nd7fRKBJeXOoIldxe0e42hJBSz2Fgxn8hALCn0XYohaquAnexkLE18SiGwDnHLlSBm6DUI\nW7wD4bqXhfDoeA5P5Gw1Yna5GmGf74tIVnYTwgZ/HG9Twj7gKkQ64gkQlw6JD0BqpogGnVrEIHUw\nBQHO0nwlG1u7/G13wrHiuXeQzhqg7WV+sD+6zpgRCgomAa9w6FBfsrNP09jYTEmJ0VfJmIuIM6AM\nagtM6YtnIoT8alpvdhvF2l2I1MVTaGpK4bqbl7PpnfnuvolECL2cxXqIFRNUZ5BC34UYombkjJzD\nO2zj99RxKbAS4YdnCP5ttC7avRKPuLyLp9qRZjr2acRm7r8gTAnZiNn/cOAW3LPPvI/g8iIxOdU5\nlyjiSR0OJ/n5m3QhrPEZwdleuuFAl/nB/uiM8/c5sJ+9jlMMPbCP4rl3BDRTTU9P8wpUqq52kpDw\nCps3N+F0HkJsjJ8EsuFoA5CDGHyNOrqVCE8oc2bRM/rr5u9LpH22n9nN/BfmhSzvfjC0N6DWOBz8\n48F76Llrj+jPjAyaho+ImP3aWIH0sx/m5KAhIW1HrJigOoMU+i6ixuFg9/FjFCHSXo0HXmYadRgV\noF7HEwRlDAXWNMI1eGbpyZbXjGPzgJv144bjMSc86Tk28SgcbYLi38BWT2ZKp8vJ/BfmwZ7prFt3\nO2CjvNx3BGd7xUcCXeYH+6Mzzl88dw4Ld2zDZq9C27E9qJmqIfyTJ39AeXkPRH6b3+K937EBzx7I\nVMQgang8fYVYdSXi/Z18W7z/zCneK/wuc/eItAeRyHzY3oDqNeMHVtmr+GmQ/RkKvNtTFtJ2xIoJ\nqjNIoe8iSvPnsVBPrDUNWNwriaOMgXqzjX0CQpCbEWJiLdqdjshvc0w/ZprpNaP6URnCbFOByKUO\nngEAcUzCfnC+ChTBruGwexSkfQGzNoiZ+aG+mAXLVwSntfjIgh89yuzHZrO3Zn+HSgR29kcXSnur\nsN2nIFZE5rNm4/F2Mp7LwDN7PwXMwTNY699Jr48hvgTO/Jm6MzWsW7eBzZvfZ/LkXixZMiGseW7a\nG1Ct/WhMGyJlv5Z29K5FCn0XYb1xR424iPccVVS4vW8Mt71a4G7EjHIgYtM0F7HxeitiZrkAT5Rs\nNULYL0YMEuYw/TcQIqUh8rH8AZK2Q2MWYl8gHrgJtFSofgOKe5L9056QXaPP5IVg+YrgNBcfcTic\nTJqxHPvpPEirp3zqmpCWCGyLztpbzWaqrKxGEhPLaGjoi/cAexKxOjI/1wcx6FYjVlevIdZpbwCn\nSUysJDElmbOn42hxm31uw+m08dZbGg0N4c1z096Aau1HY9oQKfu1tKN3LVLoQ8jhAwco+dFUhlQ7\n2KfBdXgkeK/jFCX257ifSvaicDDBxelGJ2Jm/w9amw56IuzCLv35NIRZZgUiYtPIrW5OXlaPyLKY\nBfwU2AJ1dcBi07lXIUwSKXB6JHtr1pAz9G98/4Za9n1ezyC2cX3jKWqqJ/q1kebnb8K+50/inHYN\nmMGh71eEqBfbprP21vz8TV5mqr59l9DQkIzo/2xETptmPBHLycD/Iurd2/GOhn0a4bp6koaGJTSc\nMHvkeHtFRVueGyOBWM/de9h7StjoC4dfFDH7tfG9Chv9heelHb0rkUIfQkp+NNVtrtEQ5porLr6E\nmuwchh7YR6a9ijcpYiY3U96oZ1V0C4YhCjWIr+UqxMz9JN4zy3iET/zLCB/58XiGkySEqC8H3kes\nFLwLbQi3zWrgDFy4jd0Dd7Jb28mVLV9x0Km3vQQKExL8zggPWUw9OEeS3Ss8t1JnTT/Wtp89OwD4\nOR7Pp0GIPi5C9KtRoD1df87cl0buIUu66V5HgO1Q76nmFW15bswJxK6NdGPwfK9d6aFU43Dw3kMP\nUKsnzHONHcf1f/zzeRE8JYU+hBh50EH3bLfBhPc+BIT7obZjOzZgG+asijaEmaYIsYB2AUvwbNje\nj5hZJgJfIkwI+XiE/zGEl0gSYhZqbAru1f+22v0TwPYk9N8OUze4m+BI8G57WzbSbIupx8hd3x2w\ntr2l5TRiYKzF0+8a2H4PfT4FMuGMsWoyDBz6MUmfQHqmyOl/xuSR0/efMLMISneRdOpqpk3MYcmS\n68J9qRILpfnzSCtZz33o31TJegoTEs+LjVop9CHEyINuSGqlaaZgNjmkl2/HW3zT8HjO/De+g6IO\nIoSoGG/XvwSEPX4hRqFvIVopeCJqjQyYieJxn90wboPHXVKDjMYMNDxtb8tGavil2+3pDBpU7c5d\n3x0w2v7OO3W0tKQgNrBTEekjbLh947UB0HAEmhvxfFdTEPsiFxA3ZAN9vvsBp79TI1LVv26HlpEi\ne+XUIvgC+HERl5Z+wX+t2IarOT4SlxtxwpV+IJDPST1UQTwEPKGJJaTQh5Apa9azdOZNDKl2UJme\nwZQ1692vmU0Oky+7mK+OPUILExEzbyOfeQ3CB968YbsBITAvIET+c0QQlFES8AGE6C9DmHFqEWmJ\nixBmnCyEn7hhftAgeTuJzYnYSuMYlpdLXqrCb5Y/SuETvwvI9m24J0ZjLc32MNqel/dXnE5z8e9M\nPLmBdO+aRmPFtBhhSqsF/g1SFjHtXwFtEuu0NWJBNr4ILi5yn82WDD9eBc+pVZTcd5+7XnB3wyqg\noxY8ytZlSwISbiNFt9uc2YnArfaEPJAAsUCL2sQiUuhDyIXDcvl5+e52j7u1aAPVY0bzPD1w5zEH\nhKg/hGcTUAVbP9DeRWSwtCGiZ81551chZu85CNNCPSKCdggio9Z04G2gRNiOc/8Jw4pouFy8Pa9e\ncXvLZJ8HS1iDsWPN0bFGkrg3aJ2q+LtABdg+BttQbImLGX/rPgru1oVbdzndc3w39Uqd+3SD7fCm\nXg6xz9dfh/HKQkurur9lnwUk3IbIX6gfC52bQfsS8glPPuMW/xMVB6lBrGn9fU6gRW1iESn0EeDC\nYblcNf1mXt8Ap119ETPGHMRGqSkcn2ZI/Azqc/CYFZrxFqJkxMzejrDn/xetI2gHANOg92LILIJL\nPW/vjlkVQ8Ef//hDNO0FPvkkjtraI7hcSQgz1wa8zWq1QA5c8gH8eDnadsjof4s7ZsAYJL8+dJCZ\nT9xEdbyDRHsLI50NXHUN5ByDGwYPCkmbw52Fscbh4OTmjRQjIgeagKwjdt5A9FQabec0WmivapXj\nM9AZtPVa+xw80GrAsAZ9mbMP+fqcQIra+OrjSAS8hRop9GHAV0KwCQXPMvzTZXx1NBN4EGG2eQrv\nn8VuGLoNjjhFbVfWIezs5mM+B4biyUd/Eu+BQEPE5mrgOinM+r30l+tg57bt5P1iKGMHjeOP9/05\noKCnWCA9PY3ExAScTuFq6ZEK657GDyFlkbC728RTvgbHYdm5lP+3WM3NWfYvbOhbDDaRp99Vb8Of\nB31HxNs6q13R2EhSQmJIhb/G4WDjLx+k5ycfUVFby+Mul7eQappXAo6TAwdRPHdOK9NO3HslvAGM\n0491ARUDs8hsbAgolXWrlcSgwa0GDGusSn1aGqtzcjuV5sDXymHE2reDOlc0IYU+DPhKCLby4UJW\nbV7EmDHv43TaECKu4F2QOglaesKc5+Gto3BsMp6cOEbxkaEIN8rH9ef74z0QlONOjHZmGmy101f7\nkLrmOlzNLlzXuHDanJRo60l4IZGVDxeeN/m7W7mJ0giUAi769v2G3NyRHD27iKNxh+DV34iN1mFF\nZA/NafO89ha712mrmqr8HtuR5GNWYav9eAv3OZ2dtn9b2zP33WJstHYmTTH93ZiUROHkKbgaG5jr\nz7SDZ0BYOmgwA0ddztyS4oDaa1P3uO/yM0B6cjKF02/x2kMqnf+QV5CVbeIkJnTy+q193PPAPt6Y\nPZvEvfu79W9BCn0Y8JcQzOGoob7+IB4TwRHgSkTKg3igN+yfBasaILMFjp1E2PNvwxPPWIfww78A\n8ZMSEbRxiSdIyPyY+saL4KTp1j03kt7xZZwec9rjgQlQD5t3bWTyE9cSv/0YJRurxPZkN8h8aI52\nzcs7F3C6AaurpfBgmgZoXHfdK6xceT233XaYox+MB1LAfjF9j/Rk5qF9FO/xn1Aty5aF+bSDew72\n24aOhP5bo0czCb0Hibk9FmdSjG13DWiZPIWpKwspnXytVxusLsbGgDC74Bm2zb4l4Pbur/ja7OzK\n4sOH+Y8tn3sd0xXJyqx9/I3DwcK33grpYBoJpNCHAX8JwX70oyLq6+9BeHY0IVLhnkWIvGnD9dR2\nOLEMIeLLERGc3yDy2exE2PWrgKfp0eMsGUMrGTD5c76prKD+QB2cNP1c65s5+dX34JK3PUWobMA+\ncF7hpNz2JUyA+6vEZmIwAtJeNsxQrxis0a6BphswXC0PHOjNyZN7OHcujbi4AsaOTaGgYCoAX3xR\nj7nAS4/qndxRvdoroZrVNDdhZyN9vvHU0r3hEs1vGzoS+m8VNldjA1rJ+pB6kJjbMwVxtw1IS6Np\n7HhcaKw+csRLVK3tt7oYGwNCR691uM17EBtua31MR4PnArnvrH2cd3A/NnuVux3d1R1TCn0YsCYE\nM4KLqquzgM2IvCpmT5qX8brNtVz9cRpwH8KWb8x3jHzp84BVNDf/hBOVizhRlAPJg+G4AkkPQMtw\naMiC+vk0VaZC8QyYVgTboWdTPL0Te3PaVuP+yK8HAGpwAmIVXms2zFDnSreaYPylG/D1Q3/yyeuY\nNOkVjh4diRhk/5WEhHdIT0/DUe3gbF0vvCUn0/2X8aO3muZOnElji4qwrAHv9LX7bXtHZqVWYaup\ndlDottGHZkY7oeBZVja66PnxR5wC+o4dxzVtRI+a239y4CASXA08da7Wp1eL9VpHLXjUy75v3vi0\nWwYMewjMJYHcd9Y+Lp57B9r2bSEdTCOBFPowYE4IZkbTDiBcIMFbTE7gmWpXIwqHG49TcRcidR9v\nSXPsGgtHnsDLDyH+Gf1vQZpjIjn1lWRfJAae+X95SPiEG3d0z8Gs/s6AoASkPeENdaZCqwnGX7oB\nXz/0dUzHbr8Pke+/D/A8Bw4MBYSAN+U0gfqg+9wT+Sfg+dE7qh1s3rVRvLUBuAgOJde645ynAGeH\nDfPb9kBmpf5mol2Rfjc1PYOZL7/eoeONNhTPncMjun1fQ6TRMA8QrUV0jt+Nz7ZiUoK+tiDuOyMn\nkLDRd99c9lLoI8iwYRexe3clwjZstoYeRdQtzQB2gPbvwFLAmHViOf6sj//N22c10HwCTPIzcUw/\nVi760N2WVquO558J2gOnPeENdaZCcwWpvLw6v+kGjB+6URcq7r0StiaOQ4i8kYJ4Gg7HMiHgFRth\nulOsfpwjSW7ZxSXa5/y1/0jO5o5gQsEzPPLiPJxXOD1fxXb4XqXLHee8dNBgHlixAlezfu1BmK02\n/vJB9wapVv4lKxtdPsU40pvoHRXSto5vLyYlmGsN5r4z5wTqzkihjxAOh5OamiqEW+QeRGTrpYhN\n2dF4Zt9ngUI8bpUaopbs4wgf+mTEwPBrhMfN/yCCpMzbZxugxQjx1xg0aBkFBbd7tcffqiMYrKX7\nCgq8hTfUm2jmClJtResaP3R37GtdHevrNlPB3ZhXIJmZeeS/OA+nyyk20WcVgVbEd1bBt1U4fsXl\nzNJnptaN9uT6OJ5XW4yHXNJ/AGkZGe42BWO26vnJR96eIB9/5JUp1Zjxbl36u6BNYqEYJDoqpJ0Z\n8IPpx1ELHmNp2WeePlv4aMCf192RQh8h8vM3YbcvwDMVfA6R7wa8C1pUIOwCD5uOXYT46h4xPbcK\nOAEpn0LyfkjdBs4m6LEbTqSByyMV/ft/q0uLYFhL91mJVMUfY4CJe68EW10dAM9TxMb4HE65PEVd\ncnPPCQG/CJFtIgHSD0ORKra9n/p4i/uc1o32b9VnkY7YvPMlXoHOes3C+3Vtrdf67RSWTKl1VSye\nOBZlxIigTWLtRZ4GIv4dHcA7I7zBmGG2LvudV58VLv0dF3ZDD5pgkEIfARwOJ5s3W0Ptv0EIfArC\nn3setpRUejRk0NRoLTE4GFvPRrQm83PJgAYXVsGsFeLpz4GeG+DTm+HIPMymFF9BXFpLXLu1Y7sz\nnnKEd6CtW4MNIdyP3fA1HyR4r0Dmv7CW8l5fwmUIdT0M9ymwQjW2Y4UYX7+jkROONOz94VvDx/P4\n8t+3mTMo0FmsWXivQ3i/XIpYo/UeO47+H270uiPy6uv4xuEIOo+LL+Hs6Ky5owO4L+G9IsDgpKDM\nMOdxFSsp9BHgl7/8O05nEt529kTMbnywnN5p20htvgy73bo5W4XWlG55/y7gXjhVDm/dDM6R4NoG\ndxbB0CJYPQPOjqRX730sXLTQ4ymi2SgvHsLm/3mb3nH17lWGv9qxoSDStmTrzHNKwTP8xPL5xp7F\n5oqNOF1OqsfBW71AWwU35I4HhBg/uGE9v0DffJyeQHZObps5gwKd9ZpFKR3h4ujKyaUhO4exCxaz\netP3vL59O5CXmUnhlWPaPLe57+vzRnDVkgJS0zNaCefRrCxq9fQHZxDxwqEWxs4IbzDmv/O5ipUU\n+gjwySdxiEyTTyIKXeyBhExoNN/2WdTaz1DbvABhljHiBHcictnopQVtp0DLBO4FUuH0WThmKmpS\nNAO+XQR3F4GtiHoNlr7t8tiWi6fBrrU4seG0xEJu3XySmmpHyEU41O6VHUH4+G/m0KHb3auWVB+r\nlvS0DJ668xmu/9UonKM9z3+Wl8rSp/4EBCdUgc56raJkjvosnjuHe+rr3HfPfuAuYFPuiHbP3arv\nG5qYurKwlXD2bGzkESPqFuG/1RBiYeyM8AZj/uuKAKvughT6MONwOKmtrQA2ARcCNwHToHkJ3jN0\nG8T1g2bj8W3erxmlBQcshr7vwjc7wJUB9X0Rg4IG3Iit6jtolxR5WX4Mc0259qWY+btf9I6FvNK5\nmdL5W0LvwhfmJbR5Frv8+NWU2UUZxLZWLUb2xayUGr65AnfX96/t4x74unKG2JYopR6qIAex/Q7w\nclISmyZPCUi4/PW9VTitEa/1aWlcE2JhDLfwRmpvKBqQQh9m8vM34XI9jvcmKtC8GHd64rj/hRHb\nSTydQsNRIdieRFtHEGH6+vsztgE9od6asfI24A1S+lfQp2Uwdq3K/bI7aOsFG5t7H8bpFvcpZMY/\nRK4rhWFsYwVFfHjoipD3QbiX0OZZ7HJuJJDgqtL8eVxmryIHyF4F+wZA4jH46aGTFM+9g1ELHsPV\n6OKltDROAsljx/EDk1CZ90Dy+o5gye0FHXJXbUuUrP1njj5tj0D73teKItQru/NZeMONFPow0zqR\nVh/ET8mTntiW+gUDlK+46IJL2F56J5wZSe+4Ouz2+/X3vEFaWj29B3yBfWqRSLjV6pw26HWEJOUf\nZGRlQjlkDskkt88ICu5+xu1OWX2Xk/nzPRuR1zce4MGS4i4V4bDP5Eyz2GFso8y0aok//j6lk5e0\n2itIPVThjlhYpYp/wtO+AW3dGn7/6Sf85ugRdz8VJiR6CaE1WrbhhSa/7qsd3bMItv9qHA6vwSlj\n4kSu9fPe89nMEYtIoQ8z1mCitLQ99O7diN1uzNKryUpK5FzZI5RmbIaZL0MSjD05DdbYqK4eQnr6\nYdasmU5a+vXMf+Eo6+N20+QjgKpXRhnHLrNzLMkOA+DK+jGtxMbqCllTPVFENHbhDzzcM7mjWQN5\nvVz4M11HEfsH3k/LwB8Qf/x9SuzPkWnHnXlx9qYt7s3J68q/5CVEhINRBhz9/8HHj7VpfvKXyM4X\nXe3dYv6cuaZBfFViot8BRc62Y4uQCr2iKJWInIgAH6uquiiU548FWgcT/YTq6hpmzHiC48cHoWn7\nsdt/BaTD0YegZgb8tIhP/tGPat0jpq5OY+lSYVte+XAhk5wT2FE6AxwjofYUiYn9+OG4VzgwcA87\nAhQbg1j8gfckzmuHI+7ySma+fD2lk5eQqaehsQGX2asonT/PvTn5Wtln/FZ3/3sN7x2Uirg4tJYW\nr5WPeWYen3EMJuBlLvNHuPYsrJ/TnStfSTpGyIReUZThwBeqqk4P1TljEV/BRPPnb+Lo0UW0trHb\n4OQ1sKMITl+GP9vy8NRcdvx4DdiKQIMf1t/Cyod/xdyn1rJD2xqQ2HSGSLtLtke/I3Yvget3Vaeq\nMAAADGpJREFURKi71Q5di/fm5CX9B7gzF96ESAatINwN69LSKRw3wZIf3TMznwhMYTCuywaQlzqC\nJXcX+G1fuPYsrJ/TVg6eriba75lYI5Qz+tHAEEVRNgLngHmqqu5t5z0SfNntk/W/NWjoR/y2W7lm\nTBrr1vrOH+MvO6a/50NNR0wPnfmBB/tef0I6oeBZlpZ9xmX2KndJ9bUmkTW/LxVRql1EMcDwfv1a\nZz40zZgzgV87BjBh0YftFlEPlz3c+jkzTTl4wk0kXWzPR4ISekVR7kJUsTavZh8Alqqq+raiKOOA\nV4GrQtXQWKZ1AYyd+t9nABfJrrG8+MJM7tJ854/xl6cmlPlrrNQ4HPzjwXtI3Ls/oMLMAIcPHGD1\ndd8jr74OFbit/Es2+fmB+xL1QJN7WfEnpKnpGczetIXS+eJz1lpE1vy+L/fs5uH6OowwtaUH9uve\nN6J0XuqhCnYfP0Y1uI/x69Hi49rCIXJWs1xaRtsDUJe25TyOUo0ImqaF5F9eXl5SXl5evOnx4QDe\nJ9E07dSpam3QoCc0eFGDP2hQrYGm/1unzZjxUqSb2IrXZ83SWvRGtoD2mv63A7RlQ4ZoRVdeqb02\na5ZWfeqU+z3Lhgzxes8y0IquvDKg878+a5b2Unq6ZuoY8ThMrL78cq/PLjKuwXpN+rW/brn29q7t\nfOM12QedpUP6HErTzaOAA/iDoiijgMOBvCka03+2t9QOPT3o3/9b2O3XAivwxCImY+vxEQsW/AyI\nrr5K3Lu/VUDN6pxcdh8/xsLKSmyVlWhlZe7IS4CsU6e83jMIONyvPy/N+FErc4z1/Il793NYL0xt\nrHtOaFqrPrF+d/7MPR01AzmH5qB99ZWXX5PNxzXl9evP1es/AMDVLL4za5t8XVskvtvw3+cerlpS\nQGFDk2eVtaTAZ19FA9Hapo4QSqFfDryqKMqNiLp4c0J47phHmG9SERWkViDiHm1ozdNYuvQV1q69\nNLINtOA3RH/ytX5Lr1nLzO3tlcRQbF622hWNjSQlJKJVHOQ1xCZoKsIMkpw1kDdKNpCCJ7lXe/iz\nBXfURmyYcbTNG+nldHKjfg3WawpkI/V8zrliEIveXdFMyIReVdUaRMimJAgWLBjNhg2P4XKNQBQc\naT96M5L4q7zTlohZqwbdsmY9Ff92l9fstvbjLdxnyrHyVFoa/SZOcp+/NGEevQ5V0JCd4xWJ6g9f\ntuAahwNt88ZWz7d5Hl2YaqodlM6fx0YjIdrCRylc6j9bpb++k8FIknAiA6aihGXLvjSlRvD22vZX\nGi+S+Ku805aI+aoatNUyMGTi7X80PCfXncwL6PAs0NfAU5o/j0Sn08sMFOis2tdMtKM5zeVsVhJu\npNBHCd4uljcRH7+Uiy9WyM0916pCU6D4yjkfbHnAQElNz2DUrxeL6kfqbt4s+5Qpa9Zz4bBcn8db\nBwZXYwNayfo2Bbgj9nVfA8+22bcwCU8+0D1paczsRrNq6YMu6ShS6KMEbxfLVG688UJWrvxBp85p\nzbfCCzaf7pahFg5r9aOlM2/yW//TOrutqXaIvDFtmDU6Yl9PTc/wqpRUOv8hGrOySC0XIWkacLIL\nEnZ1JdIHXdJRpNBHCe3VWQ2GQPOthFo4hlQ7vMwvQ6odAb83ELNGR32wrdf33JSbKJx+S7e1kUsf\ndElHkUIfJbRXZzUYrPVM/aVACLVwWD1RKkM8W+6o14r1+rKOHGHCex+GtE3hRHrtSDqKFPoYJtAU\nCKEWDqt3zZQ1631/bpAmo456rcSaMEqvHUlHsWmaFsnP16ItEAGiN0Ciq9pkuAyahSMQwe1sm4rn\nzvGYVIDC6bcIF0bLADBqwWNsXfa7gAYEX20K9vpCRTTeTxCd7ZJtCowLLkixtX+UBzmjl4TE3S+Y\n2bk/k5HVpr607DPP5q5pD8HXZ/qKGJTujJLzHSn0kpAQzIauP5OKdQCwbu76GxAKsTFi7duhvTCJ\nJAaQQi8JCcFs6PqzNVsHAH9pBqT3iUQSGFLoJSEhmA1PfyYV6wDgL81ArG2ySiRdhRT6KMThcJKf\nv0n3qa+hoGBSh7PVhZtQeoIEmmZAep9IJIEhhT4Kyc/fxLp1twM2PVr2Fdau/Vmkm9UmkdjwlJus\nEklgxEW6AZLWWEsLRmP2SolE0n2QQh+FZGfXIKzOEK3ZKyUSSfdBmm6ikK7IeyORSM5fpNBHIV2R\n90YikZy/SNONRCKRxDhS6CUSiSTGkUIvkUgkMY4UeolEIolx5GZsN8BR7eDBP93D3pr9Yav9KpFI\nYgcp9N0Ad+3X9LZrv0okEokvpOmmG3CovsIcKOu39qtEIpH4Qgp9NyA7MdscKOu39qtEIpH4Qppu\nugEF9zxL4qvx7HXub7P2q0QikfhCCn03ID0tgzcffzPq6lZKJJLugTTdSCQSSYwjhV4ikUhiHCn0\nEolEEuNIoZdIJJIYRwq9RCKRxDhS6CUSiSTGkUIvkUgkMY4UeolEIolxpNBLJBJJjCOFXiKRSGKc\nTqVAUBRlJnCrqqr/qj8eA/w/wAW8r6rq7zrfRIlEIpF0hqBn9Iqi/BF4Ak8CXYDngZ+oqjoBGKMo\nyqhOtk8ikUgknaQzppstwH3GA0VRUoAEVVUr9Kf+DtzQifNLJBKJJAS0a7pRFOUu4CFERnSb/v+d\nqqr+VVGUiaZD+wKnTY/PAMNC2FaJRCKRBEG7Qq+q6ovAiwGc6zRC7A1SAGc777FdcEFKAKcOP9HY\nLtmmwJBtCpxobJdsU+gJmdeNqqpngAZFUYYpimIDfgCUhur8EolEIgmOUBceuRd4HTGAvKeqalmI\nzy+RSCSSDmLTNK39oyQSiUTSbZEBUxKJRBLjSKGXSCSSGEcKvUQikcQ4od6MDRgf6RNmAE8B3+iH\nPKaqali9dqI9pYOiKJXAXv3hx6qqLopAG2zAc8AooB64R1XVg+Fuhy8URfkCqNEffq2q6t0RbMsY\nYLmqqtcpijIcKARagB2qqj4QBW36DlCM535aoarqX8PYlp4It+0cIAERZb+LCPeTn3YdJrJ9FQes\nBBRE39wLNNCBvoqI0OvpEyYD5aanRwO/UlV1TRS16XlgpqqqFYqirFcUZZSqqlsj1L7hwBeqqk6P\nxOebmAEkqqr6PV04ntGfiyiKoiQCqKo6KQra8ivgduCs/tQzwEJVVUsVRVmhKMp0VVXXRbhNo4Gn\nVVV9NpztMPFT4KSqqj9TFCUN2Ir47UW0nyztStfb9DiR7atpgKaq6ng9SHUpIng14L6KlOnGK32C\nzmjgLkVR/qkoylP6KBaxNkVhSofRwBBFUTYqilKsKEpehNoxHngXQFXVT4HvRqgdVkYByYqi/F1R\nlH/og1Ck2A/MND0ebVqdlhCZ+6hVm4CbFEXZrCjKXxRFSQ5ze94CFut/9wCagCuioJ/M7YpDrOZH\nA1Mj1Ve6gP9cf5gNVNPBvupSMVUU5S5FUbYrirLN9P9oP8ue94BfqKp6DdAHsTyJZJt8pXRI7Yo2\nBdJG4AiwVJ+xLgNeDUdbfNAXj3kEoCkCg7IvzgF/UFX1B4gB+7VItUtflTaZnjIn/gvbfWTGR5s+\nRaygJwIHgd+GuT3nVFWt1SdUfwUWER39ZG3Xb4DPgEci1Vd6u1oURXkJ+E9ErFKH+qpLTTcdSJ8A\n8JKqqoaArANuiXCbgknpEBJ8tVFRlCT0H6qqqlsURckKR1t8cBrRFwZxqqq2RKgtZvYiZq2oqrpP\nUZRTQBZQFdFWCcz9E7b7qB3Wmn5vaxACElYURbkQWA38SVXVVYqiFJhejlg/+WhXaqT7CkBV1TsV\nRckHyoAk00vt9lU0zMQMtiqKMkj/+3rgi0g2JgpTOjwK/BJAT/98OELt2ALcqLfjamB7hNph5U7g\naQD9PkpBrIKigS8VRblG/3sK0ZEa5F1FUQyzW9h/b4qiDECYQ+erqvqy/vRXke4nP+2KdF/drijK\nAv1hPdAMfG5KKtluX0XM68YHdwGrFUWpA3YidpkjTTSldFgOvKooyo2Imf2cCLVjDfB9RVG26I/v\njFA7rLwAvKgoyj8RGVbvipKVBsAjwEpFUeKB3cDfItweEPf2nxVFaQSO4rEBh4sFQBqwWFGURxHf\n2X8A/z/C/eSrXb8E/hjBvvobUKgoymaEZv87sAf4S6B9JVMgSCQSSYwTTaYbiUQikXQBUuglEokk\nxpFCL5FIJDGOFHqJRCKJcaTQSyQSSYwjhV4ikUhiHCn0EolEEuNIoZdIJJIY5/8AW25Qgx4FEvkA\nAAAASUVORK5CYII=\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x1a8529f98>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"image/png": "iVBORw0KGgoAAAANSUhEUgAAAXIAAAEKCAYAAAAPVd6lAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nEXJBEASLI0IuCIJgcUTIBUEQLM7/AzdWcWseC/85AAAAAElFTkSuQmCC\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x165eee438>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"from sklearn.lda import LDA\n",
"from sklearn.decomposition import PCA\n",
"\n",
"trialtypes = ['Anterior Pole','Posterior Pole','No Go']\n",
"\n",
"pca = PCA(n_components=2)\n",
"X_r = pca.fit(both_c).transform(both_c)\n",
"\n",
"lda = LDA(n_components=2)\n",
"X_r2 = lda.fit(both_c, tt_c).transform(both_c)\n",
"\n",
"# Percentage of variance explained for each components\n",
"print('explained variance ratio (first two components): %s'\n",
" % str(pca.explained_variance_ratio_))\n",
"\n",
"plt.figure()\n",
"for c, i, trialtypes in zip(\"rgb\", [0, 1, 2], trialtypes):\n",
" plt.scatter(X_r[tt_c.ravel() == i+1, 0], X_r[tt_c.ravel() == i+1, 1], c=c, label=trialtypes)\n",
"plt.legend()\n",
"plt.title('PCA')\n",
"\n",
"trialtypes = ['Anterior Pole','Posterior Pole','No Go']\n",
"plt.figure()\n",
"for c, i, trialtypes in zip(\"rgb\", [0, 1, 2], trialtypes):\n",
" plt.scatter(X_r2[tt_c.squeeze() == i+1, 0], X_r2[tt_c.squeeze() == i+1, 1], c=c, label=trialtypes)\n",
"plt.legend()\n",
"plt.title('LDA')\n",
"\n"
]
},
{
"cell_type": "code",
"execution_count": 653,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"0.55268292682926834"
]
},
"execution_count": 653,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"lda.score(both_c,tt_c)"
]
},
{
"cell_type": "code",
"execution_count": 183,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"0.504249291785\n",
"0.610544217687\n"
]
}
],
"source": [
"# Logistic regression (once)\n",
"lr = LogisticRegression()\n",
"lr.fit(traindata,trainlabs1D)\n",
"# lr.fit(both_c,tt_c.squeeze())\n",
"\n",
"print(accuracy_score(testlabs1D,lr.predict(testdata)))\n",
"print(accuracy_score(tt_c.squeeze(),lr.predict(both_c)))"
]
},
{
"cell_type": "code",
"execution_count": 184,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"[ 0.5480226 0.55932203 0.50991501 0.52840909 0.50712251]\n",
"0.530558248993\n"
]
}
],
"source": [
"# Logistic regression with 5-fold cross validation\n",
"lr = LogisticRegression()\n",
"scores_lr = cross_validation.cross_val_score(lr, both_c, tt_c.squeeze(), cv=5,scoring='accuracy')\n",
"\n",
"print(scores_lr)\n",
"print(np.mean(scores_lr))"
]
},
{
"cell_type": "code",
"execution_count": 196,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Step #100, epoch #8, avg. train loss: 0.92758\n",
"Step #200, epoch #16, avg. train loss: 0.67038\n",
"Step #300, epoch #25, avg. train loss: 0.55278\n",
"Step #400, epoch #33, avg. train loss: 0.45024\n",
"Step #500, epoch #41, avg. train loss: 0.38716\n",
"Step #600, epoch #50, avg. train loss: 0.32412\n",
"Step #700, epoch #58, avg. train loss: 0.29467\n",
"Step #800, epoch #66, avg. train loss: 0.25110\n",
"Step #900, epoch #75, avg. train loss: 0.22444\n",
"Step #1000, epoch #83, avg. train loss: 0.21086\n",
"Step #100, epoch #8, avg. train loss: 0.92909\n",
"Step #200, epoch #16, avg. train loss: 0.68200\n",
"Step #300, epoch #25, avg. train loss: 0.56637\n",
"Step #400, epoch #33, avg. train loss: 0.45796\n",
"Step #500, epoch #41, avg. train loss: 0.39562\n",
"Step #600, epoch #50, avg. train loss: 0.33806\n",
"Step #700, epoch #58, avg. train loss: 0.30751\n",
"Step #800, epoch #66, avg. train loss: 0.26710\n",
"Step #900, epoch #75, avg. train loss: 0.24038\n",
"Step #1000, epoch #83, avg. train loss: 0.22661\n",
"Step #100, epoch #8, avg. train loss: 0.94208\n",
"Step #200, epoch #16, avg. train loss: 0.68829\n",
"Step #300, epoch #25, avg. train loss: 0.54336\n",
"Step #400, epoch #33, avg. train loss: 0.46410\n",
"Step #500, epoch #41, avg. train loss: 0.39993\n",
"Step #600, epoch #50, avg. train loss: 0.32984\n",
"Step #700, epoch #58, avg. train loss: 0.28884\n",
"Step #800, epoch #66, avg. train loss: 0.24457\n",
"Step #900, epoch #75, avg. train loss: 0.22282\n",
"Step #1000, epoch #83, avg. train loss: 0.20779\n",
"Step #100, epoch #8, avg. train loss: 0.92933\n",
"Step #200, epoch #16, avg. train loss: 0.61408\n",
"Step #300, epoch #25, avg. train loss: 0.47380\n",
"Step #400, epoch #33, avg. train loss: 0.36005\n",
"Step #500, epoch #41, avg. train loss: 0.30394\n",
"Step #600, epoch #50, avg. train loss: 0.24771\n",
"Step #700, epoch #58, avg. train loss: 0.21006\n",
"Step #800, epoch #66, avg. train loss: 0.17243\n",
"Step #900, epoch #75, avg. train loss: 0.15369\n",
"Step #1000, epoch #83, avg. train loss: 0.12665\n",
"Step #100, epoch #8, avg. train loss: 0.88987\n",
"Step #200, epoch #16, avg. train loss: 0.58779\n",
"Step #300, epoch #25, avg. train loss: 0.45963\n",
"Step #400, epoch #33, avg. train loss: 0.37728\n",
"Step #500, epoch #41, avg. train loss: 0.31244\n",
"Step #600, epoch #50, avg. train loss: 0.26547\n",
"Step #700, epoch #58, avg. train loss: 0.23508\n",
"Step #800, epoch #66, avg. train loss: 0.19069\n",
"Step #900, epoch #75, avg. train loss: 0.16507\n",
"Step #1000, epoch #83, avg. train loss: 0.13658\n",
"[ 0.65358438 0.76238333 0.74622796 0.64693205 0.60144287]\n",
"0.682114118242\n"
]
}
],
"source": [
"# 2 layer NN\n",
"import tensorflow.contrib.learn as skflow\n",
"\n",
"NN = skflow.TensorFlowDNNClassifier(hidden_units=[50, 50], n_classes=3,batch_size=128, steps=1000, optimizer = 'Adam',learning_rate=0.001,verbose=0)\n",
"\n",
"scores_NN = cross_validation.cross_val_score(NN, both_c, tt_c.squeeze()-1, cv=5,scoring='f1_weighted')\n",
"\n",
"print(scores_NN)\n",
"print(np.mean(scores_NN))"
]
},
{
"cell_type": "code",
"execution_count": 197,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"[ 0.66369289 0.75233545 0.74937916 0.66041971 0.58509503]\n",
"0.682184449569\n"
]
}
],
"source": [
"# One hidden layer neural network\n",
"\n",
"NN = skflow.TensorFlowDNNClassifier(hidden_units=[100], n_classes=3,batch_size=128, steps=1000, optimizer = 'Adam',learning_rate=0.001,verbose=0)\n",
"\n",
"scores_NN1 = cross_validation.cross_val_score(NN, both_c, tt_c.squeeze()-1, cv=5,scoring='f1_weighted')\n",
"\n",
"print(scores_NN1)\n",
"print(np.mean(scores_NN1))"
]
},
{
"cell_type": "code",
"execution_count": 199,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Step #100, epoch #8, avg. train loss: 1.14869\n",
"Step #200, epoch #16, avg. train loss: 1.04110\n",
"Step #300, epoch #25, avg. train loss: 1.01401\n",
"Step #400, epoch #33, avg. train loss: 1.00868\n",
"Step #500, epoch #41, avg. train loss: 0.99367\n",
"Step #600, epoch #50, avg. train loss: 0.99882\n",
"Step #700, epoch #58, avg. train loss: 0.97174\n",
"Step #800, epoch #66, avg. train loss: 0.94220\n",
"Step #900, epoch #75, avg. train loss: 0.94878\n",
"Step #1000, epoch #83, avg. train loss: 0.95615\n",
"Step #1100, epoch #91, avg. train loss: 0.96497\n",
"Step #1200, epoch #100, avg. train loss: 0.94810\n",
"Step #1300, epoch #108, avg. train loss: 0.96115\n",
"Step #1400, epoch #116, avg. train loss: 0.94362\n",
"Step #1500, epoch #125, avg. train loss: 0.93616\n",
"Step #1600, epoch #133, avg. train loss: 0.92822\n",
"Step #1700, epoch #141, avg. train loss: 0.94027\n",
"Step #1800, epoch #150, avg. train loss: 0.92864\n",
"Step #1900, epoch #158, avg. train loss: 0.92282\n",
"Step #2000, epoch #166, avg. train loss: 0.94435\n",
"Step #100, epoch #8, avg. train loss: 1.15985\n",
"Step #200, epoch #16, avg. train loss: 1.03994\n",
"Step #300, epoch #25, avg. train loss: 0.99027\n",
"Step #400, epoch #33, avg. train loss: 1.00230\n",
"Step #500, epoch #41, avg. train loss: 0.96287\n",
"Step #600, epoch #50, avg. train loss: 0.93045\n",
"Step #700, epoch #58, avg. train loss: 0.93116\n",
"Step #800, epoch #66, avg. train loss: 0.93291\n",
"Step #900, epoch #75, avg. train loss: 0.92715\n",
"Step #1000, epoch #83, avg. train loss: 0.94475\n",
"Step #1100, epoch #91, avg. train loss: 0.94337\n",
"Step #1200, epoch #100, avg. train loss: 0.88984\n",
"Step #1300, epoch #108, avg. train loss: 0.90813\n",
"Step #1400, epoch #116, avg. train loss: 0.91796\n",
"Step #1500, epoch #125, avg. train loss: 0.90387\n",
"Step #1600, epoch #133, avg. train loss: 0.91400\n",
"Step #1700, epoch #141, avg. train loss: 0.90965\n",
"Step #1800, epoch #150, avg. train loss: 0.89358\n",
"Step #1900, epoch #158, avg. train loss: 0.91560\n",
"Step #2000, epoch #166, avg. train loss: 0.93284\n",
"Step #100, epoch #8, avg. train loss: 1.16572\n",
"Step #200, epoch #16, avg. train loss: 1.01939\n",
"Step #300, epoch #25, avg. train loss: 0.95911\n",
"Step #400, epoch #33, avg. train loss: 0.96130\n",
"Step #500, epoch #41, avg. train loss: 0.93773\n",
"Step #600, epoch #50, avg. train loss: 0.92886\n",
"Step #700, epoch #58, avg. train loss: 0.92879\n",
"Step #800, epoch #66, avg. train loss: 0.90902\n",
"Step #900, epoch #75, avg. train loss: 0.92708\n",
"Step #1000, epoch #83, avg. train loss: 0.94649\n",
"Step #1100, epoch #91, avg. train loss: 0.98825\n",
"Step #1200, epoch #100, avg. train loss: 0.92764\n",
"Step #1300, epoch #108, avg. train loss: 0.92938\n",
"Step #1400, epoch #116, avg. train loss: 0.95013\n",
"Step #1500, epoch #125, avg. train loss: 0.92809\n",
"Step #1600, epoch #133, avg. train loss: 0.92355\n",
"Step #1700, epoch #141, avg. train loss: 0.91293\n",
"Step #1800, epoch #150, avg. train loss: 0.89765\n",
"Step #1900, epoch #158, avg. train loss: 0.88897\n",
"Step #2000, epoch #166, avg. train loss: 0.91069\n",
"Step #100, epoch #8, avg. train loss: 1.14496\n",
"Step #200, epoch #16, avg. train loss: 1.01918\n",
"Step #300, epoch #25, avg. train loss: 0.97850\n",
"Step #400, epoch #33, avg. train loss: 0.94890\n",
"Step #500, epoch #41, avg. train loss: 0.96045\n",
"Step #600, epoch #50, avg. train loss: 0.92744\n",
"Step #700, epoch #58, avg. train loss: 0.92457\n",
"Step #800, epoch #66, avg. train loss: 0.91286\n",
"Step #900, epoch #75, avg. train loss: 0.91005\n",
"Step #1000, epoch #83, avg. train loss: 0.92114\n",
"Step #1100, epoch #91, avg. train loss: 0.90968\n",
"Step #1200, epoch #100, avg. train loss: 0.88736\n",
"Step #1300, epoch #108, avg. train loss: 0.89987\n",
"Step #1400, epoch #116, avg. train loss: 0.89299\n",
"Step #1500, epoch #125, avg. train loss: 0.87710\n",
"Step #1600, epoch #133, avg. train loss: 0.87049\n",
"Step #1700, epoch #141, avg. train loss: 0.87700\n",
"Step #1800, epoch #150, avg. train loss: 0.89919\n",
"Step #1900, epoch #158, avg. train loss: 0.86781\n",
"Step #2000, epoch #166, avg. train loss: 0.88358\n",
"Step #100, epoch #8, avg. train loss: 1.14680\n",
"Step #200, epoch #16, avg. train loss: 1.04248\n",
"Step #300, epoch #25, avg. train loss: 0.99218\n",
"Step #400, epoch #33, avg. train loss: 0.93282\n",
"Step #500, epoch #41, avg. train loss: 0.93449\n",
"Step #600, epoch #50, avg. train loss: 0.90859\n",
"Step #700, epoch #58, avg. train loss: 0.90043\n",
"Step #800, epoch #66, avg. train loss: 0.89334\n",
"Step #900, epoch #75, avg. train loss: 0.90603\n",
"Step #1000, epoch #83, avg. train loss: 0.92463\n",
"Step #1100, epoch #91, avg. train loss: 0.96414\n",
"Step #1200, epoch #100, avg. train loss: 0.94658\n",
"Step #1300, epoch #108, avg. train loss: 0.89849\n",
"Step #1400, epoch #116, avg. train loss: 0.88866\n",
"Step #1500, epoch #125, avg. train loss: 0.86922\n",
"Step #1600, epoch #133, avg. train loss: 0.86260\n",
"Step #1700, epoch #141, avg. train loss: 0.86635\n",
"Step #1800, epoch #150, avg. train loss: 0.87086\n",
"Step #1900, epoch #158, avg. train loss: 0.86440\n",
"Step #2000, epoch #166, avg. train loss: 0.88345\n",
"[ 0.57223796 0.60056657 0.50991501 0.50991501 0.59375 ]\n",
"0.557276912181\n"
]
}
],
"source": [
"# Deep net with dropout\n",
"def my_model(X, y):\n",
" \"\"\"This is DNN with 10, 20, 10 hidden layers, and dropout of 0.5 probability.\"\"\"\n",
" layers = skflow.ops.dnn(X, [10, 20, 10], dropout=0.5)\n",
" return skflow.models.logistic_regression(layers, y)\n",
"\n",
"NN_drop = skflow.TensorFlowEstimator(model_fn=my_model, n_classes=3,batch_size=128, steps=2000, optimizer = 'Adam',learning_rate=0.01,verbose=1)\n",
"scores_NN_drop = cross_validation.cross_val_score(NN_drop, both_c, tt_c.squeeze()-1, cv=5,scoring='accuracy') #'f1_weighted')\n",
"\n",
"print(scores_NN_drop)\n",
"print(np.mean(scores_NN_drop))"
]
},
{
"cell_type": "code",
"execution_count": 259,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"[ 0.53107345 0.5480226 0.5184136 0.51988636 0.49287749]\n",
"0.522054699889\n"
]
}
],
"source": [
"# SVM\n",
"from sklearn import svm\n",
"lin_svm = svm.LinearSVC()\n",
"scores_svm = cross_validation.cross_val_score(lin_svm, both_c, tt_c.squeeze(),cv=5,scoring='accuracy')\n",
"print(scores_svm)\n",
"print(np.mean(scores_svm))"
]
},
{
"cell_type": "code",
"execution_count": 248,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"# Naive bayes\n",
"cross_validation.cross_val_predict?"
]
},
{
"cell_type": "code",
"execution_count": 140,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"LogisticRegression(C=1.0, class_weight=None, dual=False, fit_intercept=True,\n",
" intercept_scaling=1, max_iter=100, multi_class='ovr', n_jobs=1,\n",
" penalty='l2', random_state=None, solver='liblinear', tol=0.0001,\n",
" verbose=0, warm_start=False)"
]
},
"execution_count": 140,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# Test all 3 models with all of the data (will overfit but we'll try and fix this later...)\n",
"lr.fit(both_c,tt_c.squeeze())\n",
"NN.fit(both_c,tt_c.squeeze()-1)\n",
"# NN_drop.fit(both_c,tt_c.squeeze()-1)"
]
},
{
"cell_type": "code",
"execution_count": 137,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Log Reg: 0.456349206349\n",
"NN: 0.989795918367\n",
"Deep NN: 0.537414965986\n"
]
}
],
"source": [
"print('Log Reg: ',accuracy_score(tt_c.squeeze(),lr.predict(both_c)))\n",
"print('NN: ',accuracy_score(tt_c.squeeze()-1,NN.predict(both_c)))\n",
"print('Deep NN: ',accuracy_score(tt_c.squeeze()-1,NN_drop.predict(both_c)))"
]
},
{
"cell_type": "code",
"execution_count": 297,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"image/png": "iVBORw0KGgoAAAANSUhEUgAAAXkAAAFxCAYAAACWUNhQAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\ngg==\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x13081d320>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"# ROC for mouse\n",
"fig, ax = plt.subplots(figsize=(6,6))\n",
"trialtypes = ['Posterior Pole','Anterior Pole','No Go'] # 36\n",
"\n",
"# Mouse score\n",
"mouse_choice = ch[clean.squeeze()].values\n",
"\n",
"fpr = dict()\n",
"tpr = dict()\n",
"roc_auc = dict()\n",
"n_classes = 3\n",
"for i in range(0,3):\n",
" these_trials = tt_c == i+1\n",
" binary_trials = np.zeros_like(tt_c.squeeze()) \n",
" binary_trials[these_trials.squeeze()] = 1\n",
" \n",
" wrong = mouse_choice != i+1\n",
" binary_preds = np.ones_like(mouse_choice)\n",
" binary_preds[wrong] = 0\n",
" fpr[i], tpr[i], thresholds = metrics.roc_curve(binary_trials,binary_preds)\n",
" roc_auc[i] = metrics.auc(fpr[i], tpr[i])\n",
" plt.plot(fpr[i], tpr[i], lw=1, label='ROC ' + trialtypes[i] +' (area = %0.2f)' % (roc_auc[i]))\n",
" \n",
"\n",
"# Compute macro-average ROC following sklearn docs\n",
"\n",
"# First aggregate all false positive rates\n",
"all_fpr = np.unique(np.concatenate([fpr[i] for i in range(n_classes)]))\n",
"# Then interpolate all ROC curves at this points\n",
"mean_tpr = np.zeros_like(all_fpr)\n",
"for i in range(n_classes):\n",
" mean_tpr += interp(all_fpr, fpr[i], tpr[i])\n",
"# Finally average it and compute AUC\n",
"mean_tpr /= n_classes\n",
"fpr[\"macro\"] = all_fpr\n",
"tpr[\"macro\"] = mean_tpr\n",
"roc_auc[\"macro\"] = metrics.auc(fpr[\"macro\"], tpr[\"macro\"])\n",
"plt.plot(fpr[\"macro\"], tpr[\"macro\"],\n",
" label='macro-average ROC curve (area = {0:0.2f})'''.format(roc_auc[\"macro\"]),linewidth=2)\n",
"\n",
"plt.plot([0, 1], [0, 1], '--', color=(0.6, 0.6, 0.6), label='Chance')\n",
"plt.legend(loc=4)\n",
"plt.savefig('ROC_Mouse_trialtype_'+ mouse_name +'.png')"
]
},
{
"cell_type": "code",
"execution_count": 523,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"/Users/mathew/miniconda/envs/py35/lib/python3.5/site-packages/ipykernel/__main__.py:5: FutureWarning: in the future, boolean array-likes will be handled as a boolean array index\n"
]
},
{
"data": {
"text/plain": [
"(2177, 5000)"
]
},
"execution_count": 523,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# plt.scatter(theta,kappa)\n",
"tt1 = tt == 1\n",
"sum(tt1)\n",
"t = theta.values\n",
"t2 = t[tt1.squeeze()]\n",
"t2.shape\n",
"# tt1.shape\n",
"# fig, ax = plt.subplots(figsize=(10,5))\n",
"# _ =plt.plot(t[tt1.squeeze()])\n",
"\n"
]
},
{
"cell_type": "code",
"execution_count": 517,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"'Right'"
]
},
"execution_count": 517,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# plt.plot(tt_c[these_trials])\n",
"# plt.imshow(both_c[these_trials.squeeze()],aspect = 0.1)\n",
"# both_c.shape\n",
"# these_trials.shape\n",
"# plt.plot(NN.predict(both_c[these_trials.squeeze()]))\n",
"# plt.plot(tt_c[these_trials.squeeze()]-1)\n",
"# preds.shape\n",
"# wrong.shape\n",
"# binary_preds\n",
"# tt_c[these_trials.squeeze()]\n",
"# i?\n",
"# np.ones_like(binary_preds)\n",
"# NN.predict_proba(both_c[these_trials.squeeze()]).shape\n",
"# tt_c.shape\n",
"trialtypes = ['Left','Right','No Go']\n",
"trialtypes[1]"
]
},
{
"cell_type": "code",
"execution_count": 452,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"# Conv net\n",
"def max_pool_2x1(tensor_in):\n",
" return tf.nn.max_pool(tensor_in, ksize=[1, 2, 1, 1], strides=[1, 2, 1, 1],\n",
" padding='SAME')\n",
"\n",
"def conv_model(X, y):\n",
" # reshape X to 4d tensor with 2nd and 3rd dimensions being image width and height\n",
" # final dimension being the number of color channels\n",
" X = tf.reshape(X, [-1, 100, 1, 1])\n",
" # first conv layer will compute 32 features for each 5x1 strip\n",
" with tf.variable_scope('conv_layer1'):\n",
" h_conv1 = skflow.ops.conv2d(X, n_filters=32, filter_shape=[5, 1], \n",
" bias=True, activation=tf.nn.relu)\n",
" h_pool1 = max_pool_2x1(h_conv1)\n",
" # second conv layer will compute 64 features for each 5x1 strip\n",
" with tf.variable_scope('conv_layer2'):\n",
" h_conv2 = skflow.ops.conv2d(h_pool1, n_filters=64, filter_shape=[5, 1], \n",
" bias=True, activation=tf.nn.relu)\n",
" h_pool2 = max_pool_2x1(h_conv2)\n",
" # reshape tensor into a batch of vectors\n",
" h_pool2_flat = tf.reshape(h_pool2, [-1, 5 * 5 * 64])\n",
" # densely connected layer with 1024 neurons\n",
" h_fc1 = skflow.ops.dnn(h_pool2_flat, [1024], activation=tf.nn.relu, dropout=0.5)\n",
" return skflow.models.logistic_regression(h_fc1, y)\n",
"\n",
"# Training and predicting\n",
"classifier3 = skflow.TensorFlowEstimator(\n",
" model_fn=conv_model, n_classes=10, batch_size=100, steps=20000,\n",
" learning_rate=0.001)"
]
},
{
"cell_type": "code",
"execution_count": 122,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Step #100, epoch #5, avg. train loss: 1.35360\n",
"Step #200, epoch #11, avg. train loss: 1.20178\n",
"Step #300, epoch #16, avg. train loss: 1.16640\n",
"Step #400, epoch #22, avg. train loss: 1.13603\n",
"Step #500, epoch #27, avg. train loss: 1.12254\n",
"Step #600, epoch #33, avg. train loss: 1.11399\n",
"Step #700, epoch #38, avg. train loss: 1.10485\n",
"Step #800, epoch #44, avg. train loss: 1.09972\n",
"Step #900, epoch #50, avg. train loss: 1.09586\n",
"Step #1000, epoch #55, avg. train loss: 1.09505\n",
"Step #1100, epoch #61, avg. train loss: 1.09069\n",
"Step #1200, epoch #66, avg. train loss: 1.08123\n",
"Step #1300, epoch #72, avg. train loss: 1.07818\n",
"Step #1400, epoch #77, avg. train loss: 1.07795\n",
"Step #1500, epoch #83, avg. train loss: 1.07521\n",
"Step #1600, epoch #88, avg. train loss: 1.06800\n",
"Step #1700, epoch #94, avg. train loss: 1.06861\n",
"Step #1800, epoch #100, avg. train loss: 1.06602\n",
"Step #1900, epoch #105, avg. train loss: 1.05996\n",
"Step #2000, epoch #111, avg. train loss: 1.05853\n",
"Step #2100, epoch #116, avg. train loss: 1.05690\n",
"Step #2200, epoch #122, avg. train loss: 1.05516\n",
"Step #2300, epoch #127, avg. train loss: 1.04927\n",
"Step #2400, epoch #133, avg. train loss: 1.04840\n",
"Step #2500, epoch #138, avg. train loss: 1.04748\n",
"Step #2600, epoch #144, avg. train loss: 1.04489\n",
"Step #2700, epoch #150, avg. train loss: 1.04213\n",
"Step #2800, epoch #155, avg. train loss: 1.04057\n",
"Step #2900, epoch #161, avg. train loss: 1.03852\n",
"Step #3000, epoch #166, avg. train loss: 1.03876\n",
"Step #3100, epoch #172, avg. train loss: 1.03233\n",
"Step #3200, epoch #177, avg. train loss: 1.03345\n",
"Step #3300, epoch #183, avg. train loss: 1.02899\n",
"Step #3400, epoch #188, avg. train loss: 1.02781\n",
"Step #3500, epoch #194, avg. train loss: 1.02408\n",
"Step #3600, epoch #200, avg. train loss: 1.02472\n",
"Step #3700, epoch #205, avg. train loss: 1.01973\n",
"Step #3800, epoch #211, avg. train loss: 1.01985\n",
"Step #3900, epoch #216, avg. train loss: 1.02125\n",
"Step #4000, epoch #222, avg. train loss: 1.01848\n",
"Step #4100, epoch #227, avg. train loss: 1.01682\n",
"Step #4200, epoch #233, avg. train loss: 1.01100\n",
"Step #4300, epoch #238, avg. train loss: 1.00799\n",
"Step #4400, epoch #244, avg. train loss: 1.00743\n",
"Step #4500, epoch #250, avg. train loss: 1.00214\n",
"Step #4600, epoch #255, avg. train loss: 1.00455\n",
"Step #4700, epoch #261, avg. train loss: 1.00507\n",
"Step #4800, epoch #266, avg. train loss: 1.00197\n",
"Step #4900, epoch #272, avg. train loss: 0.99705\n",
"Step #5000, epoch #277, avg. train loss: 0.99659\n",
"Step #5100, epoch #283, avg. train loss: 0.99129\n",
"Step #5200, epoch #288, avg. train loss: 0.99076\n",
"Step #5300, epoch #294, avg. train loss: 0.99584\n",
"Step #5400, epoch #300, avg. train loss: 0.98942\n",
"Step #5500, epoch #305, avg. train loss: 0.98545\n",
"Step #5600, epoch #311, avg. train loss: 0.98348\n",
"Step #5700, epoch #316, avg. train loss: 0.98443\n",
"Step #5800, epoch #322, avg. train loss: 0.97916\n",
"Step #5900, epoch #327, avg. train loss: 0.97974\n",
"Step #6000, epoch #333, avg. train loss: 0.97791\n",
"Step #6100, epoch #338, avg. train loss: 0.97882\n",
"Step #6200, epoch #344, avg. train loss: 0.97301\n",
"Step #6300, epoch #350, avg. train loss: 0.97029\n",
"Step #6400, epoch #355, avg. train loss: 0.97093\n",
"Step #6500, epoch #361, avg. train loss: 0.96477\n",
"Step #6600, epoch #366, avg. train loss: 0.96557\n",
"Step #6700, epoch #372, avg. train loss: 0.96516\n",
"Step #6800, epoch #377, avg. train loss: 0.96477\n",
"Step #6900, epoch #383, avg. train loss: 0.95679\n",
"Step #7000, epoch #388, avg. train loss: 0.96028\n",
"Step #7100, epoch #394, avg. train loss: 0.95860\n",
"Step #7200, epoch #400, avg. train loss: 0.95298\n",
"Step #7300, epoch #405, avg. train loss: 0.95307\n",
"Step #7400, epoch #411, avg. train loss: 0.94568\n",
"Step #7500, epoch #416, avg. train loss: 0.94520\n",
"Step #7600, epoch #422, avg. train loss: 0.94963\n",
"Step #7700, epoch #427, avg. train loss: 0.94878\n",
"Step #7800, epoch #433, avg. train loss: 0.94069\n",
"Step #7900, epoch #438, avg. train loss: 0.94069\n",
"Step #8000, epoch #444, avg. train loss: 0.93292\n",
"Step #8100, epoch #450, avg. train loss: 0.93767\n",
"Step #8200, epoch #455, avg. train loss: 0.93352\n",
"Step #8300, epoch #461, avg. train loss: 0.93247\n",
"Step #8400, epoch #466, avg. train loss: 0.92782\n",
"Step #8500, epoch #472, avg. train loss: 0.92752\n",
"Step #8600, epoch #477, avg. train loss: 0.92698\n",
"Step #8700, epoch #483, avg. train loss: 0.92483\n",
"Step #8800, epoch #488, avg. train loss: 0.91828\n",
"Step #8900, epoch #494, avg. train loss: 0.92395\n",
"Step #9000, epoch #500, avg. train loss: 0.91878\n",
"Step #9100, epoch #505, avg. train loss: 0.91621\n",
"Step #9200, epoch #511, avg. train loss: 0.91375\n",
"Step #9300, epoch #516, avg. train loss: 0.91170\n",
"Step #9400, epoch #522, avg. train loss: 0.91242\n",
"Step #9500, epoch #527, avg. train loss: 0.90868\n",
"Step #9600, epoch #533, avg. train loss: 0.90472\n",
"Step #9700, epoch #538, avg. train loss: 0.90323\n",
"Step #9800, epoch #544, avg. train loss: 0.90428\n",
"Step #9900, epoch #550, avg. train loss: 0.89727\n",
"Step #10000, epoch #555, avg. train loss: 0.90293\n",
"Step #10100, epoch #561, avg. train loss: 0.89714\n",
"Step #10200, epoch #566, avg. train loss: 0.89506\n",
"Step #10300, epoch #572, avg. train loss: 0.89400\n",
"Step #10400, epoch #577, avg. train loss: 0.88804\n",
"Step #10500, epoch #583, avg. train loss: 0.88623\n",
"Step #10600, epoch #588, avg. train loss: 0.89189\n",
"Step #10700, epoch #594, avg. train loss: 0.88276\n",
"Step #10800, epoch #600, avg. train loss: 0.87996\n",
"Step #10900, epoch #605, avg. train loss: 0.88099\n",
"Step #11000, epoch #611, avg. train loss: 0.87693\n",
"Step #11100, epoch #616, avg. train loss: 0.87348\n",
"Step #11200, epoch #622, avg. train loss: 0.87230\n",
"Step #11300, epoch #627, avg. train loss: 0.86946\n",
"Step #11400, epoch #633, avg. train loss: 0.86653\n",
"Step #11500, epoch #638, avg. train loss: 0.87163\n",
"Step #11600, epoch #644, avg. train loss: 0.86561\n",
"Step #11700, epoch #650, avg. train loss: 0.86412\n",
"Step #11800, epoch #655, avg. train loss: 0.86226\n",
"Step #11900, epoch #661, avg. train loss: 0.85540\n",
"Step #12000, epoch #666, avg. train loss: 0.85878\n",
"Step #12100, epoch #672, avg. train loss: 0.85850\n",
"Step #12200, epoch #677, avg. train loss: 0.85017\n",
"Step #12300, epoch #683, avg. train loss: 0.84906\n",
"Step #12400, epoch #688, avg. train loss: 0.84274\n",
"Step #12500, epoch #694, avg. train loss: 0.84569\n",
"Step #12600, epoch #700, avg. train loss: 0.84176\n",
"Step #12700, epoch #705, avg. train loss: 0.84002\n",
"Step #12800, epoch #711, avg. train loss: 0.83904\n",
"Step #12900, epoch #716, avg. train loss: 0.83598\n",
"Step #13000, epoch #722, avg. train loss: 0.83640\n",
"Step #13100, epoch #727, avg. train loss: 0.83090\n",
"Step #13200, epoch #733, avg. train loss: 0.82868\n",
"Step #13300, epoch #738, avg. train loss: 0.82810\n",
"Step #13400, epoch #744, avg. train loss: 0.82541\n",
"Step #13500, epoch #750, avg. train loss: 0.82396\n",
"Step #13600, epoch #755, avg. train loss: 0.82530\n",
"Step #13700, epoch #761, avg. train loss: 0.81704\n",
"Step #13800, epoch #766, avg. train loss: 0.82026\n",
"Step #13900, epoch #772, avg. train loss: 0.81388\n",
"Step #14000, epoch #777, avg. train loss: 0.81057\n",
"Step #14100, epoch #783, avg. train loss: 0.81451\n",
"Step #14200, epoch #788, avg. train loss: 0.80340\n",
"Step #14300, epoch #794, avg. train loss: 0.80912\n",
"Step #14400, epoch #800, avg. train loss: 0.80419\n",
"Step #14500, epoch #805, avg. train loss: 0.80633\n",
"Step #14600, epoch #811, avg. train loss: 0.79871\n",
"Step #14700, epoch #816, avg. train loss: 0.80212\n",
"Step #14800, epoch #822, avg. train loss: 0.79353\n",
"Step #14900, epoch #827, avg. train loss: 0.79071\n",
"Step #15000, epoch #833, avg. train loss: 0.79410\n",
"Step #15100, epoch #838, avg. train loss: 0.79034\n",
"Step #15200, epoch #844, avg. train loss: 0.78865\n",
"Step #15300, epoch #850, avg. train loss: 0.78606\n",
"Step #15400, epoch #855, avg. train loss: 0.77854\n",
"Step #15500, epoch #861, avg. train loss: 0.77884\n",
"Step #15600, epoch #866, avg. train loss: 0.77971\n",
"Step #15700, epoch #872, avg. train loss: 0.77835\n",
"Step #15800, epoch #877, avg. train loss: 0.77282\n",
"Step #15900, epoch #883, avg. train loss: 0.77495\n",
"Step #16000, epoch #888, avg. train loss: 0.76658\n",
"Step #16100, epoch #894, avg. train loss: 0.76841\n",
"Step #16200, epoch #900, avg. train loss: 0.76369\n",
"Step #16300, epoch #905, avg. train loss: 0.76553\n",
"Step #16400, epoch #911, avg. train loss: 0.75882\n",
"Step #16500, epoch #916, avg. train loss: 0.75899\n",
"Step #16600, epoch #922, avg. train loss: 0.75821\n",
"Step #16700, epoch #927, avg. train loss: 0.75386\n",
"Step #16800, epoch #933, avg. train loss: 0.75398\n",
"Step #16900, epoch #938, avg. train loss: 0.74601\n",
"Step #17000, epoch #944, avg. train loss: 0.75184\n",
"Step #17100, epoch #950, avg. train loss: 0.74895\n",
"Step #17200, epoch #955, avg. train loss: 0.74207\n",
"Step #17300, epoch #961, avg. train loss: 0.74038\n",
"Step #17400, epoch #966, avg. train loss: 0.73144\n",
"Step #17500, epoch #972, avg. train loss: 0.73606\n",
"Step #17600, epoch #977, avg. train loss: 0.73566\n",
"Step #17700, epoch #983, avg. train loss: 0.73422\n",
"Step #17800, epoch #988, avg. train loss: 0.72740\n",
"Step #17900, epoch #994, avg. train loss: 0.72633\n",
"Step #18000, epoch #1000, avg. train loss: 0.72663\n",
"Step #18100, epoch #1005, avg. train loss: 0.72530\n",
"Step #18200, epoch #1011, avg. train loss: 0.72532\n",
"Step #18300, epoch #1016, avg. train loss: 0.72057\n",
"Step #18400, epoch #1022, avg. train loss: 0.71928\n",
"Step #18500, epoch #1027, avg. train loss: 0.71353\n",
"Step #18600, epoch #1033, avg. train loss: 0.71132\n",
"Step #18700, epoch #1038, avg. train loss: 0.71090\n",
"Step #18800, epoch #1044, avg. train loss: 0.70996\n",
"Step #18900, epoch #1050, avg. train loss: 0.70915\n",
"Step #19000, epoch #1055, avg. train loss: 0.70120\n",
"Step #19100, epoch #1061, avg. train loss: 0.70089\n",
"Step #19200, epoch #1066, avg. train loss: 0.70038\n",
"Step #19300, epoch #1072, avg. train loss: 0.69912\n",
"Step #19400, epoch #1077, avg. train loss: 0.69077\n",
"Step #19500, epoch #1083, avg. train loss: 0.69294\n",
"Step #19600, epoch #1088, avg. train loss: 0.69158\n",
"Step #19700, epoch #1094, avg. train loss: 0.69082\n",
"Step #19800, epoch #1100, avg. train loss: 0.68710\n",
"Step #19900, epoch #1105, avg. train loss: 0.68437\n",
"Step #20000, epoch #1111, avg. train loss: 0.68327\n"
]
},
{
"data": {
"text/plain": [
"TensorFlowEstimator(batch_size=100, class_weight=None, clip_gradients=5.0,\n",
" config=None, continue_training=False, learning_rate=0.001,\n",
" model_fn=<function conv_model at 0x11bb8af28>, n_classes=10,\n",
" optimizer='Adagrad', steps=20000, verbose=1)"
]
},
"execution_count": 122,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"classifier.fit(both_c,tt_c.squeeze()-1)"
]
},
{
"cell_type": "code",
"execution_count": 124,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"0.82256235827664403"
]
},
"execution_count": 124,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"accuracy_score(tt_c.squeeze()-1,classifier.predict(both_c))"
]
},
{
"cell_type": "code",
"execution_count": 130,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"[ 0.32294618 0.32577904 0.36543909 0.32294618 0.34090909]\n",
"0.335603914499\n"
]
}
],
"source": [
"# Convnet\n",
"convnet = skflow.TensorFlowEstimator(\n",
" model_fn=conv_model, n_classes=10, batch_size=100, steps=20000,\n",
" learning_rate=0.001,verbose=0)\n",
"\n",
"# convnet.fit(traindata,trainlabs1D-1)\n",
"# lr.fit(both_c,tt_c.squeeze())\n",
"# print(accuracy_score(testlabs1D-1,convnet.predict(testdata)))\n",
"\n",
"scores_convnet = cross_validation.cross_val_score(convnet, both_c, tt_c.squeeze()-1, cv=5,scoring='accuracy') #'f1_weighted')\n",
"\n",
"print(scores_convnet)\n",
"print(np.mean(scores_convnet))"
]
},
{
"cell_type": "code",
"execution_count": 193,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"[<matplotlib.lines.Line2D at 0x118eb6c50>]"
]
},
"execution_count": 193,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"image/png": "iVBORw0KGgoAAAANSUhEUgAAAYAAAAECCAYAAAD3vwBsAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nLgAAAABJRU5ErkJggg==\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x11747a9b0>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"\n",
"plt.plot(np.mean(both_c[tt_c.squeeze() ==1],0))\n",
"plt.plot(np.mean(both_c[tt_c.squeeze() ==2],0))\n",
"plt.plot(np.mean(both_c[tt_c.squeeze() ==3],0))"
]
},
{
"cell_type": "code",
"execution_count": 194,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"[<matplotlib.lines.Line2D at 0x12afcac50>]"
]
},
"execution_count": 194,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"image/png": "iVBORw0KGgoAAAANSUhEUgAAAYAAAAECCAYAAAD3vwBsAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nJQFACCFs6v8Bf0vNfbku2w8AAAAASUVORK5CYII=\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x118eaad68>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"plt.plot(np.mean(traindata[trainlabs1D.squeeze() ==1],0))\n",
"plt.plot(np.mean(traindata[trainlabs1D.squeeze() ==2],0))\n",
"plt.plot(np.mean(traindata[trainlabs1D.squeeze() ==3],0))"
]
},
{
"cell_type": "code",
"execution_count": 207,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"array([ True, True, True, ..., True, False, True], dtype=bool)"
]
},
"execution_count": 207,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"image/png": "iVBORw0KGgoAAAANSUhEUgAAAXwAAAECCAYAAAD9z2x7AAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nVAgDX5IK8X/eC8T1h4tfOQAAAABJRU5ErkJggg==\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x130d731d0>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"plt.hist(preds)\n",
"wrong"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"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": "3.5.1"
}
},
"nbformat": 4,
"nbformat_minor": 0
}
| mit |
APC524/tsap | demo/findata.ipynb | 1 | 120290 | {
"cells": [
{
"cell_type": "code",
"execution_count": 31,
"metadata": {
"collapsed": false,
"scrolled": true
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"The autoreload extension is already loaded. To reload it, use:\n",
" %reload_ext autoreload\n"
]
},
{
"data": {
"image/png": "iVBORw0KGgoAAAANSUhEUgAAAekAAAGHCAYAAACUMKq9AAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x10fb7a190>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"import numpy as np\n",
"\n",
"import sys\n",
"if \"../\" not in sys.path:\n",
" sys.path.append(\"../\")\n",
"from tsap.solver import Solver\n",
"from tsap.model import AR, MA\n",
"from tsap.gradient_check import eval_numerical_gradient, eval_numerical_gradient_array\n",
"\n",
"import tsap.data_processor as dp\n",
"import tsap.inference \n",
"from tsap.ts_gen import ar1_gen\n",
"\n",
"\n",
"import matplotlib.pyplot as plt\n",
"\n",
"# This is a bit of magic to make matplotlib figures appear inline in the notebook\n",
"# rather than in a new window.\n",
"%matplotlib inline\n",
"plt.rcParams['figure.figsize'] = (5.0, 4.0) # set default size of plots\n",
"plt.rcParams['image.interpolation'] = 'nearest'\n",
"plt.rcParams['image.cmap'] = 'gray'\n",
"\n",
"# Some more magic so that the notebook will reload external python modules;\n",
"# see http://stackoverflow.com/questions/1907993/autoreload-of-modules-in-ipython\n",
"%load_ext autoreload\n",
"%autoreload 2\n",
"\n",
"data = np.loadtxt(\"../data/GOOG.csv\", delimiter=',')\n",
"X = np.array([data[0:480]])\n",
"Y = dp.get_return(X)\n",
"nrow=X.shape[0]\n",
"\n",
"\n",
"plt.plot(Y[0,:])\n",
"plt.xlabel('time')\n",
"plt.ylabel('price')\n",
"plt.title(' 2015-2016 Google stock price')\n",
"plt.show()\n",
"\n"
]
},
{
"cell_type": "code",
"execution_count": 114,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"3000\n",
"the loss is 430.253935\n",
"the loss is 412.146926\n",
"the loss is 388.942897\n",
"the loss is 362.015646\n",
"the loss is 331.335921\n",
"the loss is 296.179386\n",
"the loss is 255.227146\n",
"the loss is 206.325582\n",
"the loss is 145.804687\n",
"the loss is 66.731364\n",
"the loss is -46.458306\n",
"the loss is -242.083762\n",
"the loss is -889.899309\n",
"{'phi': array([[ 0.13702195],\n",
" [-1.72331626],\n",
" [-0.33342519],\n",
" [-0.63431413],\n",
" [ 0.45486907]]), 'intercept': array([ 0.00013577]), 'sigma': array([ 0.07798653])}\n"
]
}
],
"source": [
"\n",
"lag = 5\n",
"sigma = 1.0\n",
"intercept = 0.1\n",
"phi = np.random.randn(lag,1)\n",
"AR_model = AR(lag=lag, phi=phi, sigma=sigma, intercept=intercept)\n",
"AR_model.params\n",
"\n",
"AR_solver = Solver(AR_model, Y,\n",
" update_rule='sgd_momentum',\n",
" optim_config={\n",
" 'learning_rate': 1e-6,\n",
" },\n",
" \n",
" num_epochs=3000, batch_size=1,print_every=10)\n",
"AR_solver.train()\n",
"print AR_model.params\n",
"\n"
]
},
{
"cell_type": "code",
"execution_count": 121,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"[[ 0.00346535 0.00815387 0.0004395 -0.01989656 -0.00785229]]\n",
"[[ 0.00861254 0.00338436 -0.01660526 -0.01335022 0.00344957]]\n"
]
},
{
"data": {
"image/png": "iVBORw0KGgoAAAANSUhEUgAAAfAAAAGHCAYAAABPiwCfAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x10fd5c2d0>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"[[ 764.3194906 770.55165421 770.8903124 755.55225006 749.61943318]]\n",
"[[ 768.24 770.84 758.04 747.92 750.5 ]]\n"
]
},
{
"data": {
"image/png": "iVBORw0KGgoAAAANSUhEUgAAAdcAAAGHCAYAAADiChg2AAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nriIiIhGm4ioiIhJhKq4iIiIR9v9onRQuYbU46gAAAABJRU5ErkJggg==\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x10f8d1390>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"predreturn=AR_model.predict(Y,5)\n",
"print predreturn\n",
"\n",
"truereturn=dp.get_return(np.array([data[479:485]]))\n",
"print truereturn\n",
"\n",
"plt.plot(predreturn.T,label=\"predicted return\")\n",
"plt.plot(truereturn.T,label=\"true return\")\n",
"plt.xlabel(\"time\")\n",
"plt.ylabel(\"price\")\n",
"plt.title(' predicted and true true for 5 following days')\n",
"plt.legend()\n",
"plt.show()\n",
"\n",
"\n",
"predprice = dp.get_price(data[479],pred)\n",
"print predprice\n",
"\n",
"trueprice=np.array([data[480:485]])\n",
"print trueprice\n",
"\n",
"\n",
"plt.plot(predprice.T,label=\"predicted price\")\n",
"plt.plot(trueprice.T,label=\"true price\")\n",
"plt.xlabel('time')\n",
"plt.ylabel('price')\n",
"plt.title(' predicted and true price for 5 following days')\n",
"plt.legend()\n",
"plt.show()\n"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"\n"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"\n"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": []
}
],
"metadata": {
"anaconda-cloud": {},
"kernelspec": {
"display_name": "Python 2",
"language": "python",
"name": "python2"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 2
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython2",
"version": "2.7.13"
}
},
"nbformat": 4,
"nbformat_minor": 1
}
| gpl-3.0 |
tuanavu/coursera-university-of-washington | machine_learning/1_machine_learning_foundations/lecture/week6/quiz-week6-lecture.ipynb | 2 | 4316 | {
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Deep Learning"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"<img src=\"images/pic01.png\">\n",
"\n",
"*Screenshot taken from [Coursera](https://www.coursera.org/learn/ml-foundations/exam/iPHzm/deep-learning)*\n",
"\n",
"<!--TEASER_END-->"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"**Discussion**\n",
"\n",
"- https://www.coursera.org/learn/ml-foundations/discussions/eaBhp4ENEeWe6Qot63L7mQ\n",
"- https://www.coursera.org/learn/ml-foundations/module/nqC1t/discussions/AAIUurrtEeWGphLhfbPAyQ\n",
"\n",
"**Question 2**:\n",
"- Case 1: **x1 OR x2 OR NOT x3**"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"| x1 | x2 | x3 | x1 OR x2 OR (NOT x3) |\n",
"|:---:|:---:|:---:|:---:|\n",
"| 0 | 0 | 0 | 0 |\n",
"| 0 | 0 | 1 | 0 |\n",
"| 0 | 1 | 0 | 0 |\n",
"| 0 | 1 | 1 | 0 |\n",
"| 1 | 0 | 0 | 1 |\n",
"| 1 | 0 | 1 | 1 |\n",
"| 1 | 1 | 0 | 1 |\n",
"| 1 | 1 | 1 | 1 |"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"| (x1,x2,x3) | label |\n",
"|:---:|:---:|\n",
"| (0,0,0) | - |\n",
"| (0,0,1) | - |\n",
"| (0,1,0) | - |\n",
"| (0,1,1) | - |\n",
"| (1,0,0) | + |\n",
"| (1,0,1) | + |\n",
"| (1,1,0)) | + |\n",
"| (1,1,1) | + |"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"- Case 2: **x1 AND x2 AND NOT x3**"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"| x1 | x2 | x3 | x1 AND x2 AND (NOT x3) |\n",
"|:---:|:---:|:---:|:---:|\n",
"| 0 | 0 | 0 | 0 |\n",
"| 0 | 0 | 1 | 0 |\n",
"| 0 | 1 | 0 | 0 |\n",
"| 0 | 1 | 1 | 0 |\n",
"| 1 | 0 | 0 | 0 |\n",
"| 1 | 0 | 1 | 0 |\n",
"| 1 | 1 | 0 | 1 |\n",
"| 1 | 1 | 1 | 0 |"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"| (x1,x2,x3) | label |\n",
"|:---:|:---:|\n",
"| (0,0,0) | - |\n",
"| (0,0,1) | - |\n",
"| (0,1,0) | - |\n",
"| (0,1,1) | - |\n",
"| (1,0,0) | - |\n",
"| (1,0,1) | - |\n",
"| (1,1,0)) | + |\n",
"| (1,1,1) | - |"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"- Case 3: **x1 OR (x2 AND NOT x3)**"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"| x1 | x2 | x3 | x1 OR (x2 AND NOT x3) |\n",
"|:---:|:---:|:---:|:---:|\n",
"| 0 | 0 | 0 | 0 |\n",
"| 0 | 0 | 1 | 0 |\n",
"| 0 | 1 | 0 | 1 |\n",
"| 0 | 1 | 1 | 0 |\n",
"| 1 | 0 | 0 | 0 |\n",
"| 1 | 0 | 1 | 0 |\n",
"| 1 | 1 | 0 | 1 |\n",
"| 1 | 1 | 1 | 0 |"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"| (x1,x2,x3) | label |\n",
"|:---:|:---:|\n",
"| (0,0,0) | - |\n",
"| (0,0,1) | - |\n",
"| (0,1,0) | + |\n",
"| (0,1,1) | - |\n",
"| (1,0,0) | - |\n",
"| (1,0,1) | - |\n",
"| (1,1,0)) | + |\n",
"| (1,1,1) | - |"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"<img src=\"images/pic02.png\">\n",
"<img src=\"images/pic03.png\">\n",
"<img src=\"images/pic04.png\">\n",
"*Screenshot taken from [Coursera](https://www.coursera.org/learn/ml-foundations/exam/iPHzm/deep-learning)*\n",
"\n",
"<!--TEASER_END-->"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"<img src=\"images/pic05.png\">\n",
"\n",
"*Screenshot taken from [Coursera](https://www.coursera.org/learn/ml-foundations/exam/iPHzm/deep-learning)*\n",
"\n",
"<!--TEASER_END-->"
]
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 2",
"language": "python",
"name": "python2"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 2
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython2",
"version": "2.7.10"
}
},
"nbformat": 4,
"nbformat_minor": 0
}
| mit |
JackDi/phys202-2015-work | assignments/assignment01/ProjectEuler1.ipynb | 1 | 1841 | {
"cells": [
{
"cell_type": "markdown",
"metadata": {
"nbgrader": {}
},
"source": [
"# Project Euler: Problem 1"
]
},
{
"cell_type": "markdown",
"metadata": {
"nbgrader": {}
},
"source": [
"If we list all the natural numbers below 10 that are multiples of 3 or 5, we get 3, 5, 6 and 9. The sum of these multiples is 23.\n",
"\n",
"Find the sum of all the multiples of 3 or 5 below 1000."
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {
"collapsed": false,
"deletable": false,
"nbgrader": {
"checksum": "6cff4e8e53b15273846c3aecaea84a3d",
"solution": true
}
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"233168\n"
]
}
],
"source": [
"# YOUR CODE HERE\n",
"sum=0\n",
"for i in range(1000):\n",
" if i%3 ==0 or i%5==0:\n",
" sum=sum+i\n",
" i=i+1\n",
"print (sum)\n",
" "
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true,
"deletable": false,
"nbgrader": {
"checksum": "6e498cbe102f8b3c1bc4ebd777bcc952",
"grade": true,
"grade_id": "projecteuler1",
"points": 10
}
},
"outputs": [],
"source": [
"# This cell will be used for grading, leave it at the end of the notebook."
]
}
],
"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": "3.4.0"
}
},
"nbformat": 4,
"nbformat_minor": 0
}
| mit |
ConstantineLignos/constantinelignos.github.io | pybootcamp/notebooks/Numpy_Demo.ipynb | 1 | 8988 | {
"cells": [
{
"cell_type": "code",
"execution_count": 1,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"import numpy as np\n",
"# Seed for reproducibility\n",
"np.random.seed(1)"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"# arange creates an array with a range of numbers\n",
"x = np.arange(10, 15)"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"[10 11 12 13 14]\n"
]
},
{
"data": {
"text/plain": [
"numpy.ndarray"
]
},
"execution_count": 3,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# What's in x?\n",
"print(x)\n",
"type(x)"
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"12"
]
},
"execution_count": 4,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# Basic indexing of x\n",
"x[2]"
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"array([11, 13])"
]
},
"execution_count": 5,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# Requesting specific indices\n",
"x[[1, 3]]"
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"array([11, 12, 13, 14, 15])"
]
},
"execution_count": 6,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# Elementwise addition\n",
"x + 1"
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"array([20, 22, 24, 26, 28])"
]
},
"execution_count": 7,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# Elementwise multiplication\n",
"x * 2"
]
},
{
"cell_type": "code",
"execution_count": 8,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"25"
]
},
"execution_count": 8,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# Inner product\n",
"y = np.array([0, 1, 0, 0, 1])\n",
"np.dot(x, y)"
]
},
{
"cell_type": "code",
"execution_count": 9,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"[[1 2]\n",
" [3 4]\n",
" [5 6]]\n"
]
}
],
"source": [
"# A few equivalent ways to create a matrix. Note that \"arrays\" in\n",
"# numpy are n-dimensional, while matrices are special 2-d arrays\n",
"a = np.matrix('1 2; 3 4; 5 6')\n",
"b = np.array([1, 2, 3, 4, 5, 6]).reshape(3,2)\n",
"print(a)"
]
},
{
"cell_type": "code",
"execution_count": 10,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"matrix([[ True, True],\n",
" [ True, True],\n",
" [ True, True]], dtype=bool)"
]
},
"execution_count": 10,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# Default equality is elementwise\n",
"a == b"
]
},
{
"cell_type": "code",
"execution_count": 11,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"True"
]
},
"execution_count": 11,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# Full matrix equality\n",
"np.array_equal(a, b)"
]
},
{
"cell_type": "code",
"execution_count": 12,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"array([10, 11, 12, 13, 14])"
]
},
"execution_count": 12,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# Back to our example vector for more complex indexing\n",
"x"
]
},
{
"cell_type": "code",
"execution_count": 13,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"array([ True, True, True, True, True], dtype=bool)"
]
},
"execution_count": 13,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# Boolean expressions on an array create logical indices or \"masks\"\n",
"x > 2"
]
},
{
"cell_type": "code",
"execution_count": 14,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"array([0, 1, 0, 1, 0], dtype=int32)\n"
]
},
{
"data": {
"text/plain": [
"array([False, True, False, True, False], dtype=bool)"
]
},
"execution_count": 14,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# We can use anything that is boolean-like (i.e., zero and non-zero) to create a mask, but we may need to convert it\n",
"print(repr(x % 2))\n",
"(x % 2).astype(bool)"
]
},
{
"cell_type": "code",
"execution_count": 15,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"array([10, 11, 12, 13, 14])"
]
},
"execution_count": 15,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# Using a mask\n",
"x[x > 2]"
]
},
{
"cell_type": "code",
"execution_count": 16,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"array([11, 13])"
]
},
"execution_count": 16,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# Another mask, select the odd number values\n",
"x[(x % 2).astype(bool)]"
]
},
{
"cell_type": "code",
"execution_count": 17,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"60"
]
},
"execution_count": 17,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# Lots of standard numeric methods are available, for example sum\n",
"# Press the tab key after the period to see more options\n",
"x.sum()"
]
},
{
"cell_type": "code",
"execution_count": 18,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"array([38, 13, 73, 10, 76])"
]
},
"execution_count": 18,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# Drawing random integers\n",
"c = np.random.randint(1, 100, 5)\n",
"c"
]
},
{
"cell_type": "code",
"execution_count": 19,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"4"
]
},
"execution_count": 19,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# Which index is the highest?\n",
"c.argmax()"
]
},
{
"cell_type": "code",
"execution_count": 20,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"True"
]
},
"execution_count": 20,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# All checks whether all values are True\n",
"(c > 1).all()"
]
},
{
"cell_type": "code",
"execution_count": 21,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"True"
]
},
"execution_count": 21,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# Any checks whether any value is True\n",
"(c > 50).any()"
]
}
],
"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": "3.6.0"
}
},
"nbformat": 4,
"nbformat_minor": 2
}
| gpl-3.0 |
NeuroDataDesign/seelviz | Jupyter/ClarityViz Pipeline.ipynb | 1 | 18851 | {
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# ClarityViz\n",
"\n",
"## Pipeline: .img -> histogram .nii -> graph represented as csv -> graph as graphml -> plotly\n",
"\n",
"### To run:\n",
"\n",
"### Step 1:\n",
"\n",
"First, run the following. This takes the .img, generates the localeq histogram as an nii file, gets the nodes and edges as a csv and converts the csv into a graphml\n",
"\n"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"python runclarityviz.py --token Fear199Coronal --file-type img --source-directory /cis/project/clarity/data/clarity/isoCoronal"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Step 2: \n",
"Then run this. This just converts the graphml into a plotly"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"python runclarityviz.py --token Fear199Coronal --plotly yes"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Results"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"Starting pipeline for Fear199.img\n",
"Generating Histogram...\n",
"FINISHED GENERATING HISTOGRAM\n",
"Loading: Fear199/Fear199localeq.nii\n",
"Image Loaded: Fear199/Fear199localeq.nii\n",
"FINISHED LOADING NII\n",
"Coverting to points...\n",
"token=Fear199\n",
"total=600735744\n",
"max=255.000000\n",
"threshold=0.300000\n",
"sample=0.500000\n",
"(This will take couple minutes)\n",
"Above threshold=461409948\n",
"Samples=230718301\n",
"Finished\n",
"FINISHED GETTING POINTS"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"~/clarityviztesting/Fear199Coronal$ ls\n",
"Fear199Coronal.csv\t Fear199Coronal.graphml Fear199Coronal.nodes.csv\n",
"Fear199Coronal.edges.csv Fear199Coronallocaleq.nii Fear199Coronalplotly.html"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Code"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## runclarityviz.py:"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {
"collapsed": false
},
"outputs": [
{
"ename": "SyntaxError",
"evalue": "invalid syntax (<ipython-input-3-e4f212d51a9a>, line 2)",
"output_type": "error",
"traceback": [
"\u001b[0;36m File \u001b[0;32m\"<ipython-input-3-e4f212d51a9a>\"\u001b[0;36m, line \u001b[0;32m2\u001b[0m\n\u001b[0;31m import ...\u001b[0m\n\u001b[0m ^\u001b[0m\n\u001b[0;31mSyntaxError\u001b[0m\u001b[0;31m:\u001b[0m invalid syntax\n"
]
}
],
"source": [
"from clarityviz import clarityviz\n",
"import ...\n",
"\n",
"def get_args():\n",
" parser = argparse.ArgumentParser(description=\"Description\")\n",
"\n",
" parser.add_argument(\"--token\", type=str, required=True, help=\"The token.\")\n",
" parser.add_argument(\"--file-type\", type=str, required=False, help=\"The file type.\")\n",
" parser.add_argument(\"--source-directory\", type=str, required=False,\n",
" help=\"Optional setting of the source directory.\")\n",
" parser.add_argument(\"--plotly\", type=str, required=False, help=\"Optional method to generate the plotly graphs.\")\n",
" parser.add_argument(\"--generate-nii-from-csv\", type=str, required=False, help=\"script to generate nii\")\n",
"\n",
" args = parser.parse_args()\n",
"\n",
" return args\n",
"\n",
"\n",
"def main():\n",
" print('ayyooooo')\n",
" args = get_args()\n",
"\n",
" if args.plotly == 'yes':\n",
" ## Type in the path to your csv file here\n",
" thedata = np.genfromtxt(args.token + '/' + args.token + '.csv',\n",
" delimiter=',', dtype='int', usecols = (0,1,2), names=['a','b','c'])\n",
"\n",
" trace1 = go.Scatter3d(\n",
" x = thedata['a'],\n",
" y = thedata['b'],\n",
" z = thedata['c'],\n",
" mode='markers',\n",
" marker=dict(\n",
" size=1.2,\n",
" color='purple', # set color to an array/list of desired values\n",
" colorscale='Viridis', # choose a colorscale\n",
" opacity=0.15\n",
" )\n",
" )\n",
"\n",
" data = [trace1]\n",
" layout = go.Layout(\n",
" margin=dict(\n",
" l=0,\n",
" r=0,\n",
" b=0,\n",
" t=0\n",
" )\n",
" )\n",
"\n",
" fig = go.Figure(data=data, layout=layout)\n",
" print args.token + \"plotly\"\n",
" plotly.offline.plot(fig, filename= args.token + \"/\" + args.token + \"plotly.html\")\n",
" else:\n",
" print('Starting pipeline for %s' % (args.token + '.' + args.file_type))\n",
" if args.source_directory == None:\n",
" c = clarityviz(args.token)\n",
" else:\n",
" c = clarityviz(args.token, args.source_directory)\n",
"\n",
" if args.file_type == 'img':\n",
" #c.loadEqImg()\n",
" c.generateHistogram()\n",
" print('FINISHED GENERATING HISTOGRAM')\n",
" c.loadNiiImg()\n",
" print('FINISHED LOADING NII')\n",
" elif args.file_type == 'nii':\n",
" c.loadNiiImg()\n",
" print('FINISHED LOADING NII')\n",
"\n",
" c.imgToPoints(0.3, 0.5)\n",
" print(\"FINISHED GETTING POINTS\")\n",
"\n",
" c.savePoints()\n",
"\n",
" c.plot3d()\n",
" print(\"FINISHED PLOT3D\")\n",
"\n",
" c.graphmlconvert()\n",
" print(\"FINISHED GRAPHMLCONVERT\")\n",
"\n",
"if __name__ == \"__main__\":\n",
" main()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## clarityviz.py"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"def generateHistogram(self):\n",
" print('Generating Histogram...')\n",
" if self._source_directory == None:\n",
" path = self._token + '.img'\n",
" else:\n",
" path = self._source_directory + \"/\" + self._token + \".img\"\n",
"\n",
" im = nib.load(path)\n",
"\n",
" im = im.get_data()\n",
" img = im[:,:,:]\n",
"\n",
" shape = im.shape\n",
" #affine = im.get_affine()\n",
"\n",
" x_value = shape[0]\n",
" y_value = shape[1]\n",
" z_value = shape[2]\n",
"\n",
" #####################################################\n",
"\n",
" imgflat = img.reshape(-1)\n",
"\n",
" #img_grey = np.array(imgflat * 255, dtype = np.uint8)\n",
"\n",
" #img_eq = exposure.equalize_hist(img_grey)\n",
"\n",
" #new_img = img_eq.reshape(x_value, y_value, z_value)\n",
" #globaleq = nib.Nifti1Image(new_img, np.eye(4))\n",
"\n",
" ######################################################\n",
"\n",
" #clahe = cv2.createCLAHE(clipLimit=2.0, tileGridSize=(8,8))\n",
" clahe = cv2.createCLAHE(clipLimit=2.0, tileGridSize=(8,8))\n",
"\n",
" img_grey = np.array(imgflat * 255, dtype = np.uint8)\n",
" #threshed = cv2.adaptiveThreshold(img_grey, 255, cv2.ADAPTIVE_THRESH_MEAN_C, cv2.THRESH_BINARY, 3, 0)\n",
"\n",
" cl1 = clahe.apply(img_grey)\n",
"\n",
" #cv2.imwrite('clahe_2.jpg',cl1)\n",
" #cv2.startWindowThread()\n",
" #cv2.namedWindow(\"adaptive\")\n",
" #cv2.imshow(\"adaptive\", cl1)\n",
" #cv2.imshow(\"adaptive\", threshed)\n",
" #plt.imshow(threshed)\n",
"\n",
" localimgflat = cl1 #cl1.reshape(-1)\n",
"\n",
" newer_img = localimgflat.reshape(x_value, y_value, z_value)\n",
" localeq = nib.Nifti1Image(newer_img, np.eye(4))\n",
" nib.save(localeq, self._token + '/' + self._token + 'localeq.nii')"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"def loadGeneratedNii(self, path=None, info=False):\n",
" path = self._token + '/' + self._token + 'localeq.nii'\n",
" print(\"Loading: %s\"%(path))\n",
"\n",
" #pathname = path+self._token+\".nii\"\n",
" img = nib.load(path)\n",
" if info:\n",
" print(img)\n",
" #self._img = img.get_data()[:,:,:,0]\n",
" self._img = img.get_data()\n",
" self._shape = self._img.shape\n",
" self._max = np.max(self._img)\n",
" print(\"Image Loaded: %s\"%(path))\n",
" return self"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"def imgToPoints(self, threshold=0.1, sample=0.5, optimize=True):\n",
" \"\"\"Method to extract points data from the img file.\"\"\"\n",
" if not 0 <= threshold < 1:\n",
" raise ValueError(\"Threshold should be within [0,1).\")\n",
" if not 0 < sample <= 1:\n",
" raise ValueError(\"Sample rate should be within (0,1].\")\n",
" if self._img is None:\n",
" raise ValueError(\"Img haven't loaded, please call loadImg() first.\")\n",
"\n",
" total = self._shape[0]*self._shape[1]*self._shape[2]\n",
" print(\"Coverting to points...\\ntoken=%s\\ntotal=%d\\nmax=%f\\nthreshold=%f\\nsample=%f\"\\\n",
" %(self._token,total,self._max,threshold,sample))\n",
" print(\"(This will take couple minutes)\")\n",
" # threshold\n",
" filt = self._img > threshold * self._max\n",
" x, y, z = np.where(filt)\n",
" v = self._img[filt]\n",
" if optimize:\n",
" self.discardImg()\n",
" v = np.int16(255*(np.float32(v)/np.float32(self._max)))\n",
" l = v.shape\n",
" print(\"Above threshold=%d\"%(l))\n",
" # sample\n",
" if sample < 1.0:\n",
" filt = np.random.random(size=l) < sample\n",
" x = x[filt]\n",
" y = y[filt]\n",
" z = z[filt]\n",
" v = v[filt]\n",
" self._points = np.vstack([x,y,z,v])\n",
" self._points = np.transpose(self._points)\n",
" print(\"Samples=%d\"%(self._points.shape[0]))\n",
" print(\"Finished\")\n",
" return self"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"def plot3d(self, infile = None):\n",
" \"\"\"Method for plotting the Nodes and Edges\"\"\"\n",
" filename = \"\"\n",
" points_file = None\n",
" if infile == None:\n",
" points_file = self._points\n",
" filename = self._token\n",
" else:\n",
" self.loadInitCsv(infile)\n",
" infile = self._infile\n",
" filename = self._filename\n",
"\n",
" # points is an array of arrays\n",
" points = self._points\n",
" outpath = self._token + '/'\n",
" nodename = outpath + filename + '.nodes.csv'\n",
" edgename = outpath + filename + '.edges.csv'\n",
"\n",
" with open(nodename, 'w') as nodefile:\n",
" with open(edgename, 'w') as edgefile:\n",
" for ind in range(len(points)):\n",
" #temp = points[ind].strip().split(',')\n",
" temp = points[ind]\n",
" x = temp[0]\n",
" y = temp[1]\n",
" z = temp[2]\n",
" v = temp[3]\n",
" radius = 18\n",
" nodefile.write(\"s\" + str(ind + 1) + \",\" + str(x) + \",\" + str(y) + \",\" + str(z) + \"\\n\")\n",
" for index in range(ind + 1, len(points)):\n",
" tmp = points[index]\n",
" distance = math.sqrt(math.pow(int(x) - int(tmp[0]), 2) + math.pow(int(y) - int(tmp[1]), 2) + math.pow(int(z) - int(tmp[2]), 2))\n",
" if distance < radius:\n",
" edgefile.write(\"s\" + str(ind + 1) + \",\" + \"s\" + str(index + 1) + \"\\n\")\n",
" self._nodefile = nodefile\n",
" self._edgefile = edgefile"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
" def graphmlconvert(self, nodefilename = None, edgefilename = None):\n",
" \"\"\"Method for extracting the data to a graphml file, based on the node and edge files\"\"\"\n",
" nodefile = None\n",
" edgefile = None\n",
"\n",
" # If no nodefilename was entered, used the Clarity object's nodefile\n",
" if nodefilename == None:\n",
" #nodefile = self._nodefile\n",
" #nodefile = open(self._nodefile, 'r')\n",
"\n",
" self.loadNodeCsv(self._token + \"/\" + self._token + \".nodes.csv\")\n",
" nodefile = self._nodefile\n",
" else:\n",
" self.loadNodeCsv(nodefilename)\n",
" nodefile = self._nodefile\n",
"\n",
" # If no edgefilename was entered, used the Clarity object's edgefile\n",
" if edgefilename == None:\n",
" #edgefile = self._edgefile\n",
" #edgefile = open(self._edgefile, 'r')\n",
"\n",
" self.loadEdgeCsv(self._token + \"/\" + self._token + \".edges.csv\")\n",
" edgefile = self._edgefile\n",
" else:\n",
" self.loadEdgeCsv(edgefilename)\n",
" edgefile = self._edgefile\n",
"\n",
" # Start writing to the output graphml file\n",
" path = self._token + \"/\" + self._token + \".graphml\"\n",
" with open(path, 'w') as outfile:\n",
" outfile.write(\"<?xml version=\\\"1.0\\\" encoding=\\\"UTF-8\\\"?>\\n\")\n",
" outfile.write(\"<graphml xmlns=\\\"http://graphml.graphdrawing.org/xmlns\\\"\\n\")\n",
" outfile.write(\" xmlns:xsi=\\\"http://www.w3.org/2001/XMLSchema-instance\\\"\\n\")\n",
" outfile.write(\" xsi:schemaLocation=\\\"http://graphml.graphdrawing.org/xmlns\\n\")\n",
" outfile.write(\" http://graphml.graphdrawing.org/xmlns/1.0/graphml.xsd\\\">\\n\")\n",
"\n",
" outfile.write(\" <key id=\\\"d0\\\" for=\\\"node\\\" attr.name=\\\"attr\\\" attr.type=\\\"string\\\"/>\\n\")\n",
" outfile.write(\" <key id=\\\"e_weight\\\" for=\\\"edge\\\" attr.name=\\\"weight\\\" attr.type=\\\"double\\\"/>\\n\")\n",
" outfile.write(\" <graph id=\\\"G\\\" edgedefault=\\\"undirected\\\">\\n\")\n",
"\n",
" for line in nodefile:\n",
" if len(line) == 0:\n",
" continue\n",
" line = line.strip().split(',')\n",
" outfile.write(\" <node id=\\\"\" + line[0] + \"\\\">\\n\")\n",
" outfile.write(\" <data key=\\\"d0\\\">[\" + line[1] + \", \" + line[2] + \", \" + line[3] +\"]</data>\\n\")\n",
" outfile.write(\" </node>\\n\")\n",
" \n",
" for line in edgefile:\n",
" if len(line) == 0:\n",
" continue\n",
" line = line.strip().split(',')\n",
" outfile.write(\" <edge source=\\\"\" + line[0] + \"\\\" target=\\\"\" + line[1] + \"\\\">\\n\")\n",
" outfile.write(\" <data key=\\\"e_weight\\\">1</data>\\n\")\n",
" outfile.write(\" </edge>\\n\")\n",
"\n",
" outfile.write(\" </graph>\\n</graphml>\")"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"def graphmlToPlotly(self, path):\n",
" ## Type in the path to your csv file here\n",
" thedata = np.genfromtxt('../data/points/localeq.csv', delimiter=',', dtype='int', usecols = (0,1,2), names=['a','b','c'])\n",
"\n",
" trace1 = go.Scatter3d(\n",
" x = thedata['a'],\n",
" y = thedata['b'],\n",
" z = thedata['c'],\n",
" mode='markers',\n",
" marker=dict(\n",
" size=1.2,\n",
" color='purple', # set color to an array/list of desired values\n",
" colorscale='Viridis', # choose a colorscale\n",
" opacity=0.15\n",
" )\n",
" )\n",
"\n",
" data = [trace1]\n",
" layout = go.Layout(\n",
" margin=dict(\n",
" l=0,\n",
" r=0,\n",
" b=0,\n",
" t=0\n",
" )\n",
" )\n",
"\n",
" fig = go.Figure(data=data, layout=layout)\n",
" print \"localeq\"\n",
" plotly.offline.plot(fig, filename= \"localeq\")"
]
}
],
"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": "3.5.2"
}
},
"nbformat": 4,
"nbformat_minor": 0
}
| apache-2.0 |
deepmind/offpolicy_selection_eslb | colabs/eslb_synthetic_example.ipynb | 1 | 16620 | {
"cells": [
{
"cell_type": "markdown",
"metadata": {
"id": "S4_XUChf7kag"
},
"source": [
"Implements ESLB value estimator for contextual bandit off-policy problem.\n",
"\n",
"All estimators are described in\n",
"\"Kuzborskij, I., Vernade, C., Gyorgy, A., & Szepesvári, C. (2021, March).\n",
"Confident off-policy evaluation and selection through self-normalized importance\n",
"weighting. In International Conference on Artificial Intelligence and Statistics\n",
"(pp. 640-648). PMLR.\".\n",
"In the following we occassionally refer to the statements in the paper\n",
"(e.g. Theorem 1, Proposition 1).\n",
"\n",
"class ESLB implements an Efron-Stein high probability bound for off-policy\n",
"evaluation (Theorem 1 and Algorithm 1)."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"executionInfo": {
"elapsed": 7,
"status": "ok",
"timestamp": 1635962607507,
"user": {
"displayName": "",
"photoUrl": "",
"userId": ""
},
"user_tz": -60
},
"id": "AWJ2V9Fy7gvP"
},
"outputs": [],
"source": [
"# Copyright 2021 DeepMind Technologies Limited.\n",
"#\n",
"#\n",
"# Licensed under the Apache License, Version 2.0 (the \"License\");\n",
"# you may not use this file except in compliance with the License.\n",
"# You may obtain a copy of the License at\n",
"#\n",
"# https://www.apache.org/licenses/LICENSE-2.0\n",
"#\n",
"# Unless required by applicable law or agreed to in writing, software\n",
"# distributed under the License is distributed on an \"AS IS\" BASIS,\n",
"# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n",
"# See the License for the specific language governing permissions and\n",
"# limitations under the License."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"executionInfo": {
"elapsed": 7,
"status": "ok",
"timestamp": 1635962611274,
"user": {
"displayName": "",
"photoUrl": "",
"userId": ""
},
"user_tz": -60
},
"id": "IfalmppW7nSg"
},
"outputs": [],
"source": [
"import logging\n",
"import math\n",
"import enum\n",
"import numpy as np"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "eGhEOqUQ7tv4"
},
"source": [
"# Tools"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"executionInfo": {
"elapsed": 8,
"status": "ok",
"timestamp": 1635962612546,
"user": {
"displayName": "",
"photoUrl": "",
"userId": ""
},
"user_tz": -60
},
"id": "EORt_PYf7qmg"
},
"outputs": [],
"source": [
"def sample_from_simplices_m_times(p, m):\n",
" \"\"\"Samples from each of n probability simplices for m times.\n",
"\n",
" Args:\n",
" p: n-times-K matrix where each row describes a probability simplex\n",
" m: number of times to sample\n",
"\n",
" Returns:\n",
" n-times-m matrix of indices of simplex corners.\n",
" \"\"\"\n",
" axis = 1\n",
" r = np.expand_dims(np.random.rand(p.shape[1 - axis], m), axis=axis)\n",
" p_ = np.expand_dims(p.cumsum(axis=axis), axis=2)\n",
" return (np.repeat(p_, m, axis=2) > r).argmax(axis=1)"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "cz0tGqIE7yAQ"
},
"source": [
"# Implementation of the ESLB estimator"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"executionInfo": {
"elapsed": 114,
"status": "ok",
"timestamp": 1635962613969,
"user": {
"displayName": "",
"photoUrl": "",
"userId": ""
},
"user_tz": -60
},
"id": "1EQGSySA71di"
},
"outputs": [],
"source": [
"class ESLBBiasType(enum.Enum):\n",
" \"\"\"Bias control type of ESLB estimator.\n",
"\n",
" ESLBBiasType.MultOneHot = Multiplicative bias (only for `one-hot' rewards).\n",
" ESLBBiasType.Bernstein = Bernstein bias (for rewards in [0,1], looser than above).\n",
" \"\"\"\n",
" MultOneHot = \"MultOneHot\"\n",
" Bernstein = \"Bernstein\"\n",
"\n",
" def __str__(self):\n",
" return str(self.value)\n",
"\n",
"class ESLB(object):\n",
" \"\"\"Implements a Semi-Empirical Efron-Stein bound for the SNIW (Self-normalized Importance Weighted estimator).\n",
"\n",
" Attributes:\n",
" delta: Error probability in (0,1).\n",
" n_iterations: Number of Monte-Carlo simulation iterations for approximating\n",
" a multiplicative bias and a variance proxy.\n",
" n_batch_size: Monte-Carlo simulation batch size.\n",
" bias_type: type of bias control to use (see ESLBBiasType).\n",
" \"\"\"\n",
"\n",
" def __init__(\n",
" self,\n",
" delta: float,\n",
" n_iterations: int,\n",
" n_batch_size: int,\n",
" bias_type: ESLBBiasType\n",
" ):\n",
" \"\"\"Constructs an estimator.\n",
"\n",
" The estimate holds with probability 1-delta.\n",
"\n",
" Args:\n",
" delta: delta: Error probability in (0,1) for a confidence interval.\n",
" n_iterations: Monte-Carlo simulation iterations.\n",
" n_batch_size: Monte-Carlo simulation batch size.\n",
" bias_type: type of bias control to use.\n",
" \"\"\"\n",
" self.delta = delta\n",
" self.n_iterations = n_iterations\n",
" self.n_batch_size = n_batch_size\n",
" self.bias_type = bias_type\n",
"\n",
" def get_name(self):\n",
" \"\"\"Returns the long name of the estimator.\"\"\"\n",
" return \"Semi-Empirical Efron-Stein bound for the Self-normalized Estimator\"\n",
"\n",
" def get_abbrev(self):\n",
" \"\"\"Returns the short name of the estimator.\"\"\"\n",
" return \"ESLB\"\n",
"\n",
" def __call__(\n",
" self,\n",
" t_probs: np.ndarray,\n",
" b_probs: np.ndarray,\n",
" actions: np.ndarray,\n",
" rewards: np.ndarray,\n",
" ):\n",
" \"\"\"Computes Efron-Stein lower bound of Theorem 1 as described in Algorithm 1.\n",
"\n",
" Here n is a sample size, while K is a number actions.\n",
"\n",
" Args:\n",
" t_probs: n-times-K matrix, where i-th row corresponds to π_t(. | X_i)\n",
" (target probabilities under the target policy).\n",
" b_probs: n-times-K matrix, where i-th row corresponds to π_b(. | X_i)\n",
" (target probabilities under the behavior policy).\n",
" actions: n-sized vector of actions.\n",
" rewards: n-sized reward vector.\n",
"\n",
" Returns:\n",
" A dictionary with 8 entries:\n",
" lower_bound: Corresponds to the actual lower bound.\n",
" estimate: Same as lower_bound (required by select_policy(...)).\n",
" est_value: Empirical value.\n",
" mult_bias: Multiplicative bias.\n",
" concentration_of_contexts: Hoeffding term, concentration of contexts.\n",
" var_proxy: Variance proxy.\n",
" expected_variance_proxy: Estimated expected counterpart.\n",
" \"\"\"\n",
" conf = math.log(2.0 / self.delta)\n",
" n = len(actions)\n",
" ix_1_n = np.arange(n)\n",
"\n",
" # Importance weights\n",
" weights = t_probs[ix_1_n, actions] / b_probs[ix_1_n, actions]\n",
"\n",
" weights_cumsum = weights.cumsum()\n",
" weights_cumsum = np.repeat(\n",
" np.expand_dims(weights_cumsum, axis=1), self.n_batch_size, axis=1)\n",
" weights_repeated = np.repeat(\n",
" np.expand_dims(weights, axis=1), self.n_batch_size, axis=1)\n",
"\n",
" weight_table = t_probs / b_probs\n",
"\n",
" var_proxy_unsumed = np.zeros((n,))\n",
" expected_var_proxy_unsumed = np.zeros((n,))\n",
" loo_expected_recip_weights = 0.0\n",
"\n",
" are_rewards_binary = ((rewards==0) | (rewards==1)).all()\n",
" if self.bias_type == ESLBBiasType.MultOneHot and not are_rewards_binary:\n",
" raise Exception(\"\"\"bias_type=ESLBBiasType.MultOneHot only supports one-hot rewards. \n",
"Consider using bias_type=ESLBBiasType.Bernstein\"\"\")\n",
"\n",
" logging.debug(\n",
" \"ESLB:: Running Monte-Carlo estimation of the variance proxy and bias\")\n",
" logging.debug(\"ESLB:: iterations = %d, batch size = %d\", self.n_iterations,\n",
" self.n_batch_size)\n",
"\n",
" for i in range(self.n_iterations):\n",
" actions_sampled = sample_from_simplices_m_times(\n",
" b_probs, self.n_batch_size)\n",
" weights_sampled = weight_table[ix_1_n, actions_sampled.T].T\n",
" weights_sampled_cumsum = weights_sampled[::-1, :].cumsum(axis=0)[::-1, :]\n",
" # Hybrid sums: sums of empirical and sampled weights\n",
" weights_hybrid_sums = np.copy(weights_cumsum)\n",
" weights_hybrid_sums[:-1, :] += weights_sampled_cumsum[1:, :]\n",
"\n",
" # Computing variance proxy\n",
" weights_hybrid_sums_replace_k = weights_hybrid_sums - weights_repeated + weights_sampled\n",
"\n",
" sn_weights = weights_repeated / weights_hybrid_sums\n",
" sn_weights_prime = weights_sampled / weights_hybrid_sums_replace_k\n",
"\n",
" var_proxy_t = (sn_weights + sn_weights_prime)**2\n",
" var_proxy_new_item = var_proxy_t.mean(axis=1)\n",
" var_proxy_unsumed += (var_proxy_new_item - var_proxy_unsumed) / (i + 1)\n",
"\n",
" actions_sampled_for_expected_var = sample_from_simplices_m_times(\n",
" b_probs, self.n_batch_size)\n",
" weights_sampled_for_expected_var = weight_table[\n",
" ix_1_n, actions_sampled_for_expected_var.T].T\n",
"\n",
" expected_var_proxy_new_item = (\n",
" (weights_sampled_for_expected_var /\n",
" weights_sampled_for_expected_var.sum(axis=0))**2).mean(axis=1)\n",
" expected_var_proxy_unsumed += (expected_var_proxy_new_item -\n",
" expected_var_proxy_unsumed) / (i + 1)\n",
"\n",
"\n",
" if self.bias_type == ESLBBiasType.MultOneHot:\n",
" # Computing bias (loo = leave-one-out)\n",
" # Rewards are `one-hot'\n",
" actions_sampled_for_bias = sample_from_simplices_m_times(\n",
" b_probs, self.n_batch_size)\n",
" weights_sampled_for_bias = weight_table[ix_1_n,\n",
" actions_sampled_for_bias.T].T\n",
" loo_sum_weights = np.outer(\n",
" np.ones((n,)),\n",
" np.sum(weights_sampled_for_bias, axis=0)\n",
" ) - weights_sampled_for_bias\n",
" loo_expected_recip_weights += (1 / np.min(loo_sum_weights, axis=0)).mean()\n",
"\n",
" var_proxy = var_proxy_unsumed.sum()\n",
" expected_var_proxy = expected_var_proxy_unsumed.sum()\n",
"\n",
" if self.bias_type == ESLBBiasType.MultOneHot:\n",
" loo_expected_recip_weights /= self.n_iterations\n",
" eff_sample_size = 1.0 / loo_expected_recip_weights\n",
" mult_bias = min(1.0, eff_sample_size / n)\n",
" add_bias = 0\n",
" elif self.bias_type == ESLBBiasType.Bernstein:\n",
" # Computing Bernstein bias control (based on lower tail Bernstein's inequality)\n",
" expected_sum_weights_sq = (t_probs**2 / b_probs).sum()\n",
" bias_x = math.log(n) / 2\n",
" mult_bias = 1 - math.sqrt(2 * expected_sum_weights_sq * bias_x) / n\n",
" mult_bias = max(0, mult_bias)\n",
" add_bias = math.exp(-bias_x)\n",
" \n",
" concentration = math.sqrt(\n",
" 2.0 * (var_proxy + expected_var_proxy) *\n",
" (conf + 0.5 * math.log(1 + var_proxy / expected_var_proxy)))\n",
" concentration_of_contexts = math.sqrt(conf / (2 * n))\n",
" est_value = weights.dot(rewards) / weights.sum()\n",
" lower_bound = mult_bias * (est_value\n",
" - concentration\n",
" - add_bias) - concentration_of_contexts\n",
"\n",
" return dict(\n",
" estimate=max(0, lower_bound),\n",
" lower_bound=max(0, lower_bound),\n",
" est_value=est_value,\n",
" concentration=concentration,\n",
" mult_bias=mult_bias,\n",
" concentration_of_contexts=concentration_of_contexts,\n",
" var_proxy=var_proxy,\n",
" expected_var_proxy=expected_var_proxy)"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "0zbtpZue72Zn"
},
"source": [
"Minimal working example for using ``ESLB'' class, where the behavior and target policies are Softmax policies with the mass concentrated on one action (different for each policy)."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"executionInfo": {
"elapsed": 33221,
"status": "ok",
"timestamp": 1635962648377,
"user": {
"displayName": "",
"photoUrl": "",
"userId": ""
},
"user_tz": -60
},
"id": "NZR5tI-n75ho",
"outputId": "97e1c4ca-2374-475f-b4c8-9f80687a1619"
},
"outputs": [],
"source": [
"np.random.seed(1)\n",
"\n",
"temp = 1 # temperature of Softmax policy\n",
"K = 5 # number of actions\n",
"delta_ = 0.05 # error probability of the lower bound\n",
"\n",
"b_ix = 0 # mass on the 0-th action in the behavior policy\n",
"t_ix = 1 # mass on the 1-st action in the target policy\n",
"\n",
"# Definition of the behavior Softmax policy\n",
"b_pot = np.zeros(K)\n",
"b_pot[b_ix] = 1\n",
"b_policy = np.exp(b_pot / temp)\n",
"b_policy /= b_policy.sum()\n",
"\n",
"# Definition of the target Softmax policy\n",
"t_pot = np.zeros(K)\n",
"t_pot[t_ix] = 1\n",
"t_policy = np.exp(t_pot / temp)\n",
"t_policy /= t_policy.sum()\n",
"\n",
"t_value = t_policy[t_ix] # Value of the target policy\n",
"\n",
"# Computation of the lower bound for increasing number of observations\n",
"for n_ in [100, 1000, 10000]:\n",
" actions_ = np.random.choice(range(K), size=n_, p=b_policy)\n",
" rewards_ = np.array(actions_ == t_ix, dtype=int)\n",
" b_probs_ = np.repeat(np.expand_dims(b_policy, 0), n_, axis=0)\n",
" t_probs_ = np.repeat(np.expand_dims(t_policy, 0), n_, axis=0)\n",
"\n",
" estimator = ESLB(delta=delta_, n_iterations=10, n_batch_size=1000, bias_type=ESLBBiasType.MultOneHot)\n",
" results = estimator(\n",
" t_probs=t_probs_, b_probs=b_probs_, actions=actions_, rewards=rewards_)\n",
"\n",
" print(\"------------------------------------------------------------------\")\n",
" print(\"behavior policy:\\t (%s)\" %\n",
" \", \".join(map(lambda x: \"%.2f\" % x, b_policy)))\n",
" print(\"target policy:\\t\\t (%s)\" %\n",
" \", \".join(map(lambda x: \"%.2f\" % x, t_policy)))\n",
" print(\"sample size:\\t\\t %d\" % n_)\n",
" print(\"value(pi):\\t\\t %.3f\" % t_value)\n",
" print(\"ESLB:\\t\\t\\t %.3f\" % results[\"lower_bound\"])\n",
" print(\"hat{value}(pi):\\t\\t %.3f\" % results[\"est_value\"])\n",
" print(\"conc.:\\t\\t\\t %.3f\" % results[\"concentration\"])\n",
" print(\"mult. bias:\\t\\t %.3f\" % results[\"mult_bias\"])\n",
" print(\"conc. of contexts:\\t %.3f\" % results[\"concentration_of_contexts\"])"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "5eouWbfi-HQ7"
},
"outputs": [],
"source": []
}
],
"metadata": {
"colab": {
"collapsed_sections": [],
"name": "eslb_synthetic_example.ipynb",
"provenance": []
},
"kernelspec": {
"display_name": "Python 3 (ipykernel)",
"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": "3.9.9"
}
},
"nbformat": 4,
"nbformat_minor": 1
}
| apache-2.0 |
BinRoot/TensorFlow-Book | ch02_basics/Concept02_evaluating_ops.ipynb | 1 | 3142 | {
"cells": [
{
"cell_type": "markdown",
"metadata": {
"deletable": true,
"editable": true
},
"source": [
"# Ch `02`: Concept `02`"
]
},
{
"cell_type": "markdown",
"metadata": {
"deletable": true,
"editable": true
},
"source": [
"## Evaluating ops"
]
},
{
"cell_type": "markdown",
"metadata": {
"deletable": true,
"editable": true
},
"source": [
"Import TensorFlow:"
]
},
{
"cell_type": "code",
"execution_count": 1,
"metadata": {
"collapsed": true,
"deletable": true,
"editable": true
},
"outputs": [],
"source": [
"import tensorflow as tf"
]
},
{
"cell_type": "markdown",
"metadata": {
"collapsed": true,
"deletable": true,
"editable": true
},
"source": [
"Start with a 1x2 matrix:"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {
"collapsed": false,
"deletable": true,
"editable": true
},
"outputs": [],
"source": [
"x = tf.constant([[1, 2]])"
]
},
{
"cell_type": "markdown",
"metadata": {
"deletable": true,
"editable": true
},
"source": [
"Let's negate it. Define the negation op to be run on the matrix:"
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {
"collapsed": false,
"deletable": true,
"editable": true
},
"outputs": [],
"source": [
"neg_x = tf.negative(x)"
]
},
{
"cell_type": "markdown",
"metadata": {
"deletable": true,
"editable": true
},
"source": [
"It's nothing special if you print it out. In fact, it doesn't even perform the negation computation. Check out what happens when you simply print it:"
]
},
{
"cell_type": "code",
"execution_count": 16,
"metadata": {
"collapsed": false,
"deletable": true,
"editable": true
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Tensor(\"Neg_3:0\", shape=(1, 2), dtype=int32)\n"
]
}
],
"source": [
"print(neg_x)"
]
},
{
"cell_type": "markdown",
"metadata": {
"deletable": true,
"editable": true
},
"source": [
"You need to summon a session so you can launch the negation op:"
]
},
{
"cell_type": "code",
"execution_count": 17,
"metadata": {
"collapsed": false,
"deletable": true,
"editable": true
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"[[-1 -2]]\n"
]
}
],
"source": [
"with tf.Session() as sess:\n",
" result = sess.run(neg_x)\n",
" print(result)"
]
}
],
"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": "3.5.2"
}
},
"nbformat": 4,
"nbformat_minor": 0
}
| mit |
evanmiltenburg/python-for-text-analysis | Assignments-colab/ASSIGNMENT_RESIT_A.ipynb | 1 | 24171 | {
"nbformat": 4,
"nbformat_minor": 0,
"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": "3.8.5"
},
"colab": {
"name": "ASSIGNMENT-RESIT-A.ipynb",
"provenance": [],
"include_colab_link": true
}
},
"cells": [
{
"cell_type": "markdown",
"metadata": {
"id": "view-in-github",
"colab_type": "text"
},
"source": [
"<a href=\"https://colab.research.google.com/github/cltl/python-for-text-analysis/blob/colab/Assignments-colab/ASSIGNMENT_RESIT_A.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
]
},
{
"cell_type": "code",
"metadata": {
"id": "j8tiMr4sQhXs"
},
"source": [
"%%capture\n",
"!wget https://github.com/cltl/python-for-text-analysis/raw/master/zips/Data.zip\n",
"!wget https://github.com/cltl/python-for-text-analysis/raw/master/zips/images.zip\n",
"!wget https://github.com/cltl/python-for-text-analysis/raw/master/zips/Extra_Material.zip\n",
"\n",
"!unzip Data.zip -d ../\n",
"!unzip images.zip -d ./\n",
"!unzip Extra_Material.zip -d ../\n",
"\n",
"!rm Data.zip\n",
"!rm Extra_Material.zip\n",
"!rm images.zip"
],
"execution_count": 1,
"outputs": []
},
{
"cell_type": "markdown",
"metadata": {
"id": "b5MPzjWHQgUv"
},
"source": [
"# Resit Assignment part A\n",
"\n",
"**Deadline: Friday, November 13, 2020 before 17:00** \n",
"\n",
"- Please name your files: \n",
" * ASSIGNMENT-RESIT-A.ipynb\n",
" * utils.py (from part B)\n",
" * raw_text_to_coll.py (from part B)\n",
"\n",
"Please name your zip file as follows: RESIT-ASSIGNMENT.zip and upload it via Canvas (Resit Assignment). \n",
"- Please submit your assignment on Canvas: Resit Assignment\n",
"- If you have **questions** about this topic, please contact **[email protected]**.\n",
"\n",
" \n",
"Questions and answers will be collected in [this Q&A document](https://docs.google.com/document/d/1Yf2lE6HdApz4wSgNpxWL_nnVcXED1YNW8Rg__wCKcvs/edit?usp=sharing), \n",
"so please check if your question has already been answered.\n",
"\n",
"All of the covered chapters are important to this assignment. However, please pay special attention to:\n",
"- Chapter 10 - Dictionaries\n",
"- Chapter 11 - Functions and scope\n",
"* Chapter 14 - Reading and writing text files\n",
"* Chapter 15 - Off to analyzing text \n",
"- Chapter 17 - Data Formats II (JSON)\n",
"- Chapter 19 - More about Natural Language Processing Tools (spaCy)\n",
"\n",
"\n",
"In this assignment:\n",
"* we are going to process the texts in ../Data/Dreams/*txt\n",
"* for each file, we are going to determine:\n",
" * the number of characters\n",
" * the number of sentences\n",
" * the number of words\n",
" * the longest word\n",
" * the longest sentence"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "xGo-rcx0QgUz"
},
"source": [
"## Note\n",
"This notebook should be placed in the same folder as the other Assignments!"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "wRIdUhrKQgUz"
},
"source": [
"## Loading spaCy"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "AK2sqPb8QgU0"
},
"source": [
"Please make sure that spaCy is installed on your computer"
]
},
{
"cell_type": "code",
"metadata": {
"id": "RPDXvejPQgU0"
},
"source": [
"import spacy"
],
"execution_count": 2,
"outputs": []
},
{
"cell_type": "markdown",
"metadata": {
"id": "dMw_DCmIQgU1"
},
"source": [
"Please make sure you can load the English spaCy model:"
]
},
{
"cell_type": "code",
"metadata": {
"id": "LphmBOC8QgU1"
},
"source": [
"nlp = spacy.load('en_core_web_sm')"
],
"execution_count": 3,
"outputs": []
},
{
"cell_type": "markdown",
"metadata": {
"id": "H0N7k9KMQgU2"
},
"source": [
"## Exercise 1: get paths"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "08Q0fDERQgU3"
},
"source": [
"Define a function called **get_paths** that has the following parameter: \n",
"* **input_folder**: a string\n",
"\n",
"The function:\n",
"* stores all paths to .txt files in the *input_folder* in a list\n",
"* returns a list of strings, i.e., each string is a file path"
]
},
{
"cell_type": "code",
"metadata": {
"id": "IqoYcOHIQgU3"
},
"source": [
"# your code here"
],
"execution_count": 4,
"outputs": []
},
{
"cell_type": "markdown",
"metadata": {
"id": "5Jr6byFjQgU3"
},
"source": [
"Please test your function using the following function call"
]
},
{
"cell_type": "code",
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 183
},
"id": "6NnUwUS-QgU4",
"outputId": "49c6aac1-40ed-4e12-ec1a-7cfeedfd6252"
},
"source": [
"paths = get_paths(input_folder='../Data/Dreams')\n",
"print(paths)"
],
"execution_count": 5,
"outputs": [
{
"output_type": "error",
"ename": "NameError",
"evalue": "ignored",
"traceback": [
"\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
"\u001b[0;31mNameError\u001b[0m Traceback (most recent call last)",
"\u001b[0;32m<ipython-input-5-fe4b98dbbbf6>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mpaths\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mget_paths\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0minput_folder\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m'../Data/Dreams'\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[0mprint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mpaths\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;31mNameError\u001b[0m: name 'get_paths' is not defined"
]
}
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "555Gcum3QgU4"
},
"source": [
"## Exercise 2: load text"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "4Eqk0dHXQgU4"
},
"source": [
"Define a function called **load_text** that has the following parameter: \n",
"* **txt_path**: a string\n",
"\n",
"\n",
"The function:\n",
"* opens the **txt_path** for reading and loads the contents of the file as a string\n",
"* returns a string, i.e., the content of the file"
]
},
{
"cell_type": "code",
"metadata": {
"id": "StwFWdNuQgU5"
},
"source": [
"# your code here"
],
"execution_count": 6,
"outputs": []
},
{
"cell_type": "markdown",
"metadata": {
"id": "x7xkfnLDQgU5"
},
"source": [
"## Exercise 3: return the longest"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "jLViOXz3QgU5"
},
"source": [
"Define a function called **return_the_longest** that has the following parameter: \n",
"* **list_of_strings**: a list of strings\n",
"\n",
"\n",
"The function:\n",
"* returns the string with the highest number of characters. If multiple strings have the same length, return one of them."
]
},
{
"cell_type": "code",
"metadata": {
"id": "r3FzxYGUQgU7"
},
"source": [
"def return_the_longest(list_of_strings):\n",
" \"\"\"\n",
" given a list of strings, return the longest string\n",
" if multiple strings have the same length, return one of them.\n",
" \n",
" :param str list_of_strings: a list of strings\n",
" \n",
" \"\"\""
],
"execution_count": 7,
"outputs": []
},
{
"cell_type": "markdown",
"metadata": {
"id": "IJcupO5RQgU8"
},
"source": [
"Please test you function by running the following cell:"
]
},
{
"cell_type": "code",
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 200
},
"id": "xNabtw1xQgU8",
"outputId": "b99144ea-48ba-4aa8-9266-d04ebc584f15"
},
"source": [
"a_list_of_strings = [\"this\", \"is\", \"a\", \"sentence\"]\n",
"longest_string = return_the_longest(a_list_of_strings)\n",
"\n",
"error_message = f'the longest string should be \"sentence\", you provided {longest_string}'\n",
"assert longest_string == 'sentence', error_message"
],
"execution_count": 8,
"outputs": [
{
"output_type": "error",
"ename": "AssertionError",
"evalue": "ignored",
"traceback": [
"\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
"\u001b[0;31mAssertionError\u001b[0m Traceback (most recent call last)",
"\u001b[0;32m<ipython-input-8-67458fec4e1a>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m 3\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 4\u001b[0m \u001b[0merror_message\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34mf'the longest string should be \"sentence\", you provided {longest_string}'\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 5\u001b[0;31m \u001b[0;32massert\u001b[0m \u001b[0mlongest_string\u001b[0m \u001b[0;34m==\u001b[0m \u001b[0;34m'sentence'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0merror_message\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
"\u001b[0;31mAssertionError\u001b[0m: the longest string should be \"sentence\", you provided None"
]
}
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "BxFBm1B3QgU8"
},
"source": [
"## Exercise 4: extract statistics\n",
"We are going to use spaCy to extract statistics from Vickie's dreams! Here are a few tips below about how to use spaCy:"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "KvdyebpZQgU8"
},
"source": [
"#### tip 1: process text with spaCy"
]
},
{
"cell_type": "code",
"metadata": {
"id": "SXnkO4N1QgU8"
},
"source": [
"a_text = 'this is one sentence. this is another.'\n",
"doc = nlp(a_text)"
],
"execution_count": 9,
"outputs": []
},
{
"cell_type": "markdown",
"metadata": {
"id": "MXVbcd8SQgU9"
},
"source": [
"#### tip 2: the number of characters is the length of the document"
]
},
{
"cell_type": "code",
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "bV-kDL_3QgU9",
"outputId": "851f3445-51c4-4bc1-9765-76eec3254df7"
},
"source": [
"num_chars = len(doc.text)\n",
"print(num_chars)"
],
"execution_count": 10,
"outputs": [
{
"output_type": "stream",
"name": "stdout",
"text": [
"38\n"
]
}
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "f8bcSpWhQgU9"
},
"source": [
"#### tip 3: loop through the sentences of a document"
]
},
{
"cell_type": "code",
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "rXRz3EipQgU9",
"outputId": "960dd8f8-d091-4669-d83a-65ceb9fef9fe"
},
"source": [
"for sent in doc.sents:\n",
" sent = sent.text\n",
" print(sent)"
],
"execution_count": 11,
"outputs": [
{
"output_type": "stream",
"name": "stdout",
"text": [
"this is one sentence.\n",
"this is another.\n"
]
}
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "TQQigbyVQgU9"
},
"source": [
"#### tip 4: loop through the words of a document"
]
},
{
"cell_type": "code",
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "vQsoRMNtQgU9",
"outputId": "a26e605d-141a-40f4-c39b-e77225588116"
},
"source": [
"for token in doc:\n",
" word = token.text\n",
" print(word)"
],
"execution_count": 12,
"outputs": [
{
"output_type": "stream",
"name": "stdout",
"text": [
"this\n",
"is\n",
"one\n",
"sentence\n",
".\n",
"this\n",
"is\n",
"another\n",
".\n"
]
}
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "UXfd8gY4QgU-"
},
"source": [
"Define a function called **extract_statistics** that has the following parameters: \n",
"* **nlp**: the result of calling spacy.load('en_core_web_sm')\n",
"* **txt_path**: path to a txt file, e.g., '../Data/Dreams/vickie8.txt'\n",
"\n",
"The function:\n",
"* loads the content of the file using the function **load_text**\n",
"* processes the content of the file using **nlp(content)** (see tip 1 of this exercise)\n",
"\n",
"The function returns a dictionary with five keys:\n",
"* **num_sents**: the number of sentences in the document\n",
"* **num_chars**: the number of characters in the document\n",
"* **num_tokens**: the number of words in the document\n",
"* **longest_sent**: the longest sentence in the document\n",
" * Please make a list with all the sentences and call the function **return_the_longest** to retrieve the longest sentence\n",
"* **longest_word**: the longest word in the document\n",
" * Please make a list with all the words and call the function **return_the_longest** to retrieve the longest word\n",
" \n",
"Test the function on one of the files from Vickie's dreams."
]
},
{
"cell_type": "code",
"metadata": {
"id": "KnlcLOB0QgU-"
},
"source": [
"def extract_statistics(nlp, txt_path):\n",
" \"\"\"\n",
" given a txt_path\n",
" -use the load_text function to load the text\n",
" -process the text using spaCy\n",
" \n",
" :param nlp: loaded spaCy model (result of calling spacy.load('en_core_web_sm'))\n",
" :param str txt_path: path to txt file\n",
" \n",
" :rtype: dict\n",
" :return: a dictionary with the following keys:\n",
" -\"num_sents\" : the number of sentences\n",
" -\"num_chars\" : the number of characters \n",
" -\"num_tokens\" : the number of words \n",
" -\"longest_sent\" : the longest sentence\n",
" -\"longest_word\" : the longest word\n",
" \"\"\""
],
"execution_count": 13,
"outputs": []
},
{
"cell_type": "code",
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 183
},
"id": "fqErXzWhQgU-",
"outputId": "4ce32880-8b3f-4064-f0b1-9258bcc8f955"
},
"source": [
"stats = extract_statistics(nlp, txt_path=paths[0])\n",
"stats"
],
"execution_count": 14,
"outputs": [
{
"output_type": "error",
"ename": "NameError",
"evalue": "ignored",
"traceback": [
"\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
"\u001b[0;31mNameError\u001b[0m Traceback (most recent call last)",
"\u001b[0;32m<ipython-input-14-3567329601d5>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mstats\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mextract_statistics\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mnlp\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtxt_path\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mpaths\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m0\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[0m\u001b[1;32m 2\u001b[0m \u001b[0mstats\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;31mNameError\u001b[0m: name 'paths' is not defined"
]
}
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "7SjGKr1dQgU-"
},
"source": [
"## Exercise 5: process all txt files"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "TkdsrGH_QgU-"
},
"source": [
"#### tip 1: how to obtain the basename of a file"
]
},
{
"cell_type": "code",
"metadata": {
"id": "OJYQdyiyQgU-"
},
"source": [
"import os"
],
"execution_count": 15,
"outputs": []
},
{
"cell_type": "code",
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "PvGYNffVQgU_",
"outputId": "50e1c1cd-dfcc-4faf-8a01-2136fa18655c"
},
"source": [
"basename = os.path.basename('../Data/Dreams/vickie1.txt')[:-4]\n",
"print(basename)"
],
"execution_count": 16,
"outputs": [
{
"output_type": "stream",
"name": "stdout",
"text": [
"vickie1\n"
]
}
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "D3NSsNSbQgU_"
},
"source": [
"Define a function called **process_all_txt_files** that has the following parameters: \n",
"* **nlp**: the result of calling spacy.load('en_core_web_sm')\n",
"* **input_folder**: a string (we will test it using '../Data/Dreams')\n",
"\n",
"The function:\n",
"* obtains a list of txt paths using the function **get_paths** with **input_folder** as an argument\n",
"* loops through the txt paths one by one\n",
"* for each iteration, the **extract_statistics** function is called with **txt_path** as an argument\n",
"\n",
"The function returns a dictionary:\n",
"* the keys are the basenames of the txt files (see tip 1 of this exercise)\n",
"* the values are the output of calling the function **extract_statistics** for a specific file\n",
"\n",
"Test your function using '../Data/Dreams' as a value for the parameter *input_folder*."
]
},
{
"cell_type": "code",
"metadata": {
"id": "DyS21BoYQgU_"
},
"source": [
"def process_all_txt_files(nlp, input_folder):\n",
" \"\"\"\n",
" given a list of txt_paths\n",
" -process each with the extract_statistics function\n",
" \n",
" :param nlp: loaded spaCy model (result of calling spacy.load('en_core_web_sm'))\n",
" :param list txt_paths: list of paths to txt files\n",
" \n",
" :rtype: dict\n",
" :return: dictionary mapping:\n",
" -basename -> output of extract_statistics function\n",
" \"\"\""
],
"execution_count": 17,
"outputs": []
},
{
"cell_type": "code",
"metadata": {
"id": "R2Q6po1qQgU_"
},
"source": [
"basename_to_stats = process_all_txt_files(nlp, input_folder='../Data/Dreams')\n",
"basename_to_stats"
],
"execution_count": 18,
"outputs": []
},
{
"cell_type": "markdown",
"metadata": {
"id": "L2kMZogkQgU_"
},
"source": [
"## Exercise 6: write to disk"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "XJwunv54QgU_"
},
"source": [
"In this exercise, you are going to write our results to our computer.\n",
"Please loop through **basename_to_stats** and create one JSON file for each dream.\n",
"\n",
"* the path is f'{basename}.json', i.e., 'vickie1.json', 'vickie2.json', etc. (please write them to the same folder as this notebook)\n",
"* the content of each JSON file is each value of **basename_to_stats**"
]
},
{
"cell_type": "code",
"metadata": {
"id": "v1zsDtKYQgU_"
},
"source": [
"import json"
],
"execution_count": 19,
"outputs": []
},
{
"cell_type": "code",
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 183
},
"id": "b2-mGSBkQgVA",
"outputId": "619418a2-1410-4851-f22a-1396004bcf0c"
},
"source": [
"for basename, stats in basename_to_stats.items():\n",
" pass"
],
"execution_count": 20,
"outputs": [
{
"output_type": "error",
"ename": "AttributeError",
"evalue": "ignored",
"traceback": [
"\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
"\u001b[0;31mAttributeError\u001b[0m Traceback (most recent call last)",
"\u001b[0;32m<ipython-input-20-9b1a00c298dd>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0;32mfor\u001b[0m \u001b[0mbasename\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mstats\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mbasename_to_stats\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mitems\u001b[0m\u001b[0;34m(\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[0m\u001b[1;32m 2\u001b[0m \u001b[0;32mpass\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;31mAttributeError\u001b[0m: 'NoneType' object has no attribute 'items'"
]
}
]
},
{
"cell_type": "code",
"metadata": {
"id": "9nPnFXmwQgVA"
},
"source": [
""
],
"execution_count": 20,
"outputs": []
}
]
} | apache-2.0 |
mdomarsaleem/Facial_Plan | Experiments/caffe file.ipynb | 1 | 234639 | {
"cells": [
{
"cell_type": "code",
"execution_count": 1,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"import os,sys,re\n",
"import numpy as np\n",
"os.chdir('/home/mckc/pycaffe/')\n",
"direct = os.getcwd()"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"files = os.listdir(os.getcwd())\n",
"subject = []\n",
"file_name = []\n",
"data_type = []"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Abhay_train_4512.jpg\n",
"Omar_test_72.jpg\n",
"Sharath_train_2745.jpg\n",
"Sharath_train_1702.jpg\n",
"Kinkar_train_3933.jpg\n",
"Abhay_train_5712.jpg\n",
"Gopika_train_5180.jpg\n",
"Arun_train_4752.jpg\n",
"Omar_test_103.jpg\n",
"Sharath_train_2793.jpg\n",
"Gopika_train_5424.jpg\n",
"Arun_train_1821.jpg\n",
"Sharath_train_1013.jpg\n",
"Pandian_test_18.jpg\n",
"Kinkar_train_1266.jpg\n",
"Omar_train_3466.jpg\n",
"Gopika_train_658.jpg\n",
"Kinkar_train_5004.jpg\n",
"Gopika_train_3050.jpg\n",
"Omar_train_4243.jpg\n",
"Omar_train_5622.jpg\n",
"Sharath_train_1070.jpg\n",
"Omar_train_5035.jpg\n",
"Sharath_train_1607.jpg\n",
"Omar_train_6746.jpg\n",
"Omar_train_2078.jpg\n",
"Sharath_train_4593.jpg\n",
"Kinkar_train_1336.jpg\n",
"Kinkar_train_3585.jpg\n",
"Sharath_train_3286.jpg\n",
"Sharath_train_6400.jpg\n",
"Pandian_train_4547.jpg\n",
"Abhay_train_5019.jpg\n",
"Abhay_train_1881.jpg\n",
"Gopika_train_6358.jpg\n",
"Gopika_train_1937.jpg\n",
"Kinkar_train_6468.jpg\n",
"Gopika_train_541.jpg\n",
"Kinkar_train_4047.jpg\n",
"Sharath_train_896.jpg\n",
"Kinkar_train_2040.jpg\n",
"Sharath_train_6880.jpg\n",
"Gopika_train_1347.jpg\n",
"Gopika_train_6956.jpg\n",
"Sharath_train_1190.jpg\n",
"Omar_train_6954.jpg\n",
"Pandian_train_3894.jpg\n",
"Gopika_train_4226.jpg\n",
"Kinkar_train_3412.jpg\n",
"Gopika_train_388.jpg\n",
"Omar_train_6046.jpg\n",
"Arun_train_2806.jpg\n",
"Sharath_test_70.jpg\n",
"Gopika_test_89.jpg\n",
"Sharath_train_2713.jpg\n",
"Arun_train_6787.jpg\n",
"Kinkar_train_3338.jpg\n",
"Kinkar_train_421.jpg\n",
"Omar_train_1768.jpg\n",
"Gopika_train_3258.jpg\n",
"Sharath_train_4651.jpg\n",
"Abhay_train_2784.jpg\n",
"Pandian_train_3827.jpg\n",
"Gopika_train_6003.jpg\n",
"Abhay_train_4549.jpg\n",
"Abhay_test_77.jpg\n",
"Kinkar_train_2343.jpg\n",
"Pandian_train_4075.jpg\n",
"Pandian_train_6637.jpg\n",
"Gopika_train_3764.jpg\n",
"Omar_train_1632.jpg\n",
"Gopika_train_6008.jpg\n",
"Omar_train_955.jpg\n",
"Abhay_train_2731.jpg\n",
"Abhay_train_2956.jpg\n",
"Gopika_train_3336.jpg\n",
"Sharath_train_1803.jpg\n",
"Abhay_train_6553.jpg\n",
"Kinkar_train_4096.jpg\n",
"Omar_train_6330.jpg\n",
"Sharath_train_3767.jpg\n",
"Abhay_train_784.jpg\n",
"Omar_train_949.jpg\n",
"Sharath_train_4927.jpg\n",
"Kinkar_train_2400.jpg\n",
"Sharath_train_5616.jpg\n",
"Kinkar_train_4018.jpg\n",
"Sharath_test_86.jpg\n",
"Sharath_train_2057.jpg\n",
"Sharath_train_2452.jpg\n",
"Sharath_train_2997.jpg\n",
"Sharath_train_4817.jpg\n",
"Arun_train_4563.jpg\n",
"Abhay_train_6837.jpg\n",
"Pandian_train_3387.jpg\n",
"Abhay_train_2327.jpg\n",
"Sharath_train_21.jpg\n",
"Pandian_train_5509.jpg\n",
"Sharath_train_2726.jpg\n",
"Arun_train_5635.jpg\n",
"Pandian_train_5418.jpg\n",
"Abhay_train_5010.jpg\n",
"Abhay_train_2304.jpg\n",
"Gopika_train_1335.jpg\n",
"Arun_train_3868.jpg\n",
"Kinkar_train_3728.jpg\n",
"Abhay_train_5188.jpg\n",
"Sharath_train_984.jpg\n",
"Abhay_train_4930.jpg\n",
"Pandian_test_137.jpg\n",
"Arun_train_3193.jpg\n",
"Kinkar_train_4148.jpg\n",
"Kinkar_train_5752.jpg\n",
"Omar_train_2540.jpg\n",
"Kinkar_train_5775.jpg\n",
"Abhay_train_3176.jpg\n",
"Kinkar_train_4157.jpg\n",
"Kinkar_train_1177.jpg\n",
"Abhay_train_2831.jpg\n",
"Kinkar_train_1954.jpg\n",
"Arun_train_4976.jpg\n",
"Pandian_train_2375.jpg\n",
"Kinkar_train_3804.jpg\n",
"Kinkar_train_5514.jpg\n",
"Kinkar_train_4388.jpg\n",
"Kinkar_train_540.jpg\n",
"Arun_train_2628.jpg\n",
"Arun_train_2957.jpg\n",
"Pandian_train_3048.jpg\n",
"Omar_train_3848.jpg\n",
"Pandian_train_1055.jpg\n",
"Sharath_train_6878.jpg\n",
"Sharath_train_6925.jpg\n",
"Omar_train_241.jpg\n",
"Sharath_train_5220.jpg\n",
"Sharath_train_2379.jpg\n",
"Sharath_train_6681.jpg\n",
"Omar_train_5213.jpg\n",
"Kinkar_train_5963.jpg\n",
"Arun_train_294.jpg\n",
"Omar_train_4974.jpg\n",
"Arun_train_4392.jpg\n",
"Omar_train_702.jpg\n",
"Arun_test_153.jpg\n",
"Kinkar_train_1521.jpg\n",
"Gopika_test_65.jpg\n",
"Omar_train_1696.jpg\n",
"Sharath_train_1524.jpg\n",
"Omar_train_2162.jpg\n",
"Sharath_train_51.jpg\n",
"Abhay_train_2637.jpg\n",
"Kinkar_train_5021.jpg\n",
"Kinkar_train_1710.jpg\n",
"Omar_train_6245.jpg\n",
"Sharath_train_2576.jpg\n",
"Pandian_train_2740.jpg\n",
"Abhay_train_6718.jpg\n",
"Arun_train_6070.jpg\n",
"Sharath_train_560.jpg\n",
"Pandian_train_3917.jpg\n",
"Pandian_train_292.jpg\n",
"Sharath_train_5150.jpg\n",
"Sharath_train_4291.jpg\n",
"Omar_train_3372.jpg\n",
"Kinkar_train_4365.jpg\n",
"Pandian_train_522.jpg\n",
"Arun_train_6632.jpg\n",
"Sharath_train_553.jpg\n",
"Gopika_train_3911.jpg\n",
"Arun_train_1127.jpg\n",
"Kinkar_train_172.jpg\n",
"Arun_train_2264.jpg\n",
"Kinkar_train_5642.jpg\n",
"Sharath_train_430.jpg\n",
"Omar_train_5927.jpg\n",
"Gopika_train_291.jpg\n",
"Sharath_train_5858.jpg\n",
"Sharath_train_5166.jpg\n",
"Sharath_train_6863.jpg\n",
"Kinkar_train_1061.jpg\n",
"Gopika_train_5709.jpg\n",
"Pandian_train_1209.jpg\n",
"Sharath_train_3634.jpg\n",
"Abhay_train_4914.jpg\n",
"Omar_train_1836.jpg\n",
"Sharath_train_1417.jpg\n",
"Omar_train_53.jpg\n",
"Sharath_train_1469.jpg\n",
"Arun_train_4188.jpg\n",
"Sharath_train_2572.jpg\n",
"Pandian_train_2177.jpg\n",
"Sharath_train_2603.jpg\n",
"Kinkar_train_2082.jpg\n",
"Sharath_train_4675.jpg\n",
"Abhay_train_1330.jpg\n",
"Gopika_train_6505.jpg\n",
"Pandian_train_2133.jpg\n",
"Pandian_train_1779.jpg\n",
"Kinkar_train_2868.jpg\n",
"Omar_test_22.jpg\n",
"Omar_train_3455.jpg\n",
"Omar_train_6081.jpg\n",
"Kinkar_train_5061.jpg\n",
"Sharath_train_4325.jpg\n",
"Omar_train_6689.jpg\n",
"Kinkar_train_6587.jpg\n",
"Abhay_train_5788.jpg\n",
"Gopika_train_6518.jpg\n",
"Omar_train_4505.jpg\n",
"Gopika_train_2091.jpg\n",
"Gopika_train_3905.jpg\n",
"Gopika_train_5545.jpg\n",
"Omar_train_2762.jpg\n",
"Sharath_train_4882.jpg\n",
"Pandian_train_2783.jpg\n",
"Abhay_train_5335.jpg\n",
"Sharath_train_194.jpg\n",
"Omar_train_6407.jpg\n",
"Kinkar_train_1076.jpg\n",
"Omar_train_275.jpg\n",
"Arun_train_1560.jpg\n",
"Omar_train_5723.jpg\n",
"Abhay_train_3771.jpg\n",
"Sharath_train_599.jpg\n",
"Sharath_train_738.jpg\n",
"Pandian_train_2855.jpg\n",
"Pandian_train_3088.jpg\n",
"Arun_train_4214.jpg\n",
"Arun_train_4980.jpg\n",
"Abhay_train_5966.jpg\n",
"Omar_train_5701.jpg\n",
"Omar_train_583.jpg\n",
"Kinkar_train_2866.jpg\n",
"Sharath_train_165.jpg\n",
"Gopika_train_4318.jpg\n",
"Pandian_train_5477.jpg\n",
"Gopika_train_5485.jpg\n",
"Sharath_train_58.jpg\n",
"Pandian_train_6875.jpg\n",
"Sharath_train_4899.jpg\n",
"Sharath_train_4110.jpg\n",
"Omar_train_2215.jpg\n",
"Abhay_train_3260.jpg\n",
"Sharath_train_5496.jpg\n",
"Gopika_train_6432.jpg\n",
"Abhay_train_5536.jpg\n",
"Sharath_train_5256.jpg\n",
"Gopika_train_2273.jpg\n",
"Sharath_train_866.jpg\n",
"Gopika_train_2966.jpg\n",
"Gopika_train_5420.jpg\n",
"Omar_train_1179.jpg\n",
"Kinkar_train_5138.jpg\n",
"lenet_auto_test.prototxt\n",
"Gopika_train_3817.jpg\n",
"Omar_train_1371.jpg\n",
"Gopika_train_3184.jpg\n",
"Kinkar_train_5052.jpg\n",
"Arun_test_99.jpg\n",
"Sharath_train_1054.jpg\n",
"Pandian_train_5390.jpg\n",
"Gopika_train_3019.jpg\n",
"Gopika_train_3304.jpg\n",
"Abhay_train_6193.jpg\n",
"Sharath_train_4322.jpg\n",
"Kinkar_train_1679.jpg\n",
"Arun_train_4588.jpg\n",
"Pandian_train_3644.jpg\n",
"Gopika_train_854.jpg\n",
"Omar_train_4863.jpg\n",
"Arun_train_4280.jpg\n",
"Abhay_train_6044.jpg\n",
"Omar_train_6078.jpg\n",
"Kinkar_train_2859.jpg\n",
"Gopika_train_2950.jpg\n",
"Omar_train_1573.jpg\n",
"Sharath_train_2236.jpg\n",
"Kinkar_train_5749.jpg\n",
"Sharath_train_427.jpg\n",
"Arun_train_5412.jpg\n",
"Sharath_train_5466.jpg\n",
"Omar_train_1616.jpg\n",
"Abhay_train_1079.jpg\n",
"Kinkar_train_3329.jpg\n",
"Arun_train_3673.jpg\n",
"Omar_train_2554.jpg\n",
"Abhay_train_1891.jpg\n",
"Abhay_train_3500.jpg\n",
"Sharath_train_2000.jpg\n",
"Omar_train_552.jpg\n",
"Gopika_train_5822.jpg\n",
"Sharath_train_692.jpg\n",
"Gopika_train_5309.jpg\n",
"Gopika_test_39.jpg\n",
"Abhay_train_5982.jpg\n",
"Kinkar_train_6797.jpg\n",
"Omar_train_4682.jpg\n",
"Kinkar_train_589.jpg\n",
"Omar_train_558.jpg\n",
"Sharath_train_5422.jpg\n",
"Omar_train_6941.jpg\n",
"Gopika_train_5431.jpg\n",
"Abhay_train_2227.jpg\n",
"Kinkar_train_2202.jpg\n",
"Abhay_train_5242.jpg\n",
"Sharath_train_927.jpg\n",
"Abhay_train_6292.jpg\n",
"Omar_train_249.jpg\n",
"Kinkar_train_401.jpg\n",
"Pandian_train_2383.jpg\n",
"Abhay_train_2953.jpg\n",
"Arun_train_2872.jpg\n",
"Sharath_train_1585.jpg\n",
"Kinkar_train_2908.jpg\n",
"Kinkar_train_3808.jpg\n",
"Sharath_train_2693.jpg\n",
"Omar_train_5944.jpg\n",
"Arun_train_6548.jpg\n",
"Sharath_train_5891.jpg\n",
"Sharath_train_3026.jpg\n",
"Pandian_train_2552.jpg\n",
"Omar_train_6345.jpg\n",
"Arun_train_3109.jpg\n",
"Kinkar_train_2145.jpg\n",
"Arun_train_319.jpg\n",
"Gopika_train_6501.jpg\n",
"Pandian_train_3668.jpg\n",
"Omar_train_3415.jpg\n",
"Sharath_train_6776.jpg\n",
"Kinkar_train_988.jpg\n",
"Gopika_train_1206.jpg\n",
"Pandian_train_4983.jpg\n",
"Arun_train_4118.jpg\n",
"Arun_train_5513.jpg\n",
"Arun_train_2291.jpg\n",
"Sharath_train_6435.jpg\n",
"Arun_train_5182.jpg\n",
"Abhay_train_6826.jpg\n",
"Abhay_train_2927.jpg\n",
"Arun_train_3165.jpg\n",
"Gopika_train_2126.jpg\n",
"Sharath_train_5896.jpg\n",
"Pandian_train_1428.jpg\n",
"Sharath_train_3030.jpg\n",
"Gopika_train_380.jpg\n",
"Omar_train_2627.jpg\n",
"Arun_train_3791.jpg\n",
"Abhay_train_26.jpg\n",
"Kinkar_train_2318.jpg\n",
"Sharath_train_3209.jpg\n",
"Pandian_train_2467.jpg\n",
"Arun_train_4296.jpg\n",
"Gopika_train_2320.jpg\n",
"Pandian_train_1014.jpg\n",
"Kinkar_train_518.jpg\n",
"Omar_train_6921.jpg\n",
"Sharath_train_5999.jpg\n",
"Omar_train_3301.jpg\n",
"Sharath_train_6951.jpg\n",
"Sharath_train_4548.jpg\n",
"Omar_train_756.jpg\n",
"Kinkar_train_5038.jpg\n",
"Kinkar_train_954.jpg\n",
"Gopika_train_6387.jpg\n",
"Arun_train_688.jpg\n",
"Sharath_train_6229.jpg\n",
"Arun_train_4872.jpg\n",
"Gopika_train_1900.jpg\n",
"Abhay_train_6090.jpg\n",
"Omar_train_6729.jpg\n",
"Kinkar_train_5059.jpg\n",
"Kinkar_train_3082.jpg\n",
"Arun_train_1296.jpg\n",
"Pandian_train_2588.jpg\n",
"Omar_train_4169.jpg\n",
"Pandian_train_350.jpg\n",
"Pandian_train_496.jpg\n",
"Kinkar_train_4461.jpg\n",
"Pandian_train_3601.jpg\n",
"Sharath_train_5469.jpg\n",
"Kinkar_train_2922.jpg\n",
"Arun_train_3940.jpg\n",
"Gopika_train_196.jpg\n",
"Sharath_train_3052.jpg\n",
"Gopika_train_2164.jpg\n",
"Abhay_train_754.jpg\n",
"Arun_train_3346.jpg\n",
"Arun_train_6125.jpg\n",
"Gopika_train_3895.jpg\n",
"Arun_train_5751.jpg\n",
"Sharath_train_4440.jpg\n",
"Gopika_train_6052.jpg\n",
"Sharath_train_5142.jpg\n",
"Pandian_train_4538.jpg\n",
"Pandian_train_4354.jpg\n",
"Pandian_train_6675.jpg\n",
"Gopika_train_5224.jpg\n",
"Omar_train_4368.jpg\n",
"Sharath_train_6895.jpg\n",
"Omar_train_6560.jpg\n",
"Kinkar_train_4848.jpg\n",
"Kinkar_train_1942.jpg\n",
"Arun_train_943.jpg\n",
"Sharath_train_3567.jpg\n",
"Sharath_train_6611.jpg\n",
"Kinkar_train_94.jpg\n",
"Kinkar_train_5088.jpg\n",
"Abhay_train_6527.jpg\n",
"Abhay_train_2032.jpg\n",
"Arun_train_3559.jpg\n",
"Kinkar_train_6443.jpg\n",
"Arun_train_3696.jpg\n",
"Sharath_train_3727.jpg\n",
"Gopika_train_5047.jpg\n",
"Kinkar_train_2480.jpg\n",
"Gopika_train_5429.jpg\n",
"Arun_train_5949.jpg\n",
"Sharath_train_3232.jpg\n",
"Gopika_train_2256.jpg\n",
"Sharath_train_1358.jpg\n",
"Kinkar_train_5430.jpg\n",
"Gopika_train_480.jpg\n",
"Arun_train_2924.jpg\n",
"Kinkar_train_4377.jpg\n",
"Gopika_train_462.jpg\n",
"Pandian_train_5355.jpg\n",
"Abhay_train_1166.jpg\n",
"Sharath_train_3595.jpg\n",
"Pandian_train_5841.jpg\n",
"Arun_train_6336.jpg\n",
"Abhay_train_806.jpg\n",
"Abhay_train_4166.jpg\n",
"Gopika_train_857.jpg\n",
"Gopika_train_5332.jpg\n",
"Gopika_train_1352.jpg\n",
"Omar_train_6735.jpg\n",
"Omar_train_5384.jpg\n",
"Pandian_train_3747.jpg\n",
"Sharath_train_6535.jpg\n",
"Sharath_train_4475.jpg\n",
"Gopika_train_4536.jpg\n",
"Abhay_train_6301.jpg\n",
"Omar_train_5550.jpg\n",
"Sharath_train_3368.jpg\n",
"Abhay_train_4176.jpg\n",
"Gopika_train_415.jpg\n",
"Abhay_train_6162.jpg\n",
"Pandian_train_3586.jpg\n",
"Sharath_train_2229.jpg\n",
"Gopika_train_3665.jpg\n",
"Pandian_train_3057.jpg\n",
"Sharath_train_4925.jpg\n",
"Kinkar_train_2639.jpg\n",
"Gopika_train_739.jpg\n",
"Kinkar_train_3609.jpg\n",
"Sharath_train_3325.jpg\n",
"Abhay_train_2808.jpg\n",
"Sharath_train_826.jpg\n",
"Kinkar_train_3273.jpg\n",
"Arun_train_5237.jpg\n",
"Arun_train_1653.jpg\n",
"Sharath_train_6507.jpg\n",
"Omar_train_1661.jpg\n",
"Sharath_train_1784.jpg\n",
"Sharath_train_273.jpg\n",
"Pandian_train_1078.jpg\n",
"Omar_train_2446.jpg\n",
"Arun_train_1541.jpg\n",
"Pandian_train_4540.jpg\n",
"Gopika_train_6457.jpg\n",
"Arun_train_328.jpg\n",
"Sharath_train_68.jpg\n",
"Gopika_train_722.jpg\n",
"Sharath_train_5730.jpg\n",
"Gopika_train_4666.jpg\n",
"Gopika_train_1182.jpg\n",
"Sharath_train_1069.jpg\n",
"Sharath_train_4922.jpg\n",
"Kinkar_train_4934.jpg\n",
"Omar_train_2968.jpg\n",
"Omar_train_2143.jpg\n",
"Kinkar_train_4977.jpg\n",
"Omar_train_1321.jpg\n",
"Sharath_train_655.jpg\n",
"Sharath_train_3864.jpg\n",
"Omar_train_2110.jpg\n",
"Gopika_train_6766.jpg\n",
"Arun_train_3989.jpg\n",
"Abhay_train_4615.jpg\n",
"Gopika_train_5134.jpg\n",
"Kinkar_train_5292.jpg\n",
"Kinkar_train_6607.jpg\n",
"Kinkar_train_1617.jpg\n",
"Gopika_train_6659.jpg\n",
"Sharath_train_3616.jpg\n",
"Pandian_train_3187.jpg\n",
"Abhay_train_1883.jpg\n",
"Arun_train_5.jpg\n",
"Sharath_train_114.jpg\n",
"Kinkar_train_4044.jpg\n",
"Pandian_train_2168.jpg\n",
"Kinkar_train_3976.jpg\n",
"Sharath_train_2381.jpg\n",
"Gopika_train_6211.jpg\n",
"Sharath_train_4819.jpg\n",
"Abhay_train_3055.jpg\n",
"Abhay_train_2055.jpg\n",
"Abhay_train_6313.jpg\n",
"Kinkar_train_1502.jpg\n",
"Pandian_train_4087.jpg\n",
"Sharath_train_906.jpg\n",
"Pandian_train_2020.jpg\n",
"Omar_train_571.jpg\n",
"Gopika_train_6579.jpg\n",
"Abhay_train_1446.jpg\n",
"Gopika_train_2760.jpg\n",
"Sharath_train_6525.jpg\n",
"Kinkar_train_60.jpg\n",
"Omar_train_6470.jpg\n",
"Omar_train_3206.jpg\n",
"Abhay_train_3806.jpg\n",
"Sharath_train_1523.jpg\n",
"Omar_train_3044.jpg\n",
"Sharath_train_5998.jpg\n",
"Kinkar_train_410.jpg\n",
"Kinkar_train_2835.jpg\n",
"Sharath_train_2259.jpg\n",
"Sharath_train_3152.jpg\n",
"Pandian_train_5146.jpg\n",
"Kinkar_train_940.jpg\n",
"Arun_train_1231.jpg\n",
"Omar_train_5402.jpg\n",
"Omar_train_6253.jpg\n",
"Abhay_train_871.jpg\n",
"Sharath_train_2609.jpg\n",
"Abhay_train_4648.jpg\n",
"Gopika_train_5205.jpg\n",
"Omar_train_5861.jpg\n",
"Gopika_train_4701.jpg\n",
"Kinkar_train_1156.jpg\n",
"Arun_train_795.jpg\n",
"Abhay_train_2744.jpg\n",
"Sharath_train_162.jpg\n",
"Gopika_train_3430.jpg\n",
"Gopika_train_3136.jpg\n",
"Gopika_train_2598.jpg\n",
"Sharath_train_6225.jpg\n",
"Arun_train_5185.jpg\n",
"Abhay_train_985.jpg\n",
"Omar_train_1228.jpg\n",
"Pandian_train_5989.jpg\n",
"Abhay_train_838.jpg\n",
"Omar_train_632.jpg\n",
"Gopika_train_1845.jpg\n",
"Omar_train_4433.jpg\n",
"Abhay_train_347.jpg\n",
"Pandian_train_2371.jpg\n",
"Abhay_train_6456.jpg\n",
"Arun_train_5468.jpg\n",
"Arun_train_3573.jpg\n",
"Gopika_train_1405.jpg\n",
"Sharath_train_4987.jpg\n",
"Kinkar_test_25.jpg\n",
"Abhay_train_578.jpg\n",
"Abhay_train_5498.jpg\n",
"Omar_train_5132.jpg\n",
"Gopika_train_6750.jpg\n",
"Kinkar_train_4000.jpg\n",
"Sharath_train_1497.jpg\n",
"Omar_train_4883.jpg\n",
"Arun_train_418.jpg\n",
"Gopika_train_3426.jpg\n",
"Omar_train_5288.jpg\n",
"Kinkar_train_2281.jpg\n",
"Abhay_train_913.jpg\n",
"Abhay_train_3192.jpg\n",
"Gopika_train_1437.jpg\n",
"Omar_test_46.jpg\n",
"Arun_train_6928.jpg\n",
"Sharath_train_6821.jpg\n",
"Abhay_train_173.jpg\n",
"Sharath_train_3758.jpg\n",
"Gopika_train_2303.jpg\n",
"Gopika_train_6190.jpg\n",
"Sharath_test_17.jpg\n",
"Arun_train_836.jpg\n",
"Arun_train_4582.jpg\n",
"Sharath_train_6940.jpg\n",
"Kinkar_train_6439.jpg\n",
"Arun_train_3901.jpg\n",
"Gopika_train_3768.jpg\n",
"Gopika_train_987.jpg\n",
"Kinkar_train_6187.jpg\n",
"Sharath_train_2485.jpg\n",
"Gopika_train_3523.jpg\n",
"Kinkar_train_4113.jpg\n",
"Arun_train_3169.jpg\n",
"Omar_train_246.jpg\n",
"Kinkar_train_3002.jpg\n",
"Kinkar_train_5308.jpg\n",
"Omar_train_18.jpg\n",
"Sharath_train_96.jpg\n",
"Sharath_train_3444.jpg\n",
"Arun_train_2547.jpg\n",
"Abhay_train_2939.jpg\n",
"Arun_train_247.jpg\n",
"Omar_train_5773.jpg\n",
"Abhay_test_101.jpg\n",
"Kinkar_train_2102.jpg\n",
"Sharath_train_160.jpg\n",
"Kinkar_test_76.jpg\n",
"Omar_train_6574.jpg\n",
"Kinkar_train_5445.jpg\n",
"Pandian_train_916.jpg\n",
"Kinkar_train_6098.jpg\n",
"Abhay_train_5006.jpg\n",
"Sharath_train_1728.jpg\n",
"Kinkar_train_1113.jpg\n",
"Abhay_train_5455.jpg\n",
"Abhay_train_6392.jpg\n",
"Arun_train_2818.jpg\n",
"Kinkar_train_4941.jpg\n",
"Arun_train_661.jpg\n",
"Sharath_train_4805.jpg\n",
"Omar_train_115.jpg\n",
"Abhay_train_1394.jpg\n",
"Kinkar_train_1699.jpg\n",
"Sharath_train_3853.jpg\n",
"Gopika_train_6148.jpg\n",
"Arun_train_617.jpg\n",
"Kinkar_train_4828.jpg\n",
"Gopika_train_6953.jpg\n",
"Arun_train_2915.jpg\n",
"Abhay_train_526.jpg\n",
"Omar_train_603.jpg\n",
"Gopika_train_1317.jpg\n",
"Pandian_train_1546.jpg\n",
"Sharath_train_3829.jpg\n",
"Gopika_train_4232.jpg\n",
"Gopika_train_3810.jpg\n",
"Kinkar_train_4553.jpg\n",
"Kinkar_train_1798.jpg\n",
"Sharath_train_2847.jpg\n",
"Sharath_train_3913.jpg\n",
"Sharath_train_2160.jpg\n",
"Sharath_train_1107.jpg\n",
"Pandian_train_3606.jpg\n",
"Omar_train_3838.jpg\n",
"Kinkar_test_100.jpg\n",
"Kinkar_train_4741.jpg\n",
"Gopika_test_33.jpg\n",
"Gopika_train_581.jpg\n",
"Omar_train_2917.jpg\n",
"Pandian_train_338.jpg\n",
"Kinkar_train_4710.jpg\n",
"Arun_train_5863.jpg\n",
"Pandian_train_4174.jpg\n",
"Omar_test_30.jpg\n",
"Omar_train_5068.jpg\n",
"Sharath_train_1498.jpg\n",
"Kinkar_train_1204.jpg\n",
"Abhay_train_6736.jpg\n",
"Gopika_train_2345.jpg\n",
"Sharath_train_1277.jpg\n",
"Pandian_train_3309.jpg\n",
"Abhay_train_2334.jpg\n",
"Pandian_train_4224.jpg\n",
"Kinkar_train_1169.jpg\n",
"Sharath_train_1325.jpg\n",
"Pandian_train_6271.jpg\n",
"Gopika_train_4130.jpg\n",
"Sharath_train_3946.jpg\n",
"Sharath_train_2910.jpg\n",
"Gopika_train_2038.jpg\n",
"Sharath_train_3164.jpg\n",
"Kinkar_train_3483.jpg\n",
"Omar_train_548.jpg\n",
"Gopika_train_2445.jpg\n",
"Arun_train_2720.jpg\n",
"Kinkar_train_2989.jpg\n",
"Omar_train_4884.jpg\n",
"Omar_train_5569.jpg\n",
"Arun_train_2444.jpg\n",
"Pandian_train_4878.jpg\n",
"Pandian_train_103.jpg\n",
"Pandian_train_4081.jpg\n",
"Arun_train_261.jpg\n",
"Gopika_train_3619.jpg\n",
"Sharath_train_1520.jpg\n",
"Gopika_train_5337.jpg\n",
"Omar_train_4875.jpg\n",
"Gopika_train_3074.jpg\n",
"Kinkar_train_2427.jpg\n",
"Kinkar_train_3832.jpg\n",
"Arun_train_3587.jpg\n",
"Gopika_train_4041.jpg\n",
"Gopika_train_288.jpg\n",
"Omar_train_1755.jpg\n",
"Abhay_train_1635.jpg\n",
"Gopika_train_490.jpg\n",
"Sharath_train_4058.jpg\n",
"Arun_train_4880.jpg\n",
"Omar_train_4859.jpg\n",
"Sharath_train_2486.jpg\n",
"Sharath_train_2310.jpg\n",
"Pandian_train_2571.jpg\n",
"Gopika_train_5460.jpg\n",
"Omar_train_6320.jpg\n",
"Pandian_train_1979.jpg\n",
"Omar_train_5768.jpg\n",
"Pandian_train_5951.jpg\n",
"Omar_train_3545.jpg\n",
"Omar_train_4952.jpg\n",
"Gopika_train_1965.jpg\n",
"Abhay_train_6220.jpg\n",
"Pandian_train_5273.jpg\n",
"Sharath_train_6326.jpg\n",
"Pandian_train_4640.jpg\n",
"Kinkar_train_3550.jpg\n",
"Abhay_test_108.jpg\n",
"Omar_train_3158.jpg\n",
"Omar_train_280.jpg\n",
"Abhay_train_2037.jpg\n",
"Gopika_test_148.jpg\n",
"Abhay_train_3274.jpg\n",
"Omar_train_2027.jpg\n",
"Omar_train_790.jpg\n",
"Kinkar_train_794.jpg\n",
"Arun_train_2495.jpg\n",
"Pandian_train_185.jpg\n",
"Kinkar_train_5733.jpg\n",
"Sharath_train_5049.jpg\n",
"Kinkar_train_2045.jpg\n",
"Kinkar_train_1818.jpg\n",
"Gopika_train_1027.jpg\n",
"Sharath_train_5183.jpg\n",
"Gopika_train_6007.jpg\n",
"Sharath_train_5897.jpg\n",
"Abhay_train_1850.jpg\n",
"Pandian_train_1984.jpg\n",
"Sharath_train_4434.jpg\n",
"Kinkar_train_3522.jpg\n",
"Kinkar_train_3039.jpg\n",
"Kinkar_train_6841.jpg\n",
"Pandian_train_16.jpg\n",
"Omar_train_4078.jpg\n",
"Gopika_train_147.jpg\n",
"Sharath_train_791.jpg\n",
"Sharath_train_6654.jpg\n",
"Sharath_train_3497.jpg\n",
"Sharath_train_1121.jpg\n",
"Gopika_train_6618.jpg\n",
"Kinkar_train_6604.jpg\n",
"Gopika_train_1962.jpg\n",
"Abhay_train_5606.jpg\n",
"Pandian_train_752.jpg\n",
"Sharath_train_6682.jpg\n",
"Arun_train_5058.jpg\n",
"Pandian_train_6333.jpg\n",
"Kinkar_train_2522.jpg\n",
"Kinkar_train_444.jpg\n",
"Pandian_train_1644.jpg\n",
"Pandian_train_665.jpg\n",
"Sharath_train_2619.jpg\n",
"Abhay_train_1992.jpg\n",
"Arun_train_4200.jpg\n",
"Kinkar_train_1403.jpg\n",
"Sharath_train_6782.jpg\n",
"Arun_train_4613.jpg\n",
"Sharath_train_3713.jpg\n",
"Gopika_train_4129.jpg\n",
"Gopika_train_6564.jpg\n",
"Gopika_train_4205.jpg\n",
"Sharath_train_3395.jpg\n",
"Arun_train_5377.jpg\n",
"Pandian_train_1086.jpg\n",
"Pandian_train_4595.jpg\n",
"Abhay_train_6693.jpg\n",
"Sharath_train_3880.jpg\n",
"Sharath_train_6291.jpg\n",
"Omar_train_6450.jpg\n",
"Abhay_train_3855.jpg\n",
"Abhay_train_1450.jpg\n",
"Sharath_train_3263.jpg\n",
"Sharath_train_5159.jpg\n",
"Abhay_train_4355.jpg\n",
"Pandian_train_1087.jpg\n",
"Omar_train_2482.jpg\n",
"Kinkar_train_3081.jpg\n",
"Omar_train_3942.jpg\n",
"Pandian_train_1650.jpg\n",
"Gopika_train_2394.jpg\n",
"Kinkar_train_6102.jpg\n",
"Pandian_train_1341.jpg\n",
"Sharath_train_890.jpg\n",
"Pandian_train_1618.jpg\n",
"Pandian_train_106.jpg\n",
"Sharath_train_1677.jpg\n",
"Sharath_test_57.jpg\n",
"Kinkar_train_3847.jpg\n",
"Arun_train_2527.jpg\n",
"Sharath_train_1436.jpg\n",
"Sharath_train_498.jpg\n",
"Abhay_train_845.jpg\n",
"Arun_train_4551.jpg\n",
"Gopika_train_3335.jpg\n",
"Gopika_train_3797.jpg\n",
"Kinkar_train_551.jpg\n",
"Kinkar_train_2186.jpg\n",
"Kinkar_train_6010.jpg\n",
"Kinkar_train_2566.jpg\n",
"Kinkar_train_3927.jpg\n",
"Pandian_train_6413.jpg\n",
"Omar_test_28.jpg\n",
"Arun_train_4014.jpg\n",
"Gopika_train_2205.jpg\n",
"Abhay_train_1737.jpg\n",
"Omar_train_6963.jpg\n",
"Kinkar_train_364.jpg\n",
"Sharath_train_3653.jpg\n",
"Gopika_train_2805.jpg\n",
"Omar_train_4985.jpg\n",
"Kinkar_train_5810.jpg\n",
"Gopika_train_3463.jpg\n",
"Kinkar_train_1740.jpg\n",
"Sharath_train_1420.jpg\n",
"Abhay_train_339.jpg\n",
"Gopika_train_2363.jpg\n",
"Omar_train_6018.jpg\n",
"Gopika_train_5529.jpg\n",
"Gopika_train_1773.jpg\n",
"Pandian_train_3143.jpg\n",
"Sharath_train_2961.jpg\n",
"Omar_train_2413.jpg\n",
"Kinkar_test_91.jpg\n",
"Abhay_test_16.jpg\n",
"Abhay_train_4583.jpg\n",
"Pandian_train_5356.jpg\n",
"Pandian_train_4910.jpg\n",
"Sharath_train_2488.jpg\n",
"Arun_train_6170.jpg\n",
"Omar_train_2563.jpg\n",
"Arun_train_5494.jpg\n",
"Kinkar_train_3065.jpg\n",
"Omar_train_1222.jpg\n",
"Pandian_train_5087.jpg\n",
"Gopika_train_568.jpg\n",
"Gopika_train_2247.jpg\n",
"Sharath_train_4076.jpg\n",
"Omar_train_1592.jpg\n",
"Arun_test_53.jpg\n",
"Sharath_train_2703.jpg\n",
"Pandian_train_847.jpg\n",
"Sharath_train_5227.jpg\n",
"Abhay_train_25.jpg\n",
"Kinkar_train_2073.jpg\n",
"Sharath_train_2879.jpg\n",
"Omar_train_5564.jpg\n",
"Arun_train_1200.jpg\n",
"Kinkar_train_4369.jpg\n",
"Omar_train_3645.jpg\n",
"Abhay_train_3154.jpg\n",
"Kinkar_train_2449.jpg\n",
"Pandian_train_1754.jpg\n",
"Sharath_train_5704.jpg\n",
"Omar_train_4887.jpg\n",
"Sharath_train_2631.jpg\n",
"Sharath_train_6284.jpg\n",
"Sharath_train_2469.jpg\n",
"Kinkar_train_5717.jpg\n",
"Pandian_train_809.jpg\n",
"Sharath_train_6224.jpg\n",
"Gopika_train_6203.jpg\n",
"Arun_train_5305.jpg\n",
"Omar_train_6107.jpg\n",
"Arun_train_5958.jpg\n",
"Pandian_train_2424.jpg\n",
"Arun_train_2447.jpg\n",
"Sharath_train_4726.jpg\n",
"Gopika_train_1090.jpg\n",
"Omar_train_4718.jpg\n",
"Omar_train_4346.jpg\n",
"Sharath_train_5162.jpg\n",
"Arun_train_660.jpg\n",
"Sharath_train_3174.jpg\n",
"Gopika_train_4363.jpg\n",
"Sharath_train_4680.jpg\n",
"Omar_train_5441.jpg\n",
"Sharath_train_4509.jpg\n",
"Omar_train_994.jpg\n",
"Gopika_train_5541.jpg\n",
"Pandian_train_6322.jpg\n",
"Sharath_train_2471.jpg\n",
"Sharath_train_6946.jpg\n",
"Omar_train_4458.jpg\n",
"Omar_train_5534.jpg\n",
"Kinkar_train_3382.jpg\n",
"Pandian_train_5056.jpg\n",
"Omar_train_4663.jpg\n",
"Abhay_train_5584.jpg\n",
"Omar_train_5575.jpg\n",
"Pandian_train_5075.jpg\n",
"Abhay_train_4278.jpg\n",
"Sharath_train_3219.jpg\n",
"Gopika_train_4642.jpg\n",
"Omar_train_3252.jpg\n",
"Omar_train_6848.jpg\n",
"Arun_test_150.jpg\n",
"Pandian_train_2993.jpg\n",
"Abhay_train_3241.jpg\n",
"Kinkar_train_1458.jpg\n",
"Sharath_train_4913.jpg\n",
"Omar_train_287.jpg\n",
"Sharath_test_27.jpg\n",
"Gopika_train_493.jpg\n",
"Gopika_train_6818.jpg\n",
"Pandian_train_4867.jpg\n",
"Omar_train_3470.jpg\n",
"Abhay_train_4341.jpg\n",
"Omar_train_4235.jpg\n",
"Arun_train_2095.jpg\n",
"Gopika_train_621.jpg\n",
"Sharath_train_6293.jpg\n",
"Arun_train_1424.jpg\n",
"Omar_train_1261.jpg\n",
"Sharath_train_3350.jpg\n",
"Sharath_train_781.jpg\n",
"Gopika_train_3742.jpg\n",
"Pandian_train_4948.jpg\n",
"Gopika_train_1318.jpg\n",
"Kinkar_train_6550.jpg\n",
"Gopika_train_971.jpg\n",
"Abhay_train_5786.jpg\n",
"Sharath_train_6111.jpg\n",
"Omar_train_4975.jpg\n",
"Sharath_train_4559.jpg\n",
"Abhay_train_6892.jpg\n",
"Omar_train_1552.jpg\n",
"Arun_train_1667.jpg\n",
"Sharath_train_5503.jpg\n",
"Sharath_train_6463.jpg\n",
"Gopika_train_4761.jpg\n",
"Gopika_train_4425.jpg\n",
"Gopika_train_1929.jpg\n",
"Arun_train_6305.jpg\n",
"Arun_train_1790.jpg\n",
"Omar_train_6850.jpg\n",
"Abhay_train_6375.jpg\n",
"Omar_train_4885.jpg\n",
"Sharath_train_4452.jpg\n",
"Pandian_train_1809.jpg\n",
"Kinkar_train_4404.jpg\n",
"Sharath_train_5692.jpg\n",
"Pandian_train_5336.jpg\n",
"Sharath_train_3236.jpg\n",
"Kinkar_train_122.jpg\n",
"Gopika_train_1391.jpg\n",
"Abhay_train_6614.jpg\n",
"Gopika_train_6620.jpg\n",
"Kinkar_train_3805.jpg\n",
"Kinkar_test_43.jpg\n",
"Omar_train_2280.jpg\n",
"Sharath_train_3185.jpg\n",
"Sharath_train_3540.jpg\n",
"Pandian_train_732.jpg\n",
"Kinkar_train_3355.jpg\n",
"Abhay_train_3113.jpg\n",
"Kinkar_train_4544.jpg\n",
"Kinkar_train_97.jpg\n",
"Sharath_train_3307.jpg\n",
"Arun_train_206.jpg\n",
"Omar_train_2403.jpg\n",
"Abhay_train_5266.jpg\n",
"Gopika_train_484.jpg\n",
"Kinkar_train_5678.jpg\n",
"Abhay_train_2031.jpg\n",
"Gopika_train_2612.jpg\n",
"Gopika_train_5592.jpg\n",
"Abhay_train_6196.jpg\n",
"Omar_train_6231.jpg\n",
"Kinkar_train_6464.jpg\n",
"Arun_train_5120.jpg\n",
"Gopika_train_2237.jpg\n",
"Abhay_train_2512.jpg\n",
"Sharath_train_3950.jpg\n",
"Gopika_train_5661.jpg\n",
"Kinkar_train_915.jpg\n",
"Arun_train_4063.jpg\n",
"Abhay_train_585.jpg\n",
"Kinkar_train_5067.jpg\n",
"Sharath_train_4395.jpg\n",
"Arun_train_6219.jpg\n",
"Abhay_train_495.jpg\n",
"Kinkar_train_1440.jpg\n",
"Pandian_test_58.jpg\n",
"Sharath_train_4777.jpg\n",
"Abhay_train_839.jpg\n",
"Sharath_train_4287.jpg\n",
"Abhay_train_1035.jpg\n",
"Gopika_train_2666.jpg\n",
"Pandian_train_2765.jpg\n",
"Gopika_train_2807.jpg\n",
"Abhay_train_6547.jpg\n",
"Gopika_train_2790.jpg\n",
"Abhay_train_3408.jpg\n",
"Sharath_train_1930.jpg\n",
"Gopika_train_629.jpg\n",
"Omar_train_1015.jpg\n",
"Sharath_train_4558.jpg\n",
"Pandian_train_1215.jpg\n",
"Omar_train_2559.jpg\n",
"Gopika_train_750.jpg\n",
"Arun_train_4326.jpg\n",
"Abhay_train_1395.jpg\n",
"Sharath_train_2869.jpg\n",
"Kinkar_train_2543.jpg\n",
"Sharath_train_6180.jpg\n",
"Omar_train_2142.jpg\n",
"Abhay_train_4677.jpg\n",
"Arun_train_808.jpg\n",
"Arun_train_512.jpg\n",
"Sharath_test_9.jpg\n",
"Kinkar_test_1.jpg\n",
"Sharath_train_3661.jpg\n",
"Pandian_train_5053.jpg\n",
"Omar_train_647.jpg\n",
"Sharath_train_4485.jpg\n",
"Omar_train_2802.jpg\n",
"Omar_train_441.jpg\n",
"Arun_train_5176.jpg\n",
"Gopika_train_507.jpg\n",
"Omar_train_3881.jpg\n",
"Omar_train_2701.jpg\n",
"Pandian_train_6036.jpg\n",
"Sharath_train_6151.jpg\n",
"Pandian_train_3451.jpg\n",
"Gopika_train_5665.jpg\n",
"Pandian_train_1922.jpg\n",
"Omar_train_3720.jpg\n",
"Sharath_train_778.jpg\n",
"Sharath_train_3103.jpg\n",
"Omar_train_4019.jpg\n",
"Pandian_train_533.jpg\n",
"Gopika_train_256.jpg\n",
"Gopika_train_1843.jpg\n",
"Arun_train_4499.jpg\n",
"Gopika_train_2692.jpg\n",
"Kinkar_train_2697.jpg\n",
"Sharath_train_6378.jpg\n",
"Arun_train_1470.jpg\n",
"Omar_train_3140.jpg\n",
"Sharath_train_4022.jpg\n",
"Sharath_train_3246.jpg\n",
"Sharath_train_830.jpg\n",
"Gopika_train_1636.jpg\n",
"Sharath_train_1744.jpg\n",
"Arun_train_5346.jpg\n",
"Sharath_train_6404.jpg\n",
"Arun_train_6647.jpg\n",
"Omar_train_2508.jpg\n",
"Pandian_train_5691.jpg\n",
"Sharath_train_3861.jpg\n",
"Arun_train_1072.jpg\n",
"Abhay_train_303.jpg\n",
"Pandian_train_1037.jpg\n",
"Sharath_train_1500.jpg\n",
"Pandian_train_6248.jpg\n",
"Arun_train_5495.jpg\n",
"Abhay_train_4364.jpg\n",
"Sharath_train_3317.jpg\n",
"Gopika_train_1499.jpg\n",
"Arun_train_5890.jpg\n",
"Kinkar_train_2947.jpg\n",
"Abhay_train_4013.jpg\n",
"Sharath_train_4614.jpg\n",
"Sharath_train_6599.jpg\n",
"Abhay_train_5316.jpg\n",
"Pandian_train_3011.jpg\n",
"Omar_train_2113.jpg\n",
"Pandian_train_6728.jpg\n",
"Kinkar_train_6106.jpg\n",
"Gopika_train_2602.jpg\n",
"Sharath_train_4493.jpg\n",
"Omar_train_5296.jpg\n",
"Sharath_train_6230.jpg\n",
"Arun_train_6146.jpg\n",
"Gopika_train_3646.jpg\n",
"Omar_train_723.jpg\n",
"Arun_train_628.jpg\n",
"Kinkar_train_3040.jpg\n",
"Omar_train_3710.jpg\n",
"Abhay_train_5917.jpg\n",
"Gopika_train_5774.jpg\n",
"Pandian_train_5283.jpg\n",
"Sharath_train_3211.jpg\n",
"Arun_train_6903.jpg\n",
"Sharath_train_3877.jpg\n",
"Gopika_train_1299.jpg\n",
"Kinkar_train_4643.jpg\n",
"Sharath_train_2167.jpg\n",
"Omar_train_3631.jpg\n",
"Gopika_train_4137.jpg\n",
"Abhay_train_5671.jpg\n",
"Abhay_train_6783.jpg\n",
"Gopika_train_6060.jpg\n",
"Pandian_train_1600.jpg\n",
"Gopika_train_3107.jpg\n",
"Sharath_train_3781.jpg\n",
"Omar_train_4417.jpg\n",
"Sharath_train_542.jpg\n",
"Arun_train_2176.jpg\n",
"Abhay_train_2821.jpg\n",
"Sharath_train_3721.jpg\n",
"Abhay_train_2556.jpg\n",
"Abhay_train_4964.jpg\n",
"Abhay_train_5491.jpg\n",
"Gopika_train_3824.jpg\n",
"Omar_train_5862.jpg\n",
"Kinkar_train_1572.jpg\n",
"Omar_train_5121.jpg\n",
"Omar_train_698.jpg\n",
"Pandian_train_4490.jpg\n",
"Arun_train_6095.jpg\n",
"Gopika_train_6478.jpg\n",
"Gopika_train_3138.jpg\n",
"Gopika_train_1445.jpg\n",
"Gopika_train_4601.jpg\n",
"Sharath_train_3738.jpg\n",
"Gopika_train_1910.jpg\n",
"Arun_train_811.jpg\n",
"Kinkar_train_4120.jpg\n",
"Omar_train_6710.jpg\n",
"Sharath_train_5473.jpg\n",
"Gopika_train_4696.jpg\n",
"Sharath_train_1077.jpg\n",
"Gopika_train_226.jpg\n",
"Abhay_train_1961.jpg\n",
"Kinkar_train_773.jpg\n",
"Kinkar_train_1994.jpg\n",
"Abhay_train_3693.jpg\n",
"Gopika_train_3779.jpg\n",
"Sharath_train_6924.jpg\n",
"Kinkar_train_1071.jpg\n",
"Arun_train_394.jpg\n",
"Sharath_train_2165.jpg\n",
"Abhay_train_2297.jpg\n",
"Kinkar_train_5258.jpg\n",
"Sharath_train_5579.jpg\n",
"Pandian_train_4842.jpg\n",
"Kinkar_train_2560.jpg\n",
"Omar_train_5950.jpg\n",
"Pandian_train_6047.jpg\n",
"Omar_train_5370.jpg\n",
"Abhay_train_4590.jpg\n",
"Omar_train_5392.jpg\n",
"Sharath_train_767.jpg\n",
"Kinkar_train_157.jpg\n",
"Sharath_test_140.jpg\n",
"Gopika_train_2702.jpg\n",
"Abhay_train_2137.jpg\n",
"Abhay_train_4731.jpg\n",
"Omar_train_4106.jpg\n",
"Pandian_train_1196.jpg\n",
"Gopika_train_6831.jpg\n",
"Abhay_train_6957.jpg\n",
"Gopika_train_853.jpg\n",
"Gopika_train_3038.jpg\n",
"Kinkar_train_3836.jpg\n",
"Arun_train_227.jpg\n",
"Abhay_train_6280.jpg\n",
"Gopika_train_4942.jpg\n",
"Arun_train_3839.jpg\n",
"Omar_train_3519.jpg\n",
"Kinkar_train_3850.jpg\n",
"Abhay_train_1593.jpg\n",
"Gopika_train_5893.jpg\n",
"Kinkar_train_2292.jpg\n",
"Abhay_train_3557.jpg\n",
"Omar_train_2856.jpg\n",
"Pandian_train_3535.jpg\n",
"Pandian_test_6.jpg\n",
"Gopika_train_4573.jpg\n",
"Gopika_train_2378.jpg\n",
"Kinkar_train_2127.jpg\n",
"Omar_train_4185.jpg\n",
"Sharath_train_2593.jpg\n",
"Omar_train_643.jpg\n",
"Pandian_train_2694.jpg\n",
"Abhay_train_4358.jpg\n",
"Pandian_train_6397.jpg\n",
"Pandian_train_5947.jpg\n",
"Omar_train_2502.jpg\n",
"Gopika_train_5235.jpg\n",
"Omar_train_3233.jpg\n",
"Abhay_train_6545.jpg\n",
"Kinkar_train_4797.jpg\n",
"Kinkar_train_5900.jpg\n",
"Pandian_train_3434.jpg\n",
"Sharath_train_3481.jpg\n",
"Omar_train_2173.jpg\n",
"Omar_train_2708.jpg\n",
"Omar_train_5830.jpg\n",
"Sharath_train_2683.jpg\n",
"Gopika_train_957.jpg\n",
"Abhay_train_1610.jpg\n",
"Sharath_train_4267.jpg\n",
"Kinkar_train_2083.jpg\n",
"Omar_train_840.jpg\n",
"Kinkar_train_2776.jpg\n",
"Gopika_train_3575.jpg\n",
"Arun_train_4119.jpg\n",
"Omar_train_3222.jpg\n",
"Pandian_train_4830.jpg\n",
"Abhay_train_1339.jpg\n",
"Abhay_train_4868.jpg\n",
"Abhay_train_2159.jpg\n",
"Sharath_train_2791.jpg\n",
"Arun_train_4219.jpg\n",
"Sharath_train_3873.jpg\n",
"Sharath_train_2674.jpg\n",
"Sharath_train_4655.jpg\n",
"Kinkar_train_2399.jpg\n",
"Gopika_train_2746.jpg\n",
"Gopika_train_2242.jpg\n",
"Sharath_train_5987.jpg\n",
"Sharath_train_4749.jpg\n",
"Pandian_train_2220.jpg\n",
"Sharath_train_4308.jpg\n",
"Sharath_train_2301.jpg\n",
"Abhay_train_6270.jpg\n",
"Omar_train_5349.jpg\n",
"Abhay_train_962.jpg\n",
"Omar_train_1461.jpg\n",
"Kinkar_train_1016.jpg\n",
"Sharath_train_4055.jpg\n",
"Gopika_train_1924.jpg\n",
"Pandian_train_3844.jpg\n",
"Gopika_train_6592.jpg\n",
"Omar_train_5203.jpg\n",
"Arun_train_2689.jpg\n",
"Kinkar_train_5746.jpg\n",
"Kinkar_train_2846.jpg\n",
"Pandian_train_2014.jpg\n",
"Kinkar_train_6390.jpg\n",
"Gopika_train_2991.jpg\n",
"Sharath_train_3126.jpg\n",
"Omar_train_925.jpg\n",
"Abhay_train_1826.jpg\n",
"Kinkar_train_1404.jpg\n",
"Arun_train_5624.jpg\n",
"Omar_train_3394.jpg\n",
"Omar_train_3594.jpg\n",
"Arun_train_2278.jpg\n",
"Sharath_train_4045.jpg\n",
"Omar_train_6958.jpg\n",
"Arun_train_5253.jpg\n",
"Arun_train_6075.jpg\n",
"Omar_train_1686.jpg\n",
"Gopika_train_3359.jpg\n",
"Arun_train_725.jpg\n",
"Pandian_train_2072.jpg\n",
"Gopika_train_183.jpg\n",
"Kinkar_train_1657.jpg\n",
"Gopika_train_2392.jpg\n",
"Omar_train_6884.jpg\n",
"Sharath_train_569.jpg\n",
"Pandian_train_5071.jpg\n",
"Arun_train_2672.jpg\n",
"Kinkar_train_6017.jpg\n",
"Pandian_train_5437.jpg\n",
"Pandian_train_2274.jpg\n",
"Gopika_train_1354.jpg\n",
"Sharath_test_67.jpg\n",
"Sharath_train_2132.jpg\n",
"Arun_train_1030.jpg\n",
"Gopika_train_6626.jpg\n",
"Pandian_train_5189.jpg\n",
"Omar_train_1506.jpg\n",
"Gopika_train_6172.jpg\n",
"Kinkar_train_3700.jpg\n",
"Sharath_train_6278.jpg\n",
"Abhay_train_3179.jpg\n",
"Omar_train_6012.jpg\n",
"Sharath_train_3566.jpg\n",
"Arun_train_5994.jpg\n",
"Gopika_train_4415.jpg\n",
"Kinkar_train_4221.jpg\n",
"Arun_train_3562.jpg\n",
"Sharath_train_176.jpg\n",
"Gopika_train_6373.jpg\n",
"Gopika_train_1180.jpg\n",
"Arun_train_3237.jpg\n",
"Gopika_train_1692.jpg\n",
"Omar_train_4069.jpg\n",
"Omar_train_5970.jpg\n",
"Kinkar_train_5218.jpg\n",
"Arun_train_2103.jpg\n",
"Sharath_train_2858.jpg\n",
"Abhay_train_1829.jpg\n",
"Abhay_train_2677.jpg\n",
"Omar_train_1493.jpg\n",
"Arun_train_4886.jpg\n",
"Abhay_train_1969.jpg\n",
"Abhay_train_5997.jpg\n",
"Sharath_train_3690.jpg\n",
"Arun_train_3740.jpg\n",
"Sharath_train_6027.jpg\n",
"Gopika_train_6859.jpg\n",
"Pandian_train_4794.jpg\n",
"Omar_train_31.jpg\n",
"Sharath_train_667.jpg\n",
"Kinkar_train_3656.jpg\n",
"Pandian_train_1359.jpg\n",
"Kinkar_train_4100.jpg\n",
"Sharath_train_4048.jpg\n",
"Arun_train_1996.jpg\n",
"Gopika_train_5931.jpg\n",
"Arun_train_544.jpg\n",
"Omar_train_4204.jpg\n",
"Gopika_train_4904.jpg\n",
"Sharath_train_855.jpg\n",
"Arun_train_4644.jpg\n",
"Gopika_train_6147.jpg\n",
"Sharath_train_229.jpg\n",
"Omar_train_1060.jpg\n",
"Omar_train_279.jpg\n",
"Gopika_train_2710.jpg\n",
"Kinkar_train_3896.jpg\n",
"Kinkar_train_1155.jpg\n",
"Gopika_train_6816.jpg\n",
"Kinkar_train_2219.jpg\n",
"Sharath_train_4802.jpg\n",
"Sharath_train_1279.jpg\n",
"Pandian_train_5323.jpg\n",
"Abhay_train_4356.jpg\n",
"Abhay_train_5738.jpg\n",
"Pandian_train_2678.jpg\n",
"Abhay_train_5289.jpg\n",
"Sharath_train_2870.jpg\n",
"Gopika_train_4577.jpg\n",
"Sharath_train_2411.jpg\n",
"Sharath_train_384.jpg\n",
"Omar_train_4033.jpg\n",
"Kinkar_train_1253.jpg\n",
"Arun_train_3680.jpg\n",
"Gopika_train_2992.jpg\n",
"Abhay_train_6341.jpg\n",
"Pandian_train_2285.jpg\n",
"Arun_train_1392.jpg\n",
"Sharath_train_2832.jpg\n",
"Arun_train_5271.jpg\n",
"Gopika_train_3310.jpg\n",
"Gopika_train_4800.jpg\n",
"Pandian_train_3555.jpg\n",
"Kinkar_train_6299.jpg\n",
"Omar_train_321.jpg\n",
"Sharath_train_3388.jpg\n",
"Sharath_train_1981.jpg\n",
"Arun_train_3866.jpg\n",
"Omar_train_5358.jpg\n",
"Omar_train_6802.jpg\n",
"Abhay_train_2172.jpg\n",
"Abhay_train_5546.jpg\n",
"Arun_train_2738.jpg\n",
"Abhay_train_755.jpg\n",
"Arun_train_1594.jpg\n",
"Sharath_train_4954.jpg\n",
"Abhay_train_4692.jpg\n",
"Gopika_train_5887.jpg\n",
"Gopika_train_3492.jpg\n",
"Gopika_train_4426.jpg\n",
"Sharath_train_2498.jpg\n",
"Arun_train_3292.jpg\n",
"Pandian_train_1676.jpg\n",
"Omar_train_1295.jpg\n",
"Sharath_train_1011.jpg\n",
"Abhay_train_6093.jpg\n",
"Arun_train_345.jpg\n",
"Kinkar_train_6192.jpg\n",
"Omar_train_5270.jpg\n",
"Omar_train_343.jpg\n",
"Kinkar_train_3189.jpg\n",
"Arun_train_6588.jpg\n",
"Abhay_train_5703.jpg\n",
"Arun_train_5084.jpg\n",
"Abhay_train_3997.jpg\n",
"Abhay_train_3875.jpg\n",
"Omar_train_1300.jpg\n",
"Arun_train_2761.jpg\n",
"Abhay_train_852.jpg\n",
"Gopika_train_4131.jpg\n",
"Sharath_test_26.jpg\n",
"Abhay_train_3191.jpg\n",
"Abhay_train_1556.jpg\n",
"Gopika_train_5925.jpg\n",
"Arun_train_205.jpg\n",
"Omar_train_5757.jpg\n",
"Omar_train_2536.jpg\n",
"Kinkar_train_5194.jpg\n",
"Sharath_train_1402.jpg\n",
"Omar_train_6610.jpg\n",
"Pandian_train_2629.jpg\n",
"Kinkar_train_1596.jpg\n",
"Sharath_train_5033.jpg\n",
"Sharath_train_4141.jpg\n",
"Omar_train_6171.jpg\n",
"Kinkar_train_5747.jpg\n",
"Gopika_train_1817.jpg\n",
"Omar_train_4724.jpg\n",
"Kinkar_train_3144.jpg\n",
"Kinkar_train_6913.jpg\n",
"Gopika_train_6860.jpg\n",
"Gopika_train_3036.jpg\n",
"Omar_train_1492.jpg\n",
"Kinkar_train_1131.jpg\n",
"Gopika_train_5715.jpg\n",
"Gopika_train_5908.jpg\n",
"Pandian_train_609.jpg\n",
"Omar_train_4220.jpg\n",
"Sharath_train_6701.jpg\n",
"Pandian_train_2887.jpg\n",
"Arun_train_1476.jpg\n",
"Kinkar_train_2865.jpg\n",
"Abhay_train_2118.jpg\n",
"Abhay_train_3429.jpg\n",
"Kinkar_train_5168.jpg\n",
"Omar_train_614.jpg\n",
"Kinkar_train_6272.jpg\n",
"Gopika_train_5985.jpg\n",
"Gopika_train_2541.jpg\n",
"Arun_train_4370.jpg\n",
"Abhay_train_3842.jpg\n",
"Arun_train_1350.jpg\n",
"Omar_test_111.jpg\n",
"Pandian_train_6132.jpg\n",
"Pandian_train_295.jpg\n",
"Sharath_train_1082.jpg\n",
"Kinkar_train_2780.jpg\n",
"Sharath_train_3642.jpg\n",
"Sharath_train_757.jpg\n",
"Gopika_train_4367.jpg\n",
"Arun_train_6709.jpg\n",
"Omar_train_6813.jpg\n",
"Omar_train_6.jpg\n",
"Pandian_train_366.jpg\n",
"Omar_train_751.jpg\n",
"Kinkar_train_1621.jpg\n",
"Sharath_train_6489.jpg\n",
"Sharath_train_3264.jpg\n",
"Sharath_train_1780.jpg\n",
"Arun_train_3820.jpg\n",
"Kinkar_train_6194.jpg\n",
"Omar_train_1668.jpg\n",
"Sharath_train_3041.jpg\n",
"Sharath_train_3007.jpg\n",
"Kinkar_train_3314.jpg\n",
"Omar_train_4299.jpg\n",
"Sharath_train_4605.jpg\n",
"Sharath_train_1208.jpg\n",
"Arun_train_5668.jpg\n",
"Omar_train_4903.jpg\n",
"Gopika_train_99.jpg\n",
"Sharath_train_2269.jpg\n",
"Arun_train_184.jpg\n",
"Abhay_train_1810.jpg\n",
"Kinkar_train_5229.jpg\n",
"Sharath_train_4506.jpg\n",
"Gopika_train_2663.jpg\n",
"Omar_train_683.jpg\n",
"Abhay_test_10.jpg\n",
"Pandian_train_882.jpg\n",
"Kinkar_train_2100.jpg\n",
"Gopika_train_1576.jpg\n",
"Gopika_train_598.jpg\n",
"Sharath_train_3452.jpg\n",
"Sharath_train_3796.jpg\n",
"Sharath_train_6508.jpg\n",
"Pandian_train_3774.jpg\n",
"Abhay_train_3244.jpg\n",
"Omar_train_6658.jpg\n",
"Abhay_train_6770.jpg\n",
"Gopika_train_1494.jpg\n",
"Gopika_train_4498.jpg\n",
"Arun_train_671.jpg\n",
"Arun_train_5238.jpg\n",
"Arun_train_314.jpg\n",
"Gopika_train_4483.jpg\n",
"Arun_train_5760.jpg\n",
"Gopika_train_6388.jpg\n",
"Omar_train_5314.jpg\n",
"Gopika_train_3420.jpg\n",
"Abhay_train_1008.jpg\n",
"Omar_train_2817.jpg\n",
"Pandian_train_892.jpg\n",
"Sharath_train_3399.jpg\n",
"Arun_train_2682.jpg\n",
"Pandian_train_4946.jpg\n",
"Sharath_train_4834.jpg\n",
"Abhay_train_4198.jpg\n",
"Pandian_train_4004.jpg\n",
"Sharath_train_938.jpg\n",
"Gopika_train_435.jpg\n",
"Abhay_train_959.jpg\n",
"Pandian_train_4268.jpg\n",
"Arun_train_4090.jpg\n",
"Omar_train_2093.jpg\n",
"Gopika_train_2336.jpg\n",
"Gopika_train_5393.jpg\n",
"Pandian_train_3428.jpg\n",
"Pandian_train_4457.jpg\n",
"Omar_train_6319.jpg\n",
"Sharath_train_127.jpg\n",
"Gopika_train_4135.jpg\n",
"Pandian_train_6208.jpg\n",
"Sharath_train_6608.jpg\n",
"Sharath_train_4579.jpg\n",
"Kinkar_train_1769.jpg\n",
"Abhay_train_810.jpg\n",
"lenet_auto_train.prototxt\n",
"Abhay_train_5192.jpg\n",
"Abhay_train_223.jpg\n",
"Gopika_train_1685.jpg\n",
"Kinkar_train_2688.jpg\n",
"Pandian_train_3343.jpg\n",
"Pandian_train_659.jpg\n",
"Omar_train_891.jpg\n",
"Sharath_train_5008.jpg\n",
"Arun_train_5834.jpg\n",
"Kinkar_train_3604.jpg\n",
"Arun_train_1028.jpg\n",
"Arun_train_5063.jpg\n",
"Omar_train_4520.jpg\n",
"Kinkar_train_2490.jpg\n",
"Gopika_train_2395.jpg\n",
"Sharath_train_5651.jpg\n",
"Omar_train_3320.jpg\n",
"Gopika_train_3330.jpg\n",
"Gopika_train_5713.jpg\n",
"Gopika_train_6029.jpg\n",
"Gopika_train_126.jpg\n",
"Sharath_train_1316.jpg\n",
"Arun_train_5081.jpg\n",
"Kinkar_train_2475.jpg\n",
"Sharath_train_2267.jpg\n",
"Gopika_train_4259.jpg\n",
"Pandian_test_60.jpg\n",
"Sharath_train_4293.jpg\n",
"Gopika_train_4611.jpg\n",
"Abhay_train_5500.jpg\n",
"Abhay_train_307.jpg\n",
"Arun_train_124.jpg\n",
"Abhay_train_3407.jpg\n",
"Arun_train_1505.jpg\n",
"Omar_train_6415.jpg\n",
"Omar_train_2120.jpg\n",
"Gopika_train_4578.jpg\n",
"Omar_train_2599.jpg\n",
"Kinkar_train_6032.jpg\n",
"Arun_train_35.jpg\n",
"Kinkar_train_6377.jpg\n",
"Gopika_train_2998.jpg\n",
"Gopika_train_5340.jpg\n",
"Pandian_train_2414.jpg\n",
"Sharath_train_5072.jpg\n",
"Sharath_train_3242.jpg\n",
"Sharath_train_3702.jpg\n",
"Kinkar_train_539.jpg\n",
"Sharath_train_6791.jpg\n",
"Omar_train_3969.jpg\n",
"Arun_train_1772.jpg\n",
"Omar_train_4507.jpg\n",
"Arun_test_98.jpg\n",
"Arun_train_3961.jpg\n",
"Kinkar_train_5679.jpg\n",
"Sharath_train_1550.jpg\n",
"Abhay_train_4342.jpg\n",
"Gopika_train_933.jpg\n",
"Omar_train_5193.jpg\n",
"Pandian_train_158.jpg\n",
"Omar_train_4236.jpg\n",
"Sharath_train_278.jpg\n",
"Abhay_train_3823.jpg\n",
"Omar_train_4873.jpg\n",
"Kinkar_train_2184.jpg\n",
"Sharath_train_3533.jpg\n",
"Kinkar_train_1806.jpg\n",
"Abhay_train_5979.jpg\n",
"Omar_train_3027.jpg\n",
"Sharath_test_90.jpg\n",
"Sharath_train_3342.jpg\n",
"Sharath_train_6134.jpg\n",
"Gopika_train_5934.jpg\n",
"Sharath_train_6116.jpg\n",
"Pandian_train_776.jpg\n",
"Pandian_train_5439.jpg\n",
"Gopika_train_3253.jpg\n",
"Abhay_train_3389.jpg\n",
"Omar_train_555.jpg\n",
"Kinkar_train_6429.jpg\n",
"Gopika_train_6481.jpg\n",
"Kinkar_train_1764.jpg\n",
"Sharath_train_3655.jpg\n",
"Omar_train_1063.jpg\n",
"Pandian_train_1858.jpg\n",
"Omar_train_234.jpg\n",
"Gopika_train_4634.jpg\n",
"Abhay_train_4603.jpg\n",
"Gopika_train_6555.jpg\n",
"Sharath_train_5447.jpg\n",
"Sharath_train_3341.jpg\n",
"Sharath_train_3475.jpg\n",
"Sharath_train_2597.jpg\n",
"Pandian_train_4494.jpg\n",
"Sharath_train_5780.jpg\n",
"Sharath_train_6761.jpg\n",
"Gopika_train_5878.jpg\n",
"Abhay_train_1312.jpg\n",
"Kinkar_train_149.jpg\n",
"Kinkar_train_101.jpg\n",
"Abhay_train_1797.jpg\n",
"Arun_train_3046.jpg\n",
"Sharath_train_425.jpg\n",
"Omar_train_5907.jpg\n",
"Arun_train_4829.jpg\n",
"Kinkar_train_4757.jpg\n",
"Omar_train_5386.jpg\n",
"Kinkar_train_1332.jpg\n",
"Kinkar_train_143.jpg\n",
"Arun_train_3484.jpg\n",
"Arun_train_5294.jpg\n",
"Sharath_train_5670.jpg\n",
"Sharath_train_4898.jpg\n",
"Pandian_train_5804.jpg\n",
"Gopika_train_2455.jpg\n",
"Sharath_train_6779.jpg\n",
"Omar_train_405.jpg\n",
"Pandian_train_4907.jpg\n",
"Arun_train_928.jpg\n",
"Pandian_train_4798.jpg\n",
"Omar_train_1148.jpg\n",
"Abhay_train_6656.jpg\n",
"Sharath_train_6459.jpg\n",
"Arun_train_595.jpg\n",
"Sharath_train_1248.jpg\n",
"Kinkar_train_4313.jpg\n",
"Kinkar_train_5886.jpg\n",
"Abhay_train_5607.jpg\n",
"Kinkar_train_2456.jpg\n",
"Pandian_train_3376.jpg\n",
"Gopika_train_4923.jpg\n",
"Kinkar_train_5062.jpg\n",
"Omar_train_3528.jpg\n",
"Omar_train_4171.jpg\n",
"Sharath_train_4451.jpg\n",
"Gopika_train_1205.jpg\n",
"Pandian_train_4767.jpg\n",
"Pandian_train_3963.jpg\n",
"Omar_train_6683.jpg\n",
"Sharath_train_3157.jpg\n",
"Sharath_train_4217.jpg\n",
"Gopika_train_6643.jpg\n",
"Arun_train_1066.jpg\n",
"Gopika_train_5362.jpg\n",
"Sharath_train_741.jpg\n",
"Kinkar_train_2250.jpg\n",
"Sharath_test_34.jpg\n",
"Sharath_train_1729.jpg\n",
"Omar_train_6795.jpg\n",
"Kinkar_train_825.jpg\n",
"Abhay_train_6057.jpg\n",
"Sharath_train_4382.jpg\n",
"Abhay_train_2429.jpg\n",
"Omar_train_3906.jpg\n",
"Omar_train_3785.jpg\n",
"Omar_train_6572.jpg\n",
"Omar_train_5228.jpg\n",
"Omar_train_5074.jpg\n",
"Gopika_train_499.jpg\n",
"Sharath_train_4722.jpg\n",
"Pandian_train_3122.jpg\n",
"Omar_train_2377.jpg\n",
"Pandian_train_2007.jpg\n",
"Kinkar_train_80.jpg\n",
"Abhay_train_5411.jpg\n",
"Omar_train_989.jpg\n",
"Omar_train_3863.jpg\n",
"Pandian_train_159.jpg\n",
"Arun_train_5281.jpg\n",
"Omar_train_258.jpg\n",
"Abhay_train_4518.jpg\n",
"Gopika_train_792.jpg\n",
"Arun_train_641.jpg\n",
"Sharath_train_5813.jpg\n",
"Sharath_train_2996.jpg\n",
"Gopika_train_4901.jpg\n",
"Kinkar_train_2330.jpg\n",
"Abhay_train_3815.jpg\n",
"Sharath_train_2174.jpg\n",
"Arun_train_1360.jpg\n",
"Arun_train_3778.jpg\n",
"Abhay_train_5055.jpg\n",
"Pandian_train_3794.jpg\n",
"Gopika_train_1935.jpg\n",
"Pandian_train_6045.jpg\n",
"Gopika_train_6825.jpg\n",
"Sharath_train_3793.jpg\n",
"Arun_train_5885.jpg\n",
"Arun_train_6749.jpg\n",
"Sharath_train_5721.jpg\n",
"Abhay_train_5070.jpg\n",
"Sharath_train_3199.jpg\n",
"Omar_train_3205.jpg\n",
"Sharath_train_5363.jpg\n",
"Arun_train_6277.jpg\n",
"Kinkar_train_3633.jpg\n",
"Sharath_train_6793.jpg\n",
"Arun_train_178.jpg\n",
"Arun_train_1212.jpg\n",
"Sharath_train_413.jpg\n",
"Omar_train_4435.jpg\n",
"Sharath_train_2130.jpg\n",
"Pandian_train_3032.jpg\n",
"Kinkar_train_3578.jpg\n",
"Arun_train_2119.jpg\n",
"Omar_train_5741.jpg\n",
"Sharath_train_4652.jpg\n",
"Pandian_train_3416.jpg\n",
"Pandian_train_2656.jpg\n",
"Sharath_train_6879.jpg\n",
"Omar_train_2391.jpg\n",
"Omar_train_2491.jpg\n",
"Arun_train_663.jpg\n",
"Abhay_train_3235.jpg\n",
"Sharath_train_3792.jpg\n",
"Abhay_train_3379.jpg\n",
"Abhay_train_5154.jpg\n",
"Abhay_train_4040.jpg\n",
"Gopika_train_4473.jpg\n",
"Sharath_train_6212.jpg\n",
"Abhay_train_2324.jpg\n",
"Arun_train_2882.jpg\n",
"Pandian_train_171.jpg\n",
"Arun_train_2839.jpg\n",
"Kinkar_train_6381.jpg\n",
"Pandian_train_1298.jpg\n",
"Gopika_train_204.jpg\n",
"Kinkar_train_2270.jpg\n",
"Sharath_train_250.jpg\n",
"Pandian_train_4737.jpg\n",
"Sharath_train_3306.jpg\n",
"Abhay_train_3190.jpg\n",
"Pandian_train_22.jpg\n",
"Kinkar_train_4809.jpg\n",
"Omar_train_5909.jpg\n",
"Sharath_train_1291.jpg\n",
"Kinkar_train_308.jpg\n",
"Gopika_train_5829.jpg\n",
"Sharath_train_5799.jpg\n",
"Arun_train_4713.jpg\n",
"Gopika_train_6122.jpg\n",
"Sharath_train_373.jpg\n",
"Abhay_train_1944.jpg\n",
"Sharath_train_6389.jpg\n",
"Arun_train_6603.jpg\n",
"Abhay_train_2150.jpg\n",
"Sharath_train_5464.jpg\n",
"Sharath_train_1507.jpg\n",
"Arun_train_4298.jpg\n",
"Sharath_train_5969.jpg\n",
"Sharath_train_2723.jpg\n",
"Sharath_train_3871.jpg\n",
"Omar_train_709.jpg\n",
"Sharath_train_5838.jpg\n",
"Arun_train_5136.jpg\n",
"Gopika_train_3974.jpg\n",
"Gopika_train_4492.jpg\n",
"Omar_train_3277.jpg\n",
"Kinkar_train_1970.jpg\n",
"Omar_train_6901.jpg\n",
"Kinkar_train_3340.jpg\n",
"Arun_train_1047.jpg\n",
"Arun_train_1722.jpg\n",
"Abhay_train_201.jpg\n",
"Kinkar_train_1788.jpg\n",
"Sharath_train_2431.jpg\n",
"Kinkar_train_1045.jpg\n",
"Sharath_train_4253.jpg\n",
"Kinkar_train_3637.jpg\n",
"Omar_train_2940.jpg\n",
"Omar_train_2511.jpg\n",
"Gopika_train_1396.jpg\n",
"Abhay_train_4516.jpg\n",
"Sharath_train_4994.jpg\n",
"Sharath_train_706.jpg\n",
"Abhay_train_5625.jpg\n",
"Abhay_train_3924.jpg\n",
"Kinkar_train_392.jpg\n",
"Omar_train_5511.jpg\n",
"Abhay_train_1249.jpg\n",
"Sharath_test_160.jpg\n",
"Kinkar_train_3453.jpg\n",
"Abhay_train_1473.jpg\n",
"Kinkar_train_6593.jpg\n",
"Gopika_train_2829.jpg\n",
"Gopika_train_3398.jpg\n",
"Arun_train_2528.jpg\n",
"Omar_train_3015.jpg\n",
"Arun_train_2513.jpg\n",
"Sharath_train_1581.jpg\n",
"Abhay_train_6930.jpg\n",
"Kinkar_train_1828.jpg\n",
"Abhay_train_1448.jpg\n",
"Gopika_train_6828.jpg\n",
"Arun_train_271.jpg\n",
"Pandian_train_1412.jpg\n",
"Omar_train_782.jpg\n",
"Sharath_train_2880.jpg\n",
"Pandian_train_3067.jpg\n",
"Arun_train_2152.jpg\n",
"Omar_train_674.jpg\n",
"Gopika_train_2955.jpg\n",
"Omar_train_4376.jpg\n",
"Omar_train_133.jpg\n",
"Pandian_train_4447.jpg\n",
"Arun_train_2648.jpg\n",
"Sharath_train_2520.jpg\n",
"Sharath_train_5078.jpg\n",
"Abhay_train_6617.jpg\n",
"Pandian_train_6234.jpg\n",
"Sharath_train_3628.jpg\n",
"Pandian_train_3196.jpg\n",
"Arun_train_3145.jpg\n",
"Sharath_train_3425.jpg\n",
"Kinkar_train_786.jpg\n",
"Sharath_train_3862.jpg\n",
"Omar_train_6238.jpg\n",
"Abhay_train_5823.jpg\n",
"Omar_train_5647.jpg\n",
"Abhay_test_107.jpg\n",
"Kinkar_train_958.jpg\n",
"Pandian_train_4765.jpg\n",
"Sharath_train_6453.jpg\n",
"Kinkar_train_3681.jpg\n",
"Omar_train_601.jpg\n",
"Sharath_train_638.jpg\n",
"Pandian_train_3589.jpg\n",
"Sharath_train_4487.jpg\n",
"Omar_train_2457.jpg\n",
"Kinkar_train_5914.jpg\n",
"Pandian_train_3926.jpg\n",
"Abhay_train_3060.jpg\n",
"Gopika_train_5600.jpg\n",
"Arun_train_3496.jpg\n",
"Sharath_train_1175.jpg\n",
"Kinkar_train_6803.jpg\n",
"Sharath_train_4324.jpg\n",
"Gopika_train_3766.jpg\n",
"Gopika_train_2644.jpg\n",
"Sharath_train_2759.jpg\n",
"Gopika_train_2787.jpg\n",
"Gopika_train_973.jpg\n",
"Gopika_train_6169.jpg\n",
"Omar_train_4459.jpg\n",
"Kinkar_train_2294.jpg\n",
"Omar_train_6182.jpg\n",
"Abhay_train_5784.jpg\n",
"Omar_train_5754.jpg\n",
"Sharath_train_6687.jpg\n",
"Arun_train_727.jpg\n",
"Arun_train_6699.jpg\n",
"Gopika_train_4833.jpg\n",
"Omar_train_5113.jpg\n",
"Sharath_train_6223.jpg\n",
"Sharath_train_5821.jpg\n",
"Arun_train_2640.jpg\n",
"Sharath_train_2390.jpg\n",
"Abhay_train_620.jpg\n",
"Sharath_train_701.jpg\n",
"Pandian_train_1606.jpg\n",
"Arun_train_5955.jpg\n",
"Sharath_train_6385.jpg\n",
"Kinkar_train_587.jpg\n",
"Pandian_train_1193.jpg\n",
"Sharath_train_5866.jpg\n",
"Kinkar_train_6653.jpg\n",
"Omar_train_353.jpg\n",
"Pandian_train_5894.jpg\n",
"Sharath_train_1297.jpg\n",
"Omar_train_6080.jpg\n",
"Arun_train_2590.jpg\n",
"Kinkar_train_6311.jpg\n",
"Sharath_train_2235.jpg\n",
"Sharath_train_506.jpg\n",
"Gopika_train_1604.jpg\n",
"Omar_train_4467.jpg\n",
"Sharath_train_4265.jpg\n",
"Sharath_train_1931.jpg\n",
"Sharath_train_5957.jpg\n",
"Sharath_train_3493.jpg\n",
"Omar_train_2841.jpg\n",
"Gopika_train_517.jpg\n",
"Pandian_train_6307.jpg\n",
"Kinkar_train_1663.jpg\n",
"Pandian_train_1481.jpg\n",
"Omar_train_3066.jpg\n",
"Pandian_train_6077.jpg\n",
"Kinkar_train_3058.jpg\n",
"Sharath_train_1419.jpg\n",
"Kinkar_train_5265.jpg\n",
"Omar_train_6537.jpg\n",
"Abhay_train_5208.jpg\n",
"Pandian_train_2029.jpg\n",
"Pandian_train_180.jpg\n",
"Omar_train_1985.jpg\n",
"Abhay_train_400.jpg\n",
"Omar_train_5199.jpg\n",
"Gopika_train_1407.jpg\n",
"Sharath_train_3085.jpg\n",
"Abhay_train_2715.jpg\n",
"Arun_train_613.jpg\n",
"Abhay_train_6716.jpg\n",
"Gopika_train_6418.jpg\n",
"Sharath_train_4438.jpg\n",
"Gopika_train_6741.jpg\n",
"Arun_train_883.jpg\n",
"Sharath_train_6747.jpg\n",
"Arun_train_5149.jpg\n",
"Gopika_train_2837.jpg\n",
"Gopika_train_4289.jpg\n",
"Pandian_train_5764.jpg\n",
"Kinkar_train_5126.jpg\n",
"Arun_train_5454.jpg\n",
"Kinkar_test_88.jpg\n",
"Pandian_train_1659.jpg\n",
"Gopika_train_1108.jpg\n",
"Pandian_train_2596.jpg\n",
"Omar_train_1835.jpg\n",
"Sharath_train_6949.jpg\n",
"Gopika_train_483.jpg\n",
"Kinkar_train_4528.jpg\n",
"Kinkar_train_4824.jpg\n",
"Pandian_train_3983.jpg\n",
"Omar_train_4097.jpg\n",
"Omar_train_2681.jpg\n",
"Arun_train_3552.jpg\n",
"Gopika_train_357.jpg\n",
"Gopika_train_3364.jpg\n",
"Gopika_train_284.jpg\n",
"Pandian_train_1223.jpg\n",
"Sharath_train_4142.jpg\n",
"Pandian_train_4759.jpg\n",
"Pandian_train_3411.jpg\n",
"Omar_train_1609.jpg\n",
"Kinkar_train_1085.jpg\n",
"Omar_train_1614.jpg\n",
"Kinkar_train_5800.jpg\n",
"Pandian_train_6363.jpg\n",
"Arun_train_3937.jpg\n",
"Abhay_train_505.jpg\n",
"Sharath_train_6297.jpg\n",
"Pandian_train_1639.jpg\n",
"Omar_train_3234.jpg\n",
"Kinkar_train_6817.jpg\n",
"Arun_train_4150.jpg\n",
"Kinkar_train_4521.jpg\n",
"Omar_train_1766.jpg\n",
"Sharath_train_2170.jpg\n",
"Omar_train_369.jpg\n",
"Kinkar_train_6636.jpg\n",
"Omar_train_1943.jpg\n",
"Omar_train_3726.jpg\n",
"Sharath_train_79.jpg\n",
"Kinkar_train_3223.jpg\n",
"Sharath_train_670.jpg\n",
"Kinkar_train_5881.jpg\n",
"Sharath_train_516.jpg\n",
"Arun_train_5739.jpg\n",
"Kinkar_train_950.jpg\n",
"Sharath_train_2662.jpg\n",
"Gopika_train_6066.jpg\n",
"Gopika_train_2059.jpg\n",
"Sharath_train_1867.jpg\n",
"Arun_train_455.jpg\n",
"Kinkar_test_20.jpg\n",
"Kinkar_train_2712.jpg\n",
"Gopika_train_1543.jpg\n",
"Pandian_train_6812.jpg\n",
"Sharath_train_1042.jpg\n",
"Pandian_train_4992.jpg\n",
"Sharath_train_5549.jpg\n",
"Omar_train_6824.jpg\n",
"Sharath_train_1584.jpg\n",
"Omar_train_6523.jpg\n",
"Pandian_train_2561.jpg\n",
"Pandian_train_6406.jpg\n",
"Kinkar_train_4821.jpg\n",
"Sharath_train_470.jpg\n",
"Gopika_train_6500.jpg\n",
"Arun_train_5480.jpg\n",
"Arun_train_1684.jpg\n",
"Kinkar_train_4508.jpg\n",
"Sharath_train_1501.jpg\n",
"Kinkar_train_2730.jpg\n",
"Sharath_train_3622.jpg\n",
"Omar_train_1912.jpg\n",
"Sharath_train_6426.jpg\n",
"Sharath_train_4403.jpg\n",
"Sharath_train_724.jpg\n",
"Arun_train_139.jpg\n",
"Arun_train_6222.jpg\n",
"Arun_train_6698.jpg\n",
"Sharath_train_4399.jpg\n",
"Arun_train_4474.jpg\n",
"Kinkar_train_2166.jpg\n",
"Arun_train_1005.jpg\n",
"Gopika_train_2661.jpg\n",
"Omar_train_917.jpg\n",
"Gopika_train_1917.jpg\n",
"Pandian_train_998.jpg\n",
"Gopika_train_3812.jpg\n",
"Kinkar_train_4262.jpg\n",
"Arun_train_4915.jpg\n",
"Gopika_test_31.jpg\n",
"Sharath_train_3689.jpg\n",
"Omar_train_3469.jpg\n",
"Sharath_train_4753.jpg\n",
"Arun_train_6068.jpg\n",
"Omar_train_6026.jpg\n",
"Abhay_train_4177.jpg\n",
"Sharath_train_2226.jpg\n",
"Abhay_train_2362.jpg\n",
"Kinkar_train_905.jpg\n",
"Omar_train_4525.jpg\n",
"Kinkar_train_3114.jpg\n",
"Pandian_train_75.jpg\n",
"Abhay_train_4596.jpg\n",
"Gopika_train_6065.jpg\n",
"Gopika_train_6885.jpg\n",
"Kinkar_train_4775.jpg\n",
"Omar_train_6315.jpg\n",
"Sharath_train_5772.jpg\n",
"Gopika_train_1125.jpg\n",
"Omar_train_6872.jpg\n",
"Sharath_train_6034.jpg\n",
"Sharath_train_4269.jpg\n",
"Gopika_train_2084.jpg\n",
"Arun_train_4565.jpg\n",
"Omar_train_5173.jpg\n",
"Kinkar_test_142.jpg\n",
"Gopika_train_4841.jpg\n",
"Abhay_train_4770.jpg\n",
"Gopika_train_4420.jpg\n",
"Sharath_train_4950.jpg\n",
"Arun_train_5341.jpg\n",
"Sharath_train_2361.jpg\n",
"Abhay_train_3508.jpg\n",
"Sharath_train_200.jpg\n",
"Abhay_train_606.jpg\n",
"Sharath_train_95.jpg\n",
"Abhay_train_926.jpg\n",
"Abhay_test_83.jpg\n",
"Kinkar_train_3712.jpg\n",
"Sharath_train_423.jpg\n",
"Gopika_train_407.jpg\n",
"Kinkar_train_1577.jpg\n",
"Arun_train_885.jpg\n",
"Gopika_train_2575.jpg\n",
"Sharath_train_4921.jpg\n",
"Omar_train_2574.jpg\n",
"Pandian_train_4799.jpg\n",
"Sharath_train_749.jpg\n",
"Kinkar_train_198.jpg\n",
"Pandian_train_5276.jpg\n",
"Arun_train_6868.jpg\n",
"Kinkar_train_4571.jpg\n",
"Omar_train_2551.jpg\n",
"Gopika_train_1110.jpg\n",
"Arun_train_4598.jpg\n",
"Sharath_train_302.jpg\n",
"Sharath_train_5653.jpg\n",
"Gopika_train_2098.jpg\n",
"Gopika_train_3874.jpg\n",
"Arun_train_2396.jpg\n",
"Gopika_train_1207.jpg\n",
"Gopika_train_4066.jpg\n",
"Gopika_train_820.jpg\n",
"Kinkar_train_491.jpg\n",
"Sharath_train_4160.jpg\n",
"Gopika_train_4717.jpg\n",
"Arun_train_5236.jpg\n",
"Kinkar_train_1163.jpg\n",
"Kinkar_train_6832.jpg\n",
"Abhay_train_3348.jpg\n",
"Arun_train_296.jpg\n",
"Sharath_train_3053.jpg\n",
"Abhay_train_964.jpg\n",
"Abhay_train_5155.jpg\n",
"Arun_train_911.jpg\n",
"Pandian_train_2421.jpg\n",
"Kinkar_train_2131.jpg\n",
"Sharath_train_1770.jpg\n",
"Abhay_train_1999.jpg\n",
"Pandian_train_1982.jpg\n",
"Omar_train_3123.jpg\n",
"Gopika_train_3224.jpg\n",
"Sharath_train_6372.jpg\n",
"Omar_train_186.jpg\n",
"Arun_train_4482.jpg\n",
"Gopika_train_5699.jpg\n",
"Arun_train_1532.jpg\n",
"Omar_train_2194.jpg\n",
"Abhay_train_5497.jpg\n",
"Gopika_train_835.jpg\n",
"Arun_train_3818.jpg\n",
"Abhay_train_6028.jpg\n",
"Omar_train_2684.jpg\n",
"Kinkar_train_5416.jpg\n",
"Sharath_train_3436.jpg\n",
"Pandian_train_4881.jpg\n",
"Abhay_train_3352.jpg\n",
"Gopika_train_4795.jpg\n",
"Omar_train_6694.jpg\n",
"Kinkar_train_4206.jpg\n",
"Sharath_train_2673.jpg\n",
"Kinkar_train_1926.jpg\n",
"Omar_train_6393.jpg\n",
"Sharath_train_2326.jpg\n",
"Sharath_train_2231.jpg\n",
"Pandian_train_5427.jpg\n",
"Gopika_train_6684.jpg\n",
"Kinkar_train_5274.jpg\n",
"Gopika_train_362.jpg\n",
"Sharath_train_2430.jpg\n",
"Sharath_train_3830.jpg\n",
"Sharath_train_6598.jpg\n",
"Omar_train_3663.jpg\n",
"Kinkar_train_3902.jpg\n",
"Pandian_test_94.jpg\n",
"Arun_train_610.jpg\n",
"Kinkar_train_5864.jpg\n",
"Kinkar_train_1146.jpg\n",
"Omar_train_1151.jpg\n",
"Gopika_train_3288.jpg\n",
"Abhay_train_1870.jpg\n",
"Sharath_train_685.jpg\n",
"Kinkar_train_4413.jpg\n",
"Arun_train_6257.jpg\n",
"Kinkar_train_1280.jpg\n",
"Sharath_train_6752.jpg\n",
"Pandian_train_1128.jpg\n",
"Omar_train_4255.jpg\n",
"Sharath_train_4351.jpg\n",
"Kinkar_train_1904.jpg\n",
"Omar_train_3449.jpg\n",
"Gopika_train_4599.jpg\n",
"Sharath_train_1839.jpg\n",
"Omar_train_2346.jpg\n",
"Omar_train_1844.jpg\n",
"Arun_test_102.jpg\n",
"Gopika_train_1270.jpg\n",
"Kinkar_train_672.jpg\n",
"Gopika_train_4449.jpg\n",
"Kinkar_train_5198.jpg\n",
"Omar_train_1337.jpg\n",
"Kinkar_train_3960.jpg\n",
"Sharath_train_5077.jpg\n",
"Gopika_train_3515.jpg\n",
"Abhay_train_6084.jpg\n",
"Omar_train_3615.jpg\n",
"Abhay_train_3532.jpg\n",
"Sharath_train_5935.jpg\n",
"Abhay_train_887.jpg\n",
"Sharath_train_5676.jpg\n",
"Abhay_train_4463.jpg\n",
"Omar_train_3610.jpg\n",
"Sharath_train_1219.jpg\n",
"Omar_train_3097.jpg\n",
"Kinkar_train_4496.jpg\n",
"Sharath_train_6526.jpg\n",
"Sharath_train_864.jpg\n",
"Omar_train_4163.jpg\n",
"Pandian_train_4905.jpg\n",
"Pandian_train_6020.jpg\n",
"Gopika_train_5404.jpg\n",
"Arun_train_2043.jpg\n",
"Gopika_train_5646.jpg\n",
"Kinkar_train_1819.jpg\n",
"Abhay_train_4189.jpg\n",
"Pandian_train_3291.jpg\n",
"Pandian_test_82.jpg\n",
"Pandian_test_56.jpg\n",
"Sharath_train_5339.jpg\n",
"Gopika_train_1387.jpg\n",
"Sharath_train_6064.jpg\n",
"Omar_train_6383.jpg\n",
"Arun_train_3353.jpg\n",
"Gopika_train_2088.jpg\n",
"Gopika_train_6042.jpg\n",
"Sharath_train_3898.jpg\n",
"Omar_train_5922.jpg\n",
"Pandian_train_6717.jpg\n",
"Pandian_train_2646.jpg\n",
"Abhay_train_3131.jpg\n",
"Omar_train_5115.jpg\n",
"Gopika_train_5750.jpg\n",
"Kinkar_train_5576.jpg\n",
"Abhay_train_2767.jpg\n",
"Omar_train_6772.jpg\n",
"Arun_train_5512.jpg\n",
"Pandian_train_5590.jpg\n",
"Pandian_train_6337.jpg\n",
"Pandian_train_1307.jpg\n",
"Arun_train_5767.jpg\n",
"Omar_train_5706.jpg\n",
"Kinkar_train_5488.jpg\n",
"Pandian_train_6834.jpg\n",
"Gopika_train_3132.jpg\n",
"Kinkar_train_1057.jpg\n",
"Arun_train_5165.jpg\n",
"Omar_train_2232.jpg\n",
"Arun_train_640.jpg\n",
"Omar_train_1.jpg\n",
"Kinkar_train_5260.jpg\n",
"Sharath_train_4310.jpg\n",
"Pandian_train_341.jpg\n",
"Kinkar_train_2721.jpg\n",
"Abhay_train_5145.jpg\n",
"Arun_train_6380.jpg\n",
"Gopika_train_889.jpg\n",
"Arun_train_6264.jpg\n",
"Omar_train_439.jpg\n",
"Abhay_train_1533.jpg\n",
"Sharath_train_3991.jpg\n",
"Abhay_train_3819.jpg\n",
"Abhay_train_5233.jpg\n",
"Arun_train_5105.jpg\n",
"Sharath_train_6743.jpg\n",
"Kinkar_train_2705.jpg\n",
"Omar_train_270.jpg\n",
"Abhay_train_2615.jpg\n",
"Omar_train_6273.jpg\n",
"Sharath_train_4774.jpg\n",
"Pandian_train_6131.jpg\n",
"Gopika_train_5940.jpg\n",
"Sharath_train_5002.jpg\n",
"Sharath_train_2985.jpg\n",
"Sharath_test_0.jpg\n",
"Abhay_train_6947.jpg\n",
"Omar_train_3285.jpg\n",
"Abhay_train_2951.jpg\n",
"Omar_train_5451.jpg\n",
"Abhay_train_1774.jpg\n",
"Omar_train_1695.jpg\n",
"Omar_test_44.jpg\n",
"Arun_train_6815.jpg\n",
"Arun_train_5157.jpg\n",
"Abhay_train_2529.jpg\n",
"Kinkar_train_464.jpg\n",
"Sharath_train_336.jpg\n",
"Abhay_train_4855.jpg\n",
"Abhay_train_5639.jpg\n",
"Omar_train_5758.jpg\n",
"Omar_train_2155.jpg\n",
"Pandian_train_1490.jpg\n",
"Kinkar_train_3214.jpg\n",
"Arun_train_6926.jpg\n",
"Pandian_train_657.jpg\n",
"Arun_train_4906.jpg\n",
"Gopika_train_2325.jpg\n",
"Pandian_train_4387.jpg\n",
"Arun_train_1562.jpg\n",
"Arun_train_1540.jpg\n",
"Arun_train_4114.jpg\n",
"Gopika_train_6329.jpg\n",
"Arun_train_6700.jpg\n",
"Abhay_train_3255.jpg\n",
"Arun_train_1567.jpg\n",
"Sharath_train_1250.jpg\n",
"Sharath_train_6214.jpg\n",
"Arun_train_1831.jpg\n",
"Omar_train_6086.jpg\n",
"Pandian_train_867.jpg\n",
"Sharath_train_3439.jpg\n",
"Sharath_train_4998.jpg\n",
"Sharath_train_4497.jpg\n",
"Abhay_train_1272.jpg\n",
"Omar_train_6623.jpg\n",
"Pandian_train_6522.jpg\n",
"Sharath_train_265.jpg\n",
"Sharath_train_764.jpg\n",
"Kinkar_train_4183.jpg\n",
"Sharath_train_2153.jpg\n",
"Omar_train_4099.jpg\n",
"Gopika_train_6844.jpg\n",
"Omar_train_1890.jpg\n",
"Omar_train_2191.jpg\n",
"Kinkar_train_260.jpg\n",
"Kinkar_train_2224.jpg\n",
"Arun_train_4738.jpg\n",
"Arun_train_2282.jpg\n",
"Arun_train_41.jpg\n",
"Abhay_train_833.jpg\n",
"Omar_train_23.jpg\n",
"Arun_train_2171.jpg\n",
"Abhay_train_3602.jpg\n",
"Omar_train_807.jpg\n",
"Sharath_train_6410.jpg\n",
"Abhay_train_4621.jpg\n",
"Kinkar_train_5961.jpg\n",
"Sharath_train_6356.jpg\n",
"Pandian_train_5196.jpg\n",
"Omar_train_5401.jpg\n",
"Kinkar_train_5510.jpg\n",
"Abhay_train_3682.jpg\n",
"Sharath_train_5153.jpg\n",
"Gopika_train_1184.jpg\n",
"Gopika_train_1259.jpg\n",
"Sharath_train_5766.jpg\n",
"Gopika_train_2234.jpg\n",
"Omar_train_5826.jpg\n",
"Omar_train_2516.jpg\n",
"Kinkar_test_14.jpg\n",
"Arun_train_2944.jpg\n",
"Omar_train_6019.jpg\n",
"Sharath_train_6967.jpg\n",
"Kinkar_train_6671.jpg\n",
"Gopika_train_6887.jpg\n",
"Abhay_train_521.jpg\n",
"Kinkar_train_735.jpg\n",
"Pandian_train_2248.jpg\n",
"Kinkar_train_3718.jpg\n",
"Abhay_train_584.jpg\n",
"Arun_train_6583.jpg\n",
"Abhay_train_5065.jpg\n",
"Kinkar_train_123.jpg\n",
"Pandian_train_492.jpg\n",
"Sharath_train_6304.jpg\n",
"Arun_train_6768.jpg\n",
"Omar_train_2188.jpg\n",
"Omar_train_1239.jpg\n",
"Arun_train_1229.jpg\n",
"Abhay_train_6591.jpg\n",
"Abhay_train_1216.jpg\n",
"Arun_train_6152.jpg\n",
"Omar_train_2214.jpg\n",
"Gopika_train_2852.jpg\n",
"Omar_train_2988.jpg\n",
"Arun_train_2221.jpg\n",
"Pandian_train_132.jpg\n",
"Kinkar_train_233.jpg\n",
"Abhay_train_4931.jpg\n",
"Kinkar_train_5433.jpg\n",
"Gopika_train_6504.jpg\n",
"Sharath_train_3970.jpg\n",
"Pandian_train_5929.jpg\n",
"Arun_train_1827.jpg\n",
"Gopika_train_2024.jpg\n",
"Sharath_train_4766.jpg\n",
"Gopika_train_5030.jpg\n",
"Pandian_train_1869.jpg\n",
"Sharath_train_5453.jpg\n",
"Sharath_train_1641.jpg\n",
"Arun_train_3882.jpg\n",
"Gopika_train_4495.jpg\n",
"Omar_train_1408.jpg\n",
"Omar_train_4060.jpg\n",
"Abhay_train_5025.jpg\n",
"Kinkar_test_143.jpg\n",
"Sharath_train_937.jpg\n",
"Pandian_train_6849.jpg\n",
"Pandian_train_2209.jpg\n",
"Kinkar_train_2042.jpg\n",
"Arun_train_1050.jpg\n",
"Sharath_train_4786.jpg\n",
"Omar_train_5032.jpg\n",
"Sharath_train_6153.jpg\n",
"Gopika_train_6217.jpg\n",
"Gopika_test_66.jpg\n",
"Arun_train_217.jpg\n",
"Abhay_train_4750.jpg\n",
"Sharath_train_5069.jpg\n",
"Sharath_train_6177.jpg\n",
"Sharath_train_4409.jpg\n",
"Sharath_train_6624.jpg\n",
"Kinkar_train_1130.jpg\n",
"Kinkar_train_6764.jpg\n",
"Arun_train_1139.jpg\n",
"Sharath_train_6702.jpg\n",
"Omar_train_4050.jpg\n",
"Sharath_train_3954.jpg\n",
"Sharath_train_3750.jpg\n",
"Gopika_test_164.jpg\n",
"Abhay_train_1598.jpg\n",
"Sharath_train_1386.jpg\n",
"Gopika_train_1549.jpg\n",
"Gopika_train_6883.jpg\n",
"Arun_train_1159.jpg\n",
"Kinkar_train_4010.jpg\n",
"Pandian_train_2435.jpg\n",
"Kinkar_train_1203.jpg\n",
"Gopika_train_1633.jpg\n",
"Kinkar_train_5585.jpg\n",
"Arun_train_779.jpg\n",
"Kinkar_train_6714.jpg\n",
"Omar_train_3576.jpg\n",
"Pandian_train_5195.jpg\n",
"Kinkar_train_1278.jpg\n",
"Arun_train_5743.jpg\n",
"Arun_train_3092.jpg\n",
"Pandian_train_5268.jpg\n",
"Gopika_train_5515.jpg\n",
"Arun_train_577.jpg\n",
"Kinkar_train_3064.jpg\n",
"Gopika_train_2225.jpg\n",
"Sharath_train_5527.jpg\n",
"Sharath_train_93.jpg\n",
"Gopika_train_5556.jpg\n",
"Omar_train_3259.jpg\n",
"Sharath_train_1213.jpg\n",
"Pandian_train_6368.jpg\n",
"Gopika_train_5756.jpg\n",
"Arun_train_2382.jpg\n",
"Kinkar_train_4344.jpg\n",
"Kinkar_train_5759.jpg\n",
"Abhay_train_6157.jpg\n",
"Pandian_train_4290.jpg\n",
"Omar_train_6200.jpg\n",
"Sharath_train_50.jpg\n",
"Pandian_train_2716.jpg\n",
"Sharath_train_6334.jpg\n",
"Arun_train_6340.jpg\n",
"Gopika_train_125.jpg\n",
"Pandian_train_2658.jpg\n",
"Omar_train_6186.jpg\n",
"Gopika_train_1514.jpg\n",
"Abhay_train_3617.jpg\n",
"Omar_train_2360.jpg\n",
"Omar_train_570.jpg\n",
"Omar_train_3328.jpg\n",
"Sharath_train_5889.jpg\n",
"Omar_train_1725.jpg\n",
"Omar_train_6421.jpg\n",
"Sharath_train_1563.jpg\n",
"Omar_train_290.jpg\n",
"Gopika_train_6713.jpg\n",
"Sharath_train_1089.jpg\n",
"Pandian_train_2796.jpg\n",
"Arun_train_2729.jpg\n",
"Omar_train_3506.jpg\n",
"Arun_train_6062.jpg\n",
"Omar_train_4823.jpg\n",
"Arun_train_1147.jpg\n",
"Sharath_train_6856.jpg\n",
"Omar_train_4471.jpg\n",
"Arun_train_730.jpg\n",
"Sharath_train_5643.jpg\n",
"Gopika_train_4695.jpg\n",
"Pandian_train_2058.jpg\n",
"Abhay_train_5883.jpg\n",
"Kinkar_train_272.jpg\n",
"Kinkar_train_4054.jpg\n",
"Gopika_train_5267.jpg\n",
"Kinkar_train_6948.jpg\n",
"Arun_train_893.jpg\n",
"Kinkar_train_1165.jpg\n",
"Omar_train_2341.jpg\n",
"Gopika_train_4464.jpg\n",
"Abhay_train_1039.jpg\n",
"Abhay_train_3442.jpg\n",
"Sharath_train_6800.jpg\n",
"Arun_train_1908.jpg\n",
"Sharath_train_382.jpg\n",
"Omar_train_812.jpg\n",
"Kinkar_train_1956.jpg\n",
"Omar_train_5533.jpg\n",
"Omar_train_2714.jpg\n",
"Sharath_train_474.jpg\n",
"Pandian_train_6422.jpg\n",
"Abhay_train_3383.jpg\n",
"Gopika_train_2611.jpg\n",
"Arun_train_5812.jpg\n",
"Arun_train_151.jpg\n",
"Omar_train_2728.jpg\n",
"Omar_train_5104.jpg\n",
"Abhay_train_5779.jpg\n",
"Sharath_train_2337.jpg\n",
"Omar_train_930.jpg\n",
"Kinkar_train_3828.jpg\n",
"Arun_train_5382.jpg\n",
"Sharath_train_1516.jpg\n",
"Arun_train_5221.jpg\n",
"Sharath_train_5824.jpg\n",
"Arun_train_3588.jpg\n",
"Omar_train_5023.jpg\n",
"Sharath_train_6442.jpg\n",
"Kinkar_train_2946.jpg\n",
"Kinkar_train_4625.jpg\n",
"Pandian_train_4389.jpg\n",
"Pandian_train_59.jpg\n",
"Sharath_train_3732.jpg\n",
"Abhay_train_3181.jpg\n",
"Pandian_train_2201.jpg\n",
"Abhay_train_6514.jpg\n",
"Kinkar_train_2066.jpg\n",
"Sharath_train_1595.jpg\n",
"Gopika_train_6842.jpg\n",
"Sharath_train_3761.jpg\n",
"Sharath_train_3772.jpg\n",
"Sharath_train_40.jpg\n",
"Sharath_train_2238.jpg\n",
"Sharath_train_3516.jpg\n",
"Sharath_train_886.jpg\n",
"Arun_train_2410.jpg\n",
"Gopika_train_1978.jpg\n",
"Sharath_train_5493.jpg\n",
"Omar_train_6104.jpg\n",
"Sharath_train_2158.jpg\n",
"Omar_train_3624.jpg\n",
"Kinkar_train_3279.jpg\n",
"Abhay_train_3568.jpg\n",
"Sharath_train_1941.jpg\n",
"Abhay_train_477.jpg\n",
"Omar_train_2329.jpg\n",
"Omar_train_1036.jpg\n",
"Sharath_train_1351.jpg\n",
"Abhay_train_922.jpg\n",
"Sharath_train_1244.jpg\n",
"Sharath_train_6370.jpg\n",
"Abhay_train_974.jpg\n",
"Gopika_train_3929.jpg\n",
"Gopika_train_5073.jpg\n",
"Abhay_train_5870.jpg\n",
"Sharath_test_135.jpg\n",
"Arun_train_5287.jpg\n",
"Kinkar_train_1103.jpg\n",
"Omar_train_3988.jpg\n",
"Sharath_train_164.jpg\n",
"Sharath_train_6072.jpg\n",
"Gopika_train_2668.jpg\n",
"Gopika_train_2332.jpg\n",
"Abhay_train_422.jpg\n",
"Gopika_train_2405.jpg\n",
"Omar_train_4450.jpg\n",
"Kinkar_train_6516.jpg\n",
"Pandian_train_3975.jpg\n",
"Omar_train_3467.jpg\n",
"Arun_test_84.jpg\n",
"Arun_train_5965.jpg\n",
"Pandian_train_1429.jpg\n",
"Gopika_train_5843.jpg\n",
"Pandian_train_6394.jpg\n",
"Abhay_train_2003.jpg\n",
"Arun_train_1007.jpg\n",
"Kinkar_train_3488.jpg\n",
"Arun_train_4460.jpg\n",
"Kinkar_train_5557.jpg\n",
"Abhay_train_4418.jpg\n",
"Kinkar_train_5151.jpg\n",
"Sharath_train_3243.jpg\n",
"Arun_train_4840.jpg\n",
"Pandian_train_3457.jpg\n",
"Gopika_train_163.jpg\n",
"Abhay_train_5190.jpg\n",
"Sharath_train_687.jpg\n",
"Kinkar_train_1775.jpg\n",
"Sharath_train_3400.jpg\n",
"Gopika_train_1327.jpg\n",
"Kinkar_train_1620.jpg\n",
"Arun_train_5079.jpg\n",
"Abhay_train_6349.jpg\n",
"Sharath_train_6295.jpg\n",
"Arun_train_72.jpg\n",
"Pandian_train_975.jpg\n",
"Kinkar_train_2769.jpg\n",
"Gopika_train_6503.jpg\n",
"Arun_test_127.jpg\n",
"Kinkar_train_579.jpg\n",
"Abhay_train_4107.jpg\n",
"Sharath_train_6425.jpg\n",
"Abhay_train_1950.jpg\n",
"Omar_train_460.jpg\n",
"Sharath_train_2283.jpg\n",
"Sharath_train_4572.jpg\n",
"Pandian_train_326.jpg\n",
"Abhay_train_2460.jpg\n",
"Omar_train_5567.jpg\n",
"Gopika_train_3786.jpg\n",
"Omar_train_2049.jpg\n",
"Gopika_train_5040.jpg\n",
"Pandian_train_1214.jpg\n",
"Kinkar_train_556.jpg\n",
"Pandian_train_4962.jpg\n",
"Omar_train_3000.jpg\n",
"Arun_train_2023.jpg\n",
"Sharath_train_4771.jpg\n",
"Abhay_train_6475.jpg\n",
"Sharath_train_6529.jpg\n",
"Gopika_train_6512.jpg\n",
"Gopika_train_5919.jpg\n",
"Omar_train_6386.jpg\n",
"Pandian_train_3501.jpg\n",
"Kinkar_train_1313.jpg\n",
"Gopika_train_4281.jpg\n",
"Pandian_train_3374.jpg\n",
"Gopika_train_152.jpg\n",
"Gopika_train_447.jpg\n",
"Sharath_train_5251.jpg\n",
"Gopika_train_5619.jpg\n",
"Arun_train_1646.jpg\n",
"Sharath_train_5171.jpg\n",
"Pandian_train_2182.jpg\n",
"Arun_train_2402.jpg\n",
"Abhay_train_6881.jpg\n",
"Omar_train_5847.jpg\n",
"Kinkar_train_1586.jpg\n",
"Pandian_train_3891.jpg\n",
"Kinkar_train_3614.jpg\n",
"Sharath_train_2789.jpg\n",
"Omar_train_1526.jpg\n",
"Abhay_train_3268.jpg\n",
"Pandian_train_2048.jpg\n",
"Kinkar_train_1002.jpg\n",
"Abhay_train_3730.jpg\n",
"Kinkar_train_469.jpg\n",
"Abhay_train_2886.jpg\n",
"Sharath_train_4530.jpg\n",
"Arun_train_4051.jpg\n",
"Kinkar_train_5147.jpg\n",
"Pandian_train_6289.jpg\n",
"Sharath_train_3770.jpg\n",
"Abhay_train_2904.jpg\n",
"Sharath_train_5269.jpg\n",
"Gopika_train_2641.jpg\n",
"Arun_train_1698.jpg\n",
"Gopika_train_2458.jpg\n",
"Sharath_train_408.jpg\n",
"Abhay_train_6651.jpg\n",
"Sharath_train_4850.jpg\n",
"Abhay_train_4893.jpg\n",
"Arun_train_4539.jpg\n",
"Gopika_train_63.jpg\n",
"Omar_train_3375.jpg\n",
"Omar_train_2794.jpg\n",
"Pandian_train_4735.jpg\n",
"Pandian_train_3099.jpg\n",
"Abhay_train_5761.jpg\n",
"Omar_test_121.jpg\n",
"Omar_train_3564.jpg\n",
"Gopika_test_162.jpg\n",
"Pandian_train_4138.jpg\n",
"Omar_train_5284.jpg\n",
"Pandian_train_3448.jpg\n",
"Pandian_train_3834.jpg\n",
"Kinkar_train_777.jpg\n",
"Sharath_train_4132.jpg\n",
"Sharath_train_461.jpg\n",
"Sharath_train_1700.jpg\n",
"Kinkar_train_2124.jpg\n",
"Arun_train_71.jpg\n",
"Arun_train_3443.jpg\n",
"Omar_train_6629.jpg\n",
"Arun_train_4146.jpg\n",
"Gopika_train_2494.jpg\n",
"Pandian_train_3993.jpg\n",
"Kinkar_train_1612.jpg\n",
"Omar_train_76.jpg\n",
"Abhay_train_197.jpg\n",
"Arun_train_211.jpg\n",
"Arun_train_5523.jpg\n",
"Gopika_train_3075.jpg\n",
"Abhay_train_1329.jpg\n",
"Abhay_train_6836.jpg\n",
"Sharath_train_1263.jpg\n",
"Arun_train_3076.jpg\n",
"Pandian_train_4218.jpg\n",
"Abhay_train_1320.jpg\n",
"Kinkar_train_5086.jpg\n",
"Gopika_test_104.jpg\n",
"Kinkar_train_3059.jpg\n",
"Sharath_train_1771.jpg\n",
"Sharath_train_2366.jpg\n",
"Sharath_train_6175.jpg\n",
"Sharath_train_3603.jpg\n",
"Sharath_train_3777.jpg\n",
"Gopika_train_150.jpg\n",
"Arun_train_5223.jpg\n",
"Sharath_train_4554.jpg\n",
"Gopika_train_4422.jpg\n",
"Arun_train_5259.jpg\n",
"Kinkar_train_1977.jpg\n",
"Omar_train_1426.jpg\n",
"Pandian_train_5598.jpg\n",
"Omar_train_5502.jpg\n",
"Pandian_train_1957.jpg\n",
"Kinkar_train_175.jpg\n",
"Kinkar_train_62.jpg\n",
"Sharath_train_501.jpg\n",
"Pandian_train_5210.jpg\n",
"Kinkar_train_404.jpg\n",
"Gopika_train_2423.jpg\n",
"Kinkar_train_6063.jpg\n",
"Sharath_train_1805.jpg\n",
"Gopika_train_3313.jpg\n",
"Omar_train_6240.jpg\n",
"Kinkar_train_4089.jpg\n",
"Omar_train_5582.jpg\n",
"Kinkar_train_2557.jpg\n",
"Arun_train_508.jpg\n",
"Pandian_train_4531.jpg\n",
"Sharath_train_4501.jpg\n",
"Pandian_test_78.jpg\n",
"Kinkar_train_5170.jpg\n",
"Kinkar_train_4869.jpg\n",
"Arun_train_1837.jpg\n",
"Arun_train_2352.jpg\n",
"Arun_train_936.jpg\n",
"Gopika_train_5128.jpg\n",
"Gopika_train_349.jpg\n",
"Gopika_train_6778.jpg\n",
"Arun_train_6205.jpg\n",
"Omar_train_4093.jpg\n",
"Sharath_train_5874.jpg\n",
"Pandian_train_3305.jpg\n",
"Kinkar_train_5971.jpg\n",
"Kinkar_train_4105.jpg\n",
"Kinkar_train_6633.jpg\n",
"Gopika_train_3290.jpg\n",
"Gopika_train_3623.jpg\n",
"Omar_train_3226.jpg\n",
"Sharath_train_5347.jpg\n",
"Sharath_train_2260.jpg\n",
"Pandian_train_1043.jpg\n",
"Abhay_train_4153.jpg\n",
"Arun_train_1822.jpg\n",
"Sharath_train_2344.jpg\n",
"Kinkar_train_282.jpg\n",
"Pandian_train_1629.jpg\n",
"Abhay_train_3654.jpg\n",
"Sharath_train_468.jpg\n",
"Sharath_train_1648.jpg\n",
"Gopika_train_1183.jpg\n",
"Gopika_train_1095.jpg\n",
"Sharath_train_4937.jpg\n",
"Arun_train_6445.jpg\n",
"Kinkar_train_6296.jpg\n",
"Omar_train_2.jpg\n",
"Kinkar_train_745.jpg\n",
"Abhay_train_202.jpg\n",
"Omar_train_2261.jpg\n",
"Omar_train_611.jpg\n",
"Gopika_train_4589.jpg\n",
"Arun_train_494.jpg\n",
"Pandian_train_456.jpg\n",
"Sharath_train_2241.jpg\n",
"Gopika_train_6128.jpg\n",
"Kinkar_train_3073.jpg\n",
"Kinkar_train_6496.jpg\n",
"Sharath_train_3297.jpg\n",
"Abhay_train_3435.jpg\n",
"Abhay_train_1484.jpg\n",
"Arun_train_5771.jpg\n",
"Kinkar_train_4210.jpg\n",
"Gopika_train_5064.jpg\n",
"Arun_train_5681.jpg\n",
"Pandian_train_6931.jpg\n",
"Arun_train_1381.jpg\n",
"Sharath_train_6462.jpg\n",
"Sharath_train_1936.jpg\n",
"Arun_train_2296.jpg\n",
"Arun_train_3110.jpg\n",
"Kinkar_train_1993.jpg\n",
"Arun_train_330.jpg\n",
"Sharath_train_3051.jpg\n",
"Kinkar_train_3825.jpg\n",
"Sharath_train_2903.jpg\n",
"Gopika_train_6043.jpg\n",
"Omar_train_1356.jpg\n",
"Pandian_train_5627.jpg\n",
"Sharath_train_219.jpg\n",
"Omar_train_4023.jpg\n",
"Kinkar_train_5293.jpg\n",
"Sharath_train_5247.jpg\n",
"Gopika_train_563.jpg\n",
"Pandian_train_5111.jpg\n",
"Sharath_train_5599.jpg\n",
"Arun_train_538.jpg\n",
"Gopika_train_6420.jpg\n",
"Sharath_train_2999.jpg\n",
"Gopika_train_3676.jpg\n",
"Kinkar_train_4535.jpg\n",
"Sharath_train_2275.jpg\n",
"Gopika_train_1198.jpg\n",
"Arun_train_4294.jpg\n",
"Arun_train_766.jpg\n",
"Pandian_train_6050.jpg\n",
"Sharath_train_38.jpg\n",
"Omar_train_5054.jpg\n",
"Abhay_train_1092.jpg\n",
"Kinkar_train_918.jpg\n",
"Omar_train_5769.jpg\n",
"Arun_train_6053.jpg\n",
"Arun_train_1726.jpg\n",
"Sharath_train_5343.jpg\n",
"Sharath_train_2338.jpg\n",
"Abhay_train_5660.jpg\n",
"Arun_train_5167.jpg\n",
"Pandian_train_6542.jpg\n",
"Omar_train_5869.jpg\n",
"Pandian_train_1906.jpg\n",
"Gopika_train_1044.jpg\n",
"Arun_train_6275.jpg\n",
"Sharath_train_4995.jpg\n",
"Omar_train_2067.jpg\n",
"Abhay_train_6339.jpg\n",
"Abhay_train_1878.jpg\n",
"Abhay_train_37.jpg\n",
"Gopika_train_4065.jpg\n",
"Gopika_train_5645.jpg\n",
"Arun_train_2112.jpg\n",
"Gopika_train_486.jpg\n",
"Gopika_train_2895.jpg\n",
"Sharath_train_3171.jpg\n",
"Omar_train_1052.jpg\n",
"Omar_train_5051.jpg\n",
"Kinkar_train_5912.jpg\n",
"Omar_train_4928.jpg\n",
"Gopika_train_1666.jpg\n",
"Abhay_train_3790.jpg\n",
"Abhay_train_2196.jpg\n",
"Sharath_train_4943.jpg\n",
"Kinkar_train_4400.jpg\n",
"Arun_train_2515.jpg\n",
"Sharath_train_6181.jpg\n",
"Pandian_train_1062.jpg\n",
"Sharath_train_5574.jpg\n",
"Gopika_train_2295.jpg\n",
"Sharath_train_4412.jpg\n",
"Abhay_train_6609.jpg\n",
"Omar_train_4491.jpg\n",
"Omar_train_6256.jpg\n",
"Pandian_train_561.jpg\n",
"Pandian_train_5901.jpg\n",
"Abhay_train_3062.jpg\n",
"Sharath_train_5425.jpg\n",
"Sharath_train_6327.jpg\n",
"Abhay_train_2492.jpg\n",
"Arun_train_4699.jpg\n",
"Omar_train_829.jpg\n",
"Pandian_train_6120.jpg\n",
"Sharath_train_1564.jpg\n",
"Abhay_test_120.jpg\n",
"Pandian_train_6695.jpg\n",
"Kinkar_train_6350.jpg\n",
"Omar_train_1940.jpg\n",
"Kinkar_train_2583.jpg\n",
"Kinkar_train_3941.jpg\n",
"Sharath_train_2719.jpg\n",
"Kinkar_train_4209.jpg\n",
"Sharath_train_6798.jpg\n",
"Abhay_train_1539.jpg\n",
"Pandian_train_6652.jpg\n",
"Gopika_train_47.jpg\n",
"Gopika_train_6846.jpg\n",
"Gopika_train_4550.jpg\n",
"Omar_train_4295.jpg\n",
"Kinkar_train_146.jpg\n",
"Pandian_train_3499.jpg\n",
"Kinkar_train_6403.jpg\n",
"Omar_train_3928.jpg\n",
"Sharath_train_2519.jpg\n",
"Gopika_train_1308.jpg\n",
"Sharath_train_2704.jpg\n",
"Abhay_train_1188.jpg\n",
"Omar_train_5244.jpg\n",
"Arun_train_3360.jpg\n",
"Sharath_train_3918.jpg\n",
"Gopika_train_3108.jpg\n",
"Pandian_train_4283.jpg\n",
"Omar_train_2257.jpg\n",
"Sharath_train_5298.jpg\n",
"Kinkar_train_3980.jpg\n",
"Kinkar_train_1491.jpg\n",
"Omar_train_485.jpg\n",
"Abhay_train_4909.jpg\n",
"Sharath_train_768.jpg\n",
"Arun_train_1972.jpg\n",
"Omar_train_5875.jpg\n",
"Sharath_train_73.jpg\n",
"Abhay_train_2290.jpg\n",
"Omar_train_615.jpg\n",
"Kinkar_train_4230.jpg\n",
"Omar_train_2195.jpg\n",
"Sharath_train_1849.jpg\n",
"Omar_train_192.jpg\n",
"Arun_train_2253.jpg\n",
"Sharath_train_6908.jpg\n",
"Arun_train_1830.jpg\n",
"Sharath_train_3001.jpg\n",
"Gopika_train_4562.jpg\n",
"Abhay_train_5419.jpg\n",
"Omar_train_1479.jpg\n",
"Omar_train_3456.jpg\n",
"Gopika_train_1331.jpg\n",
"Gopika_train_3762.jpg\n",
"Gopika_train_5532.jpg\n",
"Abhay_train_4.jpg\n",
"Gopika_train_4029.jpg\n",
"Omar_train_2581.jpg\n",
"Abhay_test_12.jpg\n",
"Omar_train_3017.jpg\n",
"Kinkar_train_5470.jpg\n",
"Abhay_train_4015.jpg\n",
"Omar_train_6276.jpg\n",
"Abhay_train_2316.jpg\n",
"Sharath_train_3094.jpg\n",
"Sharath_train_1795.jpg\n",
"Kinkar_train_1875.jpg\n",
"Omar_train_117.jpg\n",
"Sharath_train_2415.jpg\n",
"Omar_train_110.jpg\n",
"Pandian_train_6283.jpg\n",
"Kinkar_train_5311.jpg\n",
"Omar_train_4984.jpg\n",
"Abhay_train_4912.jpg\n",
"Pandian_train_3556.jpg\n",
"Omar_train_107.jpg\n",
"Omar_train_4511.jpg\n",
"Gopika_train_4092.jpg\n",
"Kinkar_train_5791.jpg\n",
"Sharath_train_5428.jpg\n",
"Omar_train_2331.jpg\n",
"Arun_train_312.jpg\n",
"Gopika_train_901.jpg\n",
"Gopika_train_1443.jpg\n",
"Pandian_train_2483.jpg\n",
"Sharath_train_5895.jpg\n",
"Pandian_train_1880.jpg\n",
"Sharath_train_1660.jpg\n",
"Gopika_train_1995.jpg\n",
"Kinkar_train_1462.jpg\n",
"Sharath_train_2311.jpg\n",
"Abhay_train_1553.jpg\n",
"Sharath_train_4808.jpg\n",
"Gopika_train_4155.jpg\n",
"Abhay_train_2178.jpg\n",
"Pandian_train_4951.jpg\n",
"Sharath_train_4691.jpg\n",
"Abhay_train_3089.jpg\n",
"Omar_train_3666.jpg\n",
"Kinkar_train_3971.jpg\n",
"Sharath_train_6664.jpg\n",
"Gopika_train_2121.jpg\n",
"Gopika_train_2503.jpg\n",
"Sharath_train_131.jpg\n",
"Omar_train_4653.jpg\n",
"Abhay_train_850.jpg\n",
"Abhay_train_6808.jpg\n",
"Gopika_train_2149.jpg\n",
"Arun_train_6121.jpg\n",
"Gopika_train_4179.jpg\n",
"Kinkar_train_1545.jpg\n",
"Arun_train_1411.jpg\n",
"Abhay_train_1123.jpg\n",
"Omar_train_4911.jpg\n",
"Gopika_train_251.jpg\n",
"Abhay_train_6161.jpg\n",
"Kinkar_train_5278.jpg\n",
"Kinkar_train_2815.jpg\n",
"Arun_train_4149.jpg\n",
"Gopika_train_20.jpg\n",
"Sharath_train_6760.jpg\n",
"Arun_train_2090.jpg\n",
"Arun_train_5373.jpg\n",
"Abhay_train_1919.jpg\n",
"Kinkar_test_95.jpg\n",
"Arun_train_6855.jpg\n",
"Arun_train_3272.jpg\n",
"Omar_train_5130.jpg\n",
"Sharath_train_5630.jpg\n",
"Gopika_train_3220.jpg\n",
"Abhay_train_1096.jpg\n",
"Gopika_train_4503.jpg\n",
"Gopika_train_1355.jpg\n",
"Gopika_train_5551.jpg\n",
"Gopika_train_2905.jpg\n",
"Arun_train_1846.jpg\n",
"Omar_train_1927.jpg\n",
"Sharath_train_5239.jpg\n",
"Arun_train_4266.jpg\n",
"Gopika_train_5135.jpg\n",
"Sharath_train_1689.jpg\n",
"Gopika_train_6493.jpg\n",
"Kinkar_train_2439.jpg\n",
"Sharath_train_3650.jpg\n",
"Abhay_train_105.jpg\n",
"Pandian_train_6669.jpg\n",
"Abhay_train_3635.jpg\n",
"Gopika_train_1427.jpg\n",
"Abhay_train_5299.jpg\n",
"Pandian_train_6263.jpg\n",
"Gopika_train_6014.jpg\n",
"Kinkar_train_4918.jpg\n",
"Abhay_train_2079.jpg\n",
"Kinkar_train_3221.jpg\n",
"Pandian_train_454.jpg\n",
"Sharath_train_5589.jpg\n",
"Gopika_train_3987.jpg\n",
"Abhay_train_4543.jpg\n",
"Gopika_train_2840.jpg\n",
"Sharath_train_746.jpg\n",
"Kinkar_train_1693.jpg\n",
"Arun_train_4057.jpg\n",
"Omar_train_2474.jpg\n",
"Sharath_train_4009.jpg\n",
"Omar_train_5696.jpg\n",
"Gopika_train_3651.jpg\n",
"Pandian_train_1438.jpg\n",
"Pandian_train_6096.jpg\n",
"Arun_train_448.jpg\n",
"Sharath_train_6719.jpg\n",
"Kinkar_train_2885.jpg\n",
"Pandian_train_2679.jpg\n",
"Gopika_train_649.jpg\n",
"Gopika_train_980.jpg\n",
"Pandian_train_3454.jpg\n",
"Pandian_train_5093.jpg\n",
"Gopika_train_6755.jpg\n",
"Abhay_train_3865.jpg\n",
"Abhay_train_6249.jpg\n",
"Pandian_train_523.jpg\n",
"Sharath_train_1697.jpg\n",
"Kinkar_train_856.jpg\n",
"Omar_train_6051.jpg\n",
"Sharath_train_4708.jpg\n",
"Abhay_train_1333.jpg\n",
"Pandian_train_1645.jpg\n",
"Sharath_train_1334.jpg\n",
"Kinkar_train_5099.jpg\n",
"Gopika_train_2558.jpg\n",
"Sharath_train_3939.jpg\n",
"Kinkar_train_1568.jpg\n",
"Abhay_train_2314.jpg\n",
"Pandian_train_3511.jpg\n",
"Sharath_test_139.jpg\n",
"Sharath_train_406.jpg\n",
"Abhay_train_2453.jpg\n",
"Omar_train_3640.jpg\n",
"Abhay_train_1145.jpg\n",
"Gopika_train_2021.jpg\n",
"Kinkar_train_5457.jpg\n",
"Kinkar_train_6235.jpg\n",
"Arun_train_5537.jpg\n",
"Omar_train_4231.jpg\n",
"Sharath_train_6233.jpg\n",
"Pandian_train_2617.jpg\n",
"Arun_train_6491.jpg\n",
"Sharath_train_4837.jpg\n",
"Kinkar_train_6198.jpg\n",
"Omar_train_355.jpg\n",
"Abhay_train_6451.jpg\n",
"Arun_train_9.jpg\n",
"Pandian_train_5066.jpg\n",
"Gopika_train_1444.jpg\n",
"Kinkar_train_6554.jpg\n",
"Gopika_train_5942.jpg\n",
"Omar_train_1923.jpg\n",
"Arun_train_567.jpg\n",
"Arun_train_5092.jpg\n",
"Omar_train_3267.jpg\n",
"Omar_train_2690.jpg\n",
"Kinkar_train_5101.jpg\n",
"Arun_train_529.jpg\n",
"Kinkar_train_1901.jpg\n",
"Kinkar_test_19.jpg\n",
"Kinkar_train_2157.jpg\n",
"Abhay_train_733.jpg\n",
"Kinkar_train_1132.jpg\n",
"Sharath_train_1569.jpg\n",
"Kinkar_train_6721.jpg\n",
"Sharath_train_851.jpg\n",
"Omar_train_3979.jpg\n",
"Gopika_train_5119.jpg\n",
"Kinkar_train_3402.jpg\n",
"Arun_train_5633.jpg\n",
"Kinkar_train_4852.jpg\n",
"Kinkar_train_1732.jpg\n",
"Abhay_train_3289.jpg\n",
"Kinkar_train_3577.jpg\n",
"Abhay_train_4778.jpg\n",
"Kinkar_train_83.jpg\n",
"Pandian_train_3161.jpg\n",
"Sharath_train_4335.jpg\n",
"Omar_train_4011.jpg\n",
"Arun_train_378.jpg\n",
"Pandian_train_399.jpg\n",
"Abhay_train_842.jpg\n",
"Sharath_train_5262.jpg\n",
"Omar_train_1815.jpg\n",
"Sharath_train_2135.jpg\n",
"Omar_train_5553.jpg\n",
"Sharath_train_1480.jpg\n",
"Abhay_train_5303.jpg\n",
"Kinkar_train_3890.jpg\n",
"Pandian_train_3788.jpg\n",
"Arun_train_3978.jpg\n",
"Gopika_train_1430.jpg\n",
"Arun_train_4659.jpg\n",
"Sharath_train_2222.jpg\n",
"Pandian_train_4760.jpg\n",
"Abhay_train_2154.jpg\n",
"Sharath_train_3687.jpg\n",
"Gopika_train_3119.jpg\n",
"Sharath_train_3539.jpg\n",
"Sharath_train_5948.jpg\n",
"Abhay_train_2861.jpg\n",
"Gopika_train_2994.jpg\n",
"Pandian_train_4104.jpg\n",
"Abhay_train_1958.jpg\n",
"Omar_train_4484.jpg\n",
"Sharath_train_1886.jpg\n",
"Abhay_train_3560.jpg\n",
"Abhay_train_161.jpg\n",
"Kinkar_train_775.jpg\n",
"Sharath_train_6145.jpg\n",
"Kinkar_train_666.jpg\n",
"Kinkar_train_5548.jpg\n",
"Gopika_train_1792.jpg\n",
"Sharath_train_1237.jpg\n",
"Kinkar_train_2350.jpg\n",
"Arun_train_5440.jpg\n",
"Arun_train_438.jpg\n",
"Sharath_train_6565.jpg\n",
"Gopika_train_5380.jpg\n",
"Gopika_train_3035.jpg\n",
"Gopika_train_348.jpg\n",
"Gopika_train_5835.jpg\n",
"Arun_train_3851.jpg\n",
"Arun_train_2134.jpg\n",
"Arun_train_6822.jpg\n",
"Kinkar_train_3554.jpg\n",
"Sharath_train_3465.jpg\n",
"Pandian_train_1012.jpg\n",
"Sharath_train_5350.jpg\n",
"Gopika_train_4256.jpg\n",
"Kinkar_train_2200.jpg\n",
"Arun_train_5972.jpg\n",
"Abhay_train_2101.jpg\n",
"Arun_train_4481.jpg\n",
"Sharath_train_4127.jpg\n",
"Gopika_train_6262.jpg\n",
"Omar_train_6906.jpg\n",
"Pandian_train_4877.jpg\n",
"Sharath_train_2884.jpg\n",
"Omar_train_3003.jpg\n",
"Omar_train_1041.jpg\n",
"Abhay_train_5612.jpg\n",
"Gopika_train_6726.jpg\n",
"Gopika_train_6697.jpg\n",
"Gopika_train_412.jpg\n",
"Omar_train_5781.jpg\n",
"Pandian_train_3404.jpg\n",
"Sharath_train_3831.jpg\n",
"Abhay_train_5611.jpg\n",
"Sharath_train_1168.jpg\n",
"Omar_train_1920.jpg\n",
"Sharath_train_74.jpg\n",
"Omar_train_6644.jpg\n",
"Omar_train_1051.jpg\n",
"Pandian_train_2481.jpg\n",
"Sharath_train_2302.jpg\n",
"Sharath_train_5285.jpg\n",
"Arun_train_1856.jpg\n",
"Sharath_test_116.jpg\n",
"Gopika_train_1264.jpg\n",
"Sharath_train_6473.jpg\n",
"Arun_train_3078.jpg\n",
"Sharath_train_1269.jpg\n",
"Sharath_train_1422.jpg\n",
"Omar_train_4027.jpg\n",
"Kinkar_train_5689.jpg\n",
"Gopika_train_33.jpg\n",
"Sharath_train_5638.jpg\n",
"Arun_train_4070.jpg\n",
"Omar_train_4073.jpg\n",
"Sharath_train_5745.jpg\n",
"Omar_train_1495.jpg\n",
"Kinkar_train_3204.jpg\n",
"Kinkar_train_3175.jpg\n",
"Omar_train_1559.jpg\n",
"Arun_train_6552.jpg\n",
"Abhay_train_4479.jpg\n",
"Omar_train_956.jpg\n",
"Gopika_train_1823.jpg\n",
"Sharath_train_3248.jpg\n",
"Sharath_train_6485.jpg\n",
"Sharath_train_4158.jpg\n",
"Omar_train_5714.jpg\n",
"Gopika_train_4168.jpg\n",
"Gopika_train_2816.jpg\n",
"Abhay_train_6056.jpg\n",
"Arun_train_3284.jpg\n",
"Arun_train_1432.jpg\n",
"Omar_train_4406.jpg\n",
"Sharath_train_2010.jpg\n",
"Omar_train_2845.jpg\n",
"Gopika_train_4546.jpg\n",
"Kinkar_train_5160.jpg\n",
"Abhay_train_6197.jpg\n",
"Gopika_train_187.jpg\n",
"Omar_train_1173.jpg\n",
"Gopika_train_6845.jpg\n",
"Kinkar_train_3570.jpg\n",
"Sharath_train_3100.jpg\n",
"Pandian_train_3754.jpg\n",
"Pandian_train_1605.jpg\n",
"Abhay_train_2843.jpg\n",
"Sharath_train_3378.jpg\n",
"Sharath_train_1938.jpg\n",
"Gopika_train_6663.jpg\n",
"Omar_train_6362.jpg\n",
"Sharath_train_5301.jpg\n",
"Gopika_train_4860.jpg\n",
"Arun_train_6534.jpg\n",
"Gopika_train_4908.jpg\n",
"Abhay_train_6049.jpg\n",
"Omar_train_3789.jpg\n",
"Kinkar_train_3344.jpg\n",
"Kinkar_train_6494.jpg\n",
"Kinkar_train_2925.jpg\n",
"Sharath_train_935.jpg\n",
"Arun_train_6317.jpg\n",
"Pandian_train_377.jpg\n",
"Kinkar_train_1575.jpg\n",
"Gopika_train_6785.jpg\n",
"Abhay_train_4407.jpg\n",
"Gopika_train_4362.jpg\n",
"Pandian_train_1852.jpg\n",
"Sharath_train_1406.jpg\n",
"Arun_train_3423.jpg\n",
"Pandian_train_5850.jpg\n",
"Arun_train_1630.jpg\n",
"Sharath_train_2943.jpg\n",
"Arun_train_642.jpg\n",
"Pandian_train_2636.jpg\n",
"Sharath_train_5811.jpg\n",
"Pandian_train_4140.jpg\n",
"Gopika_train_531.jpg\n",
"Arun_train_5988.jpg\n",
"Arun_train_4480.jpg\n",
"Abhay_train_1401.jpg\n",
"Omar_train_1324.jpg\n",
"Gopika_test_158.jpg\n",
"Arun_train_3141.jpg\n",
"Sharath_train_4421.jpg\n",
"Sharath_train_3326.jpg\n",
"Omar_train_1181.jpg\n",
"Omar_train_6310.jpg\n",
"Gopika_train_4533.jpg\n",
"Sharath_train_4068.jpg\n",
"Pandian_train_1065.jpg\n",
"Abhay_train_5432.jpg\n",
"Arun_train_3447.jpg\n",
"Kinkar_train_6912.jpg\n",
"Abhay_train_2268.jpg\n",
"Arun_train_6001.jpg\n",
"Kinkar_train_2504.jpg\n",
"Abhay_train_6266.jpg\n",
"Kinkar_train_2592.jpg\n",
"Kinkar_train_6312.jpg\n",
"Pandian_train_2288.jpg\n",
"Arun_train_6141.jpg\n",
"Sharath_train_1513.jpg\n",
"Sharath_train_2594.jpg\n",
"Omar_train_992.jpg\n",
"Kinkar_train_2605.jpg\n",
"Sharath_train_5554.jpg\n",
"Kinkar_train_2970.jpg\n",
"Gopika_train_4787.jpg\n",
"Omar_train_983.jpg\n",
"Omar_train_4566.jpg\n",
"Arun_train_2271.jpg\n",
"Arun_train_3207.jpg\n",
"Omar_train_4606.jpg\n",
"Kinkar_train_3485.jpg\n",
"Pandian_train_3265.jpg\n",
"Pandian_train_6109.jpg\n",
"Abhay_train_2181.jpg\n",
"Pandian_train_5572.jpg\n",
"Sharath_test_11.jpg\n",
"Kinkar_test_119.jpg\n",
"Gopika_train_5920.jpg\n",
"Gopika_train_6767.jpg\n",
"Pandian_train_6819.jpg\n",
"Kinkar_train_6662.jpg\n",
"Gopika_train_5675.jpg\n",
"Arun_train_3349.jpg\n",
"Sharath_train_4664.jpg\n",
"Abhay_train_2454.jpg\n",
"Pandian_train_6734.jpg\n",
"Sharath_train_2407.jpg\n",
"Pandian_train_4307.jpg\n",
"Sharath_train_4408.jpg\n",
"Omar_train_6135.jpg\n",
"Abhay_train_2942.jpg\n",
"Sharath_train_2255.jpg\n",
"Gopika_train_6724.jpg\n",
"Arun_train_4002.jpg\n",
"Omar_train_2106.jpg\n",
"Gopika_train_5903.jpg\n",
"Sharath_train_1672.jpg\n",
"Omar_train_2809.jpg\n",
"Omar_test_93.jpg\n",
"Sharath_train_6346.jpg\n",
"Kinkar_train_1135.jpg\n",
"Sharath_train_5686.jpg\n",
"Kinkar_train_2279.jpg\n",
"Kinkar_train_1393.jpg\n",
"Sharath_train_1601.jpg\n",
"Omar_train_3315.jpg\n",
"Sharath_train_5372.jpg\n",
"Sharath_train_2018.jpg\n",
"Gopika_train_5986.jpg\n",
"Abhay_train_3090.jpg\n",
"Gopika_train_4053.jpg\n",
"Sharath_train_824.jpg\n",
"Sharath_train_473.jpg\n",
"Gopika_train_4814.jpg\n",
"Abhay_train_3433.jpg\n",
"Sharath_train_6228.jpg\n",
"Pandian_train_2967.jpg\n",
"Arun_train_1112.jpg\n",
"Sharath_train_2507.jpg\n",
"Pandian_train_4084.jpg\n",
"Pandian_train_1877.jpg\n",
"Omar_train_5819.jpg\n",
"Omar_train_2030.jpg\n",
"Omar_train_4685.jpg\n",
"Abhay_train_742.jpg\n",
"Kinkar_train_5649.jpg\n",
"Pandian_train_3069.jpg\n",
"Sharath_train_5342.jpg\n",
"Pandian_train_1020.jpg\n",
"Gopika_train_3876.jpg\n",
"Pandian_train_5604.jpg\n",
"Gopika_train_277.jpg\n",
"Abhay_train_4062.jpg\n",
"Sharath_train_1400.jpg\n",
"Pandian_train_559.jpg\n",
"Gopika_train_2687.jpg\n",
"Pandian_train_4086.jpg\n",
"Abhay_train_2123.jpg\n",
"Kinkar_train_6357.jpg\n",
"Pandian_train_4686.jpg\n",
"Arun_train_1749.jpg\n",
"Omar_train_907.jpg\n",
"Omar_train_1508.jpg\n",
"Omar_train_174.jpg\n",
"Pandian_train_285.jpg\n",
"Sharath_train_77.jpg\n",
"Sharath_train_1756.jpg\n",
"Omar_train_1467.jpg\n",
"Arun_train_1762.jpg\n",
"Pandian_train_4319.jpg\n",
"Kinkar_train_6209.jpg\n",
"Kinkar_train_325.jpg\n",
"Gopika_train_3746.jpg\n",
"Gopika_train_1812.jpg\n",
"Gopika_train_6597.jpg\n",
"Omar_train_3162.jpg\n",
"Omar_train_5698.jpg\n",
"Kinkar_train_5571.jpg\n",
"Gopika_train_5658.jpg\n",
"Sharath_train_4320.jpg\n",
"Pandian_train_5090.jpg\n",
"Gopika_train_403.jpg\n",
"Pandian_train_4926.jpg\n",
"Arun_train_2461.jpg\n",
"Sharath_train_4273.jpg\n",
"Sharath_train_45.jpg\n",
"Abhay_train_2655.jpg\n",
"Gopika_train_1292.jpg\n",
"Gopika_train_1342.jpg\n",
"Kinkar_train_2800.jpg\n",
"Arun_train_2901.jpg\n",
"Arun_train_1118.jpg\n",
"Omar_train_4810.jpg\n",
"Pandian_train_4282.jpg\n",
"Pandian_train_2322.jpg\n",
"Abhay_train_6088.jpg\n",
"Pandian_train_6823.jpg\n",
"Sharath_train_4816.jpg\n",
"Sharath_train_6634.jpg\n",
"Arun_train_1544.jpg\n",
"Gopika_train_682.jpg\n",
"Gopika_train_482.jpg\n",
"Abhay_train_3086.jpg\n",
"Sharath_train_4746.jpg\n",
"Sharath_train_6023.jpg\n",
"Abhay_train_6869.jpg\n",
"Abhay_train_2530.jpg\n",
"Sharath_train_6178.jpg\n",
"Sharath_train_5578.jpg\n",
"Kinkar_train_6274.jpg\n",
"Abhay_train_166.jpg\n",
"Sharath_train_3998.jpg\n",
"Gopika_train_5921.jpg\n",
"Arun_train_4193.jpg\n",
"Omar_train_1176.jpg\n",
"Pandian_train_4836.jpg\n",
"Arun_train_1068.jpg\n",
"Pandian_train_3714.jpg\n",
"Gopika_train_1916.jpg\n",
"Omar_train_3999.jpg\n",
"Omar_train_5186.jpg\n",
"Omar_train_4712.jpg\n",
"Omar_train_2569.jpg\n",
"Omar_train_6680.jpg\n",
"Sharath_train_2468.jpg\n",
"Abhay_train_481.jpg\n",
"Sharath_train_3167.jpg\n",
"Abhay_train_3859.jpg\n",
"Gopika_train_28.jpg\n",
"Kinkar_train_5879.jpg\n",
"Gopika_train_3403.jpg\n",
"Sharath_train_716.jpg\n",
"Sharath_train_6577.jpg\n",
"Abhay_train_43.jpg\n",
"Gopika_train_3362.jpg\n",
"Omar_train_1895.jpg\n",
"Gopika_train_4933.jpg\n",
"Kinkar_train_5277.jpg\n",
"Sharath_train_269.jpg\n",
"Gopika_train_4728.jpg\n",
"Arun_train_4734.jpg\n",
"Gopika_train_2801.jpg\n",
"Gopika_train_5562.jpg\n",
"Abhay_train_881.jpg\n",
"Sharath_train_4025.jpg\n",
"Kinkar_train_931.jpg\n",
"Pandian_train_2047.jpg\n",
"Gopika_train_1022.jpg\n",
"Abhay_train_5728.jpg\n",
"Kinkar_train_3487.jpg\n",
"Gopika_train_2065.jpg\n",
"Kinkar_train_2223.jpg\n",
"Abhay_train_2772.jpg\n",
"Kinkar_train_429.jpg\n",
"Pandian_train_5984.jpg\n",
"Gopika_train_4768.jpg\n",
"Kinkar_train_5744.jpg\n",
"Kinkar_train_2076.jpg\n",
"Kinkar_train_4667.jpg\n",
"Gopika_train_4067.jpg\n",
"Omar_train_1571.jpg\n",
"Arun_train_2109.jpg\n",
"Abhay_train_2459.jpg\n",
"Kinkar_train_5732.jpg\n",
"Omar_train_3321.jpg\n",
"Kinkar_test_138.jpg\n",
"Abhay_train_4858.jpg\n",
"Pandian_train_3990.jpg\n",
"Gopika_train_622.jpg\n",
"Abhay_train_11.jpg\n",
"Omar_train_4128.jpg\n",
"Arun_train_4337.jpg\n",
"Arun_train_1120.jpg\n",
"Arun_train_4733.jpg\n",
"Abhay_train_510.jpg\n",
"Omar_train_4763.jpg\n",
"Gopika_train_3662.jpg\n",
"Omar_train_15.jpg\n",
"Kinkar_train_4398.jpg\n",
"Kinkar_train_170.jpg\n",
"Omar_train_6287.jpg\n",
"Kinkar_train_6441.jpg\n",
"Sharath_train_2739.jpg\n",
"Omar_test_40.jpg\n",
"Pandian_train_4311.jpg\n",
"Sharath_train_5330.jpg\n",
"Sharath_train_4619.jpg\n",
"Omar_train_5231.jpg\n",
"Gopika_train_390.jpg\n",
"Gopika_train_2524.jpg\n",
"Kinkar_train_4920.jpg\n",
"Gopika_train_4241.jpg\n",
"Gopika_train_2476.jpg\n",
"Abhay_train_6777.jpg\n",
"Pandian_train_1373.jpg\n",
"Gopika_train_3270.jpg\n",
"Omar_train_1084.jpg\n",
"Abhay_train_3814.jpg\n",
"Abhay_train_2265.jpg\n",
"Abhay_train_1243.jpg\n",
"Sharath_train_1349.jpg\n",
"Arun_train_634.jpg\n",
"Kinkar_test_114.jpg\n",
"Abhay_train_2538.jpg\n",
"Kinkar_train_2823.jpg\n",
"Arun_train_946.jpg\n",
"Sharath_train_4077.jpg\n",
"Gopika_train_6030.jpg\n",
"Sharath_train_4257.jpg\n",
"Omar_train_6247.jpg\n",
"Arun_train_2606.jpg\n",
"Gopika_train_3715.jpg\n",
"Abhay_train_4242.jpg\n",
"Gopika_train_3982.jpg\n",
"Kinkar_train_3703.jpg\n",
"Arun_train_6149.jpg\n",
"Sharath_train_4671.jpg\n",
"Pandian_train_3965.jpg\n",
"Pandian_train_4730.jpg\n",
"Sharath_train_3137.jpg\n",
"Gopika_train_6399.jpg\n",
"Kinkar_test_110.jpg\n",
"Pandian_train_6646.jpg\n",
"Arun_train_2070.jpg\n",
"Arun_train_2249.jpg\n",
"Arun_train_4321.jpg\n",
"Sharath_train_2987.jpg\n",
"Kinkar_train_2923.jpg\n",
"Omar_train_4502.jpg\n",
"Pandian_train_803.jpg\n",
"Abhay_train_2828.jpg\n",
"Abhay_train_5215.jpg\n",
"Kinkar_train_6790.jpg\n",
"Gopika_train_4411.jpg\n",
"Omar_train_1235.jpg\n",
"Sharath_train_1136.jpg\n",
"Sharath_train_3505.jpg\n",
"Sharath_train_2218.jpg\n",
"Omar_train_6492.jpg\n",
"Sharath_train_4790.jpg\n",
"Kinkar_train_5716.jpg\n",
"Kinkar_train_2621.jpg\n",
"Abhay_train_4343.jpg\n",
"Gopika_train_2616.jpg\n",
"Pandian_train_3563.jpg\n",
"Gopika_train_3699.jpg\n",
"Sharath_train_6923.jpg\n",
"Gopika_train_3583.jpg\n",
"Arun_train_5535.jpg\n",
"Pandian_train_4003.jpg\n",
"Abhay_train_6911.jpg\n",
"Sharath_train_4165.jpg\n",
"Arun_train_116.jpg\n",
"Gopika_train_1952.jpg\n",
"Gopika_train_6774.jpg\n",
"Kinkar_train_4960.jpg\n",
"Sharath_train_2122.jpg\n",
"Gopika_train_3909.jpg\n",
"Kinkar_train_2409.jpg\n",
"Arun_train_5860.jpg\n",
"Sharath_train_6294.jpg\n",
"Pandian_train_6771.jpg\n",
"Gopika_train_3016.jpg\n",
"Arun_train_1760.jpg\n",
"Abhay_train_4716.jpg\n",
"Sharath_train_2533.jpg\n",
"Arun_train_1220.jpg\n",
"Pandian_train_4016.jpg\n",
"Kinkar_train_3546.jpg\n",
"Omar_train_4034.jpg\n",
"Sharath_train_3694.jpg\n",
"Arun_train_2315.jpg\n",
"Arun_train_4331.jpg\n",
"Pandian_train_1425.jpg\n",
"Gopika_train_6968.jpg\n",
"Abhay_train_5042.jpg\n",
"Gopika_train_1374.jpg\n",
"Gopika_train_489.jpg\n",
"Arun_train_1535.jpg\n",
"Arun_train_4469.jpg\n",
"Sharath_train_6543.jpg\n",
"Pandian_train_2240.jpg\n",
"Kinkar_train_3510.jpg\n",
"Omar_train_6692.jpg\n",
"Arun_train_4031.jpg\n",
"Sharath_train_2633.jpg\n",
"Omar_train_2015.jpg\n",
"Omar_train_1340.jpg\n",
"Omar_train_1757.jpg\n",
"Arun_train_2254.jpg\n",
"Gopika_train_3596.jpg\n",
"Sharath_train_1888.jpg\n",
"Sharath_train_5083.jpg\n",
"Sharath_train_834.jpg\n",
"Pandian_test_105.jpg\n",
"Arun_train_6575.jpg\n",
"Sharath_train_4806.jpg\n",
"Sharath_train_424.jpg\n",
"Sharath_train_2626.jpg\n",
"Kinkar_train_1380.jpg\n",
"Abhay_train_1913.jpg\n",
"Arun_train_3280.jpg\n",
"Sharath_train_2002.jpg\n",
"Kinkar_train_2397.jpg\n",
"Abhay_train_42.jpg\n",
"Kinkar_train_3803.jpg\n",
"Abhay_train_1434.jpg\n",
"Gopika_train_3121.jpg\n",
"Arun_train_545.jpg\n",
"Arun_test_73.jpg\n",
"Arun_train_5169.jpg\n",
"Sharath_train_5034.jpg\n",
"Arun_train_4698.jpg\n",
"Kinkar_train_3393.jpg\n",
"Sharath_train_2625.jpg\n",
"Sharath_test_23.jpg\n",
"Arun_train_3071.jpg\n",
"Abhay_train_1674.jpg\n",
"Kinkar_train_6944.jpg\n",
"Gopika_train_2587.jpg\n",
"Sharath_train_5923.jpg\n",
"Omar_train_5369.jpg\n",
"Pandian_train_1081.jpg\n",
"Gopika_train_3080.jpg\n",
"Abhay_train_4264.jpg\n",
"Sharath_train_235.jpg\n",
"Omar_train_897.jpg\n",
"Sharath_train_4453.jpg\n",
"Kinkar_train_1460.jpg\n",
"Gopika_train_6511.jpg\n",
"Pandian_train_4175.jpg\n",
"Arun_train_1715.jpg\n",
"Omar_train_2691.jpg\n",
"Abhay_train_4958.jpg\n",
"Gopika_train_5731.jpg\n",
"Abhay_train_2374.jpg\n",
"Kinkar_train_1031.jpg\n",
"Pandian_train_27.jpg\n",
"Sharath_train_2532.jpg\n",
"Sharath_train_2736.jpg\n",
"Abhay_train_1442.jpg\n",
"Arun_train_4552.jpg\n",
"Kinkar_train_1247.jpg\n",
"Arun_train_55.jpg\n",
"Gopika_train_1285.jpg\n",
"Kinkar_train_6179.jpg\n",
"Kinkar_train_3936.jpg\n",
"Sharath_train_4627.jpg\n",
"Omar_train_3701.jpg\n",
"Omar_train_5797.jpg\n",
"Omar_train_3780.jpg\n",
"Omar_train_169.jpg\n",
"Kinkar_train_3025.jpg\n",
"Gopika_train_6561.jpg\n",
"Sharath_train_3707.jpg\n",
"Pandian_train_888.jpg\n",
"Sharath_train_3835.jpg\n",
"Kinkar_train_120.jpg\n",
"Sharath_test_71.jpg\n",
"Sharath_train_1416.jpg\n",
"Arun_train_2140.jpg\n",
"Kinkar_train_3611.jpg\n",
"Sharath_train_4122.jpg\n",
"Gopika_train_2926.jpg\n",
"Pandian_train_3390.jpg\n",
"Abhay_train_34.jpg\n",
"Abhay_train_1948.jpg\n",
"Kinkar_train_618.jpg\n",
"Sharath_train_2384.jpg\n",
"Arun_train_3230.jpg\n",
"Kinkar_train_4228.jpg\n",
"Pandian_train_3683.jpg\n",
"Pandian_train_1126.jpg\n",
"Sharath_test_68.jpg\n",
"Kinkar_train_2207.jpg\n",
"Pandian_train_4955.jpg\n",
"Sharath_train_1088.jpg\n",
"Abhay_train_1825.jpg\n",
"Abhay_train_3612.jpg\n",
"Omar_train_5423.jpg\n",
"Sharath_train_6927.jpg\n",
"Omar_train_1853.jpg\n",
"Sharath_train_1488.jpg\n",
"Arun_train_5399.jpg\n",
"Pandian_train_6476.jpg\n",
"Kinkar_train_3915.jpg\n",
"Abhay_train_2963.jpg\n",
"Omar_train_6237.jpg\n",
"Omar_train_6440.jpg\n",
"Sharath_train_2595.jpg\n",
"Pandian_train_2333.jpg\n",
"Omar_train_2518.jpg\n",
"Kinkar_train_1384.jpg\n",
"Kinkar_train_3180.jpg\n",
"Omar_train_381.jpg\n",
"Omar_train_1682.jpg\n",
"Pandian_train_2289.jpg\n",
"Omar_train_2074.jpg\n",
"Sharath_train_5474.jpg\n",
"Gopika_train_6059.jpg\n",
"Sharath_train_144.jpg\n",
"Sharath_train_815.jpg\n",
"Sharath_train_3593.jpg\n",
"Gopika_train_437.jpg\n",
"Pandian_train_5110.jpg\n",
"Gopika_train_3385.jpg\n",
"Sharath_train_5109.jpg\n",
"Gopika_train_2136.jpg\n",
"Omar_train_814.jpg\n",
"Kinkar_train_3153.jpg\n",
"Omar_train_3912.jpg\n",
"Pandian_train_2830.jpg\n",
"Sharath_train_5184.jpg\n",
"Pandian_train_1733.jpg\n",
"Kinkar_train_2138.jpg\n",
"Omar_train_4292.jpg\n",
"Arun_train_1854.jpg\n",
"Gopika_train_5037.jpg\n",
"Sharath_train_4401.jpg\n",
"Abhay_train_6071.jpg\n",
"Sharath_train_1233.jpg\n",
"Omar_train_1288.jpg\n",
"Omar_train_3520.jpg\n",
"Gopika_train_4560.jpg\n",
"Abhay_train_2650.jpg\n",
"Sharath_train_4340.jpg\n",
"Abhay_train_704.jpg\n",
"Pandian_train_2012.jpg\n",
"Pandian_train_6308.jpg\n",
"Pandian_train_70.jpg\n",
"Gopika_train_3093.jpg\n",
"Sharath_train_636.jpg\n",
"Gopika_train_3887.jpg\n",
"Omar_train_6201.jpg\n",
"Gopika_train_3269.jpg\n",
"Kinkar_train_1230.jpg\n",
"Abhay_train_3104.jpg\n",
"Gopika_train_2722.jpg\n",
"Sharath_train_4697.jpg\n",
"Arun_train_3124.jpg\n",
"Gopika_train_6851.jpg\n",
"Kinkar_train_1983.jpg\n",
"Gopika_train_4240.jpg\n",
"Abhay_train_3418.jpg\n",
"Kinkar_train_5504.jpg\n",
"Omar_train_4095.jpg\n",
"Arun_train_6110.jpg\n",
"Kinkar_train_155.jpg\n",
"Arun_train_3031.jpg\n",
"Gopika_train_4723.jpg\n",
"Sharath_train_2952.jpg\n",
"Arun_train_3888.jpg\n",
"Gopika_train_5595.jpg\n",
"Sharath_train_4575.jpg\n",
"Omar_train_4375.jpg\n",
"Gopika_train_3972.jpg\n",
"Kinkar_train_2659.jpg\n",
"Omar_train_387.jpg\n",
"Kinkar_train_2161.jpg\n",
"Omar_train_6920.jpg\n",
"Kinkar_train_4879.jpg\n",
"Omar_train_6438.jpg\n",
"Arun_train_6382.jpg\n",
"Arun_train_1893.jpg\n",
"Gopika_train_3028.jpg\n",
"Gopika_train_1975.jpg\n",
"Abhay_train_4275.jpg\n",
"Arun_train_4608.jpg\n",
"Arun_train_873.jpg\n",
"Pandian_train_6236.jpg\n",
"Sharath_test_21.jpg\n",
"Abhay_train_3995.jpg\n",
"Sharath_train_1315.jpg\n",
"Abhay_train_1947.jpg\n",
"Kinkar_train_3932.jpg\n",
"Sharath_train_2983.jpg\n",
"Sharath_train_721.jpg\n",
"Gopika_train_1431.jpg\n",
"Arun_train_140.jpg\n",
"Omar_train_5108.jpg\n",
"Sharath_train_3745.jpg\n",
"Arun_train_32.jpg\n",
"Omar_train_2128.jpg\n",
"Arun_train_3672.jpg\n",
"Sharath_train_5212.jpg\n",
"Abhay_train_6897.jpg\n",
"Sharath_train_1976.jpg\n",
"Sharath_train_3931.jpg\n",
"Gopika_train_6835.jpg\n",
"Arun_train_2979.jpg\n",
"Omar_train_2426.jpg\n",
"Kinkar_train_6882.jpg\n",
"Gopika_train_5610.jpg\n",
"Abhay_train_4250.jpg\n",
"Omar_train_5682.jpg\n",
"Sharath_train_1091.jpg\n",
"Kinkar_test_146.jpg\n",
"Gopika_train_4727.jpg\n",
"Pandian_train_5388.jpg\n",
"Gopika_train_4186.jpg\n",
"Pandian_train_3033.jpg\n",
"Arun_train_4949.jpg\n",
"Omar_train_3679.jpg\n",
"Arun_train_1570.jpg\n",
"Sharath_train_3692.jpg\n",
"Gopika_train_4468.jpg\n",
"Sharath_train_759.jpg\n",
"Pandian_train_228.jpg\n",
"Kinkar_train_6015.jpg\n",
"Pandian_train_5139.jpg\n",
"Arun_train_690.jpg\n",
"Sharath_train_436.jpg\n",
"Kinkar_train_2080.jpg\n",
"Gopika_train_1137.jpg\n",
"Kinkar_train_411.jpg\n",
"Kinkar_train_2351.jpg\n",
"Kinkar_train_6580.jpg\n",
"Pandian_train_2664.jpg\n",
"Arun_train_6016.jpg\n",
"Gopika_train_1512.jpg\n",
"Gopika_train_582.jpg\n",
"Pandian_train_4103.jpg\n",
"Gopika_test_80.jpg\n",
"Pandian_train_1189.jpg\n",
"Arun_train_4969.jpg\n",
"Sharath_train_3659.jpg\n",
"Gopika_train_3018.jpg\n",
"Sharath_train_6352.jpg\n",
"Omar_train_6532.jpg\n",
"Arun_train_6794.jpg\n",
"Abhay_train_1451.jpg\n",
"Omar_train_5085.jpg\n",
"Sharath_train_6401.jpg\n",
"Omar_train_2539.jpg\n",
"Omar_train_86.jpg\n",
"Arun_train_2642.jpg\n",
"Sharath_train_2632.jpg\n",
"Sharath_train_6184.jpg\n",
"Kinkar_train_243.jpg\n",
"Arun_train_3858.jpg\n",
"Sharath_train_4707.jpg\n",
"Omar_train_1711.jpg\n",
"Omar_train_3238.jpg\n",
"Abhay_train_1421.jpg\n",
"Abhay_train_3351.jpg\n",
"Sharath_train_2388.jpg\n",
"Kinkar_train_5905.jpg\n",
"Arun_train_1482.jpg\n",
"Gopika_train_4725.jpg\n",
"Sharath_train_5655.jpg\n",
"Arun_train_5331.jpg\n",
"Abhay_train_868.jpg\n",
"Kinkar_train_4874.jpg\n",
"Sharath_train_372.jpg\n",
"Pandian_train_3885.jpg\n",
"Pandian_train_2387.jpg\n",
"Gopika_train_616.jpg\n",
"Kinkar_train_3056.jpg\n",
"Arun_train_1872.jpg\n",
"Omar_train_2317.jpg\n",
"Sharath_train_4020.jpg\n",
"Kinkar_train_1833.jpg\n",
"Sharath_train_2163.jpg\n",
"Gopika_train_121.jpg\n",
"Kinkar_train_2747.jpg\n",
"Pandian_train_6206.jpg\n",
"Sharath_train_4462.jpg\n",
"Sharath_train_6613.jpg\n",
"Pandian_train_2210.jpg\n",
"Omar_train_2035.jpg\n",
"Omar_train_5122.jpg\n",
"Abhay_train_4856.jpg\n",
"Sharath_train_4773.jpg\n",
"Arun_train_726.jpg\n",
"Gopika_train_4372.jpg\n",
"Gopika_train_2408.jpg\n",
"Pandian_train_4350.jpg\n",
"Arun_train_6041.jpg\n",
"Gopika_train_4657.jpg\n",
"Sharath_train_5544.jpg\n",
"Sharath_train_1793.jpg\n",
"Gopika_train_4845.jpg\n",
"Kinkar_train_6411.jpg\n",
"Arun_train_3994.jpg\n",
"Kinkar_train_3377.jpg\n",
"Kinkar_train_5462.jpg\n",
"Arun_train_4742.jpg\n",
"Gopika_train_1122.jpg\n",
"Sharath_train_4704.jpg\n",
"Sharath_train_1615.jpg\n",
"Kinkar_train_5391.jpg\n",
"Omar_train_1144.jpg\n",
"Abhay_train_863.jpg\n",
"Sharath_train_2813.jpg\n",
"Abhay_train_4693.jpg\n",
"Pandian_train_689.jpg\n",
"Arun_train_4541.jpg\n",
"Arun_train_694.jpg\n",
"Omar_train_4556.jpg\n",
"Omar_train_10.jpg\n",
"Sharath_train_2033.jpg\n",
"Pandian_train_3667.jpg\n",
"Kinkar_train_3944.jpg\n",
"Sharath_train_4258.jpg\n",
"Arun_train_5924.jpg\n",
"Abhay_train_6254.jpg\n",
"Abhay_train_3063.jpg\n",
"Kinkar_train_3574.jpg\n",
"Omar_train_6578.jpg\n",
"Sharath_train_6679.jpg\n",
"Sharath_train_1383.jpg\n",
"Arun_train_3327.jpg\n",
"Gopika_train_239.jpg\n",
"Sharath_train_5117.jpg\n",
"Kinkar_train_5820.jpg\n",
"Gopika_train_1662.jpg\n",
"Omar_train_5872.jpg\n",
"Gopika_train_2139.jpg\n",
"Kinkar_train_4999.jpg\n",
"Pandian_train_375.jpg\n",
"Kinkar_train_4208.jpg\n",
"Omar_train_1968.jpg\n",
"Abhay_train_1959.jpg\n",
"Kinkar_train_6796.jpg\n",
"Omar_train_1509.jpg\n",
"Omar_train_2893.jpg\n",
"Pandian_train_1478.jpg\n",
"Arun_train_6606.jpg\n",
"Arun_train_5282.jpg\n",
"Kinkar_train_4633.jpg\n",
"Sharath_train_4329.jpg\n",
"Gopika_train_4720.jpg\n",
"Sharath_train_3949.jpg\n",
"Gopika_train_4978.jpg\n",
"Sharath_train_3118.jpg\n",
"Sharath_train_4254.jpg\n",
"Omar_train_4437.jpg\n",
"Sharath_train_1750.jpg\n",
"Sharath_train_3195.jpg\n",
"Abhay_train_3250.jpg\n",
"Gopika_train_1717.jpg\n",
"Omar_train_5254.jpg\n",
"Omar_train_3605.jpg\n",
"Sharath_train_6907.jpg\n",
"Kinkar_train_2005.jpg\n",
"Sharath_train_6252.jpg\n",
"Sharath_train_5656.jpg\n",
"Arun_train_6642.jpg\n",
"Arun_train_4161.jpg\n",
"Omar_train_729.jpg\n",
"Omar_train_3188.jpg\n",
"Kinkar_train_1099.jpg\n",
"Pandian_train_1138.jpg\n",
"Pandian_train_2582.jpg\n",
"Gopika_train_2358.jpg\n",
"Arun_train_385.jpg\n",
"Sharath_train_3958.jpg\n",
"Kinkar_train_6484.jpg\n",
"Sharath_train_827.jpg\n",
"Omar_train_5516.jpg\n",
"Arun_train_4172.jpg\n",
"Pandian_train_4972.jpg\n",
"Omar_train_5882.jpg\n",
"Kinkar_train_4470.jpg\n",
"Kinkar_train_6048.jpg\n",
"Sharath_train_5304.jpg\n",
"Arun_train_619.jpg\n",
"Gopika_train_3529.jpg\n",
"Pandian_train_2364.jpg\n",
"Pandian_train_6472.jpg\n",
"Arun_train_3769.jpg\n",
"Arun_train_3257.jpg\n",
"Gopika_train_3363.jpg\n",
"Omar_train_4074.jpg\n",
"Sharath_train_5172.jpg\n",
"Sharath_train_2053.jpg\n",
"Arun_train_6314.jpg\n",
"Arun_train_2509.jpg\n",
"Arun_train_4276.jpg\n",
"Omar_train_4427.jpg\n",
"Pandian_train_4178.jpg\n",
"Omar_train_2770.jpg\n",
"Abhay_train_5028.jpg\n",
"Gopika_train_2804.jpg\n",
"Sharath_train_4046.jpg\n",
"Sharath_train_4465.jpg\n",
"Sharath_train_2252.jpg\n",
"Gopika_train_2752.jpg\n",
"Arun_train_5016.jpg\n",
"Kinkar_train_3784.jpg\n",
"Omar_train_1859.jpg\n",
"Sharath_train_5249.jpg\n",
"Omar_train_2401.jpg\n",
"Arun_train_2465.jpg\n",
"Pandian_train_2914.jpg\n",
"Gopika_train_2613.jpg\n",
"Omar_train_6873.jpg\n",
"Abhay_train_2085.jpg\n",
"Sharath_train_1776.jpg\n",
"Sharath_train_2737.jpg\n",
"Omar_train_4660.jpg\n",
"Sharath_train_3756.jpg\n",
"Omar_train_5724.jpg\n",
"Kinkar_train_1865.jpg\n",
"Kinkar_train_6630.jpg\n",
"Abhay_test_118.jpg\n",
"Sharath_train_1255.jpg\n",
"Kinkar_train_4658.jpg\n",
"Sharath_train_5459.jpg\n",
"Pandian_train_627.jpg\n",
"Sharath_train_4271.jpg\n",
"Gopika_train_5978.jpg\n",
"Omar_train_2709.jpg\n",
"Gopika_train_2718.jpg\n",
"Omar_train_6586.jpg\n",
"Omar_test_48.jpg\n",
"Arun_train_6446.jpg\n",
"Sharath_train_371.jpg\n",
"Omar_train_145.jpg\n",
"Omar_train_5091.jpg\n",
"Pandian_train_1194.jpg\n",
"Abhay_train_4423.jpg\n",
"Sharath_train_713.jpg\n",
"Abhay_train_129.jpg\n",
"Arun_train_6268.jpg\n",
"Sharath_train_5884.jpg\n",
"Arun_train_2775.jpg\n",
"Omar_train_4532.jpg\n",
"Kinkar_train_3867.jpg\n",
"Gopika_train_5381.jpg\n",
"Sharath_train_2011.jpg\n",
"Pandian_train_4782.jpg\n",
"Pandian_train_2920.jpg\n",
"Kinkar_train_4477.jpg\n",
"Pandian_train_320.jpg\n",
"Sharath_train_3014.jpg\n",
"Omar_train_1753.jpg\n",
"Sharath_train_5118.jpg\n",
"Gopika_train_1574.jpg\n",
"Sharath_train_6073.jpg\n",
"Sharath_train_3708.jpg\n",
"Arun_train_5939.jpg\n",
"Pandian_train_3801.jpg\n",
"Sharath_train_4312.jpg\n",
"Omar_train_4769.jpg\n",
"Kinkar_train_3590.jpg\n",
"Arun_train_1338.jpg\n",
"Pandian_train_654.jpg\n",
"Abhay_train_4121.jpg\n",
"Pandian_train_1449.jpg\n",
"Abhay_train_4585.jpg\n",
"Gopika_test_51.jpg\n",
"Kinkar_train_4567.jpg\n",
"Omar_train_5868.jpg\n",
"Pandian_train_6309.jpg\n",
"Omar_train_6118.jpg\n",
"Sharath_train_1274.jpg\n",
"Arun_train_1681.jpg\n",
"Gopika_train_2357.jpg\n",
"Omar_test_134.jpg\n",
"Sharath_train_1345.jpg\n",
"Kinkar_train_4191.jpg\n",
"Sharath_train_2470.jpg\n",
"Kinkar_train_1040.jpg\n",
"Abhay_train_4604.jpg\n",
"Sharath_train_5740.jpg\n",
"Omar_train_5174.jpg\n",
"Sharath_train_4124.jpg\n",
"Arun_train_3106.jpg\n",
"Gopika_train_4779.jpg\n",
"Gopika_test_38.jpg\n",
"Kinkar_train_5461.jpg\n",
"Arun_train_2349.jpg\n",
"Kinkar_train_1974.jpg\n",
"Pandian_train_6108.jpg\n",
"Pandian_train_5785.jpg\n",
"Kinkar_train_118.jpg\n",
"Pandian_train_5302.jpg\n",
"Abhay_train_3724.jpg\n",
"Abhay_train_6300.jpg\n",
"Omar_train_3538.jpg\n",
"Omar_train_6074.jpg\n",
"Abhay_train_2440.jpg\n",
"Omar_train_1534.jpg\n",
"Omar_train_5938.jpg\n",
"Omar_train_5163.jpg\n",
"Gopika_train_4182.jpg\n",
"Kinkar_train_4679.jpg\n",
"Kinkar_train_2894.jpg\n",
"Sharath_train_297.jpg\n",
"Gopika_train_4522.jpg\n",
"Arun_train_5876.jpg\n",
"Arun_train_4504.jpg\n",
"Abhay_train_6616.jpg\n",
"Kinkar_train_6862.jpg\n",
"Omar_train_2657.jpg\n",
"Arun_train_3597.jpg\n",
"Omar_train_2448.jpg\n",
"Pandian_train_4780.jpg\n",
"Arun_train_3878.jpg\n",
"Kinkar_train_5143.jpg\n",
"Sharath_train_46.jpg\n",
"Pandian_train_331.jpg\n",
"Omar_train_3504.jpg\n",
"Sharath_train_3524.jpg\n",
"Sharath_train_4935.jpg\n",
"Omar_train_731.jpg\n",
"Arun_train_5482.jpg\n",
"Pandian_train_626.jpg\n",
"Kinkar_train_2978.jpg\n",
"Omar_train_1673.jpg\n",
"Pandian_train_6510.jpg\n",
"Pandian_train_215.jpg\n",
"Abhay_train_5409.jpg\n",
"Gopika_train_5148.jpg\n",
"Pandian_test_62.jpg\n",
"Pandian_train_1290.jpg\n",
"Pandian_train_846.jpg\n",
"Gopika_train_6351.jpg\n",
"Pandian_train_2949.jpg\n",
"Kinkar_train_3312.jpg\n",
"Abhay_train_1129.jpg\n",
"Gopika_test_136.jpg\n",
"Gopika_train_6458.jpg\n",
"Abhay_train_1625.jpg\n",
"Kinkar_train_6199.jpg\n",
"Arun_train_361.jpg\n",
"Pandian_train_3198.jpg\n",
"Omar_train_5845.jpg\n",
"Sharath_train_3857.jpg\n",
"Gopika_train_3105.jpg\n",
"Sharath_train_3215.jpg\n",
"Kinkar_train_259.jpg\n",
"Omar_train_1745.jpg\n",
"Omar_train_2565.jpg\n",
"Gopika_train_6365.jpg\n",
"Sharath_train_3381.jpg\n",
"Gopika_train_5568.jpg\n",
"Arun_train_5827.jpg\n",
"Arun_train_6861.jpg\n",
"Abhay_train_2428.jpg\n",
"Omar_train_566.jpg\n",
"Arun_train_2756.jpg\n",
"Abhay_train_54.jpg\n",
"Gopika_train_2096.jpg\n",
"Abhay_train_979.jpg\n",
"Gopika_train_4001.jpg\n",
"Gopika_train_1483.jpg\n",
"Omar_train_6660.jpg\n",
"Arun_train_3632.jpg\n",
"Abhay_train_3752.jpg\n",
"Kinkar_train_6243.jpg\n",
"Gopika_train_3973.jpg\n",
"Sharath_train_4945.jpg\n",
"Gopika_train_4729.jpg\n",
"Arun_train_4706.jpg\n",
"Omar_train_6891.jpg\n",
"Omar_train_1851.jpg\n",
"Sharath_train_1590.jpg\n",
"Omar_train_3736.jpg\n",
"Arun_train_3759.jpg\n",
"Omar_train_1004.jpg\n",
"Sharath_train_5524.jpg\n",
"Sharath_train_6585.jpg\n",
"Abhay_train_6318.jpg\n",
"Arun_train_4793.jpg\n",
"Omar_train_6202.jpg\n",
"Abhay_train_5588.jpg\n",
"Kinkar_train_3095.jpg\n",
"Omar_train_6673.jpg\n",
"Pandian_train_6549.jpg\n",
"Abhay_train_440.jpg\n",
"Sharath_train_252.jpg\n",
"Kinkar_train_3636.jpg\n",
"Kinkar_train_85.jpg\n",
"Sharath_test_42.jpg\n",
"Sharath_test_32.jpg\n",
"Gopika_train_6739.jpg\n",
"Arun_train_4683.jpg\n",
"Gopika_train_232.jpg\n",
"Sharath_train_3151.jpg\n",
"Kinkar_train_527.jpg\n",
"Sharath_train_3860.jpg\n",
"Pandian_train_4888.jpg\n",
"Abhay_train_3964.jpg\n",
"Sharath_train_2335.jpg\n",
"Gopika_train_5107.jpg\n",
"Pandian_train_3319.jpg\n",
"Pandian_train_2579.jpg\n",
"Arun_train_5479.jpg\n",
"Omar_train_396.jpg\n",
"Arun_train_6902.jpg\n",
"Omar_train_1101.jpg\n",
"Pandian_train_6465.jpg\n",
"Arun_train_884.jpg\n",
"Pandian_train_1709.jpg\n",
"Kinkar_train_6479.jpg\n",
"Gopika_train_1119.jpg\n",
"Omar_train_519.jpg\n",
"Sharath_test_52.jpg\n",
"Sharath_train_6725.jpg\n",
"Sharath_train_1003.jpg\n",
"Omar_train_2437.jpg\n",
"Omar_train_525.jpg\n",
"Gopika_train_1058.jpg\n",
"Kinkar_train_1536.jpg\n",
"Arun_train_4839.jpg\n",
"Omar_train_6711.jpg\n",
"Sharath_train_5518.jpg\n",
"Pandian_train_2727.jpg\n",
"Kinkar_train_2749.jpg\n",
"Gopika_train_3512.jpg\n",
"Kinkar_train_4947.jpg\n",
"Omar_train_4916.jpg\n",
"Arun_train_208.jpg\n",
"Abhay_train_6801.jpg\n",
"Abhay_train_3592.jpg\n",
"Omar_train_5359.jpg\n",
"Gopika_train_5736.jpg\n",
"Kinkar_train_6244.jpg\n",
"Kinkar_train_5094.jpg\n",
"Omar_train_6156.jpg\n",
"Pandian_train_3323.jpg\n",
"Arun_train_6625.jpg\n",
"Gopika_train_528.jpg\n",
"Gopika_train_5241.jpg\n",
"Arun_train_1656.jpg\n",
"Arun_train_6101.jpg\n",
"Arun_train_1884.jpg\n",
"Gopika_train_5022.jpg\n",
"Kinkar_train_1694.jpg\n",
"Pandian_train_5718.jpg\n",
"Gopika_train_859.jpg\n",
"Abhay_train_2976.jpg\n",
"Gopika_train_3735.jpg\n",
"Sharath_train_771.jpg\n",
"Gopika_train_5526.jpg\n",
"Kinkar_train_4239.jpg\n",
"Gopika_train_4687.jpg\n",
"Kinkar_train_156.jpg\n",
"Sharath_train_4656.jpg\n",
"Sharath_train_6966.jpg\n",
"Sharath_train_714.jpg\n",
"Pandian_train_3951.jpg\n",
"Pandian_train_6952.jpg\n",
"Omar_train_789.jpg\n",
"Arun_train_168.jpg\n",
"Omar_train_6763.jpg\n",
"Sharath_train_5387.jpg\n",
"Arun_train_1305.jpg\n",
"Arun_train_2748.jpg\n",
"Pandian_train_2695.jpg\n",
"Omar_train_306.jpg\n",
"Gopika_train_929.jpg\n",
"Sharath_train_5543.jpg\n",
"Pandian_train_5178.jpg\n",
"Sharath_train_3489.jpg\n",
"Sharath_train_6286.jpg\n",
"Arun_train_4380.jpg\n",
"Sharath_train_5667.jpg\n",
"Arun_train_6530.jpg\n",
"Gopika_train_1889.jpg\n",
"Kinkar_train_3281.jpg\n",
"Kinkar_train_4315.jpg\n",
"Sharath_train_5825.jpg\n",
"Kinkar_train_6221.jpg\n",
"Kinkar_train_1747.jpg\n",
"Arun_train_1018.jpg\n",
"Gopika_train_6874.jpg\n",
"Gopika_train_4776.jpg\n",
"Omar_train_1783.jpg\n",
"Arun_train_6324.jpg\n",
"Pandian_train_1834.jpg\n",
"Abhay_train_2108.jpg\n",
"Omar_train_5312.jpg\n",
"Arun_train_2117.jpg\n",
"Gopika_train_4919.jpg\n",
"Kinkar_train_376.jpg\n",
"Kinkar_train_13.jpg\n",
"Abhay_train_4748.jpg\n",
"Kinkar_train_3959.jpg\n",
"Sharath_train_1551.jpg\n",
"Sharath_train_5735.jpg\n",
"Gopika_train_6781.jpg\n",
"Sharath_train_3688.jpg\n",
"Omar_test_15.jpg\n",
"Gopika_train_4304.jpg\n",
"Abhay_train_4781.jpg\n",
"Pandian_train_1006.jpg\n",
"Pandian_train_6215.jpg\n",
"Kinkar_train_1377.jpg\n",
"Sharath_train_1098.jpg\n",
"Gopika_train_5996.jpg\n",
"Kinkar_train_2146.jpg\n",
"Sharath_train_5007.jpg\n",
"Abhay_train_1418.jpg\n",
"Arun_train_5114.jpg\n",
"Kinkar_train_3935.jpg\n",
"Abhay_train_6076.jpg\n",
"Sharath_train_2406.jpg\n",
"Pandian_train_1433.jpg\n",
"Sharath_train_3526.jpg\n",
"Arun_train_5116.jpg\n",
"Sharath_train_4162.jpg\n",
"Sharath_train_445.jpg\n",
"Omar_train_2463.jpg\n",
"Abhay_train_1172.jpg\n",
"Abhay_train_1860.jpg\n",
"Kinkar_train_6970.jpg\n",
"Pandian_train_5396.jpg\n",
"Kinkar_train_5334.jpg\n",
"Kinkar_train_3957.jpg\n",
"Sharath_train_821.jpg\n",
"Pandian_train_5521.jpg\n",
"Gopika_train_1911.jpg\n",
"Arun_train_1080.jpg\n",
"Pandian_test_55.jpg\n",
"Abhay_train_379.jpg\n",
"Kinkar_train_443.jpg\n",
"Abhay_train_5177.jpg\n",
"Abhay_train_4338.jpg\n",
"Arun_train_6748.jpg\n",
"Sharath_train_5617.jpg\n",
"Omar_train_6434.jpg\n",
"Omar_train_5782.jpg\n",
"Omar_train_631.jpg\n",
"Sharath_train_4079.jpg\n",
"Kinkar_train_1367.jpg\n",
"Arun_train_6969.jpg\n",
"Omar_train_4314.jpg\n",
"Pandian_train_5394.jpg\n",
"Gopika_train_5202.jpg\n",
"Arun_train_182.jpg\n",
"Kinkar_train_6915.jpg\n",
"Omar_train_1106.jpg\n",
"Kinkar_train_4616.jpg\n",
"Pandian_train_82.jpg\n",
"Sharath_train_5648.jpg\n",
"Pandian_train_5708.jpg\n",
"Omar_train_5313.jpg\n",
"Sharath_train_6918.jpg\n",
"Omar_train_3706.jpg\n",
"Sharath_train_2834.jpg\n",
"Omar_train_4846.jpg\n",
"Abhay_train_300.jpg\n",
"Kinkar_train_4895.jpg\n",
"Gopika_train_4684.jpg\n",
"Kinkar_train_4059.jpg\n",
"Gopika_train_630.jpg\n",
"Sharath_train_3649.jpg\n",
"Abhay_train_4965.jpg\n",
"Gopika_train_1310.jpg\n",
"Gopika_train_6784.jpg\n",
"Abhay_train_3120.jpg\n",
"Omar_train_5015.jpg\n",
"Gopika_train_3826.jpg\n",
"Abhay_train_5618.jpg\n",
"Pandian_train_758.jpg\n",
"Abhay_train_5219.jpg\n",
"Omar_train_221.jpg\n",
"Omar_train_1053.jpg\n",
"Abhay_train_3518.jpg\n",
"Kinkar_train_1960.jpg\n",
"Sharath_train_995.jpg\n",
"Arun_train_3478.jpg\n",
"Gopika_train_4957.jpg\n",
"Gopika_train_3569.jpg\n",
"Omar_train_6159.jpg\n",
"Abhay_train_4825.jpg\n",
"Sharath_train_740.jpg\n",
"Kinkar_train_4227.jpg\n",
"Omar_train_6185.jpg\n",
"Abhay_train_4181.jpg\n",
"Omar_train_960.jpg\n",
"Abhay_train_1892.jpg\n",
"Pandian_train_3116.jpg\n",
"Sharath_train_4339.jpg\n",
"Kinkar_train_6705.jpg\n",
"Omar_train_6089.jpg\n",
"Arun_train_1178.jpg\n",
"Gopika_train_6964.jpg\n",
"Sharath_train_6408.jpg\n",
"Sharath_train_4665.jpg\n",
"Sharath_train_574.jpg\n",
"Gopika_train_153.jpg\n",
"Arun_train_4996.jpg\n",
"Omar_train_1463.jpg\n",
"Gopika_train_2004.jpg\n",
"Abhay_train_3384.jpg\n",
"Omar_train_5017.jpg\n",
"Sharath_train_6595.jpg\n",
"Omar_train_2933.jpg\n",
"Abhay_train_2779.jpg\n",
"Sharath_train_6590.jpg\n",
"Abhay_train_6858.jpg\n",
"Omar_train_1678.jpg\n",
"Sharath_train_5753.jpg\n",
"Pandian_train_5345.jpg\n",
"Abhay_train_650.jpg\n",
"Gopika_train_5657.jpg\n",
"Arun_train_4719.jpg\n",
"Sharath_train_2228.jpg\n",
"Pandian_train_1390.jpg\n",
"Pandian_train_4986.jpg\n",
"Kinkar_train_5080.jpg\n",
"Pandian_train_457.jpg\n",
"Gopika_train_4557.jpg\n",
"Pandian_train_3247.jpg\n",
"Kinkar_train_4223.jpg\n",
"Pandian_train_4938.jpg\n",
"Sharath_train_1346.jpg\n",
"Kinkar_train_3763.jpg\n",
"Kinkar_train_1286.jpg\n",
"Arun_train_6751.jpg\n",
"Gopika_train_1814.jpg\n",
"Gopika_train_902.jpg\n",
"Omar_train_2580.jpg\n",
"Sharath_train_1861.jpg\n",
"Sharath_train_3495.jpg\n",
"Arun_train_4570.jpg\n",
"Omar_train_1752.jpg\n",
"Arun_train_315.jpg\n",
"Sharath_train_324.jpg\n",
"Gopika_train_6213.jpg\n",
"Sharath_train_1001.jpg\n",
"Kinkar_train_8.jpg\n",
"Gopika_train_2570.jpg\n",
"Sharath_train_3986.jpg\n",
"Omar_train_1140.jpg\n",
"Pandian_train_4302.jpg\n",
"Kinkar_train_2897.jpg\n",
"Gopika_train_4439.jpg\n",
"Arun_train_6922.jpg\n",
"Sharath_train_195.jpg\n",
"Sharath_train_6139.jpg\n",
"Arun_train_2534.jpg\n",
"Gopika_train_2052.jpg\n",
"Sharath_train_5124.jpg\n",
"Kinkar_train_1241.jpg\n",
"Abhay_train_1167.jpg\n",
"Omar_train_17.jpg\n",
"Abhay_train_5933.jpg\n",
"Kinkar_train_2707.jpg\n",
"Abhay_train_828.jpg\n",
"Arun_train_6067.jpg\n",
"Sharath_train_2755.jpg\n",
"Abhay_train_5640.jpg\n",
"Omar_train_1195.jpg\n",
"Pandian_train_397.jpg\n",
"Kinkar_train_5175.jpg\n",
"Sharath_train_5442.jpg\n",
"Sharath_train_4215.jpg\n",
"Kinkar_train_3716.jpg\n",
"Sharath_train_6218.jpg\n",
"Omar_train_2398.jpg\n",
"Gopika_train_5472.jpg\n",
"Omar_train_6557.jpg\n",
"Sharath_train_4891.jpg\n",
"Abhay_train_3525.jpg\n",
"Kinkar_train_2706.jpg\n",
"Gopika_train_6360.jpg\n",
"Arun_train_4102.jpg\n",
"Arun_train_1953.jpg\n",
"Arun_train_5814.jpg\n",
"Pandian_test_128.jpg\n",
"Omar_train_2438.jpg\n",
"Omar_train_1987.jpg\n",
"Pandian_train_1115.jpg\n",
"Kinkar_train_2960.jpg\n",
"Omar_train_2212.jpg\n",
"Gopika_train_6035.jpg\n",
"Arun_train_3548.jpg\n",
"Gopika_train_5097.jpg\n",
"Pandian_train_1547.jpg\n",
"Arun_test_3.jpg\n",
"Sharath_train_783.jpg\n",
"Gopika_test_129.jpg\n",
"Kinkar_train_5014.jpg\n",
"Omar_train_1217.jpg\n",
"Omar_train_787.jpg\n",
"Kinkar_train_1242.jpg\n",
"Omar_train_3509.jpg\n",
"Sharath_train_5652.jpg\n",
"Arun_train_4970.jpg\n",
"Omar_test_75.jpg\n",
"Gopika_train_4154.jpg\n",
"Sharath_train_3299.jpg\n",
"Gopika_train_5629.jpg\n",
"Pandian_train_1487.jpg\n",
"Sharath_train_2099.jpg\n",
"Kinkar_train_4012.jpg\n",
"Abhay_train_2624.jpg\n",
"Omar_train_4587.jpg\n",
"Pandian_train_4197.jpg\n",
"Arun_train_4006.jpg\n",
"Sharath_train_148.jpg\n",
"Gopika_train_3580.jpg\n",
"Sharath_train_1454.jpg\n",
"Arun_train_3029.jpg\n",
"Kinkar_train_5446.jpg\n",
"Abhay_train_6932.jpg\n",
"Kinkar_train_6298.jpg\n",
"Gopika_train_6092.jpg\n",
"Gopika_train_3783.jpg\n",
"Omar_train_5992.jpg\n",
"Gopika_train_860.jpg\n",
"Abhay_train_1033.jpg\n",
"Arun_train_1376.jpg\n",
"Gopika_train_3021.jpg\n",
"Gopika_train_367.jpg\n",
"Sharath_train_2669.jpg\n",
"Omar_train_5952.jpg\n",
"Abhay_train_365.jpg\n",
"Gopika_train_6888.jpg\n",
"Kinkar_train_3674.jpg\n",
"Omar_train_802.jpg\n",
"Kinkar_train_3889.jpg\n",
"Arun_train_1225.jpg\n",
"Omar_train_4390.jpg\n",
"Omar_train_2359.jpg\n",
"Sharath_train_3549.jpg\n",
"Omar_train_203.jpg\n",
"Sharath_train_4126.jpg\n",
"Sharath_train_2620.jpg\n",
"Abhay_train_6657.jpg\n",
"Omar_train_6143.jpg\n",
"Gopika_train_1398.jpg\n",
"Gopika_train_333.jpg\n",
"Sharath_test_49.jpg\n",
"Gopika_train_2272.jpg\n",
"Gopika_train_1587.jpg\n",
"Omar_train_4591.jpg\n",
"Sharath_train_4091.jpg\n",
"Arun_train_6251.jpg\n",
"Gopika_train_5499.jpg\n",
"Abhay_train_6419.jpg\n",
"Omar_train_2724.jpg\n",
"Sharath_train_4865.jpg\n",
"Sharath_train_3541.jpg\n",
"Gopika_train_6409.jpg\n",
"Sharath_train_5683.jpg\n",
"Sharath_train_5644.jpg\n",
"Kinkar_train_4755.jpg\n",
"Abhay_train_6290.jpg\n",
"Gopika_train_2842.jpg\n",
"Omar_train_6384.jpg\n",
"Kinkar_train_2733.jpg\n",
"Gopika_train_1794.jpg\n",
"Arun_train_1453.jpg\n",
"Kinkar_train_1221.jpg\n",
"Gopika_train_3678.jpg\n",
"Arun_train_3925.jpg\n",
"Arun_train_2622.jpg\n",
"Abhay_train_2368.jpg\n",
"Omar_train_6745.jpg\n",
"Sharath_train_2372.jpg\n",
"Gopika_train_6173.jpg\n",
"Gopika_train_134.jpg\n",
"Arun_train_4711.jpg\n",
"Kinkar_train_6677.jpg\n",
"Kinkar_train_5539.jpg\n",
"Kinkar_train_607.jpg\n",
"Kinkar_train_2510.jpg\n",
"Abhay_train_4424.jpg\n",
"Kinkar_train_3787.jpg\n",
"Gopika_train_2517.jpg\n",
"Sharath_train_4890.jpg\n",
"Gopika_train_5844.jpg\n",
"Sharath_train_3392.jpg\n",
"Pandian_train_590.jpg\n",
"Pandian_train_1385.jpg\n",
"Omar_train_4831.jpg\n",
"Sharath_train_3948.jpg\n",
"Arun_train_2881.jpg\n",
"Kinkar_train_3816.jpg\n",
"Sharath_train_5434.jpg\n",
"Pandian_train_967.jpg\n",
"Sharath_train_3127.jpg\n",
"Gopika_train_3841.jpg\n",
"Omar_train_4636.jpg\n",
"Arun_train_6909.jpg\n",
"Pandian_train_5641.jpg\n",
"Omar_train_4620.jpg\n",
"Omar_train_502.jpg\n",
"Omar_train_6830.jpg\n",
"Pandian_train_4414.jpg\n",
"Pandian_train_6971.jpg\n",
"Kinkar_test_24.jpg\n",
"Sharath_test_79.jpg\n",
"Kinkar_train_5794.jpg\n",
"Abhay_train_1866.jpg\n",
"Kinkar_train_4519.jpg\n",
"Pandian_train_3473.jpg\n",
"Arun_train_5953.jpg\n",
"Arun_train_3647.jpg\n",
"Kinkar_train_588.jpg\n",
"Sharath_train_3741.jpg\n",
"Kinkar_train_2217.jpg\n",
"Kinkar_train_2742.jpg\n",
"Sharath_train_3962.jpg\n",
"Gopika_train_36.jpg\n",
"Omar_test_156.jpg\n",
"Abhay_train_1456.jpg\n",
"Sharath_train_1503.jpg\n",
"Omar_train_5222.jpg\n",
"Kinkar_train_2877.jpg\n",
"Sharath_train_5102.jpg\n",
"Sharath_train_2634.jpg\n",
"Sharath_train_4534.jpg\n",
"Abhay_train_6398.jpg\n",
"Sharath_train_2462.jpg\n",
"Abhay_train_1154.jpg\n",
"Pandian_train_5725.jpg\n",
"Omar_train_2711.jpg\n",
"Kinkar_train_1056.jpg\n",
"Gopika_train_386.jpg\n",
"Sharath_train_6807.jpg\n",
"Omar_train_130.jpg\n",
"Gopika_train_3734.jpg\n",
"Kinkar_train_4510.jpg\n",
"Arun_train_286.jpg\n",
"Sharath_train_5586.jpg\n",
"Abhay_train_3079.jpg\n",
"Gopika_train_5243.jpg\n",
"Kinkar_train_253.jpg\n",
"Arun_train_5125.jpg\n",
"Kinkar_train_6285.jpg\n",
"Pandian_train_1647.jpg\n",
"Omar_train_1799.jpg\n",
"Abhay_train_2479.jpg\n",
"Abhay_train_3660.jpg\n",
"Sharath_train_1741.jpg\n",
"Omar_train_4586.jpg\n",
"Abhay_train_1152.jpg\n",
"Abhay_test_13.jpg\n",
"Omar_train_5654.jpg\n",
"Sharath_train_1348.jpg\n",
"Pandian_train_136.jpg\n",
"Omar_train_3173.jpg\n",
"Kinkar_train_5807.jpg\n",
"Arun_train_3765.jpg\n",
"Kinkar_train_6571.jpg\n",
"Sharath_train_207.jpg\n",
"Gopika_train_1283.jpg\n",
"Abhay_train_3613.jpg\n",
"Sharath_train_6402.jpg\n",
"Pandian_train_44.jpg\n",
"Pandian_train_1227.jpg\n",
"Omar_train_684.jpg\n",
"Sharath_train_4111.jpg\n",
"Pandian_train_3139.jpg\n",
"Arun_train_6589.jpg\n",
"Arun_train_344.jpg\n",
"Pandian_train_968.jpg\n",
"Kinkar_train_1496.jpg\n",
"Omar_train_2751.jpg\n",
"Kinkar_train_5217.jpg\n",
"Gopika_train_5594.jpg\n",
"Arun_train_458.jpg\n",
"Sharath_train_6799.jpg\n",
"Arun_train_1876.jpg\n",
"Sharath_train_2654.jpg\n",
"Sharath_train_446.jpg\n",
"Gopika_train_4352.jpg\n",
"Kinkar_test_29.jpg\n",
"Pandian_train_1322.jpg\n",
"Abhay_train_2820.jpg\n",
"Sharath_train_3837.jpg\n",
"Arun_train_5597.jpg\n",
"Sharath_train_1254.jpg\n",
"Pandian_train_6754.jpg\n",
"Pandian_train_6486.jpg\n",
"Gopika_train_1457.jpg\n",
"Abhay_train_6939.jpg\n",
"Omar_train_3186.jpg\n",
"Arun_train_5328.jpg\n",
"Gopika_train_6038.jpg\n",
"Abhay_train_1441.jpg\n",
"Omar_train_1683.jpg\n",
"Abhay_train_2812.jpg\n",
"Sharath_train_2741.jpg\n",
"Abhay_train_3547.jpg\n",
"Pandian_train_4428.jpg\n",
"Arun_train_1720.jpg\n",
"Arun_train_3479.jpg\n",
"Omar_train_999.jpg\n",
"Pandian_train_6144.jpg\n",
"Omar_train_4993.jpg\n",
"Pandian_train_1719.jpg\n",
"Sharath_train_5357.jpg\n",
"Pandian_train_2889.jpg\n",
"Gopika_train_3322.jpg\n",
"Omar_train_6166.jpg\n",
"Kinkar_train_304.jpg\n",
"Omar_train_6115.jpg\n",
"Sharath_train_6742.jpg\n",
"Arun_train_6792.jpg\n",
"Omar_train_1670.jpg\n",
"Sharath_train_6232.jpg\n",
"Kinkar_train_6058.jpg\n",
"Gopika_train_472.jpg\n",
"Arun_train_245.jpg\n",
"Abhay_train_5181.jpg\n",
"Pandian_train_4416.jpg\n",
"Omar_train_4247.jpg\n",
"Pandian_train_6929.jpg\n",
"Gopika_train_6480.jpg\n",
"Arun_train_1517.jpg\n",
"Kinkar_train_4432.jpg\n",
"Omar_train_1389.jpg\n",
"Abhay_train_5685.jpg\n",
"Abhay_train_4764.jpg\n",
"Abhay_train_639.jpg\n",
"Sharath_train_1161.jpg\n",
"Pandian_train_5214.jpg\n",
"Abhay_train_4098.jpg\n",
"Omar_train_1067.jpg\n",
"Omar_train_2797.jpg\n",
"Sharath_train_2499.jpg\n",
"Sharath_train_4545.jpg\n",
"Sharath_train_4071.jpg\n",
"Arun_train_5798.jpg\n",
"Sharath_train_6471.jpg\n",
"Sharath_train_2974.jpg\n",
"Arun_train_3821.jpg\n",
"Omar_train_2671.jpg\n",
"Kinkar_train_5206.jpg\n",
"Sharath_train_6085.jpg\n",
"Kinkar_train_414.jpg\n",
"Omar_train_763.jpg\n",
"Abhay_train_3599.jpg\n",
"Kinkar_train_5873.jpg\n",
"Gopika_train_819.jpg\n",
"Sharath_train_2309.jpg\n",
"Kinkar_train_3142.jpg\n",
"Gopika_train_2921.jpg\n",
"Pandian_train_652.jpg\n",
"Kinkar_train_1652.jpg\n",
"Omar_train_3278.jpg\n",
"Kinkar_train_5011.jpg\n",
"Sharath_train_1143.jpg\n",
"Pandian_train_64.jpg\n",
"Sharath_train_5573.jpg\n",
"Arun_train_4300.jpg\n",
"Kinkar_train_5421.jpg\n",
"Gopika_train_6690.jpg\n",
"Sharath_train_1998.jpg\n",
"Sharath_train_4936.jpg\n",
"Pandian_train_6497.jpg\n",
"Abhay_train_747.jpg\n",
"Gopika_train_478.jpg\n",
"Sharath_train_4052.jpg\n",
"Arun_train_5367.jpg\n",
"Abhay_train_6521.jpg\n",
"Kinkar_train_87.jpg\n",
"Omar_train_2061.jpg\n",
"Kinkar_train_5734.jpg\n",
"Pandian_train_3675.jpg\n",
"Abhay_train_1669.jpg\n",
"Kinkar_train_4008.jpg\n",
"Omar_train_5720.jpg\n",
"Kinkar_train_12.jpg\n",
"Arun_train_3217.jpg\n",
"Sharath_train_3608.jpg\n",
"Gopika_train_5525.jpg\n",
"Omar_train_3303.jpg\n",
"Abhay_train_4123.jpg\n",
"Kinkar_train_1949.jpg\n",
"Arun_train_3276.jpg\n",
"Abhay_train_5487.jpg\n",
"Gopika_train_6124.jpg\n",
"Sharath_train_4944.jpg\n",
"Gopika_train_2896.jpg\n",
"Omar_train_3534.jpg\n",
"Abhay_train_1309.jpg\n",
"Kinkar_train_4199.jpg\n",
"Omar_train_1114.jpg\n",
"Gopika_train_1073.jpg\n",
"Kinkar_train_1787.jpg\n",
"Pandian_train_6678.jpg\n",
"Gopika_train_700.jpg\n",
"Sharath_train_2319.jpg\n",
"Omar_train_2284.jpg\n",
"Sharath_train_1104.jpg\n",
"Gopika_train_939.jpg\n",
"Abhay_train_1105.jpg\n",
"Sharath_train_2941.jpg\n",
"Kinkar_train_4740.jpg\n",
"Abhay_train_4989.jpg\n",
"Omar_train_459.jpg\n",
"Gopika_train_1510.jpg\n",
"Abhay_train_181.jpg\n",
"Pandian_train_6707.jpg\n",
"Abhay_train_1727.jpg\n",
"Abhay_train_1634.jpg\n",
"Gopika_train_299.jpg\n",
"Sharath_train_952.jpg\n",
"Kinkar_train_5976.jpg\n",
"Abhay_train_532.jpg\n",
"Sharath_train_1989.jpg\n",
"Kinkar_train_2900.jpg\n",
"Gopika_train_6551.jpg\n",
"Arun_train_1561.jpg\n",
"Arun_train_5489.jpg\n",
"Pandian_train_5776.jpg\n",
"Omar_train_6423.jpg\n",
"Arun_train_111.jpg\n",
"Arun_train_592.jpg\n",
"Abhay_train_4397.jpg\n",
"Arun_train_3410.jpg\n",
"Omar_train_5558.jpg\n",
"Sharath_train_4847.jpg\n",
"Gopika_train_2977.jpg\n",
"Kinkar_train_4442.jpg\n",
"Kinkar_train_942.jpg\n",
"Sharath_train_3916.jpg\n",
"Abhay_train_135.jpg\n",
"Pandian_train_3008.jpg\n",
"Kinkar_train_2913.jpg\n",
"Abhay_train_4285.jpg\n",
"Abhay_train_4446.jpg\n",
"Abhay_train_4202.jpg\n",
"Kinkar_train_1736.jpg\n",
"Kinkar_train_2764.jpg\n",
"Abhay_train_3155.jpg\n",
"Sharath_train_190.jpg\n",
"Sharath_train_1855.jpg\n",
"Gopika_train_4705.jpg\n",
"Abhay_train_1246.jpg\n",
"Omar_train_3843.jpg\n",
"Arun_train_2419.jpg\n",
"Pandian_train_2934.jpg\n",
"Gopika_train_3722.jpg\n",
"Sharath_train_4820.jpg\n",
"Sharath_train_1902.jpg\n",
"Abhay_train_2774.jpg\n",
"Gopika_train_3571.jpg\n",
"Gopika_train_2652.jpg\n",
"Omar_test_36.jpg\n",
"Omar_train_216.jpg\n",
"Sharath_train_2104.jpg\n",
"Pandian_train_1369.jpg\n",
"Gopika_train_3409.jpg\n",
"Sharath_train_4647.jpg\n",
"Sharath_train_1378.jpg\n",
"Omar_train_1963.jpg\n",
"Abhay_train_500.jpg\n",
"Abhay_train_6843.jpg\n",
"Pandian_train_5842.jpg\n",
"Gopika_train_2537.jpg\n",
"Gopika_train_5005.jpg\n",
"Omar_train_5693.jpg\n",
"Abhay_train_4617.jpg\n",
"Arun_train_6870.jpg\n",
"Abhay_train_5026.jpg\n",
"Sharath_train_2141.jpg\n",
"Sharath_train_3102.jpg\n",
"Pandian_train_2549.jpg\n",
"Omar_train_4094.jpg\n",
"Pandian_train_497.jpg\n",
"Sharath_train_4689.jpg\n",
"Omar_train_4929.jpg\n",
"Gopika_train_5688.jpg\n",
"Omar_train_1708.jpg\n",
"Omar_train_1472.jpg\n",
"Sharath_test_59.jpg\n",
"Gopika_train_5932.jpg\n",
"Pandian_train_904.jpg\n",
"Sharath_train_2262.jpg\n",
"Pandian_train_5848.jpg\n",
"Kinkar_train_3869.jpg\n",
"Omar_train_4584.jpg\n",
"Kinkar_train_6239.jpg\n",
"Abhay_train_4600.jpg\n",
"Gopika_train_1971.jpg\n",
"Kinkar_train_4581.jpg\n",
"Sharath_train_5570.jpg\n",
"Gopika_train_5046.jpg\n",
"Omar_train_2928.jpg\n",
"Arun_train_6267.jpg\n",
"Abhay_train_2299.jpg\n",
"Omar_train_3507.jpg\n",
"Sharath_train_4327.jpg\n",
"Omar_train_6602.jpg\n",
"Kinkar_train_3166.jpg\n",
"Omar_train_2777.jpg\n",
"Omar_train_30.jpg\n",
"Sharath_train_6853.jpg\n",
"Sharath_train_6722.jpg\n",
"Kinkar_train_3884.jpg\n",
"Kinkar_train_605.jpg\n",
"Sharath_train_780.jpg\n",
"Abhay_train_2116.jpg\n",
"Sharath_train_2732.jpg\n",
"Pandian_train_2385.jpg\n",
"Sharath_train_1964.jpg\n",
"Pandian_train_5765.jpg\n",
"Arun_train_3854.jpg\n",
"Sharath_train_1580.jpg\n",
"Arun_train_4751.jpg\n",
"Arun_train_6753.jpg\n",
"Arun_train_6762.jpg\n",
"Sharath_train_6129.jpg\n",
"Sharath_train_3367.jpg\n",
"Arun_train_1304.jpg\n",
"Omar_train_4527.jpg\n",
"Kinkar_train_924.jpg\n",
"Gopika_train_6482.jpg\n",
"Abhay_train_908.jpg\n",
"Gopika_train_6261.jpg\n",
"Gopika_train_3664.jpg\n",
"Kinkar_train_2060.jpg\n",
"Gopika_train_311.jpg\n",
"Omar_train_310.jpg\n",
"Abhay_train_2912.jpg\n",
"Kinkar_train_6130.jpg\n",
"Omar_train_1735.jpg\n",
"Gopika_train_2472.jpg\n",
"Sharath_train_653.jpg\n",
"Gopika_train_5777.jpg\n",
"Sharath_train_1353.jpg\n",
"Gopika_train_5164.jpg\n",
"Gopika_train_5795.jpg\n",
"Abhay_train_4170.jpg\n",
"Abhay_train_5973.jpg\n",
"Sharath_train_3904.jpg\n",
"Omar_train_5790.jpg\n",
"Sharath_train_4164.jpg\n",
"Pandian_train_1528.jpg\n",
"Kinkar_train_4216.jpg\n",
"Gopika_test_152.jpg\n",
"Kinkar_train_1234.jpg\n",
"Pandian_train_5888.jpg\n",
"Kinkar_train_1946.jpg\n",
"Gopika_train_3639.jpg\n",
"Gopika_train_5131.jpg\n",
"Pandian_train_3462.jpg\n",
"Sharath_train_4233.jpg\n",
"Sharath_train_1542.jpg\n",
"Sharath_train_6488.jpg\n",
"Sharath_train_6943.jpg\n",
"Omar_train_2097.jpg\n",
"Sharath_train_5264.jpg\n",
"Gopika_train_2788.jpg\n",
"Kinkar_train_6566.jpg\n",
"Omar_train_4971.jpg\n",
"Sharath_train_1649.jpg\n",
"Kinkar_train_1527.jpg\n",
"Kinkar_train_1680.jpg\n",
"Gopika_train_2230.jpg\n",
"Omar_train_604.jpg\n",
"Kinkar_train_5662.jpg\n",
"Sharath_train_2838.jpg\n",
"Abhay_train_6961.jpg\n",
"Arun_train_2339.jpg\n",
"Kinkar_train_248.jpg\n",
"Kinkar_train_5152.jpg\n",
"Arun_train_4854.jpg\n",
"Gopika_train_509.jpg\n",
"Sharath_train_4049.jpg\n",
"Sharath_train_1102.jpg\n",
"Gopika_train_6674.jpg\n",
"Pandian_train_6655.jpg\n",
"Sharath_train_5680.jpg\n",
"Pandian_train_5234.jpg\n",
"Abhay_train_5809.jpg\n",
"Arun_train_5694.jpg\n",
"Omar_train_1665.jpg\n",
"Sharath_train_3775.jpg\n",
"Omar_train_602.jpg\n",
"Gopika_train_6189.jpg\n",
"Arun_train_6011.jpg\n",
"Arun_train_4328.jpg\n",
"Kinkar_train_513.jpg\n",
"Gopika_train_675.jpg\n",
"Sharath_train_5483.jpg\n",
"Gopika_train_4394.jpg\n",
"Pandian_train_3985.jpg\n",
"Pandian_train_4861.jpg\n",
"Sharath_train_1447.jpg\n",
"Arun_train_4024.jpg\n",
"Abhay_train_5204.jpg\n",
"Arun_train_102.jpg\n",
"Omar_train_2484.jpg\n",
"Sharath_train_543.jpg\n",
"Abhay_train_2785.jpg\n",
"Omar_train_3503.jpg\n",
"Kinkar_train_2964.jpg\n",
"Sharath_train_6904.jpg\n",
"Omar_train_1232.jpg\n",
"Kinkar_train_2063.jpg\n",
"Kinkar_train_6123.jpg\n",
"Pandian_train_5778.jpg\n",
"Sharath_train_1202.jpg\n",
"Arun_train_4405.jpg\n",
"Omar_train_6804.jpg\n",
"Omar_train_2312.jpg\n",
"Sharath_train_1149.jpg\n",
"Omar_train_6428.jpg\n",
"Omar_train_6703.jpg\n",
"Omar_train_564.jpg\n",
"Abhay_train_5975.jpg\n",
"Gopika_train_1548.jpg\n",
"Gopika_train_3091.jpg\n",
"Omar_train_6416.jpg\n",
"Pandian_train_6556.jpg\n",
"Omar_train_5481.jpg\n",
"Omar_train_2586.jpg\n",
"Kinkar_train_1997.jpg\n",
"Arun_test_106.jpg\n",
"Omar_train_6154.jpg\n",
"Omar_train_6495.jpg\n",
"Abhay_train_3923.jpg\n",
"Pandian_train_283.jpg\n",
"Abhay_train_2735.jpg\n",
"Abhay_train_2009.jpg\n",
"Omar_train_1777.jpg\n",
"Sharath_train_4486.jpg\n",
"Arun_train_2353.jpg\n",
"Sharath_train_903.jpg\n",
"Kinkar_train_2356.jpg\n",
"Gopika_train_1538.jpg\n",
"Kinkar_train_1365.jpg\n",
"Omar_train_2763.jpg\n",
"Abhay_train_1029.jpg\n",
"Sharath_train_6137.jpg\n",
"Gopika_train_5024.jpg\n",
"Abhay_train_6524.jpg\n",
"Omar_train_119.jpg\n",
"Sharath_train_4184.jpg\n",
"Omar_train_2307.jpg\n",
"Gopika_train_6183.jpg\n",
"Pandian_train_6103.jpg\n",
"Sharath_train_2417.jpg\n",
"Pandian_train_1276.jpg\n",
"Kinkar_train_3782.jpg\n",
"Pandian_train_1275.jpg\n",
"Pandian_train_3148.jpg\n",
"Omar_train_5096.jpg\n",
"Kinkar_train_4441.jpg\n",
"Arun_test_113.jpg\n",
"Arun_train_351.jpg\n",
"Abhay_train_3216.jpg\n",
"Arun_train_5449.jpg\n",
"Kinkar_train_1093.jpg\n",
"Sharath_train_1589.jpg\n",
"Omar_train_220.jpg\n",
"Kinkar_train_1382.jpg\n",
"Arun_train_4353.jpg\n",
"Sharath_train_1531.jpg\n",
"Sharath_train_2017.jpg\n",
"Kinkar_train_6519.jpg\n",
"Omar_train_1966.jpg\n",
"Gopika_train_932.jpg\n",
"Pandian_train_6960.jpg\n",
"Sharath_train_108.jpg\n",
"Gopika_train_4203.jpg\n",
"Pandian_train_1019.jpg\n",
"Omar_train_1142.jpg\n",
"Gopika_train_346.jpg\n",
"Sharath_train_5964.jpg\n",
"Arun_train_2608.jpg\n",
"Kinkar_train_2286.jpg\n",
"Pandian_train_5246.jpg\n",
"Gopika_train_6347.jpg\n",
"Omar_train_5490.jpg\n",
"Omar_train_4602.jpg\n",
"Gopika_train_874.jpg\n",
"Omar_train_3302.jpg\n",
"Pandian_train_699.jpg\n",
"Sharath_train_6809.jpg\n",
"Abhay_train_6582.jpg\n",
"Kinkar_train_547.jpg\n",
"Kinkar_train_2092.jpg\n",
"Abhay_train_3450.jpg\n",
"Gopika_train_4115.jpg\n",
"Gopika_train_3240.jpg\n",
"Sharath_train_1361.jpg\n",
"Abhay_train_5700.jpg\n",
"Abhay_train_3955.jpg\n",
"Sharath_train_978.jpg\n",
"Sharath_train_5230.jpg\n",
"Sharath_train_266.jpg\n",
"Kinkar_train_5415.jpg\n",
"Gopika_train_3737.jpg\n",
"Abhay_train_3125.jpg\n",
"Sharath_train_2577.jpg\n",
"Pandian_train_4288.jpg\n",
"Pandian_train_6195.jpg\n",
"Arun_train_2081.jpg\n",
"Pandian_train_475.jpg\n",
"Pandian_train_389.jpg\n",
"Gopika_train_2853.jpg\n",
"Omar_train_1525.jpg\n",
"Kinkar_train_6164.jpg\n",
"Kinkar_train_6343.jpg\n",
"Sharath_train_4109.jpg\n",
"Sharath_test_144.jpg\n",
"Gopika_train_6083.jpg\n",
"Arun_train_5803.jpg\n",
"Sharath_train_3799.jpg\n",
"Gopika_train_5161.jpg\n",
"Arun_train_4988.jpg\n",
"Kinkar_train_2799.jpg\n",
"Abhay_train_2211.jpg\n",
"Gopika_test_155.jpg\n",
"Gopika_train_744.jpg\n",
"Kinkar_train_534.jpg\n",
"Sharath_train_1370.jpg\n",
"Abhay_train_1530.jpg\n",
"Omar_train_718.jpg\n",
"Arun_train_5050.jpg\n",
"Omar_train_662.jpg\n",
"Sharath_train_222.jpg\n",
"Abhay_train_5383.jpg\n",
"Sharath_train_2306.jpg\n",
"Omar_train_1211.jpg\n",
"Omar_train_3553.jpg\n",
"Kinkar_train_1925.jpg\n",
"Sharath_train_6216.jpg\n",
"Arun_train_6649.jpg\n",
"Kinkar_train_3992.jpg\n",
"Pandian_train_6165.jpg\n",
"Omar_train_4835.jpg\n",
"Pandian_train_4688.jpg\n",
"Gopika_train_4088.jpg\n",
"Omar_train_697.jpg\n",
"Pandian_train_81.jpg\n",
"Pandian_train_5673.jpg\n",
"Kinkar_train_4297.jpg\n",
"Kinkar_train_1675.jpg\n",
"Abhay_train_2660.jpg\n",
"Sharath_train_3490.jpg\n",
"Gopika_train_1613.jpg\n",
"Pandian_train_6628.jpg\n",
"Abhay_train_934.jpg\n",
"Gopika_train_1718.jpg\n",
"Arun_train_1038.jpg\n",
"Gopika_train_1903.jpg\n",
"Sharath_train_4385.jpg\n",
"Omar_train_4112.jpg\n",
"Pandian_train_546.jpg\n",
"Arun_train_6840.jpg\n",
"Pandian_train_6854.jpg\n",
"Gopika_train_880.jpg\n",
"Gopika_train_3168.jpg\n",
"Gopika_train_2848.jpg\n",
"Pandian_train_5471.jpg\n",
"Gopika_train_2089.jpg\n",
"Gopika_train_6942.jpg\n",
"Gopika_train_4524.jpg\n",
"Kinkar_train_154.jpg\n",
"Pandian_train_1934.jpg\n",
"Sharath_train_6621.jpg\n",
"Gopika_train_5413.jpg\n",
"Sharath_train_6933.jpg\n",
"Gopika_train_3930.jpg\n",
"Sharath_train_5374.jpg\n",
"Pandian_train_1847.jpg\n",
"Sharath_train_6447.jpg\n",
"Kinkar_train_5003.jpg\n",
"Gopika_train_2665.jpg\n",
"Arun_train_5232.jpg\n",
"Abhay_train_3228.jpg\n",
"Sharath_train_910.jpg\n",
"Kinkar_train_4827.jpg\n",
"Abhay_train_4125.jpg\n",
"Gopika_train_2810.jpg\n",
"Kinkar_train_5360.jpg\n",
"Sharath_train_3254.jpg\n",
"Omar_train_1455.jpg\n",
"Omar_train_2501.jpg\n",
"Abhay_test_47.jpg\n",
"Gopika_train_4523.jpg\n",
"Kinkar_train_4597.jpg\n",
"Arun_train_1980.jpg\n",
"Gopika_train_5581.jpg\n",
"Pandian_train_849.jpg\n",
"Kinkar_train_2824.jpg\n",
"Arun_train_1862.jpg\n",
"Kinkar_train_2766.jpg\n",
"Arun_train_2860.jpg\n",
"Omar_train_2890.jpg\n",
"Omar_train_1588.jpg\n",
"Pandian_train_3739.jpg\n",
"Sharath_train_1185.jpg\n",
"Sharath_train_1899.jpg\n",
"Gopika_train_2643.jpg\n",
"Pandian_train_4336.jpg\n",
"Abhay_train_3159.jpg\n",
"Abhay_train_453.jpg\n",
"Gopika_train_3440.jpg\n",
"Gopika_train_2450.jpg\n",
"Omar_train_6140.jpg\n",
"Sharath_train_5376.jpg\n",
"Omar_train_4436.jpg\n",
"Abhay_train_262.jpg\n",
"Pandian_train_3331.jpg\n",
"Sharath_train_5555.jpg\n",
"Sharath_train_4851.jpg\n",
"Omar_train_6414.jpg\n",
"Sharath_train_876.jpg\n",
"Sharath_train_4645.jpg\n",
"Kinkar_train_676.jpg\n",
"Sharath_test_8.jpg\n",
"Sharath_train_5517.jpg\n",
"Abhay_train_6321.jpg\n",
"Kinkar_train_3627.jpg\n",
"Kinkar_train_4133.jpg\n",
"Sharath_train_3809.jpg\n",
"Gopika_train_1832.jpg\n",
"Gopika_train_4108.jpg\n",
"Arun_train_4714.jpg\n",
"Arun_train_3115.jpg\n",
"Sharath_train_813.jpg\n",
"Abhay_train_3743.jpg\n",
"Pandian_train_1804.jpg\n",
"Arun_test_35.jpg\n",
"Sharath_train_4345.jpg\n",
"Abhay_train_264.jpg\n",
"Sharath_train_5029.jpg\n",
"Pandian_train_3527.jpg\n",
"Arun_train_4694.jpg\n",
"Omar_train_1973.jpg\n",
"Gopika_train_1439.jpg\n",
"Arun_train_1628.jpg\n",
"Abhay_train_6150.jpg\n",
"Omar_train_2618.jpg\n",
"Gopika_train_4979.jpg\n",
"Gopika_train_1218.jpg\n",
"Sharath_train_2984.jpg\n",
"Omar_train_1990.jpg\n",
"Sharath_train_4286.jpg\n",
"Sharath_train_48.jpg\n",
"Kinkar_train_137.jpg\n",
"Gopika_train_966.jpg\n",
"Gopika_train_5615.jpg\n",
"Abhay_train_5687.jpg\n",
"Sharath_train_4961.jpg\n",
"Arun_train_3163.jpg\n",
"Pandian_train_6786.jpg\n",
"Sharath_train_3203.jpg\n",
"Kinkar_train_5291.jpg\n",
"Sharath_train_4894.jpg\n",
"Omar_train_237.jpg\n",
"Gopika_train_433.jpg\n",
"Sharath_train_3977.jpg\n",
"Abhay_train_4629.jpg\n",
"Arun_train_476.jpg\n",
"Omar_train_4638.jpg\n",
"Pandian_train_6827.jpg\n",
"Abhay_train_5044.jpg\n",
"Sharath_train_5702.jpg\n",
"Omar_train_2971.jpg\n",
"Abhay_train_6461.jpg\n",
"Omar_train_4862.jpg\n",
"Arun_train_4143.jpg\n",
"Kinkar_train_3218.jpg\n",
"Gopika_train_5913.jpg\n",
"Gopika_train_6720.jpg\n",
"Pandian_train_6332.jpg\n",
"Omar_train_5351.jpg\n",
"Arun_train_1074.jpg\n",
"Kinkar_train_2054.jpg\n",
"Arun_train_6082.jpg\n",
"Sharath_train_1714.jpg\n",
"Abhay_train_5538.jpg\n",
"Pandian_train_594.jpg\n",
"Arun_train_2034.jpg\n",
"Kinkar_train_3755.jpg\n",
"Arun_train_3134.jpg\n",
"Gopika_train_6335.jpg\n",
"Sharath_train_3711.jpg\n",
"Abhay_train_281.jpg\n",
"Kinkar_train_2638.jpg\n",
"Abhay_train_3704.jpg\n",
"Arun_train_293.jpg\n",
"Kinkar_train_2151.jpg\n",
"Arun_train_242.jpg\n",
"Kinkar_train_69.jpg\n",
"Abhay_train_4784.jpg\n",
"Sharath_train_1174.jpg\n",
"Omar_train_2129.jpg\n",
"Kinkar_train_976.jpg\n",
"Gopika_train_6004.jpg\n",
"Omar_train_5859.jpg\n",
"Arun_test_122.jpg\n",
"Omar_train_1452.jpg\n",
"Omar_train_2323.jpg\n",
"Arun_train_762.jpg\n",
"Omar_train_1690.jpg\n",
"Omar_train_4924.jpg\n",
"Kinkar_train_4039.jpg\n",
"Gopika_train_5009.jpg\n",
"Arun_train_3744.jpg\n",
"Omar_train_2614.jpg\n",
"Kinkar_train_2822.jpg\n",
"Abhay_train_4284.jpg\n",
"Omar_train_5405.jpg\n",
"Kinkar_train_1945.jpg\n",
"Sharath_train_5036.jpg\n",
"Kinkar_train_2144.jpg\n",
"Omar_train_189.jpg\n",
"Gopika_train_3953.jpg\n",
"Kinkar_train_104.jpg\n",
"Sharath_test_2.jpg\n",
"Arun_train_608.jpg\n",
"Gopika_train_2591.jpg\n",
"Omar_train_4843.jpg\n",
"Arun_train_3686.jpg\n",
"Arun_train_1414.jpg\n",
"Gopika_train_4332.jpg\n",
"Kinkar_train_4815.jpg\n",
"Pandian_train_4876.jpg\n",
"Sharath_train_5632.jpg\n",
"Pandian_train_3952.jpg\n",
"Omar_train_3013.jpg\n",
"Sharath_train_5710.jpg\n",
"Omar_test_141.jpg\n",
"Arun_train_6691.jpg\n",
"Arun_train_4456.jpg\n",
"Kinkar_train_5628.jpg\n",
"Kinkar_train_5945.jpg\n",
"Omar_train_3012.jpg\n",
"Gopika_test_7.jpg\n",
"Pandian_train_6744.jpg\n",
"Arun_train_6430.jpg\n",
"Arun_train_4032.jpg\n",
"Sharath_train_5103.jpg\n",
"Abhay_train_4721.jpg\n",
"Pandian_train_2514.jpg\n",
"Sharath_train_2019.jpg\n",
"Sharath_train_6769.jpg\n",
"Kinkar_train_5048.jpg\n",
"Gopika_train_4672.jpg\n",
"Pandian_train_2464.jpg\n",
"Omar_train_4864.jpg\n",
"Sharath_train_417.jpg\n",
"Abhay_train_6938.jpg\n",
"Sharath_train_2071.jpg\n",
"Gopika_train_5486.jpg\n",
"Sharath_train_398.jpg\n",
"Kinkar_train_3396.jpg\n",
"Sharath_train_420.jpg\n",
"Omar_train_2892.jpg\n",
"Sharath_train_6685.jpg\n",
"Arun_train_3542.jpg\n",
"Pandian_train_3822.jpg\n",
"Omar_train_4637.jpg\n",
"Arun_train_4085.jpg\n",
"Kinkar_train_4576.jpg\n",
"Arun_train_4981.jpg\n",
"Sharath_train_3776.jpg\n",
"Sharath_train_2734.jpg\n",
"Sharath_train_4026.jpg\n",
"Kinkar_train_1522.jpg\n",
"Kinkar_train_6558.jpg\n",
"Omar_train_6087.jpg\n",
"Gopika_test_109.jpg\n",
"Abhay_train_6672.jpg\n",
"Abhay_train_5082.jpg\n",
"Abhay_train_1566.jpg\n",
"Kinkar_train_2180.jpg\n",
"Arun_train_6002.jpg\n",
"Pandian_train_2651.jpg\n",
"Sharath_train_3945.jpg\n",
"Gopika_train_3365.jpg\n",
"Kinkar_train_3427.jpg\n",
"Arun_train_5722.jpg\n",
"Gopika_test_97.jpg\n",
"Abhay_train_274.jpg\n",
"Pandian_train_648.jpg\n",
"Pandian_train_4036.jpg\n",
"Pandian_train_3498.jpg\n",
"Arun_train_4384.jpg\n",
"Abhay_train_870.jpg\n",
"Omar_train_5959.jpg\n",
"Arun_train_4783.jpg\n",
"Sharath_train_5252.jpg\n",
"Gopika_train_5225.jpg\n",
"Kinkar_train_2321.jpg\n",
"Sharath_train_1640.jpg\n",
"Gopika_train_2114.jpg\n",
"Kinkar_train_6635.jpg\n",
"Kinkar_train_2500.jpg\n",
"Sharath_train_3049.jpg\n",
"Pandian_train_2550.jpg\n",
"Gopika_train_5928.jpg\n",
"Sharath_train_837.jpg\n",
"Sharath_train_225.jpg\n",
"Kinkar_train_6905.jpg\n",
"Pandian_train_3098.jpg\n",
"Sharath_train_2653.jpg\n",
"Gopika_train_5129.jpg\n",
"Abhay_train_4813.jpg\n",
"Sharath_train_3406.jpg\n",
"Kinkar_train_3156.jpg\n",
"Sharath_train_4832.jpg\n",
"Pandian_train_1905.jpg\n",
"Omar_train_4849.jpg\n",
"Abhay_train_600.jpg\n",
"Kinkar_train_3266.jpg\n",
"Omar_train_193.jpg\n",
"Sharath_train_1751.jpg\n",
"Abhay_train_5385.jpg\n",
"Omar_train_760.jpg\n",
"Abhay_train_1287.jpg\n",
"Arun_train_65.jpg\n",
"Kinkar_train_1765.jpg\n",
"Abhay_train_2277.jpg\n",
"Omar_train_4445.jpg\n",
"Abhay_train_393.jpg\n",
"Pandian_train_5621.jpg\n",
"Arun_train_2827.jpg\n",
"Gopika_train_5492.jpg\n",
"Arun_train_719.jpg\n",
"Sharath_train_6040.jpg\n",
"Arun_train_3227.jpg\n",
"Arun_train_6433.jpg\n",
"Sharath_train_2436.jpg\n",
"Omar_train_2876.jpg\n",
"Sharath_train_3753.jpg\n",
"Omar_train_2553.jpg\n",
"Pandian_train_3813.jpg\n",
"Omar_train_4917.jpg\n",
"Kinkar_train_2686.jpg\n",
"Sharath_train_2287.jpg\n",
"Omar_train_1260.jpg\n",
"Sharath_train_3004.jpg\n",
"Kinkar_train_5593.jpg\n",
"Kinkar_train_3514.jpg\n",
"Omar_train_1707.jpg\n",
"Pandian_train_2525.jpg\n",
"Gopika_train_5990.jpg\n",
"Sharath_train_1111.jpg\n",
"Abhay_train_2567.jpg\n",
"Abhay_train_965.jpg\n",
"Sharath_train_3517.jpg\n",
"Omar_train_2667.jpg\n",
"Gopika_train_4785.jpg\n",
"Gopika_train_5816.jpg\n",
"Gopika_train_3773.jpg\n",
"Kinkar_train_1117.jpg\n",
"Omar_train_6467.jpg\n",
"Pandian_train_797.jpg\n",
"Gopika_train_2531.jpg\n",
"Sharath_train_1511.jpg\n",
"Gopika_train_19.jpg\n",
"Kinkar_train_5726.jpg\n",
"Arun_train_2433.jpg\n",
"Abhay_train_6024.jpg\n",
"Sharath_train_1133.jpg\n",
"Kinkar_train_5076.jpg\n",
"Abhay_train_5240.jpg\n",
"Arun_train_5408.jpg\n",
"Abhay_train_1885.jpg\n",
"Sharath_train_623.jpg\n",
"Abhay_train_3133.jpg\n",
"Abhay_train_591.jpg\n",
"Sharath_train_1703.jpg\n",
"Arun_train_1721.jpg\n",
"Kinkar_train_3042.jpg\n",
"Sharath_train_1868.jpg\n",
"Arun_train_6727.jpg\n",
"Gopika_train_656.jpg\n",
"Gopika_train_4807.jpg\n",
"Omar_train_3231.jpg\n",
"Sharath_train_2717.jpg\n",
"Gopika_train_6757.jpg\n",
"Sharath_train_1191.jpg\n",
"Gopika_train_4410.jpg\n",
"Sharath_train_2213.jpg\n",
"Pandian_train_1796.jpg\n",
"Pandian_train_6619.jpg\n",
"Gopika_test_163.jpg\n",
"Arun_train_2051.jpg\n",
"Kinkar_train_6009.jpg\n",
"Sharath_train_4635.jpg\n",
"Sharath_train_2645.jpg\n",
"Pandian_train_2111.jpg\n",
"Sharath_train_4564.jpg\n",
"Sharath_train_4747.jpg\n",
"Gopika_train_4838.jpg\n",
"Gopika_train_734.jpg\n",
"Omar_train_4252.jpg\n",
"Sharath_train_6207.jpg\n",
"Gopika_train_5307.jpg\n",
"Omar_train_363.jpg\n",
"Sharath_train_635.jpg\n",
"Abhay_train_832.jpg\n",
"Abhay_train_1915.jpg\n",
"Pandian_train_2771.jpg\n",
"Gopika_train_6596.jpg\n",
"Abhay_train_3521.jpg\n",
"Sharath_train_3432.jpg\n",
"Kinkar_train_4744.jpg\n",
"Kinkar_train_4743.jpg\n",
"Kinkar_train_2898.jpg\n",
"Pandian_test_151.jpg\n",
"Gopika_test_81.jpg\n",
"Pandian_train_4772.jpg\n",
"Omar_test_154.jpg\n",
"Abhay_train_4489.jpg\n",
"Gopika_train_5520.jpg\n",
"Gopika_train_6069.jpg\n",
"Kinkar_train_1597.jpg\n",
"Gopika_train_5946.jpg\n",
"Arun_train_3530.jpg\n",
"Abhay_train_3357.jpg\n",
"Pandian_train_3308.jpg\n",
"Abhay_train_1017.jpg\n",
"Arun_train_14.jpg\n",
"Sharath_train_4674.jpg\n",
"Omar_train_451.jpg\n",
"Omar_train_5605.jpg\n",
"Kinkar_train_4249.jpg\n",
"Sharath_train_5631.jpg\n",
"Omar_train_5324.jpg\n",
"Omar_train_3807.jpg\n",
"Sharath_train_6376.jpg\n",
"Kinkar_train_6396.jpg\n",
"Sharath_train_1761.jpg\n",
"Gopika_train_6455.jpg\n",
"Kinkar_train_6712.jpg\n",
"Kinkar_train_4330.jpg\n",
"Omar_train_4963.jpg\n",
"Gopika_train_1000.jpg\n",
"Sharath_train_6061.jpg\n",
"Sharath_train_2466.jpg\n",
"Arun_train_1464.jpg\n",
"Omar_train_4968.jpg\n",
"Arun_train_6158.jpg\n",
"Omar_train_2623.jpg\n",
"Kinkar_train_3170.jpg\n",
"Pandian_train_2773.jpg\n",
"Kinkar_train_179.jpg\n",
"Omar_train_5528.jpg\n",
"Arun_train_2647.jpg\n",
"Omar_train_774.jpg\n",
"Kinkar_train_6354.jpg\n",
"Abhay_train_3054.jpg\n",
"Omar_train_3883.jpg\n",
"Arun_train_3239.jpg\n",
"Kinkar_train_6962.jpg\n",
"Kinkar_train_3358.jpg\n",
"Omar_train_1034.jpg\n",
"Sharath_train_6886.jpg\n",
"Arun_train_6412.jpg\n",
"Gopika_train_6805.jpg\n",
"Arun_train_5272.jpg\n",
"Arun_train_2548.jpg\n",
"Sharath_train_5448.jpg\n",
"Abhay_train_5353.jpg\n",
"Kinkar_train_869.jpg\n",
"Kinkar_train_4555.jpg\n",
"Abhay_train_945.jpg\n",
"Gopika_train_3438.jpg\n",
"Pandian_train_5531.jpg\n",
"Arun_train_961.jpg\n",
"Abhay_train_4056.jpg\n",
"Sharath_train_5805.jpg\n",
"Arun_train_6788.jpg\n",
"Sharath_train_612.jpg\n",
"Kinkar_train_2891.jpg\n",
"Kinkar_train_5444.jpg\n",
"Abhay_train_6676.jpg\n",
"Omar_test_125.jpg\n",
"Pandian_train_5818.jpg\n",
"Omar_train_3658.jpg\n",
"Pandian_train_1199.jpg\n",
"Kinkar_train_4431.jpg\n",
"Omar_train_2266.jpg\n",
"Gopika_train_6704.jpg\n",
"Kinkar_train_4561.jpg\n",
"Gopika_train_313.jpg\n",
"Omar_train_2308.jpg\n",
"Sharath_train_5854.jpg\n",
"Gopika_train_209.jpg\n",
"Omar_train_4574.jpg\n",
"Arun_train_5603.jpg\n",
"Pandian_train_2497.jpg\n",
"Abhay_train_4966.jpg\n",
"Abhay_train_1951.jpg\n",
"Omar_train_6517.jpg\n",
"Gopika_train_2389.jpg\n",
"Pandian_train_4402.jpg\n",
"Sharath_train_6258.jpg\n",
"Sharath_train_3536.jpg\n",
"Sharath_train_5974.jpg\n",
"Kinkar_test_4.jpg\n",
"Pandian_train_3386.jpg\n",
"Gopika_train_5484.jpg\n",
"Kinkar_train_5852.jpg\n",
"Gopika_train_5057.jpg\n",
"Gopika_train_1258.jpg\n",
"Omar_train_5201.jpg\n",
"Gopika_train_549.jpg\n",
"Sharath_train_6773.jpg\n",
"Gopika_train_6686.jpg\n",
"Kinkar_train_2105.jpg\n",
"Omar_train_1284.jpg\n",
"Omar_train_5596.jpg\n",
"Pandian_train_2432.jpg\n",
"Abhay_train_562.jpg\n",
"Gopika_train_1863.jpg\n",
"Sharath_train_2698.jpg\n",
"Omar_train_6838.jpg\n",
"Omar_train_822.jpg\n",
"Arun_train_6538.jpg\n",
"Arun_train_1758.jpg\n",
"Sharath_train_6546.jpg\n",
"Kinkar_train_677.jpg\n",
"Arun_train_7.jpg\n",
"Arun_train_2203.jpg\n",
"Sharath_train_6648.jpg\n",
"Kinkar_train_3256.jpg\n",
"Omar_train_4383.jpg\n",
"Sharath_train_3607.jpg\n",
"Gopika_train_1529.jpg\n",
"Kinkar_train_2354.jpg\n",
"Omar_train_708.jpg\n",
"Sharath_train_3437.jpg\n",
"Omar_train_3629.jpg\n",
"Gopika_train_3852.jpg\n",
"Gopika_train_3705.jpg\n",
"Gopika_train_1724.jpg\n",
"Sharath_train_1059.jpg\n",
"Omar_train_6000.jpg\n",
"Omar_train_5354.jpg\n",
"Kinkar_train_2937.jpg\n",
"Gopika_train_6176.jpg\n",
"Kinkar_train_5248.jpg\n",
"Gopika_train_1955.jpg\n",
"Pandian_train_3846.jpg\n",
"Sharath_train_1988.jpg\n",
"Abhay_train_5410.jpg\n",
"Kinkar_train_6857.jpg\n",
"Gopika_train_4277.jpg\n",
"Omar_train_4990.jpg\n",
"Kinkar_train_2851.jpg\n",
"Pandian_train_6708.jpg\n",
"Abhay_train_4804.jpg\n",
"Abhay_train_5027.jpg\n",
"Omar_train_6706.jpg\n",
"Gopika_train_4159.jpg\n",
"Abhay_train_695.jpg\n",
"Sharath_train_2340.jpg\n",
"Arun_train_4982.jpg\n",
"Abhay_train_5257.jpg\n",
"Sharath_train_5801.jpg\n",
"Arun_train_4348.jpg\n",
"Pandian_train_4448.jpg\n",
"Sharath_train_3172.jpg\n",
"Gopika_train_1857.jpg\n",
"Sharath_train_1186.jpg\n",
"Gopika_train_3401.jpg\n",
"Omar_train_4702.jpg\n",
"Gopika_train_6594.jpg\n",
"Gopika_train_6615.jpg\n",
"Kinkar_train_2814.jpg\n",
"Omar_train_668.jpg\n",
"Sharath_train_3684.jpg\n",
"Abhay_train_3087.jpg\n",
"Abhay_train_4443.jpg\n",
"Pandian_train_6937.jpg\n",
"Abhay_train_4900.jpg\n",
"Kinkar_train_5310.jpg\n",
"Sharath_train_6877.jpg\n",
"Pandian_train_6379.jpg\n",
"Sharath_train_332.jpg\n",
"Sharath_train_4791.jpg\n",
"Abhay_train_875.jpg\n",
"Pandian_train_5060.jpg\n",
"Sharath_train_4902.jpg\n",
"Sharath_train_5368.jpg\n",
"Sharath_train_6022.jpg\n",
"Omar_train_3150.jpg\n",
"Omar_train_2013.jpg\n",
"Omar_train_409.jpg\n",
"Pandian_train_5452.jpg\n",
"Abhay_train_1778.jpg\n",
"Kinkar_train_39.jpg\n",
"Omar_train_419.jpg\n",
"Kinkar_train_2945.jpg\n",
"Kinkar_train_2376.jpg\n",
"Omar_train_2179.jpg\n",
"Omar_train_3921.jpg\n",
"Abhay_train_4472.jpg\n",
"Kinkar_train_862.jpg\n",
"Sharath_train_3897.jpg\n",
"Sharath_train_342.jpg\n",
"Omar_train_4194.jpg\n",
"Omar_train_5954.jpg\n",
"Abhay_train_1363.jpg\n",
"Gopika_train_1210.jpg\n",
"Kinkar_train_2442.jpg\n",
"Sharath_train_1158.jpg\n",
"Sharath_train_5365.jpg\n",
"Omar_train_2871.jpg\n",
"Gopika_train_316.jpg\n",
"Gopika_train_4270.jpg\n",
"Kinkar_train_4381.jpg\n",
"Kinkar_train_2562.jpg\n",
"Sharath_train_515.jpg\n",
"Pandian_train_254.jpg\n",
"Pandian_train_3491.jpg\n",
"Pandian_train_298.jpg\n",
"Sharath_train_3486.jpg\n",
"Pandian_train_1611.jpg\n",
"Abhay_train_1486.jpg\n",
"Omar_train_3419.jpg\n",
"Kinkar_train_3800.jpg\n",
"Sharath_train_785.jpg\n",
"Arun_train_4612.jpg\n",
"Sharath_train_4959.jpg\n",
"Kinkar_train_1991.jpg\n",
"Sharath_train_3405.jpg\n",
"Omar_train_947.jpg\n",
"Pandian_train_4237.jpg\n",
"Abhay_train_4082.jpg\n",
"Sharath_train_2544.jpg\n",
"Pandian_train_6581.jpg\n",
"Sharath_train_923.jpg\n",
"Abhay_train_3468.jpg\n",
"Kinkar_train_2696.jpg\n",
"Gopika_train_6600.jpg\n",
"Abhay_train_2156.jpg\n",
"Sharath_train_5319.jpg\n",
"Sharath_train_5614.jpg\n",
"Abhay_train_4301.jpg\n",
"Arun_train_3956.jpg\n",
"Arun_train_5620.jpg\n",
"Kinkar_train_5547.jpg\n",
"Arun_train_5995.jpg\n",
"Kinkar_train_2064.jpg\n",
"Omar_train_2496.jpg\n",
"Abhay_train_4030.jpg\n",
"Kinkar_train_5849.jpg\n",
"Gopika_train_4238.jpg\n",
"Omar_train_3494.jpg\n",
"Abhay_train_1306.jpg\n",
"Arun_train_4248.jpg\n",
"Kinkar_train_6919.jpg\n",
"Arun_train_537.jpg\n",
"Arun_train_1303.jpg\n",
"Pandian_train_2973.jpg\n",
"Omar_train_1800.jpg\n",
"Sharath_train_2416.jpg\n",
"Sharath_train_4892.jpg\n",
"Omar_train_2380.jpg\n",
"Omar_train_6302.jpg\n",
"Gopika_train_3641.jpg\n",
"Gopika_train_1023.jpg\n",
"Sharath_train_6100.jpg\n",
"Kinkar_train_1781.jpg\n",
"Omar_train_2183.jpg\n",
"Gopika_train_593.jpg\n",
"Gopika_train_1410.jpg\n",
"Sharath_train_4173.jpg\n",
"Gopika_train_3551.jpg\n",
"Gopika_train_805.jpg\n",
"Arun_train_4187.jpg\n",
"Abhay_train_1738.jpg\n",
"Abhay_train_3324.jpg\n",
"Omar_train_2867.jpg\n",
"Sharath_train_2016.jpg\n",
"Kinkar_train_6127.jpg\n",
"Omar_train_100.jpg\n",
"Kinkar_train_24.jpg\n",
"Abhay_train_1782.jpg\n",
"Sharath_train_3010.jpg\n",
"Kinkar_train_2206.jpg\n",
"Kinkar_train_1314.jpg\n",
"Arun_train_909.jpg\n",
"Arun_train_2505.jpg\n",
"Sharath_test_74.jpg\n",
"Pandian_train_5808.jpg\n",
"Abhay_train_681.jpg\n",
"Sharath_train_5297.jpg\n",
"Sharath_train_1967.jpg\n",
"Sharath_train_4349.jpg\n",
"Omar_train_1293.jpg\n",
"Kinkar_train_5279.jpg\n",
"Gopika_train_753.jpg\n",
"Sharath_train_3558.jpg\n",
"Arun_train_3084.jpg\n",
"Gopika_train_1691.jpg\n",
"Kinkar_train_6436.jpg\n",
"Omar_train_2546.jpg\n",
"Sharath_train_6227.jpg\n",
"Omar_train_6506.jpg\n",
"Sharath_train_5506.jpg\n",
"Abhay_train_2649.jpg\n",
"Abhay_train_1489.jpg\n",
"Abhay_train_1712.jpg\n",
"Kinkar_train_1302.jpg\n",
"Kinkar_train_4038.jpg\n",
"Pandian_train_1789.jpg\n",
"Arun_train_818.jpg\n",
"Sharath_train_4628.jpg\n",
"Gopika_train_6188.jpg\n",
"Pandian_train_3043.jpg\n",
"Arun_train_5275.jpg\n",
"Omar_train_1921.jpg\n",
"Kinkar_train_2635.jpg\n",
"Pandian_train_6431.jpg\n",
"Pandian_train_646.jpg\n",
"Abhay_train_3981.jpg\n",
"Arun_train_5565.jpg\n",
"Sharath_train_6576.jpg\n",
"Gopika_train_669.jpg\n",
"Abhay_train_1801.jpg\n",
"Kinkar_train_6544.jpg\n",
"Gopika_train_6466.jpg\n",
"Omar_train_2370.jpg\n",
"Pandian_train_5321.jpg\n",
"Omar_train_5783.jpg\n",
"Abhay_train_4234.jpg\n",
"Kinkar_train_4309.jpg\n",
"Abhay_train_6417.jpg\n",
"Arun_train_2489.jpg\n",
"Gopika_train_6279.jpg\n",
"Sharath_train_4700.jpg\n",
"Omar_train_963.jpg\n",
"Pandian_train_6328.jpg\n",
"Sharath_train_2938.jpg\n",
"Omar_train_6138.jpg\n",
"Pandian_train_2986.jpg\n",
"Omar_train_6167.jpg\n",
"Gopika_train_5315.jpg\n",
"Omar_train_6054.jpg\n",
"Arun_train_5156.jpg\n",
"Gopika_train_2600.jpg\n",
"Arun_train_3337.jpg\n",
"Gopika_train_3287.jpg\n",
"Gopika_train_3947.jpg\n",
"Omar_train_3391.jpg\n",
"Kinkar_train_3421.jpg\n",
"Omar_train_1268.jpg\n",
"Omar_train_3934.jpg\n",
"Sharath_train_798.jpg\n",
"Kinkar_train_1224.jpg\n",
"Gopika_train_214.jpg\n",
"Abhay_train_823.jpg\n",
"Sharath_train_530.jpg\n",
"Sharath_train_5501.jpg\n",
"Abhay_train_1474.jpg\n",
"Sharath_train_3149.jpg\n",
"Omar_train_645.jpg\n",
"Sharath_train_267.jpg\n",
"Omar_train_4932.jpg\n",
"Sharath_train_4444.jpg\n",
"Omar_train_4080.jpg\n",
"Kinkar_train_4537.jpg\n",
"Abhay_train_3083.jpg\n",
"Gopika_train_2094.jpg\n",
"Arun_train_5846.jpg\n",
"Kinkar_train_3339.jpg\n",
"Kinkar_train_238.jpg\n",
"Sharath_train_4167.jpg\n",
"Gopika_train_2441.jpg\n",
"Kinkar_train_6097.jpg\n",
"Abhay_train_2147.jpg\n",
"Kinkar_train_5601.jpg\n",
"Arun_train_3369.jpg\n",
"Sharath_train_340.jpg\n",
"Sharath_train_5802.jpg\n",
"Sharath_train_2263.jpg\n",
"Omar_train_1226.jpg\n",
"Gopika_train_4641.jpg\n",
"Arun_train_5450.jpg\n",
"Sharath_train_4618.jpg\n",
"Arun_train_6562.jpg\n",
"Kinkar_train_535.jpg\n",
"Gopika_train_5089.jpg\n",
"Sharath_train_1723.jpg\n",
"Sharath_train_1238.jpg\n",
"Gopika_train_3943.jpg\n",
"Pandian_train_5853.jpg\n",
"Omar_train_6395.jpg\n",
"Pandian_train_1021.jpg\n",
"Gopika_train_4592.jpg\n",
"Gopika_train_2199.jpg\n",
"Gopika_train_596.jpg\n",
"Abhay_train_5960.jpg\n",
"Kinkar_train_2811.jpg\n",
"Sharath_train_793.jpg\n",
"Sharath_train_2604.jpg\n",
"Sharath_train_317.jpg\n",
"Pandian_train_1368.jpg\n",
"Abhay_train_416.jpg\n",
"Pandian_train_2757.jpg\n",
"Sharath_train_2373.jpg\n",
"Arun_train_5187.jpg\n",
"Kinkar_train_5438.jpg\n",
"Abhay_train_2825.jpg\n",
"Sharath_train_5991.jpg\n",
"Gopika_train_2699.jpg\n",
"Abhay_train_2878.jpg\n",
"Sharath_train_4373.jpg\n",
"Omar_train_2192.jpg\n",
"Gopika_train_6255.jpg\n",
"Sharath_train_2568.jpg\n",
"Gopika_train_5690.jpg\n",
"Pandian_test_159.jpg\n",
"Abhay_train_1791.jpg\n",
"Gopika_train_2750.jpg\n",
"Sharath_train_2473.jpg\n",
"Kinkar_train_5559.jpg\n",
"Sharath_train_92.jpg\n",
"Omar_train_6168.jpg\n",
"Sharath_train_2069.jpg\n",
"Sharath_train_5899.jpg\n",
"Abhay_train_1582.jpg\n",
"Gopika_train_6688.jpg\n",
"Gopika_train_6452.jpg\n",
"Gopika_train_52.jpg\n",
"Sharath_train_919.jpg\n",
"Gopika_train_664.jpg\n",
"Omar_train_1201.jpg\n",
"Gopika_train_3600.jpg\n",
"Omar_train_6917.jpg\n",
"Kinkar_train_3648.jpg\n",
"Arun_train_895.jpg\n",
"Gopika_train_1289.jpg\n",
"Kinkar_train_3183.jpg\n",
"Sharath_train_57.jpg\n",
"Arun_train_6715.jpg\n",
"Sharath_train_2107.jpg\n",
"Kinkar_train_2039.jpg\n",
"Kinkar_train_5226.jpg\n",
"Sharath_train_6531.jpg\n",
"Arun_train_2931.jpg\n",
"Sharath_train_1192.jpg\n",
"Omar_train_4678.jpg\n",
"Pandian_train_4357.jpg\n",
"Abhay_train_1907.jpg\n",
"Sharath_train_3502.jpg\n",
"Pandian_train_5001.jpg\n",
"Pandian_train_6541.jpg\n",
"Sharath_train_141.jpg\n",
"Gopika_train_6876.jpg\n",
"Kinkar_train_4626.jpg\n",
"Omar_train_1160.jpg\n",
"Omar_train_1742.jpg\n",
"Kinkar_train_2451.jpg\n",
"Gopika_train_3531.jpg\n",
"Kinkar_train_4568.jpg\n",
"Arun_train_1515.jpg\n",
"Arun_train_3354.jpg\n",
"Omar_train_5815.jpg\n",
"Gopika_train_5915.jpg\n",
"Abhay_train_5552.jpg\n",
"Abhay_train_6477.jpg\n",
"Gopika_train_2026.jpg\n",
"Omar_train_4866.jpg\n",
"Gopika_train_6303.jpg\n",
"Abhay_train_4303.jpg\n",
"Sharath_train_5371.jpg\n",
"Gopika_train_1518.jpg\n",
"Sharath_train_712.jpg\n",
"Kinkar_train_5043.jpg\n",
"Arun_train_2542.jpg\n",
"Sharath_train_1748.jpg\n",
"Pandian_train_1807.jpg\n",
"Abhay_train_5867.jpg\n",
"Omar_train_6528.jpg\n",
"Sharath_train_1046.jpg\n",
"Arun_train_4116.jpg\n",
"Gopika_train_2781.jpg\n",
"Gopika_train_6894.jpg\n",
"Kinkar_train_6025.jpg\n",
"Pandian_train_3072.jpg\n",
"Kinkar_train_2526.jpg\n",
"Kinkar_train_1619.jpg\n",
"Pandian_train_2393.jpg\n",
"Omar_train_5398.jpg\n",
"Omar_train_5209.jpg\n",
"Gopika_train_4260.jpg\n",
"Sharath_train_953.jpg\n",
"Omar_train_5041.jpg\n",
"Arun_train_2676.jpg\n",
"Sharath_train_2902.jpg\n",
"Sharath_train_5012.jpg\n",
"Kinkar_train_748.jpg\n",
"Abhay_train_5322.jpg\n",
"Sharath_train_2854.jpg\n",
"Gopika_train_3870.jpg\n",
"Gopika_train_4211.jpg\n",
"Pandian_train_3733.jpg\n",
"Abhay_train_5563.jpg\n",
"Kinkar_train_3200.jpg\n",
"Abhay_train_4514.jpg\n",
"Kinkar_train_3061.jpg\n",
"Kinkar_train_2369.jpg\n",
"Omar_train_3840.jpg\n",
"Gopika_train_4360.jpg\n",
"Sharath_test_115.jpg\n",
"Gopika_train_3856.jpg\n",
"Abhay_train_5039.jpg\n",
"Gopika_train_5904.jpg\n",
"Sharath_train_337.jpg\n",
"Sharath_train_6732.jpg\n",
"Gopika_train_89.jpg\n",
"Omar_train_6364.jpg\n",
"Pandian_train_6730.jpg\n",
"Pandian_train_4393.jpg\n",
"Pandian_train_5317.jpg\n",
"Pandian_train_504.jpg\n",
"Gopika_train_597.jpg\n",
"Abhay_train_4117.jpg\n",
"Gopika_train_4037.jpg\n",
"Pandian_test_133.jpg\n",
"Sharath_train_4196.jpg\n",
"Sharath_train_6055.jpg\n",
"Abhay_train_972.jpg\n",
"Abhay_train_2888.jpg\n",
"Sharath_train_4005.jpg\n",
"Kinkar_train_1379.jpg\n",
"Kinkar_train_2044.jpg\n",
"Gopika_train_1409.jpg\n",
"Kinkar_train_5306.jpg\n",
"Pandian_train_240.jpg\n",
"Pandian_train_1824.jpg\n",
"Pandian_train_1413.jpg\n",
"Kinkar_train_3111.jpg\n",
"Abhay_train_90.jpg\n",
"Omar_train_3212.jpg\n",
"Omar_train_6612.jpg\n",
"Gopika_train_5280.jpg\n",
"Omar_train_1622.jpg\n",
"Abhay_train_651.jpg\n",
"Omar_train_3177.jpg\n",
"Abhay_train_1282.jpg\n",
"Kinkar_train_6696.jpg\n",
"Kinkar_train_772.jpg\n",
"Pandian_train_1153.jpg\n",
"Pandian_train_3445.jpg\n",
"Gopika_train_6914.jpg\n",
"Kinkar_train_1252.jpg\n",
"Abhay_train_5806.jpg\n",
"Abhay_test_64.jpg\n",
"Kinkar_train_3899.jpg\n",
"Pandian_train_4732.jpg\n",
"Omar_train_5361.jpg\n",
"Sharath_train_1141.jpg\n",
"Gopika_train_4622.jpg\n",
"Sharath_test_41.jpg\n",
"Kinkar_train_2975.jpg\n",
"Sharath_train_6460.jpg\n",
"Gopika_train_4144.jpg\n",
"Kinkar_train_4072.jpg\n",
"Gopika_train_2148.jpg\n",
"Arun_train_167.jpg\n",
"Gopika_train_452.jpg\n",
"Sharath_train_4083.jpg\n",
"Kinkar_train_3225.jpg\n",
"Arun_test_37.jpg\n",
"Omar_train_2958.jpg\n",
"Omar_train_301.jpg\n",
"Arun_train_2778.jpg\n",
"Pandian_train_6348.jpg\n",
"Sharath_train_5871.jpg\n",
"Abhay_test_157.jpg\n",
"Gopika_train_1094.jpg\n",
"Kinkar_train_948.jpg\n",
"Arun_train_3117.jpg\n",
"Omar_train_4386.jpg\n",
"Kinkar_train_6515.jpg\n",
"Gopika_train_1466.jpg\n",
"Arun_train_3034.jpg\n",
"Gopika_train_6916.jpg\n",
"Gopika_train_5112.jpg\n",
"Kinkar_train_434.jpg\n",
"Gopika_train_6666.jpg\n",
"Pandian_train_3128.jpg\n",
"Abhay_train_4789.jpg\n",
"Sharath_train_3691.jpg\n",
"Omar_train_3670.jpg\n",
"Sharath_train_2521.jpg\n",
"Kinkar_train_6631.jpg\n",
"Omar_train_858.jpg\n",
"Abhay_train_6091.jpg\n",
"Kinkar_train_6759.jpg\n",
"Gopika_train_6605.jpg\n",
"Omar_train_6469.jpg\n",
"Arun_train_3311.jpg\n",
"Gopika_train_6896.jpg\n",
"Abhay_train_2086.jpg\n",
"Pandian_train_6226.jpg\n",
"Kinkar_train_3893.jpg\n",
"Sharath_train_1134.jpg\n",
"Pandian_train_5707.jpg\n",
"Gopika_train_878.jpg\n",
"Omar_train_801.jpg\n",
"Gopika_train_6731.jpg\n",
"Omar_train_843.jpg\n",
"Sharath_train_2911.jpg\n",
"Kinkar_train_1362.jpg\n",
"Sharath_test_5.jpg\n",
"Kinkar_train_5375.jpg\n",
"Kinkar_train_3685.jpg\n",
"Abhay_train_2675.jpg\n",
"Abhay_train_5968.jpg\n",
"Gopika_train_466.jpg\n",
"Abhay_train_4152.jpg\n",
"Pandian_train_4261.jpg\n",
"Kinkar_train_4673.jpg\n",
"Sharath_train_4366.jpg\n",
"Pandian_train_3638.jpg\n",
"Kinkar_train_428.jpg\n",
"Pandian_train_4316.jpg\n",
"Sharath_train_2477.jpg\n",
"Arun_train_5141.jpg\n",
"Sharath_train_1933.jpg\n",
"Kinkar_train_6391.jpg\n",
"Kinkar_train_2198.jpg\n",
"Abhay_test_124.jpg\n",
"Abhay_train_1897.jpg\n",
"Omar_train_4317.jpg\n",
"Abhay_train_5467.jpg\n",
"Omar_train_4515.jpg\n",
"Omar_train_3725.jpg\n",
"Omar_train_3996.jpg\n",
"Pandian_train_2849.jpg\n",
"Abhay_train_5669.jpg\n",
"Pandian_train_2022.jpg\n",
"Gopika_train_5792.jpg\n",
"Abhay_train_5397.jpg\n",
"Gopika_train_5742.jpg\n",
"Omar_train_5636.jpg\n",
"Kinkar_train_5329.jpg\n",
"Arun_test_126.jpg\n",
"Arun_train_2006.jpg\n",
"Gopika_train_3006.jpg\n",
"Pandian_train_6338.jpg\n",
"Sharath_train_4007.jpg\n",
"Omar_train_6570.jpg\n",
"Pandian_train_861.jpg\n",
"Kinkar_train_1820.jpg\n",
"Pandian_train_5406.jpg\n",
"Sharath_train_2972.jpg\n",
"Gopika_train_6806.jpg\n",
"Pandian_train_467.jpg\n",
"Arun_train_5828.jpg\n",
"Sharath_train_4853.jpg\n",
"Kinkar_train_3380.jpg\n",
"Kinkar_train_1010.jpg\n",
"Omar_train_1664.jpg\n",
"Kinkar_train_2300.jpg\n",
"Gopika_train_1603.jpg\n",
"Gopika_train_6344.jpg\n",
"Sharath_train_4263.jpg\n",
"Gopika_train_3757.jpg\n",
"Sharath_train_3210.jpg\n",
"Sharath_train_6039.jpg\n",
"Sharath_train_2175.jpg\n",
"Sharath_train_804.jpg\n",
"Sharath_train_5684.jpg\n",
"Kinkar_train_5207.jpg\n",
"Sharath_train_817.jpg\n",
"Gopika_train_3652.jpg\n",
"Arun_train_465.jpg\n",
"Kinkar_train_1557.jpg\n",
"Kinkar_train_2348.jpg\n",
"Sharath_train_4792.jpg\n",
"Gopika_train_3333.jpg\n",
"Abhay_train_707.jpg\n",
"Pandian_train_318.jpg\n",
"Abhay_train_3695.jpg\n",
"Omar_train_761.jpg\n",
"Sharath_train_5436.jpg\n",
"Gopika_train_520.jpg\n",
"Abhay_train_1939.jpg\n",
"Abhay_train_224.jpg\n",
"Abhay_train_673.jpg\n",
"Omar_train_5967.jpg\n",
"Omar_train_88.jpg\n",
"Omar_train_3130.jpg\n",
"Abhay_train_4940.jpg\n",
"Omar_train_5877.jpg\n",
"Omar_train_6740.jpg\n",
"Pandian_train_3194.jpg\n",
"Sharath_train_6900.jpg\n",
"Sharath_train_1032.jpg\n",
"Gopika_train_2862.jpg\n",
"Pandian_train_2243.jpg\n",
"Pandian_train_5414.jpg\n",
"Abhay_train_3262.jpg\n",
"Pandian_train_2916.jpg\n",
"Omar_train_3282.jpg\n",
"Omar_train_3513.jpg\n",
"Sharath_train_3047.jpg\n",
"Arun_train_717.jpg\n",
"Omar_train_3748.jpg\n",
"Sharath_train_5943.jpg\n",
"Sharath_train_4180.jpg\n",
"Gopika_train_3886.jpg\n",
"Pandian_train_3626.jpg\n",
"Kinkar_train_487.jpg\n",
"Gopika_train_4101.jpg\n",
"Abhay_train_844.jpg\n",
"Sharath_train_4306.jpg\n",
"Gopika_train_514.jpg\n",
"Kinkar_train_113.jpg\n",
"Kinkar_train_356.jpg\n",
"Arun_train_6965.jpg\n",
"Kinkar_test_61.jpg\n",
"Abhay_train_5426.jpg\n",
"Kinkar_train_728.jpg\n",
"Arun_train_4801.jpg\n",
"Kinkar_train_5956.jpg\n",
"Omar_train_66.jpg\n",
"Pandian_train_6865.jpg\n",
"Gopika_train_6353.jpg\n",
"Gopika_train_6765.jpg\n",
"Sharath_train_4762.jpg\n",
"Sharath_train_580.jpg\n",
"Gopika_train_1423.jpg\n",
"Arun_train_3561.jpg\n",
"Sharath_train_4454.jpg\n",
"Sharath_train_4953.jpg\n",
"Arun_train_1701.jpg\n",
"Abhay_train_6342.jpg\n",
"Gopika_train_575.jpg\n",
"Omar_train_2768.jpg\n",
"Gopika_train_3146.jpg\n",
"Gopika_train_4661.jpg\n",
"Gopika_train_1009.jpg\n",
"Sharath_train_894.jpg\n",
"Omar_train_2443.jpg\n",
"Gopika_train_2578.jpg\n",
"Gopika_train_6021.jpg\n",
"Sharath_train_1554.jpg\n",
"Omar_train_5674.jpg\n",
"Sharath_train_1257.jpg\n",
"Abhay_train_6540.jpg\n",
"Sharath_train_4213.jpg\n",
"Kinkar_train_3422.jpg\n",
"Gopika_train_990.jpg\n",
"Omar_train_2864.jpg\n",
"Sharath_train_6959.jpg\n",
"Sharath_train_6191.jpg\n",
"Abhay_train_5191.jpg\n",
"Kinkar_train_3582.jpg\n",
"Abhay_train_6454.jpg\n",
"Gopika_train_2954.jpg\n",
"Sharath_train_4736.jpg\n",
"Omar_train_4430.jpg\n",
"Gopika_train_865.jpg\n",
"Sharath_train_2786.jpg\n",
"Abhay_train_1375.jpg\n",
"Abhay_train_5705.jpg\n",
"Omar_test_149.jpg\n",
"Gopika_train_6282.jpg\n",
"Kinkar_train_691.jpg\n",
"Omar_train_4703.jpg\n",
"Omar_train_736.jpg\n",
"Abhay_train_1743.jpg\n",
"Sharath_train_5666.jpg\n",
"Abhay_train_3565.jpg\n",
"Omar_train_3294.jpg\n",
"Gopika_train_1624.jpg\n",
"Sharath_train_6639.jpg\n",
"Kinkar_train_3922.jpg\n",
"Gopika_train_5937.jpg\n",
"Omar_train_5697.jpg\n",
"Pandian_train_3620.jpg\n",
"Kinkar_train_1485.jpg\n",
"Omar_train_2506.jpg\n",
"Abhay_train_1555.jpg\n",
"Kinkar_train_2792.jpg\n",
"Sharath_train_554.jpg\n",
"Pandian_train_1986.jpg\n",
"Sharath_train_5839.jpg\n",
"Omar_train_6117.jpg\n",
"Arun_train_4973.jpg\n",
"Omar_train_2293.jpg\n",
"Sharath_train_2906.jpg\n",
"Sharath_train_4639.jpg\n",
"Abhay_train_391.jpg\n",
"Gopika_train_0.jpg\n",
"Sharath_train_4347.jpg\n",
"Sharath_train_354.jpg\n",
"Omar_train_4246.jpg\n",
"Sharath_train_142.jpg\n",
"Abhay_train_2208.jpg\n",
"Sharath_train_6204.jpg\n",
"Kinkar_train_1602.jpg\n",
"Omar_test_131.jpg\n",
"Abhay_train_2545.jpg\n",
"Kinkar_train_4043.jpg\n",
"Pandian_train_6449.jpg\n",
"Sharath_train_2573.jpg\n",
"Kinkar_train_6371.jpg\n",
"Sharath_train_1265.jpg\n",
"Sharath_train_5911.jpg\n",
"Sharath_train_5941.jpg\n",
"Sharath_train_5748.jpg\n",
"Kinkar_train_1898.jpg\n",
"Kinkar_train_2193.jpg\n",
"Pandian_train_450.jpg\n",
"Arun_train_359.jpg\n",
"Sharath_train_1871.jpg\n",
"Arun_train_6246.jpg\n",
"Gopika_train_2981.jpg\n",
"Omar_train_2863.jpg\n",
"Sharath_train_3920.jpg\n",
"Kinkar_train_524.jpg\n",
"Arun_train_5507.jpg\n",
"Kinkar_train_644.jpg\n",
"Kinkar_train_3900.jpg\n",
"Gopika_train_3296.jpg\n",
"Sharath_train_4222.jpg\n",
"Gopika_train_5245.jpg\n",
"Abhay_train_2743.jpg\n",
"Omar_train_3914.jpg\n",
"Omar_train_1706.jpg\n",
"Abhay_train_6265.jpg\n",
"Sharath_train_3373.jpg\n",
"Arun_train_3371.jpg\n",
"Abhay_train_3135.jpg\n",
"Omar_train_4476.jpg\n",
"Omar_train_3045.jpg\n",
"Kinkar_train_2535.jpg\n",
"Pandian_train_3919.jpg\n",
"Omar_train_4396.jpg\n",
"Arun_train_6359.jpg\n",
"Omar_train_2245.jpg\n",
"Arun_train_6155.jpg\n",
"Sharath_train_6427.jpg\n",
"Gopika_train_5613.jpg\n",
"Pandian_train_6369.jpg\n",
"Sharath_train_2607.jpg\n",
"Pandian_train_6864.jpg\n",
"Pandian_train_5261.jpg\n",
"Kinkar_train_6367.jpg\n",
"Arun_train_1909.jpg\n",
"Arun_train_5458.jpg\n",
"Omar_test_145.jpg\n",
"Sharath_train_5435.jpg\n",
"Omar_train_1816.jpg\n",
"Kinkar_train_6306.jpg\n",
"Kinkar_train_4201.jpg\n",
"Pandian_train_3397.jpg\n",
"Kinkar_train_5663.jpg\n",
"Sharath_train_3749.jpg\n",
"Gopika_train_6374.jpg\n",
"Sharath_train_1786.jpg\n",
"Abhay_train_1397.jpg\n",
"Arun_train_3677.jpg\n",
"Gopika_train_3413.jpg\n",
"Pandian_train_383.jpg\n",
"Omar_train_3966.jpg\n",
"Omar_train_5286.jpg\n",
"Pandian_train_6638.jpg\n",
"Omar_train_1164.jpg\n",
"Omar_train_5158.jpg\n",
"Omar_train_128.jpg\n",
"Omar_train_5637.jpg\n",
"Sharath_train_3249.jpg\n",
"Gopika_train_2564.jpg\n",
"Arun_train_711.jpg\n",
"Pandian_train_1623.jpg\n",
"Omar_train_2077.jpg\n",
"Gopika_train_4272.jpg\n",
"Kinkar_train_2189.jpg\n",
"Omar_train_442.jpg\n",
"Arun_train_5018.jpg\n",
"Gopika_train_3245.jpg\n",
"Sharath_train_1475.jpg\n",
"Sharath_train_199.jpg\n",
"Kinkar_train_2875.jpg\n",
"Arun_train_4632.jpg\n",
"Kinkar_train_6955.jpg\n",
"Arun_train_3618.jpg\n",
"Omar_train_6833.jpg\n",
"Gopika_train_2365.jpg\n",
"Kinkar_train_78.jpg\n",
"Arun_train_3096.jpg\n",
"Omar_train_1655.jpg\n",
"Kinkar_train_4676.jpg\n",
"Kinkar_train_191.jpg\n",
"Abhay_train_5906.jpg\n",
"Kinkar_train_4042.jpg\n",
"Sharath_train_6568.jpg\n",
"Gopika_train_3356.jpg\n",
"Arun_train_4147.jpg\n",
"Gopika_train_3347.jpg\n",
"Pandian_train_1864.jpg\n",
"Sharath_train_6775.jpg\n",
"Sharath_train_6269.jpg\n",
"Abhay_train_1197.jpg\n",
"Sharath_train_5602.jpg\n",
"Gopika_train_3370.jpg\n",
"Gopika_train_5364.jpg\n",
"Gopika_train_4229.jpg\n",
"Omar_train_4871.jpg\n",
"Sharath_train_1187.jpg\n",
"Kinkar_train_244.jpg\n",
"Abhay_train_6573.jpg\n",
"Arun_train_1879.jpg\n",
"Pandian_train_914.jpg\n",
"Omar_train_710.jpg\n",
"Kinkar_train_3293.jpg\n",
"Omar_train_3431.jpg\n",
"Sharath_train_1928.jpg\n",
"Gopika_train_2313.jpg\n",
"Gopika_train_831.jpg\n",
"Omar_train_1256.jpg\n",
"Sharath_train_6288.jpg\n",
"Kinkar_train_4569.jpg\n",
"Gopika_train_6627.jpg\n",
"Arun_train_5400.jpg\n",
"Omar_train_5577.jpg\n",
"Abhay_test_87.jpg\n",
"Omar_train_5583.jpg\n",
"Omar_train_1100.jpg\n",
"Kinkar_train_6641.jpg\n",
"Omar_train_4466.jpg\n",
"Arun_test_132.jpg\n",
"Kinkar_train_2425.jpg\n",
"Omar_train_6142.jpg\n",
"Abhay_train_4756.jpg\n",
"Abhay_train_550.jpg\n",
"Sharath_test_96.jpg\n",
"Omar_train_5650.jpg\n",
"Sharath_train_2434.jpg\n",
"Sharath_train_4594.jpg\n",
"Abhay_train_5407.jpg\n",
"Omar_train_4529.jpg\n",
"Gopika_train_1271.jpg\n",
"Omar_train_3068.jpg\n",
"Omar_train_1558.jpg\n",
"Kinkar_train_6112.jpg\n",
"Omar_train_981.jpg\n",
"Kinkar_train_3295.jpg\n",
"Abhay_train_3275.jpg\n",
"Pandian_train_1435.jpg\n",
"Abhay_train_2204.jpg\n",
"Omar_train_2874.jpg\n",
"Gopika_train_3584.jpg\n",
"Kinkar_train_3112.jpg\n",
"Pandian_train_6325.jpg\n",
"Sharath_train_2753.jpg\n",
"Sharath_train_633.jpg\n",
"Abhay_train_2412.jpg\n",
"Pandian_train_5338.jpg\n",
"Abhay_train_5255.jpg\n",
"Omar_train_3572.jpg\n",
"Omar_train_6847.jpg\n",
"Arun_train_1245.jpg\n",
"Kinkar_train_432.jpg\n",
"Omar_train_6513.jpg\n",
"Kinkar_train_5144.jpg\n",
"Gopika_train_3334.jpg\n",
"Arun_train_3729.jpg\n",
"Arun_train_1064.jpg\n",
"Kinkar_train_5836.jpg\n",
"Abhay_train_1075.jpg\n",
"Omar_train_4669.jpg\n",
"Omar_train_921.jpg\n",
"Arun_train_788.jpg\n",
"Omar_train_3892.jpg\n",
"Sharath_train_2836.jpg\n",
"Kinkar_train_2298.jpg\n",
"Kinkar_train_5540.jpg\n",
"Kinkar_train_4623.jpg\n",
"Omar_train_2478.jpg\n",
"Sharath_train_5787.jpg\n",
"Sharath_train_993.jpg\n",
"Abhay_train_3910.jpg\n",
"Abhay_train_6559.jpg\n",
"Pandian_train_6820.jpg\n",
"Sharath_train_479.jpg\n",
"Kinkar_train_3903.jpg\n",
"Gopika_train_4145.jpg\n",
"Omar_train_1651.jpg\n",
"Kinkar_train_2125.jpg\n",
"Sharath_train_3361.jpg\n",
"Omar_train_2826.jpg\n",
"Kinkar_train_1372.jpg\n",
"Omar_train_6661.jpg\n",
"Abhay_train_5348.jpg\n",
"Kinkar_train_5796.jpg\n",
"Sharath_train_1162.jpg\n",
"Kinkar_train_3581.jpg\n",
"Kinkar_train_6499.jpg\n",
"Gopika_train_56.jpg\n",
"Omar_train_4826.jpg\n",
"Kinkar_train_5463.jpg\n",
"Gopika_train_3630.jpg\n",
"Sharath_train_4956.jpg\n",
"Sharath_train_334.jpg\n",
"Pandian_train_1811.jpg\n",
"Omar_train_4607.jpg\n",
"Arun_train_2857.jpg\n",
"Pandian_train_6031.jpg\n",
"Abhay_train_263.jpg\n",
"Pandian_train_3621.jpg\n",
"Gopika_train_1705.jpg\n",
"Gopika_train_2585.jpg\n",
"Omar_train_1896.jpg\n",
"Gopika_train_2246.jpg\n",
"Sharath_train_3537.jpg\n",
"Gopika_train_3625.jpg\n",
"Kinkar_train_720.jpg\n",
"Omar_train_4212.jpg\n",
"Pandian_train_4064.jpg\n",
"Arun_train_1848.jpg\n",
"Arun_train_3579.jpg\n",
"Kinkar_test_63.jpg\n",
"Kinkar_train_1251.jpg\n",
"Abhay_train_4190.jpg\n",
"Omar_train_360.jpg\n",
"Sharath_train_1323.jpg\n",
"Kinkar_train_6950.jpg\n",
"Sharath_train_5320.jpg\n",
"Sharath_train_1671.jpg\n",
"Arun_train_2873.jpg\n",
"Omar_train_5443.jpg\n",
"Kinkar_train_2036.jpg\n",
"Sharath_train_5216.jpg\n",
"Gopika_train_4754.jpg\n",
"Kinkar_train_4526.jpg\n",
"Abhay_test_147.jpg\n",
"Omar_train_370.jpg\n",
"Kinkar_train_3070.jpg\n",
"Pandian_train_5456.jpg\n",
"Omar_train_1704.jpg\n",
"Omar_train_1873.jpg\n",
"Arun_train_3833.jpg\n",
"Omar_train_536.jpg\n",
"Gopika_train_5211.jpg\n",
"Omar_train_1658.jpg\n",
"Abhay_train_4609.jpg\n",
"Gopika_train_5318.jpg\n",
"Arun_train_6723.jpg\n",
"Abhay_train_4478.jpg\n",
"Gopika_train_3723.jpg\n",
"Kinkar_train_1388.jpg\n",
"Abhay_train_2782.jpg\n",
"Sharath_train_488.jpg\n",
"Kinkar_train_112.jpg\n",
"Kinkar_train_796.jpg\n",
"Sharath_train_6738.jpg\n",
"Omar_train_6810.jpg\n",
"Pandian_train_4225.jpg\n",
"Sharath_train_3476.jpg\n",
"Abhay_train_2187.jpg\n",
"Omar_train_2610.jpg\n",
"Omar_train_4379.jpg\n",
"Gopika_train_2929.jpg\n",
"Sharath_train_5832.jpg\n",
"Abhay_train_3201.jpg\n",
"Pandian_train_4690.jpg\n",
"Omar_train_5580.jpg\n",
"Gopika_train_572.jpg\n",
"Sharath_train_986.jpg\n",
"Sharath_train_2948.jpg\n",
"Pandian_train_770.jpg\n",
"Omar_train_236.jpg\n",
"Pandian_train_743.jpg\n",
"Sharath_train_374.jpg\n",
"Gopika_train_2844.jpg\n",
"Abhay_train_5591.jpg\n",
"Abhay_train_6366.jpg\n",
"Sharath_train_210.jpg\n",
"Arun_train_4897.jpg\n",
"Omar_train_5727.jpg\n",
"Sharath_train_2185.jpg\n",
"Omar_train_177.jpg\n",
"Sharath_train_2493.jpg\n",
"Omar_train_213.jpg\n",
"Kinkar_train_1025.jpg\n",
"Gopika_train_4896.jpg\n",
"Arun_train_2980.jpg\n",
"Abhay_train_4646.jpg\n",
"Arun_train_920.jpg\n",
"Sharath_train_368.jpg\n",
"Pandian_train_2056.jpg\n",
"Pandian_train_2803.jpg\n",
"Omar_train_2216.jpg\n",
"Gopika_train_4333.jpg\n",
"Omar_test_85.jpg\n",
"Omar_train_1731.jpg\n",
"Sharath_train_3879.jpg\n",
"Pandian_train_2025.jpg\n",
"Omar_train_4624.jpg\n",
"Gopika_train_3698.jpg\n",
"Gopika_test_117.jpg\n",
"Abhay_train_3795.jpg\n",
"Sharath_train_5833.jpg\n",
"Kinkar_train_3147.jpg\n",
"Arun_train_5403.jpg\n",
"Sharath_train_1171.jpg\n",
"Abhay_train_4334.jpg\n",
"Sharath_train_2418.jpg\n",
"Abhay_train_693.jpg\n",
"Omar_train_6105.jpg\n",
"Abhay_train_6241.jpg\n",
"Gopika_train_6839.jpg\n",
"Omar_train_2990.jpg\n",
"Abhay_train_1687.jpg\n",
"Kinkar_train_3458.jpg\n",
"Abhay_train_6737.jpg\n",
"Kinkar_train_6852.jpg\n",
"Kinkar_train_3669.jpg\n",
"Omar_train_2630.jpg\n",
"Omar_train_6910.jpg\n",
"Gopika_train_6498.jpg\n",
"Abhay_train_1763.jpg\n",
"Kinkar_train_3020.jpg\n",
"Arun_train_1109.jpg\n",
"Kinkar_train_5530.jpg\n",
"Gopika_train_3671.jpg\n",
"Pandian_train_1281.jpg\n",
"Omar_train_3024.jpg\n",
"Omar_train_5000.jpg\n",
"Kinkar_train_5263.jpg\n",
"Sharath_train_5478.jpg\n",
"Arun_train_6867.jpg\n",
"Arun_train_2305.jpg\n",
"Omar_train_3598.jpg\n",
"Pandian_train_6331.jpg\n",
"Pandian_train_5770.jpg\n",
"Gopika_train_5729.jpg\n",
"Gopika_train_4939.jpg\n",
"Abhay_train_3229.jpg\n",
"Kinkar_train_5542.jpg\n",
"Pandian_train_2523.jpg\n",
"Omar_train_2008.jpg\n",
"Abhay_test_112.jpg\n",
"Omar_train_3213.jpg\n",
"Sharath_train_5031.jpg\n",
"Sharath_train_3751.jpg\n",
"Arun_train_6935.jpg\n",
"Sharath_train_1240.jpg\n",
"Kinkar_train_5519.jpg\n",
"Sharath_train_4500.jpg\n",
"Gopika_train_5831.jpg\n",
"Gopika_train_969.jpg\n",
"Omar_train_6013.jpg\n",
"Kinkar_train_6006.jpg\n",
"Sharath_train_2982.jpg\n",
"Abhay_train_3316.jpg\n",
"Kinkar_train_1116.jpg\n",
"Omar_train_2833.jpg\n",
"Gopika_train_6114.jpg\n",
"Omar_train_2819.jpg\n",
"Omar_train_3023.jpg\n",
"Abhay_train_6584.jpg\n",
"Sharath_train_1932.jpg\n",
"Omar_train_5020.jpg\n",
"Arun_train_872.jpg\n",
"Omar_train_1637.jpg\n",
"Sharath_train_1802.jpg\n",
"Kinkar_train_3472.jpg\n",
"Omar_train_800.jpg\n",
"Kinkar_train_3543.jpg\n",
"Sharath_train_1124.jpg\n",
"Abhay_train_4455.jpg\n",
"Abhay_train_1642.jpg\n",
"Sharath_test_130.jpg\n",
"Arun_train_1465.jpg\n",
"Kinkar_train_5197.jpg\n",
"Pandian_train_3482.jpg\n",
"Arun_train_98.jpg\n",
"Arun_train_2935.jpg\n",
"Omar_train_6437.jpg\n",
"Kinkar_train_4279.jpg\n",
"Sharath_train_5856.jpg\n",
"Abhay_train_1048.jpg\n",
"Pandian_train_2251.jpg\n",
"Kinkar_train_5522.jpg\n",
"Pandian_train_1026.jpg\n",
"Arun_train_4654.jpg\n",
"Arun_train_2584.jpg\n",
"Kinkar_train_565.jpg\n",
"Sharath_train_1157.jpg\n",
"Abhay_train_2244.jpg\n",
"Gopika_train_4812.jpg\n",
"Sharath_train_4323.jpg\n",
"Gopika_train_4745.jpg\n",
"Abhay_train_715.jpg\n",
"Sharath_train_5505.jpg\n",
"Sharath_train_2046.jpg\n",
"Gopika_train_1262.jpg\n",
"Kinkar_train_212.jpg\n",
"Sharath_train_4136.jpg\n",
"Abhay_train_3908.jpg\n",
"Abhay_train_1459.jpg\n",
"Omar_train_5344.jpg\n",
"Kinkar_train_3461.jpg\n",
"Omar_train_322.jpg\n",
"Arun_train_2909.jpg\n",
"Gopika_train_5389.jpg\n",
"Abhay_train_6405.jpg\n",
"Sharath_train_6448.jpg\n",
"Kinkar_train_3271.jpg\n",
"Omar_train_4649.jpg\n",
"Omar_train_1579.jpg\n",
"Sharath_train_4822.jpg\n",
"Pandian_train_3938.jpg\n",
"Omar_train_1311.jpg\n",
"Kinkar_train_5295.jpg\n",
"Kinkar_train_3643.jpg\n",
"Abhay_train_6898.jpg\n",
"Omar_train_6490.jpg\n",
"Sharath_train_4195.jpg\n",
"Gopika_train_3022.jpg\n",
"Gopika_train_1716.jpg\n",
"Pandian_train_6945.jpg\n",
"Pandian_train_877.jpg\n",
"Omar_train_6789.jpg\n",
"Gopika_train_6424.jpg\n",
"Gopika_train_2001.jpg\n",
"Arun_train_4371.jpg\n",
"Sharath_train_3845.jpg\n",
"Sharath_train_84.jpg\n",
"Gopika_train_327.jpg\n",
"Arun_train_2959.jpg\n",
"Omar_train_1097.jpg\n",
"Abhay_train_1838.jpg\n",
"Abhay_train_1537.jpg\n",
"Abhay_train_6099.jpg\n",
"Pandian_train_2918.jpg\n",
"Omar_train_6667.jpg\n",
"Pandian_train_402.jpg\n",
"Sharath_train_2197.jpg\n",
"Pandian_train_6569.jpg\n",
"Gopika_train_3446.jpg\n",
"Sharath_train_5465.jpg\n",
"Sharath_train_5417.jpg\n",
"Sharath_train_4513.jpg\n",
"Omar_train_4028.jpg\n",
"Kinkar_train_6934.jpg\n",
"Sharath_train_3967.jpg\n",
"Gopika_train_6483.jpg\n",
"Kinkar_train_2962.jpg\n",
"Omar_train_3802.jpg\n",
"Kinkar_train_3417.jpg\n",
"Pandian_train_941.jpg\n",
"Omar_train_997.jpg\n",
"Omar_train_3208.jpg\n",
"Sharath_train_3366.jpg\n",
"Arun_train_1024.jpg\n",
"Sharath_train_1734.jpg\n",
"Kinkar_train_1759.jpg\n",
"Arun_train_4796.jpg\n",
"Gopika_train_2347.jpg\n",
"Kinkar_train_5366.jpg\n",
"Kinkar_train_1713.jpg\n",
"Arun_train_5395.jpg\n",
"Abhay_train_3544.jpg\n",
"Pandian_train_3424.jpg\n",
"Pandian_train_4134.jpg\n",
"Sharath_train_996.jpg\n",
"Kinkar_train_3261.jpg\n",
"Sharath_train_5672.jpg\n",
"Pandian_train_2062.jpg\n",
"Omar_train_1739.jpg\n",
"Pandian_train_511.jpg\n",
"Gopika_train_1578.jpg\n",
"Kinkar_train_765.jpg\n",
"Pandian_train_4715.jpg\n",
"Omar_train_6094.jpg\n",
"Gopika_train_6601.jpg\n",
"Gopika_train_6361.jpg\n",
"Sharath_train_471.jpg\n",
"Omar_train_1631.jpg\n",
"Sharath_train_5609.jpg\n",
"Kinkar_train_1767.jpg\n",
"Gopika_train_1643.jpg\n",
"Pandian_train_6871.jpg\n",
"Gopika_train_898.jpg\n",
"Sharath_train_5892.jpg\n",
"Omar_train_2969.jpg\n",
"Gopika_train_4650.jpg\n",
"Abhay_train_1914.jpg\n",
"Omar_train_5981.jpg\n",
"Gopika_train_5123.jpg\n",
"Gopika_train_5926.jpg\n",
"Gopika_train_67.jpg\n",
"Abhay_train_5918.jpg\n",
"Abhay_train_6622.jpg\n",
"Sharath_train_3129.jpg\n",
"Pandian_test_50.jpg\n",
"Kinkar_train_1344.jpg\n",
"Gopika_train_2754.jpg\n",
"Kinkar_train_4245.jpg\n",
"Arun_train_1638.jpg\n",
"Kinkar_train_2355.jpg\n",
"Abhay_train_5902.jpg\n",
"Kinkar_train_6250.jpg\n",
"Arun_train_1083.jpg\n",
"Sharath_train_5106.jpg\n",
"Kinkar_train_3907.jpg\n",
"Sharath_train_2850.jpg\n",
"Gopika_train_4361.jpg\n",
"Sharath_train_1504.jpg\n",
"Arun_train_6533.jpg\n",
"Omar_train_1918.jpg\n",
"Arun_train_5045.jpg\n",
"Kinkar_train_5910.jpg\n",
"Omar_train_4811.jpg\n",
"Pandian_train_5695.jpg\n",
"Sharath_train_4156.jpg\n",
"Sharath_train_305.jpg\n",
"Omar_train_1808.jpg\n",
"Sharath_train_5737.jpg\n",
"Pandian_train_6444.jpg\n",
"Pandian_train_5962.jpg\n",
"Arun_train_1627.jpg\n",
"Omar_test_92.jpg\n",
"Sharath_train_5127.jpg\n",
"Gopika_train_3197.jpg\n",
"Kinkar_train_6640.jpg\n",
"Sharath_train_5133.jpg\n",
"Kinkar_train_255.jpg\n",
"Pandian_train_3471.jpg\n",
"Pandian_train_218.jpg\n",
"Pandian_train_2050.jpg\n",
"Sharath_train_2169.jpg\n",
"Kinkar_train_6260.jpg\n",
"Pandian_train_3968.jpg\n",
"Gopika_train_2386.jpg\n",
"Gopika_train_1591.jpg\n",
"Arun_train_977.jpg\n",
"Sharath_train_680.jpg\n",
"Omar_train_4517.jpg\n",
"Gopika_train_5098.jpg\n",
"Sharath_train_3202.jpg\n",
"Arun_train_3178.jpg\n",
"Sharath_train_879.jpg\n",
"Gopika_train_5623.jpg\n",
"Abhay_train_2028.jpg\n",
"Sharath_train_3077.jpg\n",
"Kinkar_train_1874.jpg\n",
"Omar_train_1267.jpg\n",
"Arun_train_1688.jpg\n",
"Kinkar_train_3298.jpg\n",
"Gopika_train_5755.jpg\n",
"Pandian_train_1468.jpg\n",
"Omar_train_6163.jpg\n",
"Sharath_train_188.jpg\n",
"Arun_train_769.jpg\n",
"Kinkar_train_5762.jpg\n",
"Sharath_train_5711.jpg\n",
"Kinkar_train_3849.jpg\n",
"Omar_train_1301.jpg\n",
"Sharath_train_2965.jpg\n",
"Sharath_train_2670.jpg\n",
"Abhay_train_1654.jpg\n",
"Sharath_train_4391.jpg\n",
"Omar_train_5789.jpg\n",
"Arun_train_2758.jpg\n",
"Sharath_train_3719.jpg\n",
"Arun_train_3459.jpg\n",
"Kinkar_train_3101.jpg\n",
"Kinkar_train_2342.jpg\n",
"Abhay_train_3591.jpg\n",
"Kinkar_train_3345.jpg\n",
"Abhay_train_335.jpg\n",
"Arun_train_5290.jpg\n",
"Arun_train_6650.jpg\n",
"Abhay_train_1294.jpg\n",
"Arun_train_5352.jpg\n",
"Abhay_train_5983.jpg\n",
"Omar_train_2115.jpg\n",
"Kinkar_train_6323.jpg\n",
"Kinkar_train_6160.jpg\n",
"Sharath_train_4991.jpg\n",
"Pandian_train_5300.jpg\n",
"Pandian_train_4035.jpg\n",
"Omar_train_6259.jpg\n",
"Sharath_train_4151.jpg\n",
"Omar_train_6563.jpg\n",
"Abhay_train_5664.jpg\n",
"Abhay_train_4967.jpg\n",
"Gopika_train_3709.jpg\n",
"Sharath_train_5508.jpg\n",
"Omar_train_4857.jpg\n",
"Kinkar_train_4251.jpg\n",
"Abhay_train_6899.jpg\n",
"Arun_train_309.jpg\n",
"Pandian_train_503.jpg\n",
"Kinkar_train_3251.jpg\n",
"Sharath_train_5857.jpg\n",
"Sharath_train_4021.jpg\n",
"Sharath_train_4630.jpg\n",
"Omar_train_6756.jpg\n",
"Sharath_train_5930.jpg\n",
"Omar_train_2239.jpg\n",
"Gopika_train_848.jpg\n",
"Gopika_train_5763.jpg\n",
"Pandian_train_6316.jpg\n",
"Kinkar_train_4305.jpg\n",
"Gopika_train_1236.jpg\n",
"Abhay_train_2422.jpg\n",
"Omar_train_6645.jpg\n",
"Pandian_train_5817.jpg\n",
"Arun_train_1477.jpg\n",
"Kinkar_train_576.jpg\n",
"Kinkar_train_4192.jpg\n",
"Kinkar_train_6780.jpg\n",
"Kinkar_train_5250.jpg\n",
"Kinkar_train_4610.jpg\n",
"Abhay_train_3798.jpg\n",
"Arun_train_1813.jpg\n",
"Gopika_train_970.jpg\n",
"Omar_train_1882.jpg\n",
"Gopika_train_5719.jpg\n",
"Arun_train_2601.jpg\n",
"Omar_train_6890.jpg\n",
"Arun_train_4244.jpg\n",
"Pandian_train_2919.jpg\n",
"Gopika_train_3300.jpg\n",
"Sharath_train_2725.jpg\n",
"Arun_train_6355.jpg\n",
"Gopika_train_1887.jpg\n",
"Pandian_train_2936.jpg\n",
"Gopika_train_109.jpg\n",
"Sharath_train_289.jpg\n",
"Sharath_train_6936.jpg\n",
"Gopika_train_2589.jpg\n",
"Sharath_train_3464.jpg\n",
"Arun_train_1626.jpg\n",
"Abhay_train_3480.jpg\n",
"Kinkar_train_323.jpg\n",
"Gopika_train_5840.jpg\n",
"Sharath_train_3657.jpg\n",
"Pandian_train_2258.jpg\n",
"Abhay_train_6119.jpg\n",
"Sharath_train_6210.jpg\n",
"Omar_train_1170.jpg\n",
"Gopika_train_1343.jpg\n",
"Kinkar_train_3872.jpg\n",
"Sharath_train_982.jpg\n",
"Pandian_train_5095.jpg\n",
"Kinkar_train_352.jpg\n",
"Arun_train_637.jpg\n",
"Abhay_train_257.jpg\n",
"Kinkar_train_329.jpg\n",
"Arun_train_1894.jpg\n",
"Kinkar_train_2798.jpg\n",
"Gopika_train_5140.jpg\n",
"Sharath_train_2233.jpg\n",
"Sharath_train_91.jpg\n",
"Omar_train_6758.jpg\n",
"Abhay_train_4374.jpg\n",
"Omar_train_6665.jpg\n",
"Omar_train_2404.jpg\n",
"Sharath_train_6814.jpg\n",
"Omar_train_5626.jpg\n",
"Abhay_train_1273.jpg\n",
"Sharath_train_5378.jpg\n",
"Gopika_train_3414.jpg\n",
"Abhay_train_6133.jpg\n",
"Omar_train_3441.jpg\n",
"Omar_train_1519.jpg\n",
"Pandian_train_5566.jpg\n",
"Kinkar_train_3474.jpg\n",
"Omar_train_4870.jpg\n",
"Sharath_train_6174.jpg\n",
"Kinkar_train_5980.jpg\n",
"Arun_train_5993.jpg\n",
"Gopika_train_6866.jpg\n",
"Gopika_train_951.jpg\n",
"Sharath_train_6079.jpg\n",
"Abhay_train_3283.jpg\n",
"Pandian_test_69.jpg\n",
"Gopika_train_5326.jpg\n",
"Arun_train_138.jpg\n",
"Gopika_train_5793.jpg\n",
"Gopika_train_463.jpg\n",
"Arun_train_3984.jpg\n",
"Pandian_train_6502.jpg\n",
"Abhay_train_2555.jpg\n",
"Omar_train_6005.jpg\n",
"Pandian_train_2041.jpg\n",
"Pandian_train_899.jpg\n",
"Arun_train_841.jpg\n",
"Kinkar_train_4818.jpg\n",
"Kinkar_train_737.jpg\n",
"Sharath_train_2932.jpg\n",
"Pandian_train_2907.jpg\n",
"Gopika_train_4709.jpg\n",
"Sharath_train_900.jpg\n",
"Omar_train_5916.jpg\n",
"Sharath_train_557.jpg\n",
"Sharath_train_6733.jpg\n",
"Sharath_train_2075.jpg\n",
"Sharath_train_4378.jpg\n",
"Gopika_train_1599.jpg\n",
"Kinkar_train_4207.jpg\n",
"Sharath_train_5179.jpg\n",
"Arun_train_696.jpg\n",
"Omar_train_1364.jpg\n",
"Omar_train_1326.jpg\n",
"Arun_train_3.jpg\n",
"Gopika_train_2685.jpg\n",
"Sharath_train_703.jpg\n",
"Gopika_train_49.jpg\n",
"Arun_train_678.jpg\n",
"Omar_train_3811.jpg\n",
"Pandian_train_6536.jpg\n",
"Sharath_train_5608.jpg\n",
"Kinkar_train_431.jpg\n",
"Sharath_train_1471.jpg\n",
"Gopika_train_5013.jpg\n",
"Arun_train_426.jpg\n",
"Kinkar_train_5865.jpg\n",
"Gopika_train_2487.jpg\n",
"Sharath_train_5333.jpg\n",
"Gopika_train_1841.jpg\n",
"Sharath_train_944.jpg\n",
"Arun_train_625.jpg\n",
"Omar_train_5100.jpg\n",
"Arun_train_231.jpg\n",
"Abhay_train_6539.jpg\n",
"Gopika_train_29.jpg\n",
"Pandian_train_5561.jpg\n",
"Gopika_train_4017.jpg\n",
"Sharath_train_573.jpg\n",
"Sharath_train_799.jpg\n",
"Kinkar_train_6113.jpg\n",
"Omar_train_2995.jpg\n",
"Sharath_train_1565.jpg\n",
"Arun_train_5677.jpg\n",
"Omar_train_5200.jpg\n",
"Arun_train_4359.jpg\n",
"Omar_test_54.jpg\n",
"Gopika_train_5137.jpg\n",
"Abhay_train_4580.jpg\n",
"Omar_train_230.jpg\n",
"Gopika_test_161.jpg\n",
"Kinkar_train_4061.jpg\n",
"Omar_train_2087.jpg\n",
"Abhay_train_1583.jpg\n",
"Sharath_train_1150.jpg\n",
"Sharath_train_4681.jpg\n",
"Sharath_train_4788.jpg\n",
"Abhay_train_4429.jpg\n",
"Abhay_train_3037.jpg\n",
"Gopika_train_5837.jpg\n",
"Gopika_train_4803.jpg\n",
"Abhay_train_2680.jpg\n",
"Gopika_train_4997.jpg\n",
"Sharath_train_5898.jpg\n",
"Sharath_train_1328.jpg\n",
"Kinkar_train_2795.jpg\n",
"Arun_test_123.jpg\n",
"Gopika_train_1840.jpg\n",
"Kinkar_train_5851.jpg\n",
"Arun_train_3460.jpg\n",
"Omar_train_6893.jpg\n",
"Arun_train_5855.jpg\n",
"Sharath_train_1366.jpg\n",
"Arun_train_6136.jpg\n",
"Abhay_train_1049.jpg\n",
"Sharath_train_2367.jpg\n",
"Kinkar_train_358.jpg\n",
"Arun_train_1399.jpg\n",
"Gopika_train_1608.jpg\n",
"Arun_train_6487.jpg\n",
"Sharath_train_4419.jpg\n",
"Kinkar_train_3005.jpg\n",
"Sharath_train_3477.jpg\n",
"Omar_train_3318.jpg\n",
"Kinkar_train_5880.jpg\n",
"Gopika_train_6281.jpg\n",
"Gopika_train_2899.jpg\n",
"Omar_train_6242.jpg\n",
"Pandian_train_4488.jpg\n",
"Arun_train_5327.jpg\n",
"Abhay_train_6668.jpg\n",
"Abhay_train_6889.jpg\n",
"Sharath_train_1746.jpg\n",
"Pandian_train_4662.jpg\n",
"Gopika_train_2068.jpg\n",
"Kinkar_train_6474.jpg\n",
"Sharath_train_3731.jpg\n",
"Gopika_train_6811.jpg\n",
"Abhay_train_3697.jpg\n",
"Sharath_train_4844.jpg\n",
"Sharath_train_5476.jpg\n",
"Omar_train_4631.jpg\n",
"Arun_train_4139.jpg\n",
"Kinkar_train_679.jpg\n",
"Arun_train_5977.jpg\n",
"Omar_train_5379.jpg\n",
"Kinkar_train_2420.jpg\n",
"Omar_train_2930.jpg\n",
"Kinkar_train_449.jpg\n",
"Kinkar_train_4758.jpg\n",
"Arun_train_6670.jpg\n",
"Gopika_train_6126.jpg\n",
"Omar_train_686.jpg\n",
"Abhay_train_3182.jpg\n",
"Abhay_train_5659.jpg\n",
"Sharath_train_586.jpg\n",
"Abhay_train_2190.jpg\n",
"Sharath_train_268.jpg\n",
"Omar_train_5325.jpg\n",
"Arun_test_45.jpg\n",
"Sharath_train_4668.jpg\n",
"Gopika_train_6033.jpg\n",
"Gopika_train_6829.jpg\n",
"Sharath_train_1730.jpg\n",
"Sharath_train_816.jpg\n",
"Kinkar_train_2700.jpg\n",
"Abhay_train_3760.jpg\n",
"Arun_train_4670.jpg\n",
"Gopika_train_2328.jpg\n",
"Abhay_train_5475.jpg\n",
"Omar_train_5560.jpg\n",
"Sharath_train_3160.jpg\n",
"Abhay_train_395.jpg\n",
"Arun_train_4542.jpg\n",
"Sharath_train_1319.jpg\n",
"Abhay_train_4274.jpg\n",
"Pandian_train_705.jpg\n",
"Gopika_train_1415.jpg\n",
"Kinkar_train_912.jpg\n",
"Abhay_train_6567.jpg\n",
"Omar_train_2276.jpg\n",
"Kinkar_train_2883.jpg\n",
"Sharath_train_1357.jpg\n",
"Sharath_train_991.jpg\n",
"Kinkar_train_5634.jpg\n",
"Omar_train_6520.jpg\n",
"Abhay_train_61.jpg\n",
"Pandian_train_4889.jpg\n",
"Omar_train_3717.jpg\n",
"Omar_train_276.jpg\n",
"Omar_train_5936.jpg\n",
"Sharath_train_624.jpg\n",
"Sharath_train_5587.jpg\n",
"Gopika_train_3009.jpg\n",
"Gopika_train_1785.jpg\n",
"Sharath_train_1842.jpg\n",
"Sharath_train_3332.jpg\n",
"Sharath_train_6037.jpg\n",
"Sharath_train_6509.jpg\n",
"Abhay_train_4739.jpg\n"
]
}
],
"source": [
"for name in files:\n",
" file_name = np.append(file_name,direct+'/' + name)\n",
" print name\n",
" index = name.index('_')\n",
" data_type = np.append(data_type,name[index:index+4])\n",
" subject = np.append(subject,name[:index])"
]
},
{
"cell_type": "code",
"execution_count": 9,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"map, Y_number = np.unique(subject, return_inverse=True)"
]
},
{
"cell_type": "code",
"execution_count": 10,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"array([0, 4, 6, ..., 6, 6, 0])"
]
},
"execution_count": 10,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"Y_number"
]
},
{
"cell_type": "code",
"execution_count": 11,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"data = np.vstack((np.array(file_name), np.array(Y_number),np.array(data_type)))\n",
"data = np.transpose(data)\n",
"data = data.astype(str)"
]
},
{
"cell_type": "code",
"execution_count": 12,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"train = data[data[:,2]=='_tra',]\n",
"test = data[data[:,2]!='_tra',]"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": []
},
{
"cell_type": "code",
"execution_count": 13,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"np.savetxt(\"train.txt\", train[:,:2], delimiter=\" \",fmt='%s')\n",
"np.savetxt(\"test.txt\", test[:,:2], delimiter=\" \",fmt='%s')"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": []
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 2",
"language": "python",
"name": "python2"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 2
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython2",
"version": "2.7.12"
}
},
"nbformat": 4,
"nbformat_minor": 0
}
| mit |
ES-DOC/esdoc-jupyterhub | notebooks/mohc/cmip6/models/hadgem3-gc31-hm/aerosol.ipynb | 1 | 84302 | {
"nbformat_minor": 0,
"nbformat": 4,
"cells": [
{
"source": [
"# ES-DOC CMIP6 Model Properties - Aerosol \n",
"**MIP Era**: CMIP6 \n",
"**Institute**: MOHC \n",
"**Source ID**: HADGEM3-GC31-HM \n",
"**Topic**: Aerosol \n",
"**Sub-Topics**: Transport, Emissions, Concentrations, Optical Radiative Properties, Model. \n",
"**Properties**: 69 (37 required) \n",
"**Model descriptions**: [Model description details](https://specializations.es-doc.org/cmip6/aerosol?client=jupyter-notebook) \n",
"**Initialized From**: -- \n",
"\n",
"**Notebook Help**: [Goto notebook help page](https://es-doc.org/cmip6-models-documenting-with-ipython) \n",
"**Notebook Initialised**: 2018-02-15 16:54:14"
],
"cell_type": "markdown",
"metadata": {}
},
{
"source": [
"### Document Setup \n",
"**IMPORTANT: to be executed each time you run the notebook** "
],
"cell_type": "markdown",
"metadata": {}
},
{
"execution_count": null,
"cell_type": "code",
"source": [
"# DO NOT EDIT ! \n",
"from pyesdoc.ipython.model_topic import NotebookOutput \n",
"\n",
"# DO NOT EDIT ! \n",
"DOC = NotebookOutput('cmip6', 'mohc', 'hadgem3-gc31-hm', 'aerosol')"
],
"outputs": [],
"metadata": {}
},
{
"source": [
"### Document Authors \n",
"*Set document authors*"
],
"cell_type": "markdown",
"metadata": {}
},
{
"execution_count": null,
"cell_type": "code",
"source": [
"# Set as follows: DOC.set_author(\"name\", \"email\") \n",
"# TODO - please enter value(s)"
],
"outputs": [],
"metadata": {}
},
{
"source": [
"### Document Contributors \n",
"*Specify document contributors* "
],
"cell_type": "markdown",
"metadata": {}
},
{
"execution_count": null,
"cell_type": "code",
"source": [
"# Set as follows: DOC.set_contributor(\"name\", \"email\") \n",
"# TODO - please enter value(s)"
],
"outputs": [],
"metadata": {}
},
{
"source": [
"### Document Publication \n",
"*Specify document publication status* "
],
"cell_type": "markdown",
"metadata": {}
},
{
"execution_count": null,
"cell_type": "code",
"source": [
"# Set publication status: \n",
"# 0=do not publish, 1=publish. \n",
"DOC.set_publication_status(0)"
],
"outputs": [],
"metadata": {}
},
{
"source": [
"### Document Table of Contents \n",
"[1. Key Properties](#1.-Key-Properties) \n",
"[2. Key Properties --> Software Properties](#2.-Key-Properties--->-Software-Properties) \n",
"[3. Key Properties --> Timestep Framework](#3.-Key-Properties--->-Timestep-Framework) \n",
"[4. Key Properties --> Meteorological Forcings](#4.-Key-Properties--->-Meteorological-Forcings) \n",
"[5. Key Properties --> Resolution](#5.-Key-Properties--->-Resolution) \n",
"[6. Key Properties --> Tuning Applied](#6.-Key-Properties--->-Tuning-Applied) \n",
"[7. Transport](#7.-Transport) \n",
"[8. Emissions](#8.-Emissions) \n",
"[9. Concentrations](#9.-Concentrations) \n",
"[10. Optical Radiative Properties](#10.-Optical-Radiative-Properties) \n",
"[11. Optical Radiative Properties --> Absorption](#11.-Optical-Radiative-Properties--->-Absorption) \n",
"[12. Optical Radiative Properties --> Mixtures](#12.-Optical-Radiative-Properties--->-Mixtures) \n",
"[13. Optical Radiative Properties --> Impact Of H2o](#13.-Optical-Radiative-Properties--->-Impact-Of-H2o) \n",
"[14. Optical Radiative Properties --> Radiative Scheme](#14.-Optical-Radiative-Properties--->-Radiative-Scheme) \n",
"[15. Optical Radiative Properties --> Cloud Interactions](#15.-Optical-Radiative-Properties--->-Cloud-Interactions) \n",
"[16. Model](#16.-Model) \n",
"\n"
],
"cell_type": "markdown",
"metadata": {}
},
{
"source": [
"# 1. Key Properties \n",
"*Key properties of the aerosol model*"
],
"cell_type": "markdown",
"metadata": {}
},
{
"source": [
"### 1.1. Model Overview\n",
"**Is Required:** TRUE **Type:** STRING **Cardinality:** 1.1\n",
"### *Overview of aerosol model.*"
],
"cell_type": "markdown",
"metadata": {}
},
{
"execution_count": null,
"cell_type": "code",
"source": [
"# PROPERTY ID - DO NOT EDIT ! \n",
"DOC.set_id('cmip6.aerosol.key_properties.model_overview') \n",
"\n",
"# PROPERTY VALUE: \n",
"# Set as follows: DOC.set_value(\"value\") \n",
"# TODO - please enter value(s)",
"\n"
],
"outputs": [],
"metadata": {
"collapsed": true
}
},
{
"source": [
"### 1.2. Model Name\n",
"**Is Required:** TRUE **Type:** STRING **Cardinality:** 1.1\n",
"### *Name of aerosol model code*"
],
"cell_type": "markdown",
"metadata": {}
},
{
"execution_count": null,
"cell_type": "code",
"source": [
"# PROPERTY ID - DO NOT EDIT ! \n",
"DOC.set_id('cmip6.aerosol.key_properties.model_name') \n",
"\n",
"# PROPERTY VALUE: \n",
"# Set as follows: DOC.set_value(\"value\") \n",
"# TODO - please enter value(s)",
"\n"
],
"outputs": [],
"metadata": {
"collapsed": true
}
},
{
"source": [
"### 1.3. Scheme Scope\n",
"**Is Required:** TRUE **Type:** ENUM **Cardinality:** 1.N\n",
"### *Atmospheric domains covered by the aerosol model*"
],
"cell_type": "markdown",
"metadata": {}
},
{
"execution_count": null,
"cell_type": "code",
"source": [
"# PROPERTY ID - DO NOT EDIT ! \n",
"DOC.set_id('cmip6.aerosol.key_properties.scheme_scope') \n",
"\n",
"# PROPERTY VALUE(S): \n",
"# Set as follows: DOC.set_value(\"value\") \n",
"# Valid Choices: \n",
"# \"troposhere\" \n",
"# \"stratosphere\" \n",
"# \"mesosphere\" \n",
"# \"mesosphere\" \n",
"# \"whole atmosphere\" \n",
"# \"Other: [Please specify]\" \n",
"# TODO - please enter value(s)",
"\n"
],
"outputs": [],
"metadata": {
"collapsed": true
}
},
{
"source": [
"### 1.4. Basic Approximations\n",
"**Is Required:** TRUE **Type:** STRING **Cardinality:** 1.1\n",
"### *Basic approximations made in the aerosol model*"
],
"cell_type": "markdown",
"metadata": {}
},
{
"execution_count": null,
"cell_type": "code",
"source": [
"# PROPERTY ID - DO NOT EDIT ! \n",
"DOC.set_id('cmip6.aerosol.key_properties.basic_approximations') \n",
"\n",
"# PROPERTY VALUE: \n",
"# Set as follows: DOC.set_value(\"value\") \n",
"# TODO - please enter value(s)",
"\n"
],
"outputs": [],
"metadata": {
"collapsed": true
}
},
{
"source": [
"### 1.5. Prognostic Variables Form\n",
"**Is Required:** TRUE **Type:** ENUM **Cardinality:** 1.N\n",
"### *Prognostic variables in the aerosol model*"
],
"cell_type": "markdown",
"metadata": {}
},
{
"execution_count": null,
"cell_type": "code",
"source": [
"# PROPERTY ID - DO NOT EDIT ! \n",
"DOC.set_id('cmip6.aerosol.key_properties.prognostic_variables_form') \n",
"\n",
"# PROPERTY VALUE(S): \n",
"# Set as follows: DOC.set_value(\"value\") \n",
"# Valid Choices: \n",
"# \"3D mass/volume ratio for aerosols\" \n",
"# \"3D number concenttration for aerosols\" \n",
"# \"Other: [Please specify]\" \n",
"# TODO - please enter value(s)",
"\n"
],
"outputs": [],
"metadata": {
"collapsed": true
}
},
{
"source": [
"### 1.6. Number Of Tracers\n",
"**Is Required:** TRUE **Type:** INTEGER **Cardinality:** 1.1\n",
"### *Number of tracers in the aerosol model*"
],
"cell_type": "markdown",
"metadata": {}
},
{
"execution_count": null,
"cell_type": "code",
"source": [
"# PROPERTY ID - DO NOT EDIT ! \n",
"DOC.set_id('cmip6.aerosol.key_properties.number_of_tracers') \n",
"\n",
"# PROPERTY VALUE: \n",
"# Set as follows: DOC.set_value(value) \n",
"# TODO - please enter value(s)",
"\n"
],
"outputs": [],
"metadata": {
"collapsed": true
}
},
{
"source": [
"### 1.7. Family Approach\n",
"**Is Required:** TRUE **Type:** BOOLEAN **Cardinality:** 1.1\n",
"### *Are aerosol calculations generalized into families of species?*"
],
"cell_type": "markdown",
"metadata": {}
},
{
"execution_count": null,
"cell_type": "code",
"source": [
"# PROPERTY ID - DO NOT EDIT ! \n",
"DOC.set_id('cmip6.aerosol.key_properties.family_approach') \n",
"\n",
"# PROPERTY VALUE: \n",
"# Set as follows: DOC.set_value(value) \n",
"# Valid Choices: \n",
"# True \n",
"# False \n",
"# TODO - please enter value(s)",
"\n"
],
"outputs": [],
"metadata": {
"collapsed": true
}
},
{
"source": [
"# 2. Key Properties --> Software Properties \n",
"*Software properties of aerosol code*"
],
"cell_type": "markdown",
"metadata": {}
},
{
"source": [
"### 2.1. Repository\n",
"**Is Required:** FALSE **Type:** STRING **Cardinality:** 0.1\n",
"### *Location of code for this component.*"
],
"cell_type": "markdown",
"metadata": {}
},
{
"execution_count": null,
"cell_type": "code",
"source": [
"# PROPERTY ID - DO NOT EDIT ! \n",
"DOC.set_id('cmip6.aerosol.key_properties.software_properties.repository') \n",
"\n",
"# PROPERTY VALUE: \n",
"# Set as follows: DOC.set_value(\"value\") \n",
"# TODO - please enter value(s)",
"\n"
],
"outputs": [],
"metadata": {
"collapsed": true
}
},
{
"source": [
"### 2.2. Code Version\n",
"**Is Required:** FALSE **Type:** STRING **Cardinality:** 0.1\n",
"### *Code version identifier.*"
],
"cell_type": "markdown",
"metadata": {}
},
{
"execution_count": null,
"cell_type": "code",
"source": [
"# PROPERTY ID - DO NOT EDIT ! \n",
"DOC.set_id('cmip6.aerosol.key_properties.software_properties.code_version') \n",
"\n",
"# PROPERTY VALUE: \n",
"# Set as follows: DOC.set_value(\"value\") \n",
"# TODO - please enter value(s)",
"\n"
],
"outputs": [],
"metadata": {
"collapsed": true
}
},
{
"source": [
"### 2.3. Code Languages\n",
"**Is Required:** FALSE **Type:** STRING **Cardinality:** 0.N\n",
"### *Code language(s).*"
],
"cell_type": "markdown",
"metadata": {}
},
{
"execution_count": null,
"cell_type": "code",
"source": [
"# PROPERTY ID - DO NOT EDIT ! \n",
"DOC.set_id('cmip6.aerosol.key_properties.software_properties.code_languages') \n",
"\n",
"# PROPERTY VALUE(S): \n",
"# Set as follows: DOC.set_value(\"value\") \n",
"# TODO - please enter value(s)",
"\n"
],
"outputs": [],
"metadata": {
"collapsed": true
}
},
{
"source": [
"# 3. Key Properties --> Timestep Framework \n",
"*Physical properties of seawater in ocean*"
],
"cell_type": "markdown",
"metadata": {}
},
{
"source": [
"### 3.1. Method\n",
"**Is Required:** TRUE **Type:** ENUM **Cardinality:** 1.1\n",
"### *Mathematical method deployed to solve the time evolution of the prognostic variables*"
],
"cell_type": "markdown",
"metadata": {}
},
{
"execution_count": null,
"cell_type": "code",
"source": [
"# PROPERTY ID - DO NOT EDIT ! \n",
"DOC.set_id('cmip6.aerosol.key_properties.timestep_framework.method') \n",
"\n",
"# PROPERTY VALUE: \n",
"# Set as follows: DOC.set_value(\"value\") \n",
"# Valid Choices: \n",
"# \"Uses atmospheric chemistry time stepping\" \n",
"# \"Specific timestepping (operator splitting)\" \n",
"# \"Specific timestepping (integrated)\" \n",
"# \"Other: [Please specify]\" \n",
"# TODO - please enter value(s)",
"\n"
],
"outputs": [],
"metadata": {
"collapsed": true
}
},
{
"source": [
"### 3.2. Split Operator Advection Timestep\n",
"**Is Required:** FALSE **Type:** INTEGER **Cardinality:** 0.1\n",
"### *Timestep for aerosol advection (in seconds)*"
],
"cell_type": "markdown",
"metadata": {}
},
{
"execution_count": null,
"cell_type": "code",
"source": [
"# PROPERTY ID - DO NOT EDIT ! \n",
"DOC.set_id('cmip6.aerosol.key_properties.timestep_framework.split_operator_advection_timestep') \n",
"\n",
"# PROPERTY VALUE: \n",
"# Set as follows: DOC.set_value(value) \n",
"# TODO - please enter value(s)",
"\n"
],
"outputs": [],
"metadata": {
"collapsed": true
}
},
{
"source": [
"### 3.3. Split Operator Physical Timestep\n",
"**Is Required:** FALSE **Type:** INTEGER **Cardinality:** 0.1\n",
"### *Timestep for aerosol physics (in seconds).*"
],
"cell_type": "markdown",
"metadata": {}
},
{
"execution_count": null,
"cell_type": "code",
"source": [
"# PROPERTY ID - DO NOT EDIT ! \n",
"DOC.set_id('cmip6.aerosol.key_properties.timestep_framework.split_operator_physical_timestep') \n",
"\n",
"# PROPERTY VALUE: \n",
"# Set as follows: DOC.set_value(value) \n",
"# TODO - please enter value(s)",
"\n"
],
"outputs": [],
"metadata": {
"collapsed": true
}
},
{
"source": [
"### 3.4. Integrated Timestep\n",
"**Is Required:** TRUE **Type:** INTEGER **Cardinality:** 1.1\n",
"### *Timestep for the aerosol model (in seconds)*"
],
"cell_type": "markdown",
"metadata": {}
},
{
"execution_count": null,
"cell_type": "code",
"source": [
"# PROPERTY ID - DO NOT EDIT ! \n",
"DOC.set_id('cmip6.aerosol.key_properties.timestep_framework.integrated_timestep') \n",
"\n",
"# PROPERTY VALUE: \n",
"# Set as follows: DOC.set_value(value) \n",
"# TODO - please enter value(s)",
"\n"
],
"outputs": [],
"metadata": {
"collapsed": true
}
},
{
"source": [
"### 3.5. Integrated Scheme Type\n",
"**Is Required:** TRUE **Type:** ENUM **Cardinality:** 1.1\n",
"### *Specify the type of timestep scheme*"
],
"cell_type": "markdown",
"metadata": {}
},
{
"execution_count": null,
"cell_type": "code",
"source": [
"# PROPERTY ID - DO NOT EDIT ! \n",
"DOC.set_id('cmip6.aerosol.key_properties.timestep_framework.integrated_scheme_type') \n",
"\n",
"# PROPERTY VALUE: \n",
"# Set as follows: DOC.set_value(\"value\") \n",
"# Valid Choices: \n",
"# \"Explicit\" \n",
"# \"Implicit\" \n",
"# \"Semi-implicit\" \n",
"# \"Semi-analytic\" \n",
"# \"Impact solver\" \n",
"# \"Back Euler\" \n",
"# \"Newton Raphson\" \n",
"# \"Rosenbrock\" \n",
"# \"Other: [Please specify]\" \n",
"# TODO - please enter value(s)",
"\n"
],
"outputs": [],
"metadata": {
"collapsed": true
}
},
{
"source": [
"# 4. Key Properties --> Meteorological Forcings \n",
"**"
],
"cell_type": "markdown",
"metadata": {}
},
{
"source": [
"### 4.1. Variables 3D\n",
"**Is Required:** FALSE **Type:** STRING **Cardinality:** 0.1\n",
"### *Three dimensionsal forcing variables, e.g. U, V, W, T, Q, P, conventive mass flux*"
],
"cell_type": "markdown",
"metadata": {}
},
{
"execution_count": null,
"cell_type": "code",
"source": [
"# PROPERTY ID - DO NOT EDIT ! \n",
"DOC.set_id('cmip6.aerosol.key_properties.meteorological_forcings.variables_3D') \n",
"\n",
"# PROPERTY VALUE: \n",
"# Set as follows: DOC.set_value(\"value\") \n",
"# TODO - please enter value(s)",
"\n"
],
"outputs": [],
"metadata": {
"collapsed": true
}
},
{
"source": [
"### 4.2. Variables 2D\n",
"**Is Required:** FALSE **Type:** STRING **Cardinality:** 0.1\n",
"### *Two dimensionsal forcing variables, e.g. land-sea mask definition*"
],
"cell_type": "markdown",
"metadata": {}
},
{
"execution_count": null,
"cell_type": "code",
"source": [
"# PROPERTY ID - DO NOT EDIT ! \n",
"DOC.set_id('cmip6.aerosol.key_properties.meteorological_forcings.variables_2D') \n",
"\n",
"# PROPERTY VALUE: \n",
"# Set as follows: DOC.set_value(\"value\") \n",
"# TODO - please enter value(s)",
"\n"
],
"outputs": [],
"metadata": {
"collapsed": true
}
},
{
"source": [
"### 4.3. Frequency\n",
"**Is Required:** FALSE **Type:** INTEGER **Cardinality:** 0.1\n",
"### *Frequency with which meteological forcings are applied (in seconds).*"
],
"cell_type": "markdown",
"metadata": {}
},
{
"execution_count": null,
"cell_type": "code",
"source": [
"# PROPERTY ID - DO NOT EDIT ! \n",
"DOC.set_id('cmip6.aerosol.key_properties.meteorological_forcings.frequency') \n",
"\n",
"# PROPERTY VALUE: \n",
"# Set as follows: DOC.set_value(value) \n",
"# TODO - please enter value(s)",
"\n"
],
"outputs": [],
"metadata": {
"collapsed": true
}
},
{
"source": [
"# 5. Key Properties --> Resolution \n",
"*Resolution in the aersosol model grid*"
],
"cell_type": "markdown",
"metadata": {}
},
{
"source": [
"### 5.1. Name\n",
"**Is Required:** TRUE **Type:** STRING **Cardinality:** 1.1\n",
"### *This is a string usually used by the modelling group to describe the resolution of this grid, e.g. ORCA025, N512L180, T512L70 etc.*"
],
"cell_type": "markdown",
"metadata": {}
},
{
"execution_count": null,
"cell_type": "code",
"source": [
"# PROPERTY ID - DO NOT EDIT ! \n",
"DOC.set_id('cmip6.aerosol.key_properties.resolution.name') \n",
"\n",
"# PROPERTY VALUE: \n",
"# Set as follows: DOC.set_value(\"value\") \n",
"# TODO - please enter value(s)",
"\n"
],
"outputs": [],
"metadata": {
"collapsed": true
}
},
{
"source": [
"### 5.2. Canonical Horizontal Resolution\n",
"**Is Required:** FALSE **Type:** STRING **Cardinality:** 0.1\n",
"### *Expression quoted for gross comparisons of resolution, eg. 50km or 0.1 degrees etc.*"
],
"cell_type": "markdown",
"metadata": {}
},
{
"execution_count": null,
"cell_type": "code",
"source": [
"# PROPERTY ID - DO NOT EDIT ! \n",
"DOC.set_id('cmip6.aerosol.key_properties.resolution.canonical_horizontal_resolution') \n",
"\n",
"# PROPERTY VALUE: \n",
"# Set as follows: DOC.set_value(\"value\") \n",
"# TODO - please enter value(s)",
"\n"
],
"outputs": [],
"metadata": {
"collapsed": true
}
},
{
"source": [
"### 5.3. Number Of Horizontal Gridpoints\n",
"**Is Required:** FALSE **Type:** INTEGER **Cardinality:** 0.1\n",
"### *Total number of horizontal (XY) points (or degrees of freedom) on computational grid.*"
],
"cell_type": "markdown",
"metadata": {}
},
{
"execution_count": null,
"cell_type": "code",
"source": [
"# PROPERTY ID - DO NOT EDIT ! \n",
"DOC.set_id('cmip6.aerosol.key_properties.resolution.number_of_horizontal_gridpoints') \n",
"\n",
"# PROPERTY VALUE: \n",
"# Set as follows: DOC.set_value(value) \n",
"# TODO - please enter value(s)",
"\n"
],
"outputs": [],
"metadata": {
"collapsed": true
}
},
{
"source": [
"### 5.4. Number Of Vertical Levels\n",
"**Is Required:** FALSE **Type:** INTEGER **Cardinality:** 0.1\n",
"### *Number of vertical levels resolved on computational grid.*"
],
"cell_type": "markdown",
"metadata": {}
},
{
"execution_count": null,
"cell_type": "code",
"source": [
"# PROPERTY ID - DO NOT EDIT ! \n",
"DOC.set_id('cmip6.aerosol.key_properties.resolution.number_of_vertical_levels') \n",
"\n",
"# PROPERTY VALUE: \n",
"# Set as follows: DOC.set_value(value) \n",
"# TODO - please enter value(s)",
"\n"
],
"outputs": [],
"metadata": {
"collapsed": true
}
},
{
"source": [
"### 5.5. Is Adaptive Grid\n",
"**Is Required:** FALSE **Type:** BOOLEAN **Cardinality:** 0.1\n",
"### *Default is False. Set true if grid resolution changes during execution.*"
],
"cell_type": "markdown",
"metadata": {}
},
{
"execution_count": null,
"cell_type": "code",
"source": [
"# PROPERTY ID - DO NOT EDIT ! \n",
"DOC.set_id('cmip6.aerosol.key_properties.resolution.is_adaptive_grid') \n",
"\n",
"# PROPERTY VALUE: \n",
"# Set as follows: DOC.set_value(value) \n",
"# Valid Choices: \n",
"# True \n",
"# False \n",
"# TODO - please enter value(s)",
"\n"
],
"outputs": [],
"metadata": {
"collapsed": true
}
},
{
"source": [
"# 6. Key Properties --> Tuning Applied \n",
"*Tuning methodology for aerosol model*"
],
"cell_type": "markdown",
"metadata": {}
},
{
"source": [
"### 6.1. Description\n",
"**Is Required:** TRUE **Type:** STRING **Cardinality:** 1.1\n",
"### *General overview description of tuning: explain and motivate the main targets and metrics retained. &Document the relative weight given to climate performance metrics versus process oriented metrics, &and on the possible conflicts with parameterization level tuning. In particular describe any struggle &with a parameter value that required pushing it to its limits to solve a particular model deficiency.*"
],
"cell_type": "markdown",
"metadata": {}
},
{
"execution_count": null,
"cell_type": "code",
"source": [
"# PROPERTY ID - DO NOT EDIT ! \n",
"DOC.set_id('cmip6.aerosol.key_properties.tuning_applied.description') \n",
"\n",
"# PROPERTY VALUE: \n",
"# Set as follows: DOC.set_value(\"value\") \n",
"# TODO - please enter value(s)",
"\n"
],
"outputs": [],
"metadata": {
"collapsed": true
}
},
{
"source": [
"### 6.2. Global Mean Metrics Used\n",
"**Is Required:** FALSE **Type:** STRING **Cardinality:** 0.N\n",
"### *List set of metrics of the global mean state used in tuning model/component*"
],
"cell_type": "markdown",
"metadata": {}
},
{
"execution_count": null,
"cell_type": "code",
"source": [
"# PROPERTY ID - DO NOT EDIT ! \n",
"DOC.set_id('cmip6.aerosol.key_properties.tuning_applied.global_mean_metrics_used') \n",
"\n",
"# PROPERTY VALUE(S): \n",
"# Set as follows: DOC.set_value(\"value\") \n",
"# TODO - please enter value(s)",
"\n"
],
"outputs": [],
"metadata": {
"collapsed": true
}
},
{
"source": [
"### 6.3. Regional Metrics Used\n",
"**Is Required:** FALSE **Type:** STRING **Cardinality:** 0.N\n",
"### *List of regional metrics of mean state used in tuning model/component*"
],
"cell_type": "markdown",
"metadata": {}
},
{
"execution_count": null,
"cell_type": "code",
"source": [
"# PROPERTY ID - DO NOT EDIT ! \n",
"DOC.set_id('cmip6.aerosol.key_properties.tuning_applied.regional_metrics_used') \n",
"\n",
"# PROPERTY VALUE(S): \n",
"# Set as follows: DOC.set_value(\"value\") \n",
"# TODO - please enter value(s)",
"\n"
],
"outputs": [],
"metadata": {
"collapsed": true
}
},
{
"source": [
"### 6.4. Trend Metrics Used\n",
"**Is Required:** FALSE **Type:** STRING **Cardinality:** 0.N\n",
"### *List observed trend metrics used in tuning model/component*"
],
"cell_type": "markdown",
"metadata": {}
},
{
"execution_count": null,
"cell_type": "code",
"source": [
"# PROPERTY ID - DO NOT EDIT ! \n",
"DOC.set_id('cmip6.aerosol.key_properties.tuning_applied.trend_metrics_used') \n",
"\n",
"# PROPERTY VALUE(S): \n",
"# Set as follows: DOC.set_value(\"value\") \n",
"# TODO - please enter value(s)",
"\n"
],
"outputs": [],
"metadata": {
"collapsed": true
}
},
{
"source": [
"# 7. Transport \n",
"*Aerosol transport*"
],
"cell_type": "markdown",
"metadata": {}
},
{
"source": [
"### 7.1. Overview\n",
"**Is Required:** TRUE **Type:** STRING **Cardinality:** 1.1\n",
"### *Overview of transport in atmosperic aerosol model*"
],
"cell_type": "markdown",
"metadata": {}
},
{
"execution_count": null,
"cell_type": "code",
"source": [
"# PROPERTY ID - DO NOT EDIT ! \n",
"DOC.set_id('cmip6.aerosol.transport.overview') \n",
"\n",
"# PROPERTY VALUE: \n",
"# Set as follows: DOC.set_value(\"value\") \n",
"# TODO - please enter value(s)",
"\n"
],
"outputs": [],
"metadata": {
"collapsed": true
}
},
{
"source": [
"### 7.2. Scheme\n",
"**Is Required:** TRUE **Type:** ENUM **Cardinality:** 1.1\n",
"### *Method for aerosol transport modeling*"
],
"cell_type": "markdown",
"metadata": {}
},
{
"execution_count": null,
"cell_type": "code",
"source": [
"# PROPERTY ID - DO NOT EDIT ! \n",
"DOC.set_id('cmip6.aerosol.transport.scheme') \n",
"\n",
"# PROPERTY VALUE: \n",
"# Set as follows: DOC.set_value(\"value\") \n",
"# Valid Choices: \n",
"# \"Uses Atmospheric chemistry transport scheme\" \n",
"# \"Specific transport scheme (eulerian)\" \n",
"# \"Specific transport scheme (semi-lagrangian)\" \n",
"# \"Specific transport scheme (eulerian and semi-lagrangian)\" \n",
"# \"Specific transport scheme (lagrangian)\" \n",
"# TODO - please enter value(s)",
"\n"
],
"outputs": [],
"metadata": {
"collapsed": true
}
},
{
"source": [
"### 7.3. Mass Conservation Scheme\n",
"**Is Required:** TRUE **Type:** ENUM **Cardinality:** 1.N\n",
"### *Method used to ensure mass conservation.*"
],
"cell_type": "markdown",
"metadata": {}
},
{
"execution_count": null,
"cell_type": "code",
"source": [
"# PROPERTY ID - DO NOT EDIT ! \n",
"DOC.set_id('cmip6.aerosol.transport.mass_conservation_scheme') \n",
"\n",
"# PROPERTY VALUE(S): \n",
"# Set as follows: DOC.set_value(\"value\") \n",
"# Valid Choices: \n",
"# \"Uses Atmospheric chemistry transport scheme\" \n",
"# \"Mass adjustment\" \n",
"# \"Concentrations positivity\" \n",
"# \"Gradients monotonicity\" \n",
"# \"Other: [Please specify]\" \n",
"# TODO - please enter value(s)",
"\n"
],
"outputs": [],
"metadata": {
"collapsed": true
}
},
{
"source": [
"### 7.4. Convention\n",
"**Is Required:** TRUE **Type:** ENUM **Cardinality:** 1.N\n",
"### *Transport by convention*"
],
"cell_type": "markdown",
"metadata": {}
},
{
"execution_count": null,
"cell_type": "code",
"source": [
"# PROPERTY ID - DO NOT EDIT ! \n",
"DOC.set_id('cmip6.aerosol.transport.convention') \n",
"\n",
"# PROPERTY VALUE(S): \n",
"# Set as follows: DOC.set_value(\"value\") \n",
"# Valid Choices: \n",
"# \"Uses Atmospheric chemistry transport scheme\" \n",
"# \"Convective fluxes connected to tracers\" \n",
"# \"Vertical velocities connected to tracers\" \n",
"# \"Other: [Please specify]\" \n",
"# TODO - please enter value(s)",
"\n"
],
"outputs": [],
"metadata": {
"collapsed": true
}
},
{
"source": [
"# 8. Emissions \n",
"*Atmospheric aerosol emissions*"
],
"cell_type": "markdown",
"metadata": {}
},
{
"source": [
"### 8.1. Overview\n",
"**Is Required:** TRUE **Type:** STRING **Cardinality:** 1.1\n",
"### *Overview of emissions in atmosperic aerosol model*"
],
"cell_type": "markdown",
"metadata": {}
},
{
"execution_count": null,
"cell_type": "code",
"source": [
"# PROPERTY ID - DO NOT EDIT ! \n",
"DOC.set_id('cmip6.aerosol.emissions.overview') \n",
"\n",
"# PROPERTY VALUE: \n",
"# Set as follows: DOC.set_value(\"value\") \n",
"# TODO - please enter value(s)",
"\n"
],
"outputs": [],
"metadata": {
"collapsed": true
}
},
{
"source": [
"### 8.2. Method\n",
"**Is Required:** TRUE **Type:** ENUM **Cardinality:** 1.N\n",
"### *Method used to define aerosol species (several methods allowed because the different species may not use the same method).*"
],
"cell_type": "markdown",
"metadata": {}
},
{
"execution_count": null,
"cell_type": "code",
"source": [
"# PROPERTY ID - DO NOT EDIT ! \n",
"DOC.set_id('cmip6.aerosol.emissions.method') \n",
"\n",
"# PROPERTY VALUE(S): \n",
"# Set as follows: DOC.set_value(\"value\") \n",
"# Valid Choices: \n",
"# \"None\" \n",
"# \"Prescribed (climatology)\" \n",
"# \"Prescribed CMIP6\" \n",
"# \"Prescribed above surface\" \n",
"# \"Interactive\" \n",
"# \"Interactive above surface\" \n",
"# \"Other: [Please specify]\" \n",
"# TODO - please enter value(s)",
"\n"
],
"outputs": [],
"metadata": {
"collapsed": true
}
},
{
"source": [
"### 8.3. Sources\n",
"**Is Required:** FALSE **Type:** ENUM **Cardinality:** 0.N\n",
"### *Sources of the aerosol species are taken into account in the emissions scheme*"
],
"cell_type": "markdown",
"metadata": {}
},
{
"execution_count": null,
"cell_type": "code",
"source": [
"# PROPERTY ID - DO NOT EDIT ! \n",
"DOC.set_id('cmip6.aerosol.emissions.sources') \n",
"\n",
"# PROPERTY VALUE(S): \n",
"# Set as follows: DOC.set_value(\"value\") \n",
"# Valid Choices: \n",
"# \"Vegetation\" \n",
"# \"Volcanos\" \n",
"# \"Bare ground\" \n",
"# \"Sea surface\" \n",
"# \"Lightning\" \n",
"# \"Fires\" \n",
"# \"Aircraft\" \n",
"# \"Anthropogenic\" \n",
"# \"Other: [Please specify]\" \n",
"# TODO - please enter value(s)",
"\n"
],
"outputs": [],
"metadata": {
"collapsed": true
}
},
{
"source": [
"### 8.4. Prescribed Climatology\n",
"**Is Required:** FALSE **Type:** ENUM **Cardinality:** 0.1\n",
"### *Specify the climatology type for aerosol emissions*"
],
"cell_type": "markdown",
"metadata": {}
},
{
"execution_count": null,
"cell_type": "code",
"source": [
"# PROPERTY ID - DO NOT EDIT ! \n",
"DOC.set_id('cmip6.aerosol.emissions.prescribed_climatology') \n",
"\n",
"# PROPERTY VALUE: \n",
"# Set as follows: DOC.set_value(\"value\") \n",
"# Valid Choices: \n",
"# \"Constant\" \n",
"# \"Interannual\" \n",
"# \"Annual\" \n",
"# \"Monthly\" \n",
"# \"Daily\" \n",
"# TODO - please enter value(s)",
"\n"
],
"outputs": [],
"metadata": {
"collapsed": true
}
},
{
"source": [
"### 8.5. Prescribed Climatology Emitted Species\n",
"**Is Required:** FALSE **Type:** STRING **Cardinality:** 0.1\n",
"### *List of aerosol species emitted and prescribed via a climatology*"
],
"cell_type": "markdown",
"metadata": {}
},
{
"execution_count": null,
"cell_type": "code",
"source": [
"# PROPERTY ID - DO NOT EDIT ! \n",
"DOC.set_id('cmip6.aerosol.emissions.prescribed_climatology_emitted_species') \n",
"\n",
"# PROPERTY VALUE: \n",
"# Set as follows: DOC.set_value(\"value\") \n",
"# TODO - please enter value(s)",
"\n"
],
"outputs": [],
"metadata": {
"collapsed": true
}
},
{
"source": [
"### 8.6. Prescribed Spatially Uniform Emitted Species\n",
"**Is Required:** FALSE **Type:** STRING **Cardinality:** 0.1\n",
"### *List of aerosol species emitted and prescribed as spatially uniform*"
],
"cell_type": "markdown",
"metadata": {}
},
{
"execution_count": null,
"cell_type": "code",
"source": [
"# PROPERTY ID - DO NOT EDIT ! \n",
"DOC.set_id('cmip6.aerosol.emissions.prescribed_spatially_uniform_emitted_species') \n",
"\n",
"# PROPERTY VALUE: \n",
"# Set as follows: DOC.set_value(\"value\") \n",
"# TODO - please enter value(s)",
"\n"
],
"outputs": [],
"metadata": {
"collapsed": true
}
},
{
"source": [
"### 8.7. Interactive Emitted Species\n",
"**Is Required:** FALSE **Type:** STRING **Cardinality:** 0.1\n",
"### *List of aerosol species emitted and specified via an interactive method*"
],
"cell_type": "markdown",
"metadata": {}
},
{
"execution_count": null,
"cell_type": "code",
"source": [
"# PROPERTY ID - DO NOT EDIT ! \n",
"DOC.set_id('cmip6.aerosol.emissions.interactive_emitted_species') \n",
"\n",
"# PROPERTY VALUE: \n",
"# Set as follows: DOC.set_value(\"value\") \n",
"# TODO - please enter value(s)",
"\n"
],
"outputs": [],
"metadata": {
"collapsed": true
}
},
{
"source": [
"### 8.8. Other Emitted Species\n",
"**Is Required:** FALSE **Type:** STRING **Cardinality:** 0.1\n",
"### *List of aerosol species emitted and specified via an "other method"*"
],
"cell_type": "markdown",
"metadata": {}
},
{
"execution_count": null,
"cell_type": "code",
"source": [
"# PROPERTY ID - DO NOT EDIT ! \n",
"DOC.set_id('cmip6.aerosol.emissions.other_emitted_species') \n",
"\n",
"# PROPERTY VALUE: \n",
"# Set as follows: DOC.set_value(\"value\") \n",
"# TODO - please enter value(s)",
"\n"
],
"outputs": [],
"metadata": {
"collapsed": true
}
},
{
"source": [
"### 8.9. Other Method Characteristics\n",
"**Is Required:** FALSE **Type:** STRING **Cardinality:** 0.1\n",
"### *Characteristics of the "other method" used for aerosol emissions*"
],
"cell_type": "markdown",
"metadata": {}
},
{
"execution_count": null,
"cell_type": "code",
"source": [
"# PROPERTY ID - DO NOT EDIT ! \n",
"DOC.set_id('cmip6.aerosol.emissions.other_method_characteristics') \n",
"\n",
"# PROPERTY VALUE: \n",
"# Set as follows: DOC.set_value(\"value\") \n",
"# TODO - please enter value(s)",
"\n"
],
"outputs": [],
"metadata": {
"collapsed": true
}
},
{
"source": [
"# 9. Concentrations \n",
"*Atmospheric aerosol concentrations*"
],
"cell_type": "markdown",
"metadata": {}
},
{
"source": [
"### 9.1. Overview\n",
"**Is Required:** TRUE **Type:** STRING **Cardinality:** 1.1\n",
"### *Overview of concentrations in atmosperic aerosol model*"
],
"cell_type": "markdown",
"metadata": {}
},
{
"execution_count": null,
"cell_type": "code",
"source": [
"# PROPERTY ID - DO NOT EDIT ! \n",
"DOC.set_id('cmip6.aerosol.concentrations.overview') \n",
"\n",
"# PROPERTY VALUE: \n",
"# Set as follows: DOC.set_value(\"value\") \n",
"# TODO - please enter value(s)",
"\n"
],
"outputs": [],
"metadata": {
"collapsed": true
}
},
{
"source": [
"### 9.2. Prescribed Lower Boundary\n",
"**Is Required:** FALSE **Type:** STRING **Cardinality:** 0.1\n",
"### *List of species prescribed at the lower boundary.*"
],
"cell_type": "markdown",
"metadata": {}
},
{
"execution_count": null,
"cell_type": "code",
"source": [
"# PROPERTY ID - DO NOT EDIT ! \n",
"DOC.set_id('cmip6.aerosol.concentrations.prescribed_lower_boundary') \n",
"\n",
"# PROPERTY VALUE: \n",
"# Set as follows: DOC.set_value(\"value\") \n",
"# TODO - please enter value(s)",
"\n"
],
"outputs": [],
"metadata": {
"collapsed": true
}
},
{
"source": [
"### 9.3. Prescribed Upper Boundary\n",
"**Is Required:** FALSE **Type:** STRING **Cardinality:** 0.1\n",
"### *List of species prescribed at the upper boundary.*"
],
"cell_type": "markdown",
"metadata": {}
},
{
"execution_count": null,
"cell_type": "code",
"source": [
"# PROPERTY ID - DO NOT EDIT ! \n",
"DOC.set_id('cmip6.aerosol.concentrations.prescribed_upper_boundary') \n",
"\n",
"# PROPERTY VALUE: \n",
"# Set as follows: DOC.set_value(\"value\") \n",
"# TODO - please enter value(s)",
"\n"
],
"outputs": [],
"metadata": {
"collapsed": true
}
},
{
"source": [
"### 9.4. Prescribed Fields Mmr\n",
"**Is Required:** FALSE **Type:** STRING **Cardinality:** 0.1\n",
"### *List of species prescribed as mass mixing ratios.*"
],
"cell_type": "markdown",
"metadata": {}
},
{
"execution_count": null,
"cell_type": "code",
"source": [
"# PROPERTY ID - DO NOT EDIT ! \n",
"DOC.set_id('cmip6.aerosol.concentrations.prescribed_fields_mmr') \n",
"\n",
"# PROPERTY VALUE: \n",
"# Set as follows: DOC.set_value(\"value\") \n",
"# TODO - please enter value(s)",
"\n"
],
"outputs": [],
"metadata": {
"collapsed": true
}
},
{
"source": [
"### 9.5. Prescribed Fields Mmr\n",
"**Is Required:** FALSE **Type:** STRING **Cardinality:** 0.1\n",
"### *List of species prescribed as AOD plus CCNs.*"
],
"cell_type": "markdown",
"metadata": {}
},
{
"execution_count": null,
"cell_type": "code",
"source": [
"# PROPERTY ID - DO NOT EDIT ! \n",
"DOC.set_id('cmip6.aerosol.concentrations.prescribed_fields_mmr') \n",
"\n",
"# PROPERTY VALUE: \n",
"# Set as follows: DOC.set_value(\"value\") \n",
"# TODO - please enter value(s)",
"\n"
],
"outputs": [],
"metadata": {
"collapsed": true
}
},
{
"source": [
"# 10. Optical Radiative Properties \n",
"*Aerosol optical and radiative properties*"
],
"cell_type": "markdown",
"metadata": {}
},
{
"source": [
"### 10.1. Overview\n",
"**Is Required:** TRUE **Type:** STRING **Cardinality:** 1.1\n",
"### *Overview of optical and radiative properties*"
],
"cell_type": "markdown",
"metadata": {}
},
{
"execution_count": null,
"cell_type": "code",
"source": [
"# PROPERTY ID - DO NOT EDIT ! \n",
"DOC.set_id('cmip6.aerosol.optical_radiative_properties.overview') \n",
"\n",
"# PROPERTY VALUE: \n",
"# Set as follows: DOC.set_value(\"value\") \n",
"# TODO - please enter value(s)",
"\n"
],
"outputs": [],
"metadata": {
"collapsed": true
}
},
{
"source": [
"# 11. Optical Radiative Properties --> Absorption \n",
"*Absortion properties in aerosol scheme*"
],
"cell_type": "markdown",
"metadata": {}
},
{
"source": [
"### 11.1. Black Carbon\n",
"**Is Required:** FALSE **Type:** FLOAT **Cardinality:** 0.1\n",
"### *Absorption mass coefficient of black carbon at 550nm (if non-absorbing enter 0)*"
],
"cell_type": "markdown",
"metadata": {}
},
{
"execution_count": null,
"cell_type": "code",
"source": [
"# PROPERTY ID - DO NOT EDIT ! \n",
"DOC.set_id('cmip6.aerosol.optical_radiative_properties.absorption.black_carbon') \n",
"\n",
"# PROPERTY VALUE: \n",
"# Set as follows: DOC.set_value(value) \n",
"# TODO - please enter value(s)",
"\n"
],
"outputs": [],
"metadata": {
"collapsed": true
}
},
{
"source": [
"### 11.2. Dust\n",
"**Is Required:** FALSE **Type:** FLOAT **Cardinality:** 0.1\n",
"### *Absorption mass coefficient of dust at 550nm (if non-absorbing enter 0)*"
],
"cell_type": "markdown",
"metadata": {}
},
{
"execution_count": null,
"cell_type": "code",
"source": [
"# PROPERTY ID - DO NOT EDIT ! \n",
"DOC.set_id('cmip6.aerosol.optical_radiative_properties.absorption.dust') \n",
"\n",
"# PROPERTY VALUE: \n",
"# Set as follows: DOC.set_value(value) \n",
"# TODO - please enter value(s)",
"\n"
],
"outputs": [],
"metadata": {
"collapsed": true
}
},
{
"source": [
"### 11.3. Organics\n",
"**Is Required:** FALSE **Type:** FLOAT **Cardinality:** 0.1\n",
"### *Absorption mass coefficient of organics at 550nm (if non-absorbing enter 0)*"
],
"cell_type": "markdown",
"metadata": {}
},
{
"execution_count": null,
"cell_type": "code",
"source": [
"# PROPERTY ID - DO NOT EDIT ! \n",
"DOC.set_id('cmip6.aerosol.optical_radiative_properties.absorption.organics') \n",
"\n",
"# PROPERTY VALUE: \n",
"# Set as follows: DOC.set_value(value) \n",
"# TODO - please enter value(s)",
"\n"
],
"outputs": [],
"metadata": {
"collapsed": true
}
},
{
"source": [
"# 12. Optical Radiative Properties --> Mixtures \n",
"**"
],
"cell_type": "markdown",
"metadata": {}
},
{
"source": [
"### 12.1. External\n",
"**Is Required:** TRUE **Type:** BOOLEAN **Cardinality:** 1.1\n",
"### *Is there external mixing with respect to chemical composition?*"
],
"cell_type": "markdown",
"metadata": {}
},
{
"execution_count": null,
"cell_type": "code",
"source": [
"# PROPERTY ID - DO NOT EDIT ! \n",
"DOC.set_id('cmip6.aerosol.optical_radiative_properties.mixtures.external') \n",
"\n",
"# PROPERTY VALUE: \n",
"# Set as follows: DOC.set_value(value) \n",
"# Valid Choices: \n",
"# True \n",
"# False \n",
"# TODO - please enter value(s)",
"\n"
],
"outputs": [],
"metadata": {
"collapsed": true
}
},
{
"source": [
"### 12.2. Internal\n",
"**Is Required:** TRUE **Type:** BOOLEAN **Cardinality:** 1.1\n",
"### *Is there internal mixing with respect to chemical composition?*"
],
"cell_type": "markdown",
"metadata": {}
},
{
"execution_count": null,
"cell_type": "code",
"source": [
"# PROPERTY ID - DO NOT EDIT ! \n",
"DOC.set_id('cmip6.aerosol.optical_radiative_properties.mixtures.internal') \n",
"\n",
"# PROPERTY VALUE: \n",
"# Set as follows: DOC.set_value(value) \n",
"# Valid Choices: \n",
"# True \n",
"# False \n",
"# TODO - please enter value(s)",
"\n"
],
"outputs": [],
"metadata": {
"collapsed": true
}
},
{
"source": [
"### 12.3. Mixing Rule\n",
"**Is Required:** FALSE **Type:** STRING **Cardinality:** 0.1\n",
"### *If there is internal mixing with respect to chemical composition then indicate the mixinrg rule*"
],
"cell_type": "markdown",
"metadata": {}
},
{
"execution_count": null,
"cell_type": "code",
"source": [
"# PROPERTY ID - DO NOT EDIT ! \n",
"DOC.set_id('cmip6.aerosol.optical_radiative_properties.mixtures.mixing_rule') \n",
"\n",
"# PROPERTY VALUE: \n",
"# Set as follows: DOC.set_value(\"value\") \n",
"# TODO - please enter value(s)",
"\n"
],
"outputs": [],
"metadata": {
"collapsed": true
}
},
{
"source": [
"# 13. Optical Radiative Properties --> Impact Of H2o \n",
"**"
],
"cell_type": "markdown",
"metadata": {}
},
{
"source": [
"### 13.1. Size\n",
"**Is Required:** TRUE **Type:** BOOLEAN **Cardinality:** 1.1\n",
"### *Does H2O impact size?*"
],
"cell_type": "markdown",
"metadata": {}
},
{
"execution_count": null,
"cell_type": "code",
"source": [
"# PROPERTY ID - DO NOT EDIT ! \n",
"DOC.set_id('cmip6.aerosol.optical_radiative_properties.impact_of_h2o.size') \n",
"\n",
"# PROPERTY VALUE: \n",
"# Set as follows: DOC.set_value(value) \n",
"# Valid Choices: \n",
"# True \n",
"# False \n",
"# TODO - please enter value(s)",
"\n"
],
"outputs": [],
"metadata": {
"collapsed": true
}
},
{
"source": [
"### 13.2. Internal Mixture\n",
"**Is Required:** TRUE **Type:** BOOLEAN **Cardinality:** 1.1\n",
"### *Does H2O impact internal mixture?*"
],
"cell_type": "markdown",
"metadata": {}
},
{
"execution_count": null,
"cell_type": "code",
"source": [
"# PROPERTY ID - DO NOT EDIT ! \n",
"DOC.set_id('cmip6.aerosol.optical_radiative_properties.impact_of_h2o.internal_mixture') \n",
"\n",
"# PROPERTY VALUE: \n",
"# Set as follows: DOC.set_value(value) \n",
"# Valid Choices: \n",
"# True \n",
"# False \n",
"# TODO - please enter value(s)",
"\n"
],
"outputs": [],
"metadata": {
"collapsed": true
}
},
{
"source": [
"# 14. Optical Radiative Properties --> Radiative Scheme \n",
"*Radiative scheme for aerosol*"
],
"cell_type": "markdown",
"metadata": {}
},
{
"source": [
"### 14.1. Overview\n",
"**Is Required:** TRUE **Type:** STRING **Cardinality:** 1.1\n",
"### *Overview of radiative scheme*"
],
"cell_type": "markdown",
"metadata": {}
},
{
"execution_count": null,
"cell_type": "code",
"source": [
"# PROPERTY ID - DO NOT EDIT ! \n",
"DOC.set_id('cmip6.aerosol.optical_radiative_properties.radiative_scheme.overview') \n",
"\n",
"# PROPERTY VALUE: \n",
"# Set as follows: DOC.set_value(\"value\") \n",
"# TODO - please enter value(s)",
"\n"
],
"outputs": [],
"metadata": {
"collapsed": true
}
},
{
"source": [
"### 14.2. Shortwave Bands\n",
"**Is Required:** TRUE **Type:** INTEGER **Cardinality:** 1.1\n",
"### *Number of shortwave bands*"
],
"cell_type": "markdown",
"metadata": {}
},
{
"execution_count": null,
"cell_type": "code",
"source": [
"# PROPERTY ID - DO NOT EDIT ! \n",
"DOC.set_id('cmip6.aerosol.optical_radiative_properties.radiative_scheme.shortwave_bands') \n",
"\n",
"# PROPERTY VALUE: \n",
"# Set as follows: DOC.set_value(value) \n",
"# TODO - please enter value(s)",
"\n"
],
"outputs": [],
"metadata": {
"collapsed": true
}
},
{
"source": [
"### 14.3. Longwave Bands\n",
"**Is Required:** TRUE **Type:** INTEGER **Cardinality:** 1.1\n",
"### *Number of longwave bands*"
],
"cell_type": "markdown",
"metadata": {}
},
{
"execution_count": null,
"cell_type": "code",
"source": [
"# PROPERTY ID - DO NOT EDIT ! \n",
"DOC.set_id('cmip6.aerosol.optical_radiative_properties.radiative_scheme.longwave_bands') \n",
"\n",
"# PROPERTY VALUE: \n",
"# Set as follows: DOC.set_value(value) \n",
"# TODO - please enter value(s)",
"\n"
],
"outputs": [],
"metadata": {
"collapsed": true
}
},
{
"source": [
"# 15. Optical Radiative Properties --> Cloud Interactions \n",
"*Aerosol-cloud interactions*"
],
"cell_type": "markdown",
"metadata": {}
},
{
"source": [
"### 15.1. Overview\n",
"**Is Required:** TRUE **Type:** STRING **Cardinality:** 1.1\n",
"### *Overview of aerosol-cloud interactions*"
],
"cell_type": "markdown",
"metadata": {}
},
{
"execution_count": null,
"cell_type": "code",
"source": [
"# PROPERTY ID - DO NOT EDIT ! \n",
"DOC.set_id('cmip6.aerosol.optical_radiative_properties.cloud_interactions.overview') \n",
"\n",
"# PROPERTY VALUE: \n",
"# Set as follows: DOC.set_value(\"value\") \n",
"# TODO - please enter value(s)",
"\n"
],
"outputs": [],
"metadata": {
"collapsed": true
}
},
{
"source": [
"### 15.2. Twomey\n",
"**Is Required:** TRUE **Type:** BOOLEAN **Cardinality:** 1.1\n",
"### *Is the Twomey effect included?*"
],
"cell_type": "markdown",
"metadata": {}
},
{
"execution_count": null,
"cell_type": "code",
"source": [
"# PROPERTY ID - DO NOT EDIT ! \n",
"DOC.set_id('cmip6.aerosol.optical_radiative_properties.cloud_interactions.twomey') \n",
"\n",
"# PROPERTY VALUE: \n",
"# Set as follows: DOC.set_value(value) \n",
"# Valid Choices: \n",
"# True \n",
"# False \n",
"# TODO - please enter value(s)",
"\n"
],
"outputs": [],
"metadata": {
"collapsed": true
}
},
{
"source": [
"### 15.3. Twomey Minimum Ccn\n",
"**Is Required:** FALSE **Type:** INTEGER **Cardinality:** 0.1\n",
"### *If the Twomey effect is included, then what is the minimum CCN number?*"
],
"cell_type": "markdown",
"metadata": {}
},
{
"execution_count": null,
"cell_type": "code",
"source": [
"# PROPERTY ID - DO NOT EDIT ! \n",
"DOC.set_id('cmip6.aerosol.optical_radiative_properties.cloud_interactions.twomey_minimum_ccn') \n",
"\n",
"# PROPERTY VALUE: \n",
"# Set as follows: DOC.set_value(value) \n",
"# TODO - please enter value(s)",
"\n"
],
"outputs": [],
"metadata": {
"collapsed": true
}
},
{
"source": [
"### 15.4. Drizzle\n",
"**Is Required:** TRUE **Type:** BOOLEAN **Cardinality:** 1.1\n",
"### *Does the scheme affect drizzle?*"
],
"cell_type": "markdown",
"metadata": {}
},
{
"execution_count": null,
"cell_type": "code",
"source": [
"# PROPERTY ID - DO NOT EDIT ! \n",
"DOC.set_id('cmip6.aerosol.optical_radiative_properties.cloud_interactions.drizzle') \n",
"\n",
"# PROPERTY VALUE: \n",
"# Set as follows: DOC.set_value(value) \n",
"# Valid Choices: \n",
"# True \n",
"# False \n",
"# TODO - please enter value(s)",
"\n"
],
"outputs": [],
"metadata": {
"collapsed": true
}
},
{
"source": [
"### 15.5. Cloud Lifetime\n",
"**Is Required:** TRUE **Type:** BOOLEAN **Cardinality:** 1.1\n",
"### *Does the scheme affect cloud lifetime?*"
],
"cell_type": "markdown",
"metadata": {}
},
{
"execution_count": null,
"cell_type": "code",
"source": [
"# PROPERTY ID - DO NOT EDIT ! \n",
"DOC.set_id('cmip6.aerosol.optical_radiative_properties.cloud_interactions.cloud_lifetime') \n",
"\n",
"# PROPERTY VALUE: \n",
"# Set as follows: DOC.set_value(value) \n",
"# Valid Choices: \n",
"# True \n",
"# False \n",
"# TODO - please enter value(s)",
"\n"
],
"outputs": [],
"metadata": {
"collapsed": true
}
},
{
"source": [
"### 15.6. Longwave Bands\n",
"**Is Required:** TRUE **Type:** INTEGER **Cardinality:** 1.1\n",
"### *Number of longwave bands*"
],
"cell_type": "markdown",
"metadata": {}
},
{
"execution_count": null,
"cell_type": "code",
"source": [
"# PROPERTY ID - DO NOT EDIT ! \n",
"DOC.set_id('cmip6.aerosol.optical_radiative_properties.cloud_interactions.longwave_bands') \n",
"\n",
"# PROPERTY VALUE: \n",
"# Set as follows: DOC.set_value(value) \n",
"# TODO - please enter value(s)",
"\n"
],
"outputs": [],
"metadata": {
"collapsed": true
}
},
{
"source": [
"# 16. Model \n",
"*Aerosol model*"
],
"cell_type": "markdown",
"metadata": {}
},
{
"source": [
"### 16.1. Overview\n",
"**Is Required:** TRUE **Type:** STRING **Cardinality:** 1.1\n",
"### *Overview of atmosperic aerosol model*"
],
"cell_type": "markdown",
"metadata": {}
},
{
"execution_count": null,
"cell_type": "code",
"source": [
"# PROPERTY ID - DO NOT EDIT ! \n",
"DOC.set_id('cmip6.aerosol.model.overview') \n",
"\n",
"# PROPERTY VALUE: \n",
"# Set as follows: DOC.set_value(\"value\") \n",
"# TODO - please enter value(s)",
"\n"
],
"outputs": [],
"metadata": {
"collapsed": true
}
},
{
"source": [
"### 16.2. Processes\n",
"**Is Required:** TRUE **Type:** ENUM **Cardinality:** 1.N\n",
"### *Processes included in the Aerosol model.*"
],
"cell_type": "markdown",
"metadata": {}
},
{
"execution_count": null,
"cell_type": "code",
"source": [
"# PROPERTY ID - DO NOT EDIT ! \n",
"DOC.set_id('cmip6.aerosol.model.processes') \n",
"\n",
"# PROPERTY VALUE(S): \n",
"# Set as follows: DOC.set_value(\"value\") \n",
"# Valid Choices: \n",
"# \"Dry deposition\" \n",
"# \"Sedimentation\" \n",
"# \"Wet deposition (impaction scavenging)\" \n",
"# \"Wet deposition (nucleation scavenging)\" \n",
"# \"Coagulation\" \n",
"# \"Oxidation (gas phase)\" \n",
"# \"Oxidation (in cloud)\" \n",
"# \"Condensation\" \n",
"# \"Ageing\" \n",
"# \"Advection (horizontal)\" \n",
"# \"Advection (vertical)\" \n",
"# \"Heterogeneous chemistry\" \n",
"# \"Nucleation\" \n",
"# TODO - please enter value(s)",
"\n"
],
"outputs": [],
"metadata": {
"collapsed": true
}
},
{
"source": [
"### 16.3. Coupling\n",
"**Is Required:** FALSE **Type:** ENUM **Cardinality:** 0.N\n",
"### *Other model components coupled to the Aerosol model*"
],
"cell_type": "markdown",
"metadata": {}
},
{
"execution_count": null,
"cell_type": "code",
"source": [
"# PROPERTY ID - DO NOT EDIT ! \n",
"DOC.set_id('cmip6.aerosol.model.coupling') \n",
"\n",
"# PROPERTY VALUE(S): \n",
"# Set as follows: DOC.set_value(\"value\") \n",
"# Valid Choices: \n",
"# \"Radiation\" \n",
"# \"Land surface\" \n",
"# \"Heterogeneous chemistry\" \n",
"# \"Clouds\" \n",
"# \"Ocean\" \n",
"# \"Cryosphere\" \n",
"# \"Gas phase chemistry\" \n",
"# \"Other: [Please specify]\" \n",
"# TODO - please enter value(s)",
"\n"
],
"outputs": [],
"metadata": {
"collapsed": true
}
},
{
"source": [
"### 16.4. Gas Phase Precursors\n",
"**Is Required:** TRUE **Type:** ENUM **Cardinality:** 1.N\n",
"### *List of gas phase aerosol precursors.*"
],
"cell_type": "markdown",
"metadata": {}
},
{
"execution_count": null,
"cell_type": "code",
"source": [
"# PROPERTY ID - DO NOT EDIT ! \n",
"DOC.set_id('cmip6.aerosol.model.gas_phase_precursors') \n",
"\n",
"# PROPERTY VALUE(S): \n",
"# Set as follows: DOC.set_value(\"value\") \n",
"# Valid Choices: \n",
"# \"DMS\" \n",
"# \"SO2\" \n",
"# \"Ammonia\" \n",
"# \"Iodine\" \n",
"# \"Terpene\" \n",
"# \"Isoprene\" \n",
"# \"VOC\" \n",
"# \"NOx\" \n",
"# \"Other: [Please specify]\" \n",
"# TODO - please enter value(s)",
"\n"
],
"outputs": [],
"metadata": {
"collapsed": true
}
},
{
"source": [
"### 16.5. Scheme Type\n",
"**Is Required:** TRUE **Type:** ENUM **Cardinality:** 1.N\n",
"### *Type(s) of aerosol scheme used by the aerosols model (potentially multiple: some species may be covered by one type of aerosol scheme and other species covered by another type).*"
],
"cell_type": "markdown",
"metadata": {}
},
{
"execution_count": null,
"cell_type": "code",
"source": [
"# PROPERTY ID - DO NOT EDIT ! \n",
"DOC.set_id('cmip6.aerosol.model.scheme_type') \n",
"\n",
"# PROPERTY VALUE(S): \n",
"# Set as follows: DOC.set_value(\"value\") \n",
"# Valid Choices: \n",
"# \"Bulk\" \n",
"# \"Modal\" \n",
"# \"Bin\" \n",
"# \"Other: [Please specify]\" \n",
"# TODO - please enter value(s)",
"\n"
],
"outputs": [],
"metadata": {
"collapsed": true
}
},
{
"source": [
"### 16.6. Bulk Scheme Species\n",
"**Is Required:** TRUE **Type:** ENUM **Cardinality:** 1.N\n",
"### *List of species covered by the bulk scheme.*"
],
"cell_type": "markdown",
"metadata": {}
},
{
"execution_count": null,
"cell_type": "code",
"source": [
"# PROPERTY ID - DO NOT EDIT ! \n",
"DOC.set_id('cmip6.aerosol.model.bulk_scheme_species') \n",
"\n",
"# PROPERTY VALUE(S): \n",
"# Set as follows: DOC.set_value(\"value\") \n",
"# Valid Choices: \n",
"# \"Sulphate\" \n",
"# \"Nitrate\" \n",
"# \"Sea salt\" \n",
"# \"Dust\" \n",
"# \"Ice\" \n",
"# \"Organic\" \n",
"# \"Black carbon / soot\" \n",
"# \"SOA (secondary organic aerosols)\" \n",
"# \"POM (particulate organic matter)\" \n",
"# \"Polar stratospheric ice\" \n",
"# \"NAT (Nitric acid trihydrate)\" \n",
"# \"NAD (Nitric acid dihydrate)\" \n",
"# \"STS (supercooled ternary solution aerosol particule)\" \n",
"# \"Other: [Please specify]\" \n",
"# TODO - please enter value(s)",
"\n"
],
"outputs": [],
"metadata": {
"collapsed": true
}
},
{
"source": [
"### \u00a92017 [ES-DOC](https://es-doc.org) \n"
],
"cell_type": "markdown",
"metadata": {}
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 2",
"name": "python2",
"language": "python"
},
"language_info": {
"mimetype": "text/x-python",
"nbconvert_exporter": "python",
"name": "python",
"file_extension": ".py",
"version": "2.7.10",
"pygments_lexer": "ipython2",
"codemirror_mode": {
"version": 2,
"name": "ipython"
}
}
}
} | gpl-3.0 |
KitwareMedical/ITKTubeTK | examples/TubeNumPyArrayAndPropertyHistograms.ipynb | 1 | 125170 | {
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"This notebook illustrates the [TubeTK](http://tubetk.org) tube NumPy array data structure and how to create histograms of the properties of a [VesselTube](http://www.itk.org/Doxygen/html/classitk_1_1VesselTubeSpatialObject.html).\n",
"\n",
"First, import the function for reading a tube file in as a NumPy array, and read in the file."
]
},
{
"cell_type": "code",
"execution_count": 1,
"metadata": {},
"outputs": [],
"source": [
"import os\n",
"import sys"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {},
"outputs": [],
"source": [
"from _tubetk_numpy import tubes_from_file\n",
"\n",
"tubes = tubes_from_file(\"data/Normal071-VascularNetwork.tre\")"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"The result is a [NumPy Record Array](http://docs.scipy.org/doc/numpy/user/basics.rec.html) where the fields of the array correspond to the properties of a [VesselTubeSpatialObjectPoint](http://www.itk.org/Doxygen/html/classitk_1_1VesselTubeSpatialObjectPoint.html)."
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"<class 'numpy.ndarray'>\n",
"[('Id', '<i4'), ('PositionInWorldSpace', '<f8', (3,)), ('Color', '<f4', (4,)), ('TangentInWorldSpace', '<f8', (3,)), ('Normal1InWorldSpace', '<f8', (3,)), ('Normal2InWorldSpace', '<f8', (3,)), ('RadiusInWorldSpace', '<f4'), ('Alpha1', '<f4'), ('Alpha2', '<f4'), ('Alpha3', '<f4'), ('Medialness', '<f4'), ('Ridgeness', '<f4'), ('Branchness', '<f4')]\n"
]
}
],
"source": [
"print(type(tubes))\n",
"print(tubes.dtype)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"The length of the array corresponds to the number of points that make up the tubes."
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"106061\n",
"(106061,)\n"
]
}
],
"source": [
"print(len(tubes))\n",
"print(tubes.shape)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Individual points can be sliced, or views can be created on individual fields."
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Entire points 0, 2:\n",
"[(-1, [121.26599451, 94.40424276, 0.30700558], [1., 0., 0., 1.], [0.82861531, 0.52673039, 0.18960951], [ 0.55138761, -0.70933917, -0.43910095], [ 0.09679036, -0.46839411, 0.87820191], 1.277065, 0., 0., 0., 0., 0., 0.)\n",
" (-1, [121.33222107, 94.44634136, 0.32216 ], [1., 0., 0., 1.], [0.85344853, 0.48634417, 0.18733647], [-0.50062039, 0.86495203, 0.03517395], [ 0.14493042, 0.12380361, -0.98166585], 1.277065, 0., 0., 0., 0., 0., 0.)]\n",
"\n",
"Position of points 0, 2\n",
"[[121.26599451 94.40424276 0.30700558]\n",
" [121.33222107 94.44634136 0.32216 ]]\n"
]
}
],
"source": [
"print('Entire points 0, 2:')\n",
"print(tubes[:4:2])\n",
"\n",
"print('\\nPosition of points 0, 2')\n",
"print(tubes['PositionInWorldSpace'][:4:2])"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"We can easily create a histogram of the radii or visualize the point positions."
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Populating the interactive namespace from numpy and matplotlib\n"
]
},
{
"data": {
"image/png": "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\n",
"text/plain": [
"<Figure size 1152x432 with 2 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"%pylab inline\n",
"from mpl_toolkits.mplot3d import Axes3D\n",
"import matplotlib.pyplot as plt\n",
"\n",
"fig = plt.figure(figsize=(16, 6))\n",
"\n",
"ax = fig.add_subplot(1, 2, 1)\n",
"ax.hist(tubes['RadiusInWorldSpace'], bins=100)\n",
"ax.set_xlabel('Radius')\n",
"ax.set_ylabel('Count')\n",
"\n",
"ax = fig.add_subplot(1, 2, 2, projection='3d')\n",
"subsample = 100\n",
"position = tubes['PositionInWorldSpace'][::subsample]\n",
"radius = tubes['RadiusInWorldSpace'][::subsample]\n",
"ax.scatter(position[:,0], position[:,1], position[:,2], s=(2*radius)**2)\n",
"ax.set_title('Point Positions')\n",
"ax.set_xlabel('X')\n",
"ax.set_ylabel('Y')\n",
"ax.set_zlabel('Z');"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": []
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.7.5"
}
},
"nbformat": 4,
"nbformat_minor": 1
}
| apache-2.0 |
turbomanage/training-data-analyst | courses/machine_learning/deepdive2/image_classification/labs/2_mnist_models.ipynb | 1 | 19653 | {
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# MNIST Image Classification with TensorFlow on Cloud ML Engine\n",
"\n",
"This notebook demonstrates how to implement different image models on MNIST using the [tf.keras API](https://www.tensorflow.org/versions/r2.0/api_docs/python/tf/keras).\n",
"\n",
"## Learning Objectives\n",
"1. Understand how to build a Dense Neural Network (DNN) for image classification\n",
"2. Understand how to use dropout (DNN) for image classification\n",
"3. Understand how to use Convolutional Neural Networks (CNN)\n",
"4. Know how to deploy and use an image classifcation model using Google Cloud's [AI Platform](https://cloud.google.com/ai-platform/)\n",
"\n",
"First things first. Configure the parameters below to match your own Google Cloud project details."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"from datetime import datetime\n",
"import os\n",
"\n",
"PROJECT = \"your-project-id-here\" # REPLACE WITH YOUR PROJECT ID\n",
"BUCKET = \"your-bucket-id-here\" # REPLACE WITH YOUR BUCKET NAME\n",
"REGION = \"us-central1\" # REPLACE WITH YOUR BUCKET REGION e.g. us-central1\n",
"\n",
"# Do not change these\n",
"os.environ[\"PROJECT\"] = PROJECT\n",
"os.environ[\"BUCKET\"] = BUCKET\n",
"os.environ[\"REGION\"] = REGION\n",
"os.environ[\"IMAGE_URI\"] = os.path.join(\"gcr.io\", PROJECT, \"mnist_models\")"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Building a dynamic model\n",
"\n",
"In the previous notebook, <a href=\"mnist_linear.ipynb\">mnist_linear.ipynb</a>, we ran our code directly from the notebook. In order to run it on the AI Platform, it needs to be packaged as a python module.\n",
"\n",
"The boilerplate structure for this module has already been set up in the folder `mnist_models`. The module lives in the sub-folder, `trainer`, and is designated as a python package with the empty `__init__.py` (`mnist_models/trainer/__init__.py`) file. It still needs the model and a trainer to run it, so let's make them.\n",
"\n",
"Let's start with the trainer file first. This file parses command line arguments to feed into the model."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"%%writefile mnist_models/trainer/task.py\n",
"import argparse\n",
"import json\n",
"import os\n",
"import sys\n",
"\n",
"from . import model\n",
"\n",
"\n",
"def _parse_arguments(argv):\n",
" \"\"\"Parses command-line arguments.\"\"\"\n",
" parser = argparse.ArgumentParser()\n",
" parser.add_argument(\n",
" '--model_type',\n",
" help='Which model type to use',\n",
" type=str, default='linear')\n",
" parser.add_argument(\n",
" '--epochs',\n",
" help='The number of epochs to train',\n",
" type=int, default=10)\n",
" parser.add_argument(\n",
" '--steps_per_epoch',\n",
" help='The number of steps per epoch to train',\n",
" type=int, default=100)\n",
" parser.add_argument(\n",
" '--job-dir',\n",
" help='Directory where to save the given model',\n",
" type=str, default='mnist_models/')\n",
" return parser.parse_known_args(argv)\n",
"\n",
"\n",
"def main():\n",
" \"\"\"Parses command line arguments and kicks off model training.\"\"\"\n",
" args = _parse_arguments(sys.argv[1:])[0]\n",
"\n",
" # Configure path for hyperparameter tuning.\n",
" trial_id = json.loads(\n",
" os.environ.get('TF_CONFIG', '{}')).get('task', {}).get('trial', '')\n",
" output_path = args.job_dir if not trial_id else args.job_dir + '/'\n",
"\n",
" model_layers = model.get_layers(args.model_type)\n",
" image_model = model.build_model(model_layers, args.job_dir)\n",
" model_history = model.train_and_evaluate(\n",
" image_model, args.epochs, args.steps_per_epoch, args.job_dir)\n",
"\n",
"\n",
"if __name__ == '__main__':\n",
" main()\n"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Next, let's group non-model functions into a util file to keep the model file simple. We'll copy over the `scale` and `load_dataset` functions from the previous lab."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"%%writefile mnist_models/trainer/util.py\n",
"import tensorflow as tf\n",
"\n",
"\n",
"def scale(image, label):\n",
" \"\"\"Scales images from a 0-255 int range to a 0-1 float range\"\"\"\n",
" image = tf.cast(image, tf.float32)\n",
" image /= 255\n",
" image = tf.expand_dims(image, -1)\n",
" return image, label\n",
"\n",
"\n",
"def load_dataset(\n",
" data, training=True, buffer_size=5000, batch_size=100, nclasses=10):\n",
" \"\"\"Loads MNIST dataset into a tf.data.Dataset\"\"\"\n",
" (x_train, y_train), (x_test, y_test) = data\n",
" x = x_train if training else x_test\n",
" y = y_train if training else y_test\n",
" # One-hot encode the classes\n",
" y = tf.keras.utils.to_categorical(y, nclasses)\n",
" dataset = tf.data.Dataset.from_tensor_slices((x, y))\n",
" dataset = dataset.map(scale).batch(batch_size)\n",
" if training:\n",
" dataset = dataset.shuffle(buffer_size).repeat()\n",
" return dataset\n"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Finally, let's code the models! The [tf.keras API](https://www.tensorflow.org/versions/r2.0/api_docs/python/tf/keras) accepts an array of [layers](https://www.tensorflow.org/api_docs/python/tf/keras/layers) into a [model object](https://www.tensorflow.org/api_docs/python/tf/keras/Model), so we can create a dictionary of layers based on the different model types we want to use. The below file has two functions: `get_layers` and `create_and_train_model`. We will build the structure of our model in `get_layers`. Last but not least, we'll copy over the training code from the previous lab into `train_and_evaluate`.\n",
"\n",
"**TODO 1**: Define the Keras layers for a DNN model \n",
"**TODO 2**: Define the Keras layers for a dropout model \n",
"**TODO 3**: Define the Keras layers for a CNN model \n",
"\n",
"Hint: These models progressively build on each other. Look at the imported `tensorflow.keras.layers` modules and the default values for the variables defined in `get_layers` for guidance."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"%%writefile mnist_models/trainer/model.py\n",
"import os\n",
"import shutil\n",
"\n",
"import matplotlib.pyplot as plt\n",
"import numpy as np\n",
"import tensorflow as tf\n",
"from tensorflow.keras import Sequential\n",
"from tensorflow.keras.callbacks import TensorBoard\n",
"from tensorflow.keras.layers import (\n",
" Conv2D, Dense, Dropout, Flatten, MaxPooling2D, Softmax)\n",
"\n",
"from . import util\n",
"\n",
"\n",
"# Image Variables\n",
"WIDTH = 28\n",
"HEIGHT = 28\n",
"\n",
"\n",
"def get_layers(\n",
" model_type,\n",
" nclasses=10,\n",
" hidden_layer_1_neurons=400,\n",
" hidden_layer_2_neurons=100,\n",
" dropout_rate=0.25,\n",
" num_filters_1=64,\n",
" kernel_size_1=3,\n",
" pooling_size_1=2,\n",
" num_filters_2=32,\n",
" kernel_size_2=3,\n",
" pooling_size_2=2):\n",
" \"\"\"Constructs layers for a keras model based on a dict of model types.\"\"\"\n",
" model_layers = {\n",
" 'linear': [\n",
" Flatten(),\n",
" Dense(nclasses),\n",
" Softmax()\n",
" ],\n",
" 'dnn': [\n",
" # TODO\n",
" ],\n",
" 'dnn_dropout': [\n",
" # TODO\n",
" ],\n",
" 'cnn': [\n",
" # TODO\n",
" ]\n",
" }\n",
" return model_layers[model_type]\n",
"\n",
"\n",
"def build_model(layers, output_dir):\n",
" \"\"\"Compiles keras model for image classification.\"\"\"\n",
" model = Sequential(layers)\n",
" model.compile(optimizer='adam',\n",
" loss='categorical_crossentropy',\n",
" metrics=['accuracy'])\n",
" return model\n",
"\n",
"\n",
"def train_and_evaluate(model, num_epochs, steps_per_epoch, output_dir):\n",
" \"\"\"Compiles keras model and loads data into it for training.\"\"\"\n",
" mnist = tf.keras.datasets.mnist.load_data()\n",
" train_data = util.load_dataset(mnist)\n",
" validation_data = util.load_dataset(mnist, training=False)\n",
"\n",
" callbacks = []\n",
" if output_dir:\n",
" tensorboard_callback = TensorBoard(log_dir=output_dir)\n",
" callbacks = [tensorboard_callback]\n",
"\n",
" history = model.fit(\n",
" train_data,\n",
" validation_data=validation_data,\n",
" epochs=num_epochs,\n",
" steps_per_epoch=steps_per_epoch,\n",
" verbose=2,\n",
" callbacks=callbacks)\n",
"\n",
" if output_dir:\n",
" export_path = os.path.join(output_dir, 'keras_export')\n",
" model.save(export_path, save_format='tf')\n",
"\n",
" return history\n"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Local Training\n",
"\n",
"With everything set up, let's run locally to test the code. Some of the previous tests have been copied over into a testing script `mnist_models/trainer/test.py` to make sure the model still passes our previous checks. On `line 13`, you can specify which model types you would like to check. `line 14` and `line 15` has the number of epochs and steps per epoch respectively.\n",
"\n",
"Moment of truth! Run the code below to check your models against the unit tests. If you see \"OK\" at the end when it's finished running, congrats! You've passed the tests!"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"!python3 -m mnist_models.trainer.test"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Now that we know that our models are working as expected, let's run it on the [Google Cloud AI Platform](https://cloud.google.com/ml-engine/docs/). We can run it as a python module locally first using the command line.\n",
"\n",
"The below cell transfers some of our variables to the command line as well as create a job directory including a timestamp. This is where our model and tensorboard data will be stored."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"current_time = datetime.now().strftime(\"%y%m%d_%H%M%S\")\n",
"model_type = 'cnn'\n",
"\n",
"os.environ[\"MODEL_TYPE\"] = model_type\n",
"os.environ[\"JOB_DIR\"] = \"mnist_models/models/{}_{}/\".format(\n",
" model_type, current_time)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"The cell below runs the local version of the code. The epochs and steps_per_epoch flag can be changed to run for longer or shorther, as defined in our `mnist_models/trainer/task.py` file."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"%%bash\n",
"python3 -m mnist_models.trainer.task \\\n",
" --job-dir=$JOB_DIR \\\n",
" --epochs=5 \\\n",
" --steps_per_epoch=50 \\\n",
" --model_type=$MODEL_TYPE"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Training on the cloud\n",
"\n",
"Since we're using an unreleased version of TensorFlow on AI Platform, we can instead use a [Deep Learning Container](https://cloud.google.com/ai-platform/deep-learning-containers/docs/overview) in order to take advantage of libraries and applications not normally packaged with AI Platform. Below is a simple [Dockerlife](https://docs.docker.com/engine/reference/builder/) which copies our code to be used in a TF2 environment."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"%%writefile mnist_models/Dockerfile\n",
"FROM gcr.io/deeplearning-platform-release/tf2-cpu\n",
"COPY mnist_models/trainer /mnist_models/trainer\n",
"ENTRYPOINT [\"python3\", \"-m\", \"mnist_models.trainer.task\"]"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"The below command builds the image and ships it off to Google Cloud so it can be used for AI Platform. When built, it will show up [here](http://console.cloud.google.com/gcr) with the name `mnist_models`. ([Click here](https://console.cloud.google.com/cloud-build) to enable Cloud Build)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"!docker build -f mnist_models/Dockerfile -t $IMAGE_URI ./"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"!docker push $IMAGE_URI"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Finally, we can kickoff the [AI Platform training job](https://cloud.google.com/sdk/gcloud/reference/ai-platform/jobs/submit/training). We can pass in our docker image using the `master-image-uri` flag."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"current_time = datetime.now().strftime(\"%y%m%d_%H%M%S\")\n",
"model_type = 'cnn'\n",
"\n",
"os.environ[\"MODEL_TYPE\"] = model_type\n",
"os.environ[\"JOB_DIR\"] = \"gs://{}/mnist_{}_{}/\".format(\n",
" BUCKET, model_type, current_time)\n",
"os.environ[\"JOB_NAME\"] = \"mnist_{}_{}\".format(\n",
" model_type, current_time)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"%%bash\n",
"echo $JOB_DIR $REGION $JOB_NAME\n",
"gcloud ai-platform jobs submit training $JOB_NAME \\\n",
" --staging-bucket=gs://$BUCKET \\\n",
" --region=$REGION \\\n",
" --master-image-uri=$IMAGE_URI \\\n",
" --scale-tier=BASIC_GPU \\\n",
" --job-dir=$JOB_DIR \\\n",
" -- \\\n",
" --model_type=$MODEL_TYPE"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Can't wait to see the results? Run the code below and copy the output into the [Google Cloud Shell](https://console.cloud.google.com/home/dashboard?cloudshell=true) to follow along with TensorBoard. Look at the web preview on port 6006."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"!echo \"tensorboard --logdir $JOB_DIR\""
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Deploying and predicting with model\n",
"\n",
"Once you have a model you're proud of, let's deploy it! All we need to do is give AI Platform the location of the model. Below uses the keras export path of the previous job, but `${JOB_DIR}keras_export/` can always be changed to a different path.\n",
"\n",
"Even though we're using a 1.14 runtime, it's compatable with TF2 exported models. Phew!\n",
"\n",
"Uncomment the delete commands below if you are getting an \"already exists error\" and want to deploy a new model."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"%%bash\n",
"MODEL_NAME=\"mnist\"\n",
"MODEL_VERSION=${MODEL_TYPE}\n",
"MODEL_LOCATION=${JOB_DIR}keras_export/\n",
"echo \"Deleting and deploying $MODEL_NAME $MODEL_VERSION from $MODEL_LOCATION ... this will take a few minutes\"\n",
"#yes | gcloud ai-platform versions delete ${MODEL_VERSION} --model ${MODEL_NAME}\n",
"#yes | gcloud ai-platform models delete ${MODEL_NAME}\n",
"gcloud ai-platform models create ${MODEL_NAME} --regions $REGION\n",
"gcloud ai-platform versions create ${MODEL_VERSION} \\\n",
" --model ${MODEL_NAME} \\\n",
" --origin ${MODEL_LOCATION} \\\n",
" --framework tensorflow \\\n",
" --runtime-version=1.14"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"To predict with the model, let's take one of the example images.\n",
"\n",
"**TODO 4**: Write a `.json` file with image data to send to an AI Platform deployed model"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"import json, codecs\n",
"import tensorflow as tf\n",
"import matplotlib.pyplot as plt\n",
"from mnist_models.trainer import util\n",
"\n",
"HEIGHT = 28\n",
"WIDTH = 28\n",
"IMGNO = 12\n",
"\n",
"mnist = tf.keras.datasets.mnist.load_data()\n",
"(x_train, y_train), (x_test, y_test) = mnist\n",
"test_image = x_test[IMGNO]\n",
"\n",
"jsondata = test_image.reshape(HEIGHT, WIDTH, 1).tolist()\n",
"json.dump(jsondata, codecs.open(\"test.json\", \"w\", encoding = \"utf-8\"))\n",
"plt.imshow(test_image.reshape(HEIGHT, WIDTH));"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Finally, we can send it to the prediction service. The output will have a 1 in the index of the corresponding digit it is predicting. Congrats! You've completed the lab!"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"%%bash\n",
"gcloud ai-platform predict \\\n",
" --model=mnist \\\n",
" --version=${MODEL_TYPE} \\\n",
" --json-instances=./test.json"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Copyright 2019 Google Inc.\n",
"Licensed under the Apache License, Version 2.0 (the \"License\"); you may not use this file except in compliance with the License. You may obtain a copy of the License at\n",
"http://www.apache.org/licenses/LICENSE-2.0\n",
"Unless required by applicable law or agreed to in writing, software distributed under the License is distributed on an \"AS IS\" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the License for the specific language governing permissions and limitations under the License."
]
}
],
"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": "3.5.3"
}
},
"nbformat": 4,
"nbformat_minor": 4
} | apache-2.0 |
maxpumperla/elephas | examples/Spark_ML_Pipeline.ipynb | 1 | 24190 | {
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Spark ML model pipelines on Distributed Deep Neural Nets\n",
"\n",
"This notebook describes how to build machine learning [pipelines with Spark ML](http://spark.apache.org/docs/latest/ml-guide.html) for distributed versions of Keras deep learning models. As data set we use the Otto Product Classification challenge from Kaggle. The reason we chose this data is that it is small and very structured. This way, we can focus more on technical components rather than prepcrocessing intricacies. Also, users with slow hardware or without a full-blown Spark cluster should be able to run this example locally, and still learn a lot about the distributed mode.\n",
"\n",
"Often, the need to distribute computation is not imposed by model training, but rather by building the data pipeline, i.e. ingestion, transformation etc. In training, deep neural networks tend to do fairly well on one or more GPUs on one machine. Most of the time, using gradient descent methods, you will process one batch after another anyway. Even so, it may still be beneficial to use frameworks like Spark to integrate your models with your surrounding infrastructure. On top of that, the convenience provided by Spark ML pipelines can be very valuable (being syntactically very close to what you might know from [```scikit-learn```](http://scikit-learn.org/stable/modules/generated/sklearn.pipeline.Pipeline.html)).\n",
"\n",
"**TL;DR:** We will show how to tackle a classification problem using distributed deep neural nets and Spark ML pipelines in an example that is essentially a distributed version of the one found [here](https://github.com/fchollet/keras/blob/master/examples/kaggle_otto_nn.py)."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Using this notebook\n",
"As we are going to use elephas, you will need access to a running Spark context to run this notebook. If you don't have it already, install Spark locally by following the [instructions provided here](https://github.com/maxpumperla/elephas/blob/master/README.md). Make sure to also export ```SPARK_HOME``` to your path and start your ipython/jupyter notebook as follows:"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"```\n",
"IPYTHON_OPTS=\"notebook\" ${SPARK_HOME}/bin/pyspark --driver-memory 4G elephas/examples/Spark_ML_Pipeline.ipynb\n",
"```"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"To test your environment, try to print the Spark context (provided as ```sc```), i.e. execute the following cell."
]
},
{
"cell_type": "code",
"execution_count": 1,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"<pyspark.context.SparkContext object at 0x1132d61d0>\n"
]
}
],
"source": [
"from __future__ import print_function\n",
"print(sc)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Otto Product Classification Data"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Training and test data is available [here](https://www.kaggle.com/c/otto-group-product-classification-challenge/data). Go ahead and download the data. Inspecting it, you will see that the provided csv files consist of an id column, 93 integer feature columns. ```train.csv``` has an additional column for labels, which ```test.csv``` is missing. The challenge is to accurately predict test labels. For the rest of this notebook, we will assume data is stored at ```data_path```, which you should modify below as needed."
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"data_path = \"./\" # <-- Make sure to adapt this to where your csv files are."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Loading data is relatively simple, but we have to take care of a few things. First, while you can shuffle rows of an RDD, it is generally not very efficient. But since data in ```train.csv``` is sorted by category, we'll have to shuffle in order to make the model perform well. This is what the function ```shuffle_csv``` below is for. Next, we read in plain text in ```load_data_rdd```, split lines by comma and convert features to float vector type. Also, note that the last column in ```train.csv``` represents the category, which has a ```Class_``` prefix. "
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Defining Data Frames\n",
"\n",
"Spark has a few core data structures, among them is the ```data frame```, which is a distributed version of the named columnar data structure many will now from either [R](https://stat.ethz.ch/R-manual/R-devel/library/base/html/data.frame.html) or [Pandas](http://pandas.pydata.org/pandas-docs/stable/generated/pandas.DataFrame.html). We need a so called ```SQLContext``` and an optional column-to-names mapping to create a data frame from scratch. "
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"from pyspark.sql import SQLContext\n",
"from pyspark.ml.linalg import Vectors\n",
"import numpy as np\n",
"import random\n",
"\n",
"sql_context = SQLContext(sc)\n",
"\n",
"def shuffle_csv(csv_file):\n",
" lines = open(csv_file).readlines()\n",
" random.shuffle(lines)\n",
" open(csv_file, 'w').writelines(lines)\n",
"\n",
"def load_data_frame(csv_file, shuffle=True, train=True):\n",
" if shuffle:\n",
" shuffle_csv(csv_file)\n",
" data = sc.textFile(data_path + csv_file) # This is an RDD, which will later be transformed to a data frame\n",
" data = data.filter(lambda x:x.split(',')[0] != 'id').map(lambda line: line.split(','))\n",
" if train:\n",
" data = data.map(\n",
" lambda line: (Vectors.dense(np.asarray(line[1:-1]).astype(np.float32)),\n",
" str(line[-1])) )\n",
" else:\n",
" # Test data gets dummy labels. We need the same structure as in Train data\n",
" data = data.map( lambda line: (Vectors.dense(np.asarray(line[1:]).astype(np.float32)),\"Class_1\") ) \n",
" return sqlContext.createDataFrame(data, ['features', 'category'])\n",
" "
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Let's load both train and test data and print a few rows of data using the convenient ```show``` method."
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Train data frame:\n",
"+--------------------+--------+\n",
"| features|category|\n",
"+--------------------+--------+\n",
"|[0.0,0.0,0.0,0.0,...| Class_8|\n",
"|[0.0,0.0,0.0,0.0,...| Class_8|\n",
"|[0.0,0.0,0.0,0.0,...| Class_2|\n",
"|[0.0,1.0,0.0,1.0,...| Class_6|\n",
"|[0.0,0.0,0.0,0.0,...| Class_9|\n",
"|[0.0,0.0,0.0,0.0,...| Class_2|\n",
"|[0.0,0.0,0.0,0.0,...| Class_2|\n",
"|[0.0,0.0,0.0,0.0,...| Class_3|\n",
"|[0.0,0.0,4.0,0.0,...| Class_8|\n",
"|[0.0,0.0,0.0,0.0,...| Class_7|\n",
"+--------------------+--------+\n",
"only showing top 10 rows\n",
"\n",
"Test data frame (note the dummy category):\n",
"+--------------------+--------+\n",
"| features|category|\n",
"+--------------------+--------+\n",
"|[1.0,0.0,0.0,1.0,...| Class_1|\n",
"|[0.0,1.0,13.0,1.0...| Class_1|\n",
"|[0.0,0.0,1.0,1.0,...| Class_1|\n",
"|[0.0,0.0,0.0,0.0,...| Class_1|\n",
"|[2.0,0.0,5.0,1.0,...| Class_1|\n",
"|[0.0,0.0,0.0,0.0,...| Class_1|\n",
"|[0.0,0.0,0.0,0.0,...| Class_1|\n",
"|[0.0,0.0,0.0,1.0,...| Class_1|\n",
"|[0.0,0.0,0.0,0.0,...| Class_1|\n",
"|[0.0,0.0,0.0,0.0,...| Class_1|\n",
"+--------------------+--------+\n",
"only showing top 10 rows\n",
"\n"
]
}
],
"source": [
"train_df = load_data_frame(\"train.csv\")\n",
"test_df = load_data_frame(\"test.csv\", shuffle=False, train=False) # No need to shuffle test data\n",
"\n",
"print(\"Train data frame:\")\n",
"train_df.show(10)\n",
"\n",
"print(\"Test data frame (note the dummy category):\")\n",
"test_df.show(10)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Preprocessing: Defining Transformers\n",
"\n",
"Up until now, we basically just read in raw data. Luckily, ```Spark ML``` has quite a few preprocessing features available, so the only thing we will ever have to do is define transformations of data frames.\n",
"\n",
"To proceed, we will first transform category strings to double values. This is done by a so called ```StringIndexer```. Note that we carry out the actual transformation here already, but that is just for demonstration purposes. All we really need is too define ```string_indexer``` to put it into a pipeline later on."
]
},
{
"cell_type": "code",
"execution_count": 8,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"from pyspark.ml.feature import StringIndexer\n",
"\n",
"string_indexer = StringIndexer(inputCol=\"category\", outputCol=\"index_category\")\n",
"fitted_indexer = string_indexer.fit(train_df)\n",
"indexed_df = fitted_indexer.transform(train_df)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Next, it's good practice to normalize the features, which is done with a ```StandardScaler```."
]
},
{
"cell_type": "code",
"execution_count": 9,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"from pyspark.ml.feature import StandardScaler\n",
"\n",
"scaler = StandardScaler(inputCol=\"features\", outputCol=\"scaled_features\", withStd=True, withMean=True)\n",
"fitted_scaler = scaler.fit(indexed_df)\n",
"scaled_df = fitted_scaler.transform(indexed_df)"
]
},
{
"cell_type": "code",
"execution_count": 10,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"The result of indexing and scaling. Each transformation adds new columns to the data frame:\n",
"+--------------------+--------+--------------+--------------------+\n",
"| features|category|index_category| scaled_features|\n",
"+--------------------+--------+--------------+--------------------+\n",
"|[0.0,0.0,0.0,0.0,...| Class_8| 2.0|[-0.2535060296260...|\n",
"|[0.0,0.0,0.0,0.0,...| Class_8| 2.0|[-0.2535060296260...|\n",
"|[0.0,0.0,0.0,0.0,...| Class_2| 0.0|[-0.2535060296260...|\n",
"|[0.0,1.0,0.0,1.0,...| Class_6| 1.0|[-0.2535060296260...|\n",
"|[0.0,0.0,0.0,0.0,...| Class_9| 4.0|[-0.2535060296260...|\n",
"|[0.0,0.0,0.0,0.0,...| Class_2| 0.0|[-0.2535060296260...|\n",
"|[0.0,0.0,0.0,0.0,...| Class_2| 0.0|[-0.2535060296260...|\n",
"|[0.0,0.0,0.0,0.0,...| Class_3| 3.0|[-0.2535060296260...|\n",
"|[0.0,0.0,4.0,0.0,...| Class_8| 2.0|[-0.2535060296260...|\n",
"|[0.0,0.0,0.0,0.0,...| Class_7| 5.0|[-0.2535060296260...|\n",
"+--------------------+--------+--------------+--------------------+\n",
"only showing top 10 rows\n",
"\n"
]
}
],
"source": [
"print(\"The result of indexing and scaling. Each transformation adds new columns to the data frame:\")\n",
"scaled_df.show(10)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Keras Deep Learning model\n",
"\n",
"Now that we have a data frame with processed features and labels, let's define a deep neural net that we can use to address the classification problem. Chances are you came here because you know a thing or two about deep learning. If so, the model below will look very straightforward to you. We build a keras model by choosing a set of three consecutive Dense layers with dropout and ReLU activations. There are certainly much better architectures for the problem out there, but we really just want to demonstrate the general flow here."
]
},
{
"cell_type": "code",
"execution_count": 12,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"from tensorflow.keras.models import Sequential\n",
"from tensorflow.keras.layers import Dense, Dropout, Activation\n",
"from tensorflow.keras.utils import to_categorical, generic_utils\n",
"\n",
"nb_classes = train_df.select(\"category\").distinct().count()\n",
"input_dim = len(train_df.select(\"features\").first()[0])\n",
"\n",
"model = Sequential()\n",
"model.add(Dense(512, input_shape=(input_dim,)))\n",
"model.add(Activation('relu'))\n",
"model.add(Dropout(0.5))\n",
"model.add(Dense(512))\n",
"model.add(Activation('relu'))\n",
"model.add(Dropout(0.5))\n",
"model.add(Dense(512))\n",
"model.add(Activation('relu'))\n",
"model.add(Dropout(0.5))\n",
"model.add(Dense(nb_classes))\n",
"model.add(Activation('softmax'))\n",
"\n",
"model.compile(loss='categorical_crossentropy', optimizer='adam')"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Distributed Elephas model\n",
"\n",
"To lift the above Keras ```model``` to Spark, we define an ```Estimator``` on top of it. An ```Estimator``` is Spark's incarnation of a model that still has to be trained. It essentially only comes with only a single (required) method, namely ```fit```. Once we call ```fit``` on a data frame, we get back a ```Model```, which is a trained model with a ```transform``` method to predict labels.\n",
"\n",
"We do this by initializing an ```ElephasEstimator``` and setting a few properties. As by now our input data frame will have many columns, we have to tell the model where to find features and labels by column name. Then we provide serialized versions of our Keras model. We can not plug in keras models into the ```Estimator``` directly, as Spark will have to serialize them anyway for communication with workers, so it's better to provide the serialization ourselves. In fact, while pyspark knows how to serialize ```model```, it is extremely inefficient and can break if models become too large. Spark ML is especially picky (and rightly so) about parameters and more or less prohibits you from providing non-atomic types and arrays of the latter. Most of the remaining parameters are optional and rather self explainatory. Plus, many of them you know if you have ever run a keras model before. We just include them here to show the full set of training configuration."
]
},
{
"cell_type": "code",
"execution_count": 14,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"ElephasEstimator_415398ab22cb1699f794"
]
},
"execution_count": 14,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"from elephas.ml_model import ElephasEstimator\n",
"from tensorflow.keras import optimizers\n",
"\n",
"\n",
"adam = optimizers.Adam(lr=0.01)\n",
"opt_conf = optimizers.serialize(adam)\n",
"\n",
"# Initialize SparkML Estimator and set all relevant properties\n",
"estimator = ElephasEstimator()\n",
"estimator.setFeaturesCol(\"scaled_features\") # These two come directly from pyspark,\n",
"estimator.setLabelCol(\"index_category\") # hence the camel case. Sorry :)\n",
"estimator.set_keras_model_config(model.to_yaml()) # Provide serialized Keras model\n",
"estimator.set_categorical_labels(True)\n",
"estimator.set_nb_classes(nb_classes)\n",
"estimator.set_num_workers(1) # We just use one worker here. Feel free to adapt it.\n",
"estimator.set_epochs(20) \n",
"estimator.set_batch_size(128)\n",
"estimator.set_verbosity(1)\n",
"estimator.set_validation_split(0.15)\n",
"estimator.set_optimizer_config(opt_conf)\n",
"estimator.set_mode(\"synchronous\")\n",
"estimator.set_loss(\"categorical_crossentropy\")\n",
"estimator.set_metrics(['acc'])"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## SparkML Pipelines\n",
"\n",
"Now for the easy part: Defining pipelines is really as easy as listing pipeline stages. We can provide any configuration of ```Transformers``` and ```Estimators``` really, but here we simply take the three components defined earlier. Note that ```string_indexer``` and ```scaler``` and interchangable, while ```estimator``` somewhat obviously has to come last in the pipeline."
]
},
{
"cell_type": "code",
"execution_count": 15,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"from pyspark.ml import Pipeline\n",
"\n",
"pipeline = Pipeline(stages=[string_indexer, scaler, estimator])"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Fitting and evaluating the pipeline\n",
"\n",
"The last step now is to fit the pipeline on training data and evaluate it. We evaluate, i.e. transform, on _training data_, since only in that case do we have labels to check accuracy of the model. If you like, you could transform the ```test_df``` as well."
]
},
{
"cell_type": "code",
"execution_count": 17,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"61878/61878 [==============================] - 0s \n",
"+--------------+----------+\n",
"|index_category|prediction|\n",
"+--------------+----------+\n",
"| 2.0| 2.0|\n",
"| 2.0| 2.0|\n",
"| 0.0| 0.0|\n",
"| 1.0| 1.0|\n",
"| 4.0| 4.0|\n",
"| 0.0| 0.0|\n",
"| 0.0| 0.0|\n",
"| 3.0| 3.0|\n",
"| 2.0| 2.0|\n",
"| 5.0| 0.0|\n",
"| 0.0| 0.0|\n",
"| 4.0| 4.0|\n",
"| 0.0| 0.0|\n",
"| 4.0| 1.0|\n",
"| 2.0| 2.0|\n",
"| 1.0| 1.0|\n",
"| 0.0| 0.0|\n",
"| 6.0| 0.0|\n",
"| 2.0| 2.0|\n",
"| 1.0| 1.0|\n",
"| 2.0| 2.0|\n",
"| 8.0| 8.0|\n",
"| 1.0| 1.0|\n",
"| 5.0| 0.0|\n",
"| 0.0| 0.0|\n",
"| 0.0| 3.0|\n",
"| 0.0| 0.0|\n",
"| 1.0| 1.0|\n",
"| 4.0| 4.0|\n",
"| 2.0| 2.0|\n",
"| 0.0| 3.0|\n",
"| 3.0| 3.0|\n",
"| 0.0| 0.0|\n",
"| 3.0| 0.0|\n",
"| 1.0| 5.0|\n",
"| 3.0| 3.0|\n",
"| 2.0| 2.0|\n",
"| 1.0| 1.0|\n",
"| 0.0| 0.0|\n",
"| 2.0| 2.0|\n",
"| 2.0| 2.0|\n",
"| 1.0| 1.0|\n",
"| 6.0| 6.0|\n",
"| 1.0| 1.0|\n",
"| 0.0| 3.0|\n",
"| 7.0| 0.0|\n",
"| 0.0| 0.0|\n",
"| 0.0| 0.0|\n",
"| 1.0| 1.0|\n",
"| 1.0| 1.0|\n",
"| 6.0| 6.0|\n",
"| 0.0| 0.0|\n",
"| 0.0| 3.0|\n",
"| 2.0| 2.0|\n",
"| 0.0| 0.0|\n",
"| 2.0| 2.0|\n",
"| 0.0| 0.0|\n",
"| 4.0| 4.0|\n",
"| 0.0| 0.0|\n",
"| 6.0| 6.0|\n",
"| 2.0| 5.0|\n",
"| 0.0| 3.0|\n",
"| 3.0| 0.0|\n",
"| 0.0| 0.0|\n",
"| 3.0| 3.0|\n",
"| 4.0| 4.0|\n",
"| 0.0| 3.0|\n",
"| 0.0| 0.0|\n",
"| 0.0| 0.0|\n",
"| 4.0| 4.0|\n",
"| 3.0| 0.0|\n",
"| 2.0| 2.0|\n",
"| 1.0| 1.0|\n",
"| 7.0| 7.0|\n",
"| 0.0| 0.0|\n",
"| 0.0| 0.0|\n",
"| 0.0| 3.0|\n",
"| 1.0| 1.0|\n",
"| 1.0| 1.0|\n",
"| 5.0| 4.0|\n",
"| 1.0| 1.0|\n",
"| 1.0| 1.0|\n",
"| 4.0| 4.0|\n",
"| 3.0| 3.0|\n",
"| 0.0| 0.0|\n",
"| 2.0| 2.0|\n",
"| 4.0| 4.0|\n",
"| 7.0| 7.0|\n",
"| 2.0| 2.0|\n",
"| 0.0| 0.0|\n",
"| 1.0| 1.0|\n",
"| 0.0| 0.0|\n",
"| 4.0| 4.0|\n",
"| 1.0| 1.0|\n",
"| 0.0| 0.0|\n",
"| 0.0| 0.0|\n",
"| 0.0| 0.0|\n",
"| 0.0| 3.0|\n",
"| 0.0| 3.0|\n",
"| 0.0| 0.0|\n",
"+--------------+----------+\n",
"only showing top 100 rows\n",
"\n",
"0.764132648114\n"
]
}
],
"source": [
"from pyspark.mllib.evaluation import MulticlassMetrics\n",
"\n",
"fitted_pipeline = pipeline.fit(train_df) # Fit model to data\n",
"\n",
"prediction = fitted_pipeline.transform(train_df) # Evaluate on train data.\n",
"# prediction = fitted_pipeline.transform(test_df) # <-- The same code evaluates test data.\n",
"pnl = prediction.select(\"index_category\", \"prediction\")\n",
"pnl.show(100)\n",
"\n",
"prediction_and_label = pnl.map(lambda row: (row.index_category, row.prediction))\n",
"metrics = MulticlassMetrics(prediction_and_label)\n",
"print(metrics.precision())"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Conclusion\n",
"\n",
"It may certainly take some time to master the principles and syntax of both Keras and Spark, depending where you come from, of course. However, we also hope you come to the conclusion that once you get beyond the stage of struggeling with defining your models and preprocessing your data, the business of building and using SparkML pipelines is quite an elegant and useful one. \n",
"\n",
"If you like what you see, consider helping further improve elephas or contributing to Keras or Spark. Do you have any constructive remarks on this notebook? Is there something you want me to clarify? In any case, feel free to contact me."
]
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 2",
"language": "python",
"name": "python2"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.6.5"
}
},
"nbformat": 4,
"nbformat_minor": 2
}
| mit |
ksu-mechatronics-research/deep-visual-odometry | python/snippets/main.ipynb | 1 | 297540 | {
"cells": [
{
"cell_type": "code",
"execution_count": 1,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"import formatData"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"Using TensorFlow backend.\n"
]
}
],
"source": [
"import alexNet"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Loading poses for sequence 00...\n",
"done.\n",
"Loading poses for sequence 01...\n",
"done.\n",
"Loading poses for sequence 02...\n",
"done.\n",
"Loading poses for sequence 03...\n",
"done.\n",
"Loading poses for sequence 04...\n",
"done.\n",
"Loading poses for sequence 05...\n",
"done.\n",
"Loading poses for sequence 06...\n",
"done.\n",
"Loading poses for sequence 07...\n",
"done.\n",
"Loading poses for sequence 08...\n",
"done.\n",
"Loading poses for sequence 09...\n",
"done.\n",
"Loading poses for sequence 10...\n",
"done.\n"
]
}
],
"source": [
"Xtr, Ytr, Xte, Yte = formatData.knownEnv(formatData.load_data(),formatData.load_poses())"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Epoch 1/5\n",
"13888/18541 [=====================>........] - ETA: 178s - loss: 0.3276 - acc: 0.6436 \b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b"
]
}
],
"source": [
"alexNet.run_model(alexNet.create_model(), Xtr, Ytr, Xte, Yte)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"alexNet = reload(alexNet)"
]
}
],
"metadata": {
"anaconda-cloud": {},
"kernelspec": {
"display_name": "Python [default]",
"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": "3.5.2"
}
},
"nbformat": 4,
"nbformat_minor": 2
}
| mit |
rddy/lentil | nb/model_explorations.ipynb | 2 | 9262 | {
"cells": [
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"from __future__ import division\n",
"\n",
"import pickle\n",
"import os\n",
"import random\n",
"\n",
"from matplotlib import pyplot as plt\n",
"import numpy as np\n",
"\n",
"import seaborn as sns\n",
"sns.set_style('whitegrid')\n",
"\n",
"from lentil import datatools\n",
"from lentil import models\n",
"from lentil import est\n",
"from lentil import evaluate\n",
"\n",
"%matplotlib inline"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"import logging\n",
"logging.getLogger().setLevel(logging.DEBUG)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Load an interaction history"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"history_path = os.path.join('data', 'assistments_2009_2010.pkl')"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"with open(history_path, 'rb') as f:\n",
" history = pickle.load(f)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"df = history.data"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Train an embedding model on the interaction history and visualize the results"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"embedding_dimension = 2\n",
"\n",
"model = models.EmbeddingModel(\n",
" history, \n",
" embedding_dimension,\n",
" using_prereqs=True,\n",
" using_lessons=True,\n",
" using_bias=True,\n",
" learning_update_variance_constant=0.5)\n",
"\n",
"estimator = est.EmbeddingMAPEstimator(\n",
" regularization_constant=1e-3,\n",
" using_scipy=True,\n",
" verify_gradient=False,\n",
" debug_mode_on=True,\n",
" ftol=1e-3)\n",
"\n",
"model.fit(estimator)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"print \"Training AUC = %f\" % (evaluate.training_auc(\n",
" model, history, plot_roc_curve=True))"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"split_history = history.split_interactions_by_type()\n",
"timestep_of_last_interaction = split_history.timestep_of_last_interaction"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"NUM_STUDENTS_TO_SAMPLE = 10\n",
"for student_id in random.sample(df['student_id'].unique(), NUM_STUDENTS_TO_SAMPLE):\n",
" student_idx = history.idx_of_student_id(student_id)\n",
"\n",
" timesteps = range(1, timestep_of_last_interaction[student_id]+1)\n",
"\n",
" for i in xrange(model.embedding_dimension):\n",
" plt.plot(timesteps, model.student_embeddings[student_idx, i, timesteps], \n",
" label='Skill %d' % (i+1))\n",
" \n",
" norms = np.linalg.norm(model.student_embeddings[student_idx, :, timesteps], axis=1)\n",
" plt.plot(timesteps, norms, label='norm')\n",
" \n",
" plt.title('student_id = %s' % student_id)\n",
" plt.xlabel('Timestep')\n",
" plt.ylabel('Skill')\n",
" plt.legend(loc='upper right')\n",
" plt.show()"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"assessment_norms = np.linalg.norm(model.assessment_embeddings, axis=1)\n",
"\n",
"plt.xlabel('Assessment embedding norm')\n",
"plt.ylabel('Frequency (number of assessments)')\n",
"plt.hist(assessment_norms, bins=20)\n",
"plt.show()"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"def get_pass_rates(grouped):\n",
" \"\"\"\n",
" Get pass rate for each group\n",
" \n",
" :param pd.GroupBy grouped: A grouped dataframe\n",
" :rtype: dict[str, float]\n",
" :return: A dictionary mapping group name to pass rate\n",
" \"\"\"\n",
" pass_rates = {}\n",
" for name, group in grouped:\n",
" vc = group['outcome'].value_counts()\n",
" if True not in vc:\n",
" pass_rates[name] = 0\n",
" else:\n",
" pass_rates[name] = vc[True] / len(group)\n",
" return pass_rates"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"grouped = df[df['module_type']==datatools.AssessmentInteraction.MODULETYPE].groupby('module_id')\n",
"pass_rates = get_pass_rates(grouped)\n",
"\n",
"assessment_norms = [np.linalg.norm(model.assessment_embeddings[history.idx_of_assessment_id(assessment_id), :]) for assessment_id in pass_rates]\n",
"\n",
"plt.xlabel('Assessment pass rate')\n",
"plt.ylabel('Assessment embedding norm')\n",
"plt.scatter(pass_rates.values(), assessment_norms)\n",
"plt.show()"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"grouped = df[df['module_type']==datatools.AssessmentInteraction.MODULETYPE].groupby('module_id')\n",
"pass_rates = get_pass_rates(grouped)\n",
"\n",
"bias_minus_norm = [model.assessment_biases[history.idx_of_assessment_id(\n",
" assessment_id)] - np.linalg.norm(\n",
" model.assessment_embeddings[history.idx_of_assessment_id(\n",
" assessment_id), :]) for assessment_id in pass_rates]\n",
"\n",
"plt.xlabel('Assessment pass rate')\n",
"plt.ylabel('Assessment bias - Assessment embedding norm')\n",
"plt.scatter(pass_rates.values(), bias_minus_norm)\n",
"plt.show()"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"grouped = df[df['module_type']==datatools.AssessmentInteraction.MODULETYPE].groupby('student_id')\n",
"pass_rates = get_pass_rates(grouped)\n",
"\n",
"biases = [model.student_biases[history.idx_of_student_id(\n",
" student_id)] for student_id in pass_rates]\n",
"\n",
"plt.xlabel('Student pass rate')\n",
"plt.ylabel('Student bias')\n",
"plt.scatter(pass_rates.values(), biases)\n",
"plt.show()"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"lesson_norms = np.linalg.norm(model.lesson_embeddings, axis=1)\n",
"\n",
"plt.xlabel('Lesson embedding norm')\n",
"plt.ylabel('Frequency (number of lessons)')\n",
"plt.hist(lesson_norms, bins=20)\n",
"plt.show()"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"prereq_norms = np.linalg.norm(model.prereq_embeddings, axis=1)\n",
"\n",
"plt.xlabel('Prereq embedding norm')\n",
"plt.ylabel('Frequency (number of lessons)')\n",
"plt.hist(prereq_norms, bins=20)\n",
"plt.show()"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"plt.xlabel('Lesson embedding norm')\n",
"plt.ylabel('Prereq embedding norm')\n",
"plt.scatter(prereq_norms, lesson_norms)\n",
"plt.show()"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"timesteps = range(model.student_embeddings.shape[2])\n",
"avg_student_norms = np.array(np.linalg.norm(np.mean(model.student_embeddings, axis=0), axis=0))\n",
"\n",
"plt.xlabel('Timestep')\n",
"plt.ylabel('Average student embedding norm')\n",
"plt.plot(timesteps, avg_student_norms)\n",
"plt.show()"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": []
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 2",
"language": "python",
"name": "python2"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 2
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython2",
"version": "2.7.10"
}
},
"nbformat": 4,
"nbformat_minor": 0
}
| apache-2.0 |
wangshiphys/HamiltonianPy | HamiltonianPy/doc/MatrixRepr.ipynb | 1 | 16269 | {
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Matrix representation of operators in Fock space"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Fock Space\n",
"\n",
"Number of single particle states: **4**\n",
"\n",
"Particle number conservation: **False**\n",
"\n",
"Base vectors:\n",
"\n",
"Index | Ket | Index | Ket | Index | Ket | Index | Ket\n",
"--- | --- | --- | --- | --- | --- | --- | ---\n",
"0 | $\\mid0000\\rangle$ | 4 | $\\mid0100\\rangle$ | 8 | $\\mid1000\\rangle$ | 12 | $\\mid1100\\rangle$\n",
"1 | $\\mid0001\\rangle$ | 5 | $\\mid0101\\rangle$ | 9 | $\\mid1001\\rangle$ | 13 | $\\mid1101\\rangle$\n",
"2 | $\\mid0010\\rangle$ | 6 | $\\mid0110\\rangle$ | 10 | $\\mid1010\\rangle$ | 14 | $\\mid1110\\rangle$\n",
"3 | $\\mid0011\\rangle$ | 7 | $\\mid0111\\rangle$ | 11 | $\\mid1011\\rangle$ | 15 | $\\mid1111\\rangle$\n",
"\n",
"Creation operators:\n",
"\n",
"- $c_0^\\dagger$\n",
"- $c_1^\\dagger$\n",
"- $c_2^\\dagger$\n",
"- $c_3^\\dagger$\n",
"\n",
"Annihilation operators:\n",
"\n",
"- $c_0$\n",
"- $c_1$\n",
"- $c_2$\n",
"- $c_3$"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Annihilation and creation operators' matrix representation\n",
"\n",
"### $c_0$\n",
"\n",
"Row Index | Column Index | Entry\n",
"--- | --- | ---\n",
"0 | 1 | 1\n",
"2 | 3 | 1\n",
"4 | 5 | 1\n",
"6 | 7 | 1\n",
"8 | 9 | 1\n",
"10 | 11 | 1\n",
"12 | 13 | 1\n",
"14 | 15 | 1\n",
"\n",
"### $c_0^{\\dagger}$\n",
"\n",
"Row Index | Column Index | Entry\n",
"--- | --- | ---\n",
"1 | 0 | 1\n",
"3 | 2 | 1\n",
"5 | 4 | 1\n",
"7 | 6 | 1\n",
"9 | 8 | 1\n",
"11 | 10 | 1\n",
"13 | 12 | 1\n",
"15 | 14 | 1\n",
"\n",
"### $c_1$\n",
"\n",
"Row Index | Column Index | Entry\n",
"--- | --- | ---\n",
"0 | 2 | 1\n",
"1 | 3 | -1\n",
"4 | 6 | 1\n",
"5 | 7 | -1\n",
"8 | 10 | 1\n",
"9 | 11 | -1\n",
"12 | 14 | 1\n",
"13 | 15 | -1\n",
"\n",
"### $c_1^{\\dagger}$\n",
"\n",
"Row Index | Column Index | Entry\n",
"--- | --- | ---\n",
"2 | 0 | 1\n",
"3 | 1 | -1\n",
"6 | 4 | 1\n",
"7 | 5 | -1\n",
"10 | 8 | 1\n",
"11 | 9 | -1\n",
"14 | 12 | 1\n",
"15 | 13 | -1\n",
"\n",
"### $c_2$\n",
"\n",
"Row Index | Column Index | Entry\n",
"--- | --- | ---\n",
"0 | 4 | 1\n",
"1 | 5 | -1\n",
"2 | 6 | -1\n",
"3 | 7 | 1\n",
"8 | 12 | 1\n",
"9 | 13 | -1\n",
"10 | 14 | -1\n",
"11 | 15 | 1\n",
"\n",
"### $c_2^{\\dagger}$\n",
"\n",
"Row Index | Column Index | Entry\n",
"--- | --- | ---\n",
"4 | 0 | 1\n",
"5 | 1 | -1\n",
"6 | 2 | -1\n",
"7 | 3 | 1\n",
"12 | 8 | 1\n",
"13 | 9 | -1\n",
"14 | 10 | -1\n",
"15 | 11 | 1\n",
"\n",
"### $c_3$\n",
"\n",
"Row Index | Column Index | Entry\n",
"--- | --- | ---\n",
"0 | 8 | 1\n",
"1 | 9 | -1\n",
"2 | 10 | -1\n",
"3 | 11 | 1\n",
"4 | 12 | -1\n",
"5 | 13 | 1\n",
"6 | 14 | 1\n",
"7 | 15 | -1\n",
"\n",
"### $c_3^{\\dagger}$\n",
"\n",
"Row Index | Column Index | Entry\n",
"--- | --- | ---\n",
"8 | 0 | 1\n",
"9 | 1 | -1\n",
"10 | 2 | -1\n",
"11 | 3 | 1\n",
"12 | 4 | -1\n",
"13 | 5 | 1\n",
"14 | 6 | 1\n",
"15 | 7 | -1"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Hopping terms' matrix representation\n",
"\n",
"### $c_0^{\\dagger} c_0$\n",
"\n",
"Row Index | Column Index | Entry\n",
"--- | --- | ---\n",
"1 | 1 | 1\n",
"3 | 3 | 1\n",
"5 | 5 | 1\n",
"7 | 7 | 1\n",
"9 | 9 | 1\n",
"11 | 11 | 1\n",
"13 | 13 | 1\n",
"15 | 15 | 1\n",
"\n",
"### $c_0^{\\dagger} c_1$\n",
"\n",
"Row Index | Column Index | Entry\n",
"--- | --- | ---\n",
"1 | 2 | 1\n",
"5 | 6 | 1\n",
"9 | 10 | 1\n",
"13 | 14 | 1\n",
"\n",
"### $c_1^{\\dagger} c_0$\n",
"\n",
"Row Index | Column Index | Entry\n",
"--- | --- | ---\n",
"2 | 1 | 1\n",
"6 | 5 | 1\n",
"10 | 9 | 1\n",
"14 | 13 | 1\n",
"\n",
"### $c_0^{\\dagger} c_2$\n",
"\n",
"Row Index | Column Index | Entry\n",
"--- | --- | ---\n",
"1 | 4 | 1\n",
"3 | 6 | -1\n",
"9 | 12 | 1\n",
"11 | 14 | -1\n",
"\n",
"### $c_2^{\\dagger} c_0$\n",
"\n",
"Row Index | Column Index | Entry\n",
"--- | --- | ---\n",
"4 | 1 | 1\n",
"6 | 3 | -1\n",
"12 | 9 | 1\n",
"14 | 11 | -1\n",
"\n",
"### $c_0^{\\dagger} c_3$\n",
"\n",
"Row Index | Column Index | Entry\n",
"--- | --- | ---\n",
"1 | 8 | 1\n",
"3 | 10 | -1\n",
"5 | 12 | -1\n",
"7 | 14 | 1\n",
"\n",
"### $c_3^{\\dagger} c_0$\n",
"\n",
"Row Index | Column Index | Entry\n",
"--- | --- | ---\n",
"8 | 1 | 1\n",
"10 | 3 | -1\n",
"12 | 5 | -1\n",
"14 | 7 | 1\n",
"\n",
"### $c_1^{\\dagger} c_1$\n",
"\n",
"Row Index | Column Index | Entry\n",
"--- | --- | ---\n",
"2 | 2 | 1\n",
"3 | 3 | 1\n",
"6 | 6 | 1\n",
"7 | 7 | 1\n",
"10 | 10 | 1\n",
"11 | 11 | 1\n",
"14 | 14 | 1\n",
"15 | 15 | 1\n",
"\n",
"### $c_1^{\\dagger} c_2$\n",
"\n",
"Row Index | Column Index | Entry\n",
"--- | --- | ---\n",
"2 | 4 | 1\n",
"3 | 5 | 1\n",
"10 | 12 | 1\n",
"11 | 13 | 1\n",
"\n",
"### $c_2^{\\dagger} c_1$\n",
"\n",
"Row Index | Column Index | Entry\n",
"--- | --- | ---\n",
"4 | 2 | 1\n",
"5 | 3 | 1\n",
"12 | 10 | 1\n",
"13 | 11 | 1\n",
"\n",
"### $c_1^{\\dagger} c_3$\n",
"\n",
"Row Index | Column Index | Entry\n",
"--- | --- | ---\n",
"2 | 8 | 1\n",
"3 | 9 | 1\n",
"6 | 12 | -1\n",
"7 | 13 | -1\n",
"\n",
"### $c_3^{\\dagger} c_1$\n",
"\n",
"Row Index | Column Index | Entry\n",
"--- | --- | ---\n",
"8 | 2 | 1\n",
"9 | 3 | 1\n",
"12 | 6 | -1\n",
"13 | 7 | -1\n",
"\n",
"### $c_2^{\\dagger} c_2$\n",
"\n",
"Row Index | Column Index | Entry\n",
"--- | --- | ---\n",
"4 | 4 | 1\n",
"5 | 5 | 1\n",
"6 | 6 | 1\n",
"7 | 7 | 1\n",
"12 | 12 | 1\n",
"13 | 13 | 1\n",
"14 | 14 | 1\n",
"15 | 15 | 1\n",
"\n",
"### $c_2^{\\dagger} c_3$\n",
"\n",
"Row Index | Column Index | Entry\n",
"--- | --- | ---\n",
"4 | 8 | 1\n",
"5 | 9 | 1\n",
"6 | 10 | 1\n",
"7 | 11 | 1\n",
"\n",
"### $c_3^{\\dagger} c_2$\n",
"\n",
"Row Index | Column Index | Entry\n",
"--- | --- | ---\n",
"8 | 4 | 1\n",
"9 | 5 | 1\n",
"10 | 6 | 1\n",
"11 | 7 | 1\n",
"\n",
"### $c_3^{\\dagger} c_3$\n",
"\n",
"Row Index | Column Index | Entry\n",
"--- | --- | ---\n",
"8 | 8 | 1\n",
"9 | 9 | 1\n",
"10 | 10 | 1\n",
"11 | 11 | 1\n",
"12 | 12 | 1\n",
"13 | 13 | 1\n",
"14 | 14 | 1\n",
"15 | 15 | 1"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Hubbard term's matrix representation\n",
"\n",
"### $n_0 n_0$\n",
"\n",
"Row Index | Column Index | Entry\n",
"--- | --- | ---\n",
"1 | 1 | 1\n",
"3 | 3 | 1\n",
"5 | 5 | 1\n",
"7 | 7 | 1\n",
"9 | 9 | 1\n",
"11 | 11 | 1\n",
"13 | 13 | 1\n",
"15 | 15 | 1\n",
"\n",
"### $n_0 n_1$\n",
"\n",
"Row Index | Column Index | Entry\n",
"--- | --- | ---\n",
"3 | 3 | 1\n",
"7 | 7 | 1\n",
"11 | 11 | 1\n",
"15 | 15 | 1\n",
"\n",
"### $n_1 n_0$\n",
"\n",
"Row Index | Column Index | Entry\n",
"--- | --- | ---\n",
"3 | 3 | 1\n",
"7 | 7 | 1\n",
"11 | 11 | 1\n",
"15 | 15 | 1\n",
"\n",
"### $n_0 n_2$\n",
"\n",
"Row Index | Column Index | Entry\n",
"--- | --- | ---\n",
"5 | 5 | 1\n",
"7 | 7 | 1\n",
"13 | 13 | 1\n",
"15 | 15 | 1\n",
"\n",
"### $n_2 n_0$\n",
"\n",
"Row Index | Column Index | Entry\n",
"--- | --- | ---\n",
"5 | 5 | 1\n",
"7 | 7 | 1\n",
"13 | 13 | 1\n",
"15 | 15 | 1\n",
"\n",
"### $n_0 n_3$\n",
"\n",
"Row Index | Column Index | Entry\n",
"--- | --- | ---\n",
"9 | 9 | 1\n",
"11 | 11 | 1\n",
"13 | 13 | 1\n",
"15 | 15 | 1\n",
"\n",
"### $n_3 n_0$\n",
"\n",
"Row Index | Column Index | Entry\n",
"--- | --- | ---\n",
"9 | 9 | 1\n",
"11 | 11 | 1\n",
"13 | 13 | 1\n",
"15 | 15 | 1\n",
"\n",
"### $n_1 n_1$\n",
"\n",
"Row Index | Column Index | Entry\n",
"--- | --- | ---\n",
"2 | 2 | 1\n",
"3 | 3 | 1\n",
"6 | 6 | 1\n",
"7 | 7 | 1\n",
"10 | 10 | 1\n",
"11 | 11 | 1\n",
"14 | 14 | 1\n",
"15 | 15 | 1\n",
"\n",
"### $n_1 n_2$\n",
"\n",
"Row Index | Column Index | Entry\n",
"--- | --- | ---\n",
"6 | 6 | 1\n",
"7 | 7 | 1\n",
"14 | 14 | 1\n",
"15 | 15 | 1\n",
"\n",
"### $n_2 n_1$\n",
"\n",
"Row Index | Column Index | Entry\n",
"--- | --- | ---\n",
"6 | 6 | 1\n",
"7 | 7 | 1\n",
"14 | 14 | 1\n",
"15 | 15 | 1\n",
"\n",
"### $n_1 n_3$\n",
"\n",
"Row Index | Column Index | Entry\n",
"--- | --- | ---\n",
"10 | 10 | 1\n",
"11 | 11 | 1\n",
"14 | 14 | 1\n",
"15 | 15 | 1\n",
"\n",
"### $n_3 n_1$\n",
"\n",
"Row Index | Column Index | Entry\n",
"--- | --- | ---\n",
"10 | 10 | 1\n",
"11 | 11 | 1\n",
"14 | 14 | 1\n",
"15 | 15 | 1\n",
"\n",
"### $n_2 n_2$\n",
"\n",
"Row Index | Column Index | Entry\n",
"--- | --- | ---\n",
"4 | 4 | 1\n",
"5 | 5 | 1\n",
"6 | 6 | 1\n",
"7 | 7 | 1\n",
"12 | 12 | 1\n",
"13 | 13 | 1\n",
"14 | 14 | 1\n",
"15 | 15 | 1\n",
"\n",
"### $n_2 n_3$\n",
"\n",
"Row Index | Column Index | Entry\n",
"--- | --- | ---\n",
"12 | 12 | 1\n",
"13 | 13 | 1\n",
"14 | 14 | 1\n",
"15 | 15 | 1\n",
"\n",
"### $n_3 n_2$\n",
"\n",
"Row Index | Column Index | Entry\n",
"--- | --- | ---\n",
"12 | 12 | 1\n",
"13 | 13 | 1\n",
"14 | 14 | 1\n",
"15 | 15 | 1\n",
"\n",
"### $n_3 n_3$\n",
"\n",
"Row Index | Column Index | Entry\n",
"--- | --- | ---\n",
"8 | 8 | 1\n",
"9 | 9 | 1\n",
"10 | 10 | 1\n",
"11 | 11 | 1\n",
"12 | 12 | 1\n",
"13 | 13 | 1\n",
"14 | 14 | 1\n",
"15 | 15 | 1"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Hole pairing term's matrix representation\n",
"\n",
"### $c_0 c_1$\n",
"\n",
"Row Index | Column Index | Entry\n",
"--- | --- | ---\n",
"0 | 3 | -1\n",
"4 | 7 | -1\n",
"8 | 11 | -1\n",
"12 | 15 | -1\n",
"\n",
"### $c_1 c_0$\n",
"\n",
"Row Index | Column Index | Entry\n",
"--- | --- | ---\n",
"0 | 3 | 1\n",
"4 | 7 | 1\n",
"8 | 11 | 1\n",
"12 | 15 | 1\n",
"\n",
"### $c_0 c_2$\n",
"\n",
"Row Index | Column Index | Entry\n",
"--- | --- | ---\n",
"0 | 5 | -1\n",
"2 | 7 | 1\n",
"8 | 13 | -1\n",
"10 | 15 | 1\n",
"\n",
"### $c_2 c_0$\n",
"\n",
"Row Index | Column Index | Entry\n",
"--- | --- | ---\n",
"0 | 5 | 1\n",
"2 | 7 | -1\n",
"8 | 13 | 1\n",
"10 | 15 | -1\n",
"\n",
"### $c_0 c_3$\n",
"\n",
"Row Index | Column Index | Entry\n",
"--- | --- | ---\n",
"0 | 9 | -1\n",
"2 | 11 | 1\n",
"4 | 13 | 1\n",
"6 | 15 | -1\n",
"\n",
"### $c_3 c_0$\n",
"\n",
"Row Index | Column Index | Entry\n",
"--- | --- | ---\n",
"0 | 9 | 1\n",
"2 | 11 | -1\n",
"4 | 13 | -1\n",
"6 | 15 | 1\n",
"\n",
"### $c_1 c_2$\n",
"\n",
"Row Index | Column Index | Entry\n",
"--- | --- | ---\n",
"0 | 6 | -1\n",
"1 | 7 | -1\n",
"8 | 14 | -1\n",
"9 | 15 | -1\n",
"\n",
"### $c_2 c_1$\n",
"\n",
"Row Index | Column Index | Entry\n",
"--- | --- | ---\n",
"0 | 6 | 1\n",
"1 | 7 | 1\n",
"8 | 14 | 1\n",
"9 | 15 | 1\n",
"\n",
"### $c_1 c_3$\n",
"\n",
"Row Index | Column Index | Entry\n",
"--- | --- | ---\n",
"0 | 10 | -1\n",
"1 | 11 | -1\n",
"4 | 14 | 1\n",
"5 | 15 | 1\n",
"\n",
"### $c_3 c_1$\n",
"\n",
"Row Index | Column Index | Entry\n",
"--- | --- | ---\n",
"0 | 10 | 1\n",
"1 | 11 | 1\n",
"4 | 14 | -1\n",
"5 | 15 | -1\n",
"\n",
"### $c_2 c_3$\n",
"\n",
"Row Index | Column Index | Entry\n",
"--- | --- | ---\n",
"0 | 12 | -1\n",
"1 | 13 | -1\n",
"2 | 14 | -1\n",
"3 | 15 | -1\n",
"\n",
"### $c_3 c_2$\n",
"\n",
"Row Index | Column Index | Entry\n",
"--- | --- | ---\n",
"0 | 12 | 1\n",
"1 | 13 | 1\n",
"2 | 14 | 1\n",
"3 | 15 | 1"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Particle pairing term's matrix representation\n",
"\n",
"### $c_0^{\\dagger} c_1^{\\dagger}$\n",
"\n",
"Row Index | Column Index | Entry\n",
"--- | --- | ---\n",
"3 | 0 | 1\n",
"7 | 4 | 1\n",
"11 | 8 | 1\n",
"15 | 12 | 1\n",
"\n",
"### $c_1^{\\dagger} c_0^{\\dagger}$\n",
"\n",
"Row Index | Column Index | Entry\n",
"--- | --- | ---\n",
"3 | 0 | -1\n",
"7 | 4 | -1\n",
"11 | 8 | -1\n",
"15 | 12 | -1\n",
"\n",
"### $c_0^{\\dagger} c_2^{\\dagger}$\n",
"\n",
"Row Index | Column Index | Entry\n",
"--- | --- | ---\n",
"5 | 0 | 1\n",
"7 | 2 | -1\n",
"13 | 8 | 1\n",
"15 | 10 | -1\n",
"\n",
"### $c_2^{\\dagger} c_0^{\\dagger}$\n",
"\n",
"Row Index | Column Index | Entry\n",
"--- | --- | ---\n",
"5 | 0 | -1\n",
"7 | 2 | 1\n",
"13 | 8 | -1\n",
"15 | 10 | 1\n",
"\n",
"### $c_0^{\\dagger} c_3^{\\dagger}$\n",
"\n",
"Row Index | Column Index | Entry\n",
"--- | --- | ---\n",
"9 | 0 | 1\n",
"11 | 2 | -1\n",
"13 | 4 | -1\n",
"15 | 6 | 1\n",
"\n",
"### $c_3^{\\dagger} c_0^{\\dagger}$\n",
"\n",
"Row Index | Column Index | Entry\n",
"--- | --- | ---\n",
"9 | 0 | -1\n",
"11 | 2 | 1\n",
"13 | 4 | 1\n",
"15 | 6 | -1\n",
"\n",
"### $c_1^{\\dagger} c_2^{\\dagger}$\n",
"\n",
"Row Index | Column Index | Entry\n",
"--- | --- | ---\n",
"6 | 0 | 1\n",
"7 | 1 | 1\n",
"14 | 8 | 1\n",
"15 | 9 | 1\n",
"\n",
"### $c_2^{\\dagger} c_1^{\\dagger}$\n",
"\n",
"Row Index | Column Index | Entry\n",
"--- | --- | ---\n",
"6 | 0 | -1\n",
"7 | 1 | -1\n",
"14 | 8 | -1\n",
"15 | 9 | -1\n",
"\n",
"### $c_1^{\\dagger} c_3^{\\dagger}$\n",
"\n",
"Row Index | Column Index | Entry\n",
"--- | --- | ---\n",
"10 | 0 | 1\n",
"11 | 1 | 1\n",
"14 | 4 | -1\n",
"15 | 5 | -1\n",
"\n",
"### $c_3^{\\dagger} c_1^{\\dagger}$\n",
"\n",
"Row Index | Column Index | Entry\n",
"--- | --- | ---\n",
"10 | 0 | -1\n",
"11 | 1 | -1\n",
"14 | 4 | 1\n",
"15 | 5 | 1\n",
"\n",
"### $c_2^{\\dagger} c_3^{\\dagger}$\n",
"\n",
"Row Index | Column Index | Entry\n",
"--- | --- | ---\n",
"12 | 0 | 1\n",
"13 | 1 | 1\n",
"14 | 2 | 1\n",
"15 | 3 | 1\n",
"\n",
"### $c_3^{\\dagger} c_2^{\\dagger}$\n",
"\n",
"Row Index | Column Index | Entry\n",
"--- | --- | ---\n",
"12 | 0 | -1\n",
"13 | 1 | -1\n",
"14 | 2 | -1\n",
"15 | 3 | -1"
]
}
],
"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": "3.6.6"
}
},
"nbformat": 4,
"nbformat_minor": 2
}
| gpl-3.0 |
adamsteer/nci-notebooks | training/py3/.ipynb_checkpoints/Python_Siphon_I-checkpoint.ipynb | 1 | 1813739 | null | apache-2.0 |
Patri-meteocat/Meteocat_ANL_collaboration | notebooks/XRAD_PPIplots_ZVVupVreg.ipynb | 4 | 6300 | {
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# N-panel plots of IRIS data and velocity dealiasing\n",
"_______________________________________________________________________________________________\n",
"\n",
"This script takes an IRIS raw volume PPI scan file as input and generates a N-panel plot for each sweep in the volume file. Each of the output plots consists of a row of panels with a panel for each selected data type. The script also uses PyArt dealiasing algorithms (phase unwrap and region based) to correct the Doppler velocity data and add corresponding corrected velocity data fields to the radar object.\n",
"\n",
"Here an example with a failure of the unwrap phase algorithm. Region based works nicely though!\n",
"\n",
"\n"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"#! /usr/bin/env python\n",
"\n",
"import matplotlib.pyplot as plt\n",
"import pylab as plb\n",
"import matplotlib as mpl\n",
"import pyart\n"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"## SETTINGS #####################################################################\n",
"\n",
"in_path = './data/'\n",
"out_path = './output/'\n",
"filename = 'CDV130618145623.RAWCBRF'\n",
"radar_abbr = filename[:3]\n",
"\n",
"lims = [150, 125, 100, 50, 50, 40, 30, 20]\n",
"\n",
"sel_data = ['reflectivity', 'velocity', 'corrected_velocity_uwp', 'corrected_velocity_reg']"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"Ndata = len(sel_data)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"## COLORMAP #####################################################################\n",
"\n",
"cdict = {'red': ((0.0, 0.0, 0.0),\n",
" (0.25, 0.0, 0.0),\n",
" (0.5, 0.9, 0.9),\n",
" (0.75, 1.0, 1.0),\n",
" (1.0, 1.0, 1.0)),\n",
" 'green': ((0.0, 0.5, 0.5),\n",
" (0.25, 0., 0.),\n",
" (0.5, 0.9, 0.9),\n",
" (0.75, 0., 0.),\n",
" (1.0, 0.5, 0.5)),\n",
" 'blue': ((0.0, 1.0, 1.0),\n",
" (0.25, 1., 1.),\n",
" (0.5, 0.9, 0.9),\n",
" (0.75, 0.0, 0.0), \n",
" (1.0, 0.0, 0.0))}\n",
"\n",
" \n",
"my_cmap=mpl.colors.LinearSegmentedColormap('my_colormap', cdict, N=33)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"## DATA ##########################################################################\n",
"\n",
"in_file = in_path + filename\n",
"\n",
"radar = pyart.io.read_rsl(in_file)\n",
"radar.metadata['instrument_name'] = radar_abbr\n",
"\n",
"nyq_vel = radar.instrument_parameters['nyquist_velocity']['data'][0]\n",
"sw_num = radar.nsweeps\n",
"sw_elevs = [radar.fixed_angle['data'][sw] for sw in range(0, sw_num-1)]\n",
"\n",
"dealias_data_uwp = pyart.correct.dealias_unwrap_phase(radar, rays_wrap_around=True, keep_original=False)\n",
"radar.add_field('corrected_velocity_uwp', dealias_data_uwp)\n",
"\n",
"dealias_data_reg = pyart.correct.dealias_region_based(radar, interval_splits=20, rays_wrap_around=True, keep_original=False)\n",
"radar.add_field('corrected_velocity_reg', dealias_data_reg)"
]
},
{
"cell_type": "code",
"execution_count": 21,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"## PLOTS ########################################################################\n",
"\n",
"display = pyart.graph.RadarDisplay(radar)\n",
"time_text = ' ' + display.time_begin.isoformat() + 'Z '\n",
"\n",
"st_range = -150\n",
"end_range = 150\n",
"i_range = -150\n",
"f_range = 150\n",
"\n",
"for sw in range(0, sw_num-1):\n",
"\n",
" if f_range>50:\n",
" i_range = st_range + sw*25\n",
" f_range = end_range - sw*25\n",
" else:\n",
" i_range = -30\n",
" f_range = 30\n",
"\n",
" out_file = out_path + filename.split('.', 1)[0]+ '_el%.0f' % (sw_elevs[sw]) + '_Vdealias.png'\n",
" fig = plt.figure(figsize=(36,6.5))\n",
" \n",
" pl_count = 1\n",
" for dd in sel_data:\n",
" \n",
" data_name = radar.fields[dd]['long_name']\n",
" data_units = radar.fields[dd]['units']\n",
" \n",
" cb_lab = data_name + '\\n' + data_units\n",
" title = radar_abbr + time_text + 'elev %.1f' % (sw_elevs[sw]) + 'deg' + '\\n' + dd \n",
" \n",
" ax = fig.add_subplot(100+Ndata*10+pl_count)\n",
" \n",
" display.plot(dd, sw, vmin=-2*nyq_vel, vmax=2*nyq_vel, \n",
" colorbar_label=cb_lab, \n",
" cmap=my_cmap, title=title, ax=ax, mask_outside=False)\n",
" \n",
" display.plot_range_rings(range(25, 150, 25))\n",
" display.plot_cross_hair(0.5)\n",
" plt.xlim((-lims[sw], lims[sw]))\n",
" plt.ylim((-lims[sw], lims[sw]))\n",
"\n",
" plt.savefig(out_file)\n",
" \n",
" pl_count += 1\n",
" \n",
" plt.close()\n"
]
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 2",
"language": "python",
"name": "python2"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 2
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython2",
"version": "2.7.10"
}
},
"nbformat": 4,
"nbformat_minor": 0
}
| bsd-2-clause |
aboucaud/python-euclid2016 | notebooks/04-Astropy.ipynb | 1 | 1224988 | null | bsd-3-clause |
ViralLeadership/Probabilistic-Programming-and-Bayesian-Methods-for-Hackers | Chapter5_LossFunctions/Chapter5.ipynb | 5 | 1481161 | null | mit |
faneshion/MatchZoo | tutorials/wikiqa/esim.ipynb | 1 | 31218 | {
"cells": [
{
"cell_type": "code",
"execution_count": 1,
"metadata": {},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"Using TensorFlow backend.\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"matchzoo version 2.1.0\n",
"\n",
"data loading ...\n",
"data loaded as `train_pack_raw` `dev_pack_raw` `test_pack_raw`\n",
"`ranking_task` initialized with metrics [normalized_discounted_cumulative_gain@3(0.0), normalized_discounted_cumulative_gain@5(0.0), mean_average_precision(0.0)]\n",
"loading embedding ...\n",
"embedding loaded as `glove_embedding`\n"
]
}
],
"source": [
"%run ./tutorials/wikiqa/init.ipynb"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {},
"outputs": [],
"source": [
"import tensorflow as tf\n",
"from keras.backend.tensorflow_backend import set_session\n",
"config = tf.ConfigProto()\n",
"config.gpu_options.visible_device_list=\"1\"\n",
"config.gpu_options.allow_growth = True # dynamically grow the memory used on the GPU\n",
"sess = tf.Session(config=config)\n",
"set_session(sess) # set this TensorFlow session as the default session for Keras"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {},
"outputs": [],
"source": [
"def load_filtered_data(preprocessor, data_type):\n",
" assert ( data_type in ['train', 'dev', 'test'])\n",
" data_pack = mz.datasets.wiki_qa.load_data(data_type, task='ranking')\n",
"\n",
" if data_type == 'train':\n",
" X, Y = preprocessor.fit_transform(data_pack).unpack()\n",
" else:\n",
" X, Y = preprocessor.transform(data_pack).unpack()\n",
"\n",
" new_idx = []\n",
" for i in range(Y.shape[0]):\n",
" if X[\"length_left\"][i] == 0 or X[\"length_right\"][i] == 0:\n",
" continue\n",
" new_idx.append(i)\n",
" new_idx = np.array(new_idx)\n",
" print(\"Removed empty data. Found \", (Y.shape[0] - new_idx.shape[0]))\n",
"\n",
" for k in X.keys():\n",
" X[k] = X[k][new_idx]\n",
" Y = Y[new_idx]\n",
"\n",
" pos_idx = (Y == 1)[:, 0]\n",
" pos_qid = X[\"id_left\"][pos_idx]\n",
" keep_idx_bool = np.array([ qid in pos_qid for qid in X[\"id_left\"]])\n",
" keep_idx = np.arange(keep_idx_bool.shape[0])\n",
" keep_idx = keep_idx[keep_idx_bool]\n",
" print(\"Removed questions with no pos label. Found \", (keep_idx_bool == 0).sum())\n",
"\n",
" print(\"shuffling...\")\n",
" np.random.shuffle(keep_idx)\n",
" for k in X.keys():\n",
" X[k] = X[k][keep_idx]\n",
" Y = Y[keep_idx]\n",
"\n",
" return X, Y, preprocessor"
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"Processing text_left with chain_transform of Tokenize => Lowercase => PuncRemoval: 100%|██████████| 2118/2118 [00:00<00:00, 12754.26it/s]\n",
"Processing text_right with chain_transform of Tokenize => Lowercase => PuncRemoval: 100%|██████████| 18841/18841 [00:02<00:00, 6500.31it/s]\n",
"Processing text_right with append: 100%|██████████| 18841/18841 [00:00<00:00, 1215206.55it/s]\n",
"Building FrequencyFilter from a datapack.: 100%|██████████| 18841/18841 [00:00<00:00, 185258.28it/s]\n",
"Processing text_right with transform: 100%|██████████| 18841/18841 [00:00<00:00, 184455.70it/s]\n",
"Processing text_left with extend: 100%|██████████| 2118/2118 [00:00<00:00, 922581.36it/s]\n",
"Processing text_right with extend: 100%|██████████| 18841/18841 [00:00<00:00, 1082236.12it/s]\n",
"Building Vocabulary from a datapack.: 100%|██████████| 404432/404432 [00:00<00:00, 3795031.47it/s]\n",
"Processing text_left with chain_transform of Tokenize => Lowercase => PuncRemoval: 100%|██████████| 2118/2118 [00:00<00:00, 13650.60it/s]\n",
"Processing text_right with chain_transform of Tokenize => Lowercase => PuncRemoval: 100%|██████████| 18841/18841 [00:02<00:00, 6764.51it/s]\n",
"Processing text_right with transform: 100%|██████████| 18841/18841 [00:00<00:00, 171037.31it/s]\n",
"Processing text_left with transform: 100%|██████████| 2118/2118 [00:00<00:00, 288623.28it/s]\n",
"Processing text_right with transform: 100%|██████████| 18841/18841 [00:00<00:00, 90725.37it/s]\n",
"Processing length_left with len: 100%|██████████| 2118/2118 [00:00<00:00, 583636.81it/s]\n",
"Processing length_right with len: 100%|██████████| 18841/18841 [00:00<00:00, 1203693.44it/s]\n",
"Processing text_left with transform: 100%|██████████| 2118/2118 [00:00<00:00, 193145.54it/s]\n",
"Processing text_right with transform: 100%|██████████| 18841/18841 [00:00<00:00, 134549.60it/s]\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"Removed empty data. Found 38\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"Processing text_left with chain_transform of Tokenize => Lowercase => PuncRemoval: 100%|██████████| 296/296 [00:00<00:00, 14135.26it/s]\n",
"Processing text_right with chain_transform of Tokenize => Lowercase => PuncRemoval: 0%| | 0/2708 [00:00<?, ?it/s]"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"Removed questions with no pos label. Found 11672\n",
"shuffling...\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"Processing text_right with chain_transform of Tokenize => Lowercase => PuncRemoval: 100%|██████████| 2708/2708 [00:00<00:00, 6731.87it/s]\n",
"Processing text_right with transform: 100%|██████████| 2708/2708 [00:00<00:00, 168473.93it/s]\n",
"Processing text_left with transform: 100%|██████████| 296/296 [00:00<00:00, 204701.40it/s]\n",
"Processing text_right with transform: 100%|██████████| 2708/2708 [00:00<00:00, 159066.95it/s]\n",
"Processing length_left with len: 100%|██████████| 296/296 [00:00<00:00, 442607.48it/s]\n",
"Processing length_right with len: 100%|██████████| 2708/2708 [00:00<00:00, 1038699.15it/s]\n",
"Processing text_left with transform: 100%|██████████| 296/296 [00:00<00:00, 149130.81it/s]\n",
"Processing text_right with transform: 100%|██████████| 2708/2708 [00:00<00:00, 140864.36it/s]\n",
"Processing text_left with chain_transform of Tokenize => Lowercase => PuncRemoval: 100%|██████████| 633/633 [00:00<00:00, 12189.39it/s]"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"Removed empty data. Found 2\n",
"Removed questions with no pos label. Found 1601\n",
"shuffling...\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"\n",
"Processing text_right with chain_transform of Tokenize => Lowercase => PuncRemoval: 100%|██████████| 5961/5961 [00:00<00:00, 7064.16it/s]\n",
"Processing text_right with transform: 100%|██████████| 5961/5961 [00:00<00:00, 187399.25it/s]\n",
"Processing text_left with transform: 100%|██████████| 633/633 [00:00<00:00, 259733.36it/s]\n",
"Processing text_right with transform: 100%|██████████| 5961/5961 [00:00<00:00, 160878.23it/s]\n",
"Processing length_left with len: 100%|██████████| 633/633 [00:00<00:00, 688714.51it/s]\n",
"Processing length_right with len: 100%|██████████| 5961/5961 [00:00<00:00, 1166965.98it/s]\n",
"Processing text_left with transform: 100%|██████████| 633/633 [00:00<00:00, 158526.06it/s]\n",
"Processing text_right with transform: 100%|██████████| 5961/5961 [00:00<00:00, 137558.64it/s]\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"Removed empty data. Found 18\n",
"Removed questions with no pos label. Found 3805\n",
"shuffling...\n"
]
}
],
"source": [
"preprocessor = mz.preprocessors.BasicPreprocessor(fixed_length_left=20,\n",
" fixed_length_right=40,\n",
" remove_stop_words=False)\n",
"train_X, train_Y, preprocessor = load_filtered_data(preprocessor, 'train')\n",
"val_X, val_Y, _ = load_filtered_data(preprocessor, 'dev')\n",
"pred_X, pred_Y, _ = load_filtered_data(preprocessor, 'test')"
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"__________________________________________________________________________________________________\n",
"Layer (type) Output Shape Param # Connected to \n",
"==================================================================================================\n",
"text_left (InputLayer) (None, 20) 0 \n",
"__________________________________________________________________________________________________\n",
"text_right (InputLayer) (None, 40) 0 \n",
"__________________________________________________________________________________________________\n",
"embedding (Embedding) multiple 5002500 text_left[0][0] \n",
" text_right[0][0] \n",
"__________________________________________________________________________________________________\n",
"dropout_1 (Dropout) multiple 0 embedding[0][0] \n",
" embedding[1][0] \n",
" dense_1[0][0] \n",
" dense_1[1][0] \n",
" dense_2[0][0] \n",
"__________________________________________________________________________________________________\n",
"lambda_1 (Lambda) multiple 0 text_left[0][0] \n",
" text_right[0][0] \n",
"__________________________________________________________________________________________________\n",
"bidirectional_1 (Bidirectional) multiple 1442400 dropout_1[0][0] \n",
" dropout_1[1][0] \n",
"__________________________________________________________________________________________________\n",
"lambda_2 (Lambda) (None, 20, 1) 0 lambda_1[0][0] \n",
"__________________________________________________________________________________________________\n",
"lambda_3 (Lambda) (None, 40, 1) 0 lambda_1[1][0] \n",
"__________________________________________________________________________________________________\n",
"multiply_1 (Multiply) (None, 20, 600) 0 bidirectional_1[0][0] \n",
" lambda_2[0][0] \n",
"__________________________________________________________________________________________________\n",
"multiply_2 (Multiply) (None, 40, 600) 0 bidirectional_1[1][0] \n",
" lambda_3[0][0] \n",
"__________________________________________________________________________________________________\n",
"lambda_4 (Lambda) (None, 20, 1) 0 lambda_1[0][0] \n",
"__________________________________________________________________________________________________\n",
"lambda_5 (Lambda) (None, 1, 40) 0 lambda_1[1][0] \n",
"__________________________________________________________________________________________________\n",
"dot_1 (Dot) (None, 20, 40) 0 multiply_1[0][0] \n",
" multiply_2[0][0] \n",
"__________________________________________________________________________________________________\n",
"multiply_3 (Multiply) (None, 20, 40) 0 lambda_4[0][0] \n",
" lambda_5[0][0] \n",
"__________________________________________________________________________________________________\n",
"permute_1 (Permute) (None, 40, 20) 0 dot_1[0][0] \n",
" multiply_3[0][0] \n",
"__________________________________________________________________________________________________\n",
"atten_mask (Lambda) multiple 0 dot_1[0][0] \n",
" multiply_3[0][0] \n",
" permute_1[0][0] \n",
" permute_1[1][0] \n",
"__________________________________________________________________________________________________\n",
"softmax_1 (Softmax) multiple 0 atten_mask[0][0] \n",
" atten_mask[1][0] \n",
"__________________________________________________________________________________________________\n",
"dot_2 (Dot) (None, 20, 600) 0 softmax_1[0][0] \n",
" multiply_2[0][0] \n",
"__________________________________________________________________________________________________\n",
"dot_3 (Dot) (None, 40, 600) 0 softmax_1[1][0] \n",
" multiply_1[0][0] \n",
"__________________________________________________________________________________________________\n",
"subtract_1 (Subtract) (None, 20, 600) 0 multiply_1[0][0] \n",
" dot_2[0][0] \n",
"__________________________________________________________________________________________________\n",
"multiply_4 (Multiply) (None, 20, 600) 0 multiply_1[0][0] \n",
" dot_2[0][0] \n",
"__________________________________________________________________________________________________\n",
"subtract_2 (Subtract) (None, 40, 600) 0 multiply_2[0][0] \n",
" dot_3[0][0] \n",
"__________________________________________________________________________________________________\n",
"multiply_5 (Multiply) (None, 40, 600) 0 multiply_2[0][0] \n",
" dot_3[0][0] \n",
"__________________________________________________________________________________________________\n",
"concatenate_1 (Concatenate) (None, 20, 2400) 0 multiply_1[0][0] \n",
" dot_2[0][0] \n",
" subtract_1[0][0] \n",
" multiply_4[0][0] \n",
"__________________________________________________________________________________________________\n",
"concatenate_2 (Concatenate) (None, 40, 2400) 0 multiply_2[0][0] \n",
" dot_3[0][0] \n",
" subtract_2[0][0] \n",
" multiply_5[0][0] \n",
"__________________________________________________________________________________________________\n",
"dense_1 (Dense) multiple 720300 concatenate_1[0][0] \n",
" concatenate_2[0][0] \n",
"__________________________________________________________________________________________________\n",
"bidirectional_2 (Bidirectional) multiple 1442400 dropout_1[2][0] \n",
" dropout_1[3][0] \n",
"__________________________________________________________________________________________________\n",
"lambda_6 (Lambda) (None, 20, 1) 0 lambda_1[0][0] \n",
"__________________________________________________________________________________________________\n",
"lambda_8 (Lambda) (None, 20, 1) 0 lambda_1[0][0] \n",
"__________________________________________________________________________________________________\n",
"lambda_10 (Lambda) (None, 40, 1) 0 lambda_1[1][0] \n",
"__________________________________________________________________________________________________\n",
"lambda_12 (Lambda) (None, 40, 1) 0 lambda_1[1][0] \n",
"__________________________________________________________________________________________________\n",
"multiply_6 (Multiply) (None, 20, 600) 0 bidirectional_2[0][0] \n",
" lambda_6[0][0] \n",
"__________________________________________________________________________________________________\n",
"multiply_7 (Multiply) (None, 20, 600) 0 bidirectional_2[0][0] \n",
" lambda_8[0][0] \n",
"__________________________________________________________________________________________________\n",
"multiply_8 (Multiply) (None, 40, 600) 0 bidirectional_2[1][0] \n",
" lambda_10[0][0] \n",
"__________________________________________________________________________________________________\n",
"multiply_9 (Multiply) (None, 40, 600) 0 bidirectional_2[1][0] \n",
" lambda_12[0][0] \n",
"__________________________________________________________________________________________________\n",
"lambda_7 (Lambda) (None, 600) 0 multiply_6[0][0] \n",
" lambda_6[0][0] \n",
"__________________________________________________________________________________________________\n",
"lambda_9 (Lambda) (None, 600) 0 multiply_7[0][0] \n",
"__________________________________________________________________________________________________\n",
"lambda_11 (Lambda) (None, 600) 0 multiply_8[0][0] \n",
" lambda_10[0][0] \n",
"__________________________________________________________________________________________________\n",
"lambda_13 (Lambda) (None, 600) 0 multiply_9[0][0] \n",
"__________________________________________________________________________________________________\n",
"concatenate_3 (Concatenate) (None, 1200) 0 lambda_7[0][0] \n",
" lambda_9[0][0] \n",
"__________________________________________________________________________________________________\n",
"concatenate_4 (Concatenate) (None, 1200) 0 lambda_11[0][0] \n",
" lambda_13[0][0] \n",
"__________________________________________________________________________________________________\n",
"concatenate_5 (Concatenate) (None, 2400) 0 concatenate_3[0][0] \n",
" concatenate_4[0][0] \n",
"__________________________________________________________________________________________________\n",
"dense_2 (Dense) (None, 300) 720300 concatenate_5[0][0] \n",
"__________________________________________________________________________________________________\n",
"dense_3 (Dense) (None, 2) 602 dropout_1[4][0] \n",
"==================================================================================================\n",
"Total params: 9,328,502\n",
"Trainable params: 4,326,002\n",
"Non-trainable params: 5,002,500\n",
"__________________________________________________________________________________________________\n"
]
}
],
"source": [
"from keras.optimizers import Adam\n",
"import matchzoo\n",
"\n",
"model = matchzoo.contrib.models.ESIM()\n",
"\n",
"# update `input_shapes` and `embedding_input_dim`\n",
"# model.params['task'] = mz.tasks.Ranking() \n",
"# or \n",
"model.params['task'] = mz.tasks.Classification(num_classes=2)\n",
"model.params.update(preprocessor.context)\n",
"\n",
"model.params['mask_value'] = 0\n",
"model.params['lstm_dim'] = 300\n",
"model.params['embedding_output_dim'] = 300\n",
"model.params['embedding_trainable'] = False\n",
"model.params['dropout_rate'] = 0.5\n",
"\n",
"model.params['mlp_num_units'] = 300\n",
"model.params['mlp_num_layers'] = 0\n",
"model.params['mlp_num_fan_out'] = 300\n",
"model.params['mlp_activation_func'] = 'tanh'\n",
"model.params['optimizer'] = Adam(lr=1e-4)\n",
"model.guess_and_fill_missing_params()\n",
"model.build()\n",
"model.compile()\n",
"model.backend.summary()"
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {},
"outputs": [],
"source": [
"embedding_matrix = glove_embedding.build_matrix(preprocessor.context['vocab_unit'].state['term_index'], initializer=lambda: 0)\n",
"model.load_embedding_matrix(embedding_matrix)"
]
},
{
"cell_type": "code",
"execution_count": 8,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Train on 8650 samples, validate on 1130 samples\n",
"Epoch 1/10\n",
"8650/8650 [==============================] - 52s 6ms/step - loss: 0.0985 - val_loss: 0.0977\n",
"Validation: mean_average_precision(0.0): 0.6377925262180991\n",
"Epoch 2/10\n",
"8650/8650 [==============================] - 52s 6ms/step - loss: 0.0947 - val_loss: 0.0939\n",
"Validation: mean_average_precision(0.0): 0.6323746460063332\n",
"Epoch 3/10\n",
"8650/8650 [==============================] - 52s 6ms/step - loss: 0.0923 - val_loss: 0.0896\n",
"Validation: mean_average_precision(0.0): 0.6447892278707743\n",
"Epoch 4/10\n",
"8650/8650 [==============================] - 52s 6ms/step - loss: 0.0895 - val_loss: 0.0904\n",
"Validation: mean_average_precision(0.0): 0.6645210508066117\n",
"Epoch 5/10\n",
"8650/8650 [==============================] - 52s 6ms/step - loss: 0.0883 - val_loss: 0.0900\n",
"Validation: mean_average_precision(0.0): 0.6622282952529867\n",
"Epoch 6/10\n",
"8650/8650 [==============================] - 52s 6ms/step - loss: 0.0839 - val_loss: 0.0900\n",
"Validation: mean_average_precision(0.0): 0.6654279587941297\n",
"Epoch 7/10\n",
"8650/8650 [==============================] - 52s 6ms/step - loss: 0.0821 - val_loss: 0.0896\n",
"Validation: mean_average_precision(0.0): 0.6668269018575894\n",
"Epoch 8/10\n",
"8650/8650 [==============================] - 52s 6ms/step - loss: 0.0792 - val_loss: 0.0885\n",
"Validation: mean_average_precision(0.0): 0.6723704781393599\n",
"Epoch 9/10\n",
"8650/8650 [==============================] - 52s 6ms/step - loss: 0.0754 - val_loss: 0.0895\n",
"Validation: mean_average_precision(0.0): 0.6552521148587158\n",
"Epoch 10/10\n",
"8650/8650 [==============================] - 52s 6ms/step - loss: 0.0731 - val_loss: 0.0910\n",
"Validation: mean_average_precision(0.0): 0.6695447388956829\n"
]
}
],
"source": [
"# train as ranking task\n",
"model.params['task'] = mz.tasks.Ranking()\n",
"evaluate = mz.callbacks.EvaluateAllMetrics(model,\n",
" x=pred_X,\n",
" y=pred_Y,\n",
" once_every=1,\n",
" batch_size=len(pred_Y))\n",
"history = model.fit(x = [train_X['text_left'],\n",
" train_X['text_right']], # (20360, 1000)\n",
" y = train_Y, # (20360, 2)\n",
" validation_data = (val_X, val_Y),\n",
" callbacks=[evaluate],\n",
" batch_size = 32,\n",
" epochs = 10)"
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Train on 8650 samples, validate on 1130 samples\n",
"Epoch 1/10\n",
"8650/8650 [==============================] - 68s 8ms/step - loss: 0.3628 - val_loss: 0.3552\n",
"Epoch 2/10\n",
"8650/8650 [==============================] - 63s 7ms/step - loss: 0.3285 - val_loss: 0.3591\n",
"Epoch 3/10\n",
"8650/8650 [==============================] - 63s 7ms/step - loss: 0.3105 - val_loss: 0.3681\n",
"Epoch 4/10\n",
"8650/8650 [==============================] - 64s 7ms/step - loss: 0.3012 - val_loss: 0.3166\n",
"Epoch 5/10\n",
"8650/8650 [==============================] - 64s 7ms/step - loss: 0.2888 - val_loss: 0.2961\n",
"Epoch 6/10\n",
"8650/8650 [==============================] - 64s 7ms/step - loss: 0.2801 - val_loss: 0.3362\n",
"Epoch 7/10\n",
"8650/8650 [==============================] - 64s 7ms/step - loss: 0.2692 - val_loss: 0.3324\n",
"Epoch 8/10\n",
"8650/8650 [==============================] - 64s 7ms/step - loss: 0.2609 - val_loss: 0.3172\n",
"Epoch 9/10\n",
"8650/8650 [==============================] - 58s 7ms/step - loss: 0.2542 - val_loss: 0.3296\n",
"Epoch 10/10\n",
"8650/8650 [==============================] - 53s 6ms/step - loss: 0.2365 - val_loss: 0.3058\n"
]
}
],
"source": [
"# train as classification task \n",
"\n",
"from keras.utils import to_categorical\n",
"train_Y = to_categorical(train_Y)\n",
"val_Y = to_categorical(val_Y)\n",
"\n",
"model.params['task'] = mz.tasks.Classification(num_classes=2)\n",
"\n",
"history = model.fit(x = [train_X['text_left'],\n",
" train_X['text_right']], # (20360, 1000)\n",
" y = train_Y, # (20360, 2)\n",
" validation_data = (val_X, val_Y),\n",
" batch_size = 32,\n",
" epochs = 10)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": []
}
],
"metadata": {
"kernelspec": {
"display_name": "mz_play",
"language": "python",
"name": "mz_play"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.6.8"
}
},
"nbformat": 4,
"nbformat_minor": 2
}
| apache-2.0 |
tjwei/tf-play | tbtest.ipynb | 1 | 2342 | {
"cells": [
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"import tensorflow as tf\n",
"import numpy as np"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"sess = tf.InteractiveSession()\n",
"accuracy = tf.Variable(0.2, name=\"xxx\")\n",
"accuracy_ = tf.placeholder(\"float\")\n",
"img_ = tf.placeholder(\"float\", shape=[None, 64, 64, 3])\n",
"img = tf.Variable(tf.zeros([5, 64,64,3]))\n",
"tf.scalar_summary(\"acc\", accuracy)\n",
"tf.image_summary(\"img\",img)\n",
"summary_op = tf.merge_all_summaries()\n",
"summary_writer = tf.train.SummaryWriter(\"log2\", graph_def=sess.graph_def)\n",
"update = tf.assign(accuracy, accuracy_)\n",
"update_img = tf.assign(img, img_)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"tf.initialize_all_variables().run()"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"img_input = np.zeros([5,64,64,3])\n",
"for i in range(1000):\n",
" update.eval(feed_dict={accuracy_: i/100.0})\n",
" for b in range(5):\n",
" for x in range(64):\n",
" for y in range(64):\n",
" for c in range(3):\n",
" img_input[b][x][y][c] = ((x*(c+2)*i**2+y*i*(c+16)+b**3)%1000)/1000.0\n",
" update_img.eval(feed_dict={img_: img_input})\n",
" summary_writer.add_summary(summary_op.eval(), i)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"np.zeros?"
]
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 2",
"language": "python",
"name": "python2"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 2
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython2",
"version": "2.7.9"
}
},
"nbformat": 4,
"nbformat_minor": 0
}
| mit |
tsavo-sevenoaks/garth | ipython_notebook_tutorial.ipynb | 1 | 23229 | {
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Text Using Markdown\n",
"\n",
"**If you double click on this cell**, you will see the text change so that all of the formatting is removed. This allows you to edit this block of text. This block of text is written using [Markdown](http://daringfireball.net/projects/markdown/syntax), which is a way to format text using headers, links, italics, and many other options. Hit shift + enter or shift + return to show the formatted text again. This is called \"running\" the cell, and you can also do it using the run button in the toolbar."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Code cells\n",
"\n",
"One great advantage of IPython notebooks is that you can show your Python code alongside the results, add comments to the code, or even add blocks of text using Markdown. These notebooks allow you to collaborate with others and share your work. The following cell is a code cell."
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"hello world11_0_11\n",
"hello world\n"
]
}
],
"source": [
"# Hit shift + enter or use the run button to run this cell and see the results\n",
"\n",
"print 'hello world11_0_11'\n",
"print 'hello world'"
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"4\n",
"6\n"
]
}
],
"source": [
"# The last line of every code cell will be displayed by default, \n",
"# even if you don't print it. Run this cell to see how this works.\n",
"\n",
"print 2 + 2 # The result of this line will not be displayed\n",
"print 3 + 3 # The result of this line will be displayed, because it is the last line of the cell"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Nicely formatted results\n",
"\n",
"IPython notebooks allow you to display nicely formatted results, such as plots and tables, directly in\n",
"the notebook. You'll learn how to use the following libraries later on in this course, but for now here's a\n",
"preview of what IPython notebook can do."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"# If you run this cell, you should see the values displayed as a table.\n",
"\n",
"# Pandas is a software library for data manipulation and analysis. You'll learn to use it later in this course.\n",
"import pandas as pd\n",
"\n",
"df = pd.DataFrame({'a': [2, 4, 6, 8], 'b': [1, 3, 5, 7]})\n",
"df"
]
},
{
"cell_type": "code",
"execution_count": 9,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Populating the interactive namespace from numpy and matplotlib\n"
]
},
{
"data": {
"text/plain": [
"<matplotlib.collections.PathCollection at 0xb58fda0>"
]
},
"execution_count": 9,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"image/png": "iVBORw0KGgoAAAANSUhEUgAAAYAAAAEACAYAAAC6d6FnAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\naigTgCQ1lAlAkhrKBCBJDWUCkKSG+v+BP9B/dcflEgAAAABJRU5ErkJggg==\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x3da2a58>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"# If you run this cell, you should see a scatter plot of the function y = x^2\n",
"\n",
"%pylab inline\n",
"import matplotlib.pyplot as plt\n",
"\n",
"xs = range(-30, 31)\n",
"ys = [x ** 2 for x in xs]\n",
"\n",
"plt.scatter(xs, ys)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Creating cells \n",
" \n",
"To create a new **code cell**, click \"Insert > Insert Cell [Above or Below]\". A code cell will automatically be created.\n",
"\n",
"To create a new **markdown cell**, first follow the process above to create a code cell, then change the type from \"Code\" to \"Markdown\" using the dropdown next to the run, stop, and restart buttons."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"#Some Markdown data "
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Re-running cells\n",
"\n",
"If you find a bug in your code, you can always update the cell and re-run it. However, any cells that come afterward won't be automatically updated. Try it out below. First run each of the three cells. The first two don't have any output, but you will be able to tell they've run because a number will appear next to them, for example, \"In [5]\". The third cell should output the message \"Intro to Data Analysis is awesome!\""
]
},
{
"cell_type": "code",
"execution_count": 16,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"class_name = \"BRUCE Woodley Intro to Data Analysis\""
]
},
{
"cell_type": "code",
"execution_count": 19,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"message = class_name + \" is awesome!\""
]
},
{
"cell_type": "code",
"execution_count": 20,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"'BRUCE Woodley Intro to Data Analysis is awesome!'"
]
},
"execution_count": 20,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"message"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Once you've run all three cells, try modifying the first one to set `class_name` to your name, rather than \"Intro to Data Analysis\", so you can print that you are awesome. Then rerun the first and third cells without rerunning the second.\n",
"\n",
"You should have seen that the third cell still printed \"Intro to Data Analysis is awesome!\" That's because you didn't rerun the second cell, so even though the `class_name` variable was updated, the `message` variable was not. Now try rerunning the second cell, and then the third.\n",
"\n",
"You should have seen the output change to \"*your name* is awesome!\" Often, after changing a cell, you'll want to rerun all the cells below it. You can do that quickly by clicking \"Cell > Run All Below\"."
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"('type(line) \\t', <type 'instance'>)\n",
"{u'status': u'canceled', u'is_udacity': u'True', u'is_canceled': u'True', u'join_date': u'2014-11-10', u'account_key': u'448', u'cancel_date': u'2015-01-14', u'days_to_cancel': u'65'}\n"
]
}
],
"source": [
"import unicodecsv\n",
"\n",
"with open(\"enrollments.csv\",\"rb\") as filein :\n",
" line = unicodecsv.DictReader(filein)\n",
" print(\"type(line) \\t\",type(line)) \n",
" enrollments = list(line)\n",
"print enrollments[0]\n",
"\n",
" "
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"{u'lessons_completed': u'0.0', u'num_courses_visited': u'1.0', u'total_minutes_visited': u'11.6793745', u'projects_completed': u'0.0', u'acct': u'0', u'utc_date': u'2015-01-09'}\n"
]
}
],
"source": [
"import unicodecsv\n",
"\n",
"with open(\"daily_engagement.csv\",\"rb\") as filein :\n",
" line = unicodecsv.DictReader(filein)\n",
" #print(\"type(line) \\t\",type(line)) \n",
" daily_engagement = list(line)\n",
"print daily_engagement[0]\n"
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"('project_submissions_fieldnames = ', \"[u'creation_date', u'completion_date', u'assigned_rating', u'account_key', u'lesson_key', u'processing_state']\")\n",
"{u'lesson_key': u'3176718735', u'processing_state': u'EVALUATED', u'account_key': u'256', u'assigned_rating': u'UNGRADED', u'completion_date': u'2015-01-16', u'creation_date': u'2015-01-14'}\n"
]
}
],
"source": [
"import unicodecsv\n",
"\n",
"with open(\"project_submissions.csv\",\"rb\") as filein :\n",
" line = unicodecsv.DictReader(filein)\n",
" project_submissions_fieldnames = line.fieldnames \n",
" #print(\"type(line) \\t\",type(line))\n",
" print(\"project_submissions_fieldnames = \",str(project_submissions_fieldnames))\n",
" project_submissions = list(line)\n",
"print project_submissions[0]\n"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Fixing Data Types.\n"
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {
"collapsed": false
},
"outputs": [
{
"ename": "NameError",
"evalue": "name 'enrollment' is not defined",
"output_type": "error",
"traceback": [
"\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
"\u001b[1;31mNameError\u001b[0m Traceback (most recent call last)",
"\u001b[1;32m<ipython-input-5-08421e9dad61>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m()\u001b[0m\n\u001b[0;32m 19\u001b[0m \u001b[1;32mreturn\u001b[0m \u001b[0mint\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mi\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 20\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m---> 21\u001b[1;33m \u001b[1;32mprint\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m\" type(enrollment) \"\u001b[0m \u001b[1;33m,\u001b[0m \u001b[0mtype\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0menrollment\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 22\u001b[0m \u001b[1;31m# Clean up the data types in the enrollments table\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 23\u001b[0m \u001b[1;32mfor\u001b[0m \u001b[0menrollment\u001b[0m \u001b[1;32min\u001b[0m \u001b[0menrollments\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
"\u001b[1;31mNameError\u001b[0m: name 'enrollment' is not defined"
]
}
],
"source": [
"# Fixing Data Types.\n",
"# Hit shift + enter or use the run button to run this cell and see the results\n",
"from datetime import datetime as dt\n",
"\n",
"# Takes a date as a string, and returns a Python datetime object. \n",
"# If there is no date given, returns None\n",
"def parse_date(date):\n",
" if date == '':\n",
" return None\n",
" else:\n",
" return dt.strptime(date, '%Y-%m-%d')\n",
" \n",
"# Takes a string which is either an empty string or represents an integer,\n",
"# and returns an int or None.\n",
"def parse_maybe_int(i):\n",
" if i == '':\n",
" return None\n",
" else:\n",
" return int(i)\n",
" \n",
"print(\" type(enrollment) \" , type(enrollment))\n",
"# Clean up the data types in the enrollments table\n",
"for enrollment in enrollments:\n",
" enrollment['cancel_date'] = parse_date(enrollment['cancel_date'])\n",
" enrollment['days_to_cancel'] = parse_maybe_int(enrollment['days_to_cancel'])\n",
" enrollment['is_canceled'] = enrollment['is_canceled'] == 'True'\n",
" enrollment['is_udacity'] = enrollment['is_udacity'] == 'True'\n",
" enrollment['join_date'] = parse_date(enrollment['join_date'])\n",
" \n",
"enrollments[0]"
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"('enrollments', <type 'list'>, ' row total: ', 1640, ' total students: ', 1302)\n",
"('daily_engagement', <type 'list'>, ' row total: ', 136240, ' total students: ', 1237)\n",
"('project_submissions', <type 'list'>, ' row total: ', 3642, ' total students: ', 743)\n",
" \n",
"REM: these are all a \"List of Dictionaries\"...!\n"
]
}
],
"source": [
"# enrollments\n",
"# daily_engagement\n",
"# project_submission\n",
"# these are all a \"List of Dictionaries\"\n",
"import sys \n",
"import os \n",
"import string \n",
"import time \n",
"\n",
"\n",
"#print(type(enrollments),len(enrollments) )\n",
"enrollments_set = set()\n",
"for line in enrollments :\n",
" enrollments_set.add(line['account_key'] ) \n",
"print(\"enrollments\",type(enrollments), \" row total: \",len(enrollments), \" total students: \", len(enrollments_set) )\n",
"\n",
"#print(type(daily_engagement), len(daily_engagement) ) \n",
"daily_engagement_set = set()\n",
"for line in daily_engagement :\n",
" daily_engagement_set.add(line['acct'] ) \n",
"print(\"daily_engagement\", type(daily_engagement),\" row total: \",len(daily_engagement), \" total students: \", len(daily_engagement_set) )\n",
"\n",
"#print(type(project_submissions), len(project_submissions) )\n",
"project_submissions_set = set()\n",
"for line in project_submissions :\n",
" project_submissions_set.add(line['account_key'] ) \n",
"print(\"project_submissions\", type(project_submissions),\" row total: \",len(project_submissions), \" total students: \", len(project_submissions_set) )\n",
"\n",
"print(\" \")\n",
"print('REM: these are all a \"List of Dictionaries\"...!')\n"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": []
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": []
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 2",
"language": "python",
"name": "python2"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 2
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython2",
"version": "2.7.10"
}
},
"nbformat": 4,
"nbformat_minor": 0
}
| gpl-3.0 |
ahellander/pyurdme | examples/simple_diffusion/Simple Diffusion-2D Periodic.ipynb | 3 | 410688 | {"nbformat_minor": 0, "cells": [{"execution_count": 1, "cell_type": "code", "source": "%matplotlib inline\nimport pyurdme\nimport numpy", "outputs": [], "metadata": {"collapsed": false, "trusted": true}}, {"execution_count": 2, "cell_type": "code", "source": "class Simple_Diffusion(pyurdme.URDMEModel):\n \"\"\" Initial condition is a delta function at and off-center point. \n The solution should be a Gaussian, and periodic boundary conditions\n should allow it to wrap. \"\"\"\n\n def __init__(self):\n\n pyurdme.URDMEModel.__init__(self,name=\"simple_diffusion\")\n\n D = 0.01\n A = pyurdme.Species(name=\"A\",diffusion_constant=D)\n\n self.add_species([A])\n\n # A unit square\n self.mesh = pyurdme.URDMEMesh.generate_unit_square_mesh(40,40, periodic=True)\n\n # Place the A molecules in the voxel nearest the center of the square\n self.set_initial_condition_place_near({A:100000},point=[0.1,0.1])\n\n self.timespan(numpy.linspace(0,5,1000))", "outputs": [], "metadata": {"collapsed": true, "trusted": true}}, {"execution_count": 3, "cell_type": "code", "source": "model = Simple_Diffusion()", "outputs": [], "metadata": {"collapsed": false, "trusted": true}}, {"execution_count": 4, "cell_type": "code", "source": "%time result = model.run()", "outputs": [{"output_type": "stream", "name": "stdout", "text": "CPU times: user 932 ms, sys: 112 ms, total: 1.04 s\nWall time: 14.7 s\n"}], "metadata": {"collapsed": false, "trusted": true}}, {"execution_count": 5, "cell_type": "code", "source": "#result.display('A',-1)", "outputs": [], "metadata": {"collapsed": false, "trusted": true}}, {"execution_count": 6, "cell_type": "code", "source": "def display(result, species, timepoint, opacity=1.0, wireframe=True, width=500):\n data = result.get_species(species,timepoint,concentration=True)\n fun = pyurdme.DolfinFunctionWrapper(result.model.mesh.get_function_space())\n vec = fun.vector()\n nd = data.shape[0]\n if nd == len(vec):\n for i in range(nd):\n vec[i]=data[i]\n else:\n v2d= result.get_v2d()\n for i in range(len(vec)):\n vec[i]=data[i]\n fun.display(opacity=opacity, wireframe=wireframe, width=width)", "outputs": [], "metadata": {"collapsed": false, "trusted": true}}, {"execution_count": 10, "cell_type": "code", "source": "display(result, 'A', 200)", "outputs": [{"output_type": "display_data", "data": {"text/plain": "<IPython.core.display.HTML object>", "text/html": "<div style=\"width: 500px; height: 375px;\" id=\"733742ec-fd23-4ab1-8efc-48092d77bdce\" ></div><script>\n renderPyURDMEMesh({\"faces\": [128, 0, 1, 42, 0, 1, 42, 128, 0, 41, 42, 0, 41, 42, 128, 1, 2, 43, 1, 2, 43, 128, 1, 42, 43, 1, 42, 43, 128, 2, 3, 44, 2, 3, 44, 128, 2, 43, 44, 2, 43, 44, 128, 3, 4, 45, 3, 4, 45, 128, 3, 44, 45, 3, 44, 45, 128, 4, 5, 46, 4, 5, 46, 128, 4, 45, 46, 4, 45, 46, 128, 5, 6, 47, 5, 6, 47, 128, 5, 46, 47, 5, 46, 47, 128, 6, 7, 48, 6, 7, 48, 128, 6, 47, 48, 6, 47, 48, 128, 7, 8, 49, 7, 8, 49, 128, 7, 48, 49, 7, 48, 49, 128, 8, 9, 50, 8, 9, 50, 128, 8, 49, 50, 8, 49, 50, 128, 9, 10, 51, 9, 10, 51, 128, 9, 50, 51, 9, 50, 51, 128, 10, 11, 52, 10, 11, 52, 128, 10, 51, 52, 10, 51, 52, 128, 11, 12, 53, 11, 12, 53, 128, 11, 52, 53, 11, 52, 53, 128, 12, 13, 54, 12, 13, 54, 128, 12, 53, 54, 12, 53, 54, 128, 13, 14, 55, 13, 14, 55, 128, 13, 54, 55, 13, 54, 55, 128, 14, 15, 56, 14, 15, 56, 128, 14, 55, 56, 14, 55, 56, 128, 15, 16, 57, 15, 16, 57, 128, 15, 56, 57, 15, 56, 57, 128, 16, 17, 58, 16, 17, 58, 128, 16, 57, 58, 16, 57, 58, 128, 17, 18, 59, 17, 18, 59, 128, 17, 58, 59, 17, 58, 59, 128, 18, 19, 60, 18, 19, 60, 128, 18, 59, 60, 18, 59, 60, 128, 19, 20, 61, 19, 20, 61, 128, 19, 60, 61, 19, 60, 61, 128, 20, 21, 62, 20, 21, 62, 128, 20, 61, 62, 20, 61, 62, 128, 21, 22, 63, 21, 22, 63, 128, 21, 62, 63, 21, 62, 63, 128, 22, 23, 64, 22, 23, 64, 128, 22, 63, 64, 22, 63, 64, 128, 23, 24, 65, 23, 24, 65, 128, 23, 64, 65, 23, 64, 65, 128, 24, 25, 66, 24, 25, 66, 128, 24, 65, 66, 24, 65, 66, 128, 25, 26, 67, 25, 26, 67, 128, 25, 66, 67, 25, 66, 67, 128, 26, 27, 68, 26, 27, 68, 128, 26, 67, 68, 26, 67, 68, 128, 27, 28, 69, 27, 28, 69, 128, 27, 68, 69, 27, 68, 69, 128, 28, 29, 70, 28, 29, 70, 128, 28, 69, 70, 28, 69, 70, 128, 29, 30, 71, 29, 30, 71, 128, 29, 70, 71, 29, 70, 71, 128, 30, 31, 72, 30, 31, 72, 128, 30, 71, 72, 30, 71, 72, 128, 31, 32, 73, 31, 32, 73, 128, 31, 72, 73, 31, 72, 73, 128, 32, 33, 74, 32, 33, 74, 128, 32, 73, 74, 32, 73, 74, 128, 33, 34, 75, 33, 34, 75, 128, 33, 74, 75, 33, 74, 75, 128, 34, 35, 76, 34, 35, 76, 128, 34, 75, 76, 34, 75, 76, 128, 35, 36, 77, 35, 36, 77, 128, 35, 76, 77, 35, 76, 77, 128, 36, 37, 78, 36, 37, 78, 128, 36, 77, 78, 36, 77, 78, 128, 37, 38, 79, 37, 38, 79, 128, 37, 78, 79, 37, 78, 79, 128, 38, 39, 80, 38, 39, 80, 128, 38, 79, 80, 38, 79, 80, 128, 39, 40, 81, 39, 40, 81, 128, 39, 80, 81, 39, 80, 81, 128, 41, 42, 83, 41, 42, 83, 128, 41, 82, 83, 41, 82, 83, 128, 42, 43, 84, 42, 43, 84, 128, 42, 83, 84, 42, 83, 84, 128, 43, 44, 85, 43, 44, 85, 128, 43, 84, 85, 43, 84, 85, 128, 44, 45, 86, 44, 45, 86, 128, 44, 85, 86, 44, 85, 86, 128, 45, 46, 87, 45, 46, 87, 128, 45, 86, 87, 45, 86, 87, 128, 46, 47, 88, 46, 47, 88, 128, 46, 87, 88, 46, 87, 88, 128, 47, 48, 89, 47, 48, 89, 128, 47, 88, 89, 47, 88, 89, 128, 48, 49, 90, 48, 49, 90, 128, 48, 89, 90, 48, 89, 90, 128, 49, 50, 91, 49, 50, 91, 128, 49, 90, 91, 49, 90, 91, 128, 50, 51, 92, 50, 51, 92, 128, 50, 91, 92, 50, 91, 92, 128, 51, 52, 93, 51, 52, 93, 128, 51, 92, 93, 51, 92, 93, 128, 52, 53, 94, 52, 53, 94, 128, 52, 93, 94, 52, 93, 94, 128, 53, 54, 95, 53, 54, 95, 128, 53, 94, 95, 53, 94, 95, 128, 54, 55, 96, 54, 55, 96, 128, 54, 95, 96, 54, 95, 96, 128, 55, 56, 97, 55, 56, 97, 128, 55, 96, 97, 55, 96, 97, 128, 56, 57, 98, 56, 57, 98, 128, 56, 97, 98, 56, 97, 98, 128, 57, 58, 99, 57, 58, 99, 128, 57, 98, 99, 57, 98, 99, 128, 58, 59, 100, 58, 59, 100, 128, 58, 99, 100, 58, 99, 100, 128, 59, 60, 101, 59, 60, 101, 128, 59, 100, 101, 59, 100, 101, 128, 60, 61, 102, 60, 61, 102, 128, 60, 101, 102, 60, 101, 102, 128, 61, 62, 103, 61, 62, 103, 128, 61, 102, 103, 61, 102, 103, 128, 62, 63, 104, 62, 63, 104, 128, 62, 103, 104, 62, 103, 104, 128, 63, 64, 105, 63, 64, 105, 128, 63, 104, 105, 63, 104, 105, 128, 64, 65, 106, 64, 65, 106, 128, 64, 105, 106, 64, 105, 106, 128, 65, 66, 107, 65, 66, 107, 128, 65, 106, 107, 65, 106, 107, 128, 66, 67, 108, 66, 67, 108, 128, 66, 107, 108, 66, 107, 108, 128, 67, 68, 109, 67, 68, 109, 128, 67, 108, 109, 67, 108, 109, 128, 68, 69, 110, 68, 69, 110, 128, 68, 109, 110, 68, 109, 110, 128, 69, 70, 111, 69, 70, 111, 128, 69, 110, 111, 69, 110, 111, 128, 70, 71, 112, 70, 71, 112, 128, 70, 111, 112, 70, 111, 112, 128, 71, 72, 113, 71, 72, 113, 128, 71, 112, 113, 71, 112, 113, 128, 72, 73, 114, 72, 73, 114, 128, 72, 113, 114, 72, 113, 114, 128, 73, 74, 115, 73, 74, 115, 128, 73, 114, 115, 73, 114, 115, 128, 74, 75, 116, 74, 75, 116, 128, 74, 115, 116, 74, 115, 116, 128, 75, 76, 117, 75, 76, 117, 128, 75, 116, 117, 75, 116, 117, 128, 76, 77, 118, 76, 77, 118, 128, 76, 117, 118, 76, 117, 118, 128, 77, 78, 119, 77, 78, 119, 128, 77, 118, 119, 77, 118, 119, 128, 78, 79, 120, 78, 79, 120, 128, 78, 119, 120, 78, 119, 120, 128, 79, 80, 121, 79, 80, 121, 128, 79, 120, 121, 79, 120, 121, 128, 80, 81, 122, 80, 81, 122, 128, 80, 121, 122, 80, 121, 122, 128, 82, 83, 124, 82, 83, 124, 128, 82, 123, 124, 82, 123, 124, 128, 83, 84, 125, 83, 84, 125, 128, 83, 124, 125, 83, 124, 125, 128, 84, 85, 126, 84, 85, 126, 128, 84, 125, 126, 84, 125, 126, 128, 85, 86, 127, 85, 86, 127, 128, 85, 126, 127, 85, 126, 127, 128, 86, 87, 128, 86, 87, 128, 128, 86, 127, 128, 86, 127, 128, 128, 87, 88, 129, 87, 88, 129, 128, 87, 128, 129, 87, 128, 129, 128, 88, 89, 130, 88, 89, 130, 128, 88, 129, 130, 88, 129, 130, 128, 89, 90, 131, 89, 90, 131, 128, 89, 130, 131, 89, 130, 131, 128, 90, 91, 132, 90, 91, 132, 128, 90, 131, 132, 90, 131, 132, 128, 91, 92, 133, 91, 92, 133, 128, 91, 132, 133, 91, 132, 133, 128, 92, 93, 134, 92, 93, 134, 128, 92, 133, 134, 92, 133, 134, 128, 93, 94, 135, 93, 94, 135, 128, 93, 134, 135, 93, 134, 135, 128, 94, 95, 136, 94, 95, 136, 128, 94, 135, 136, 94, 135, 136, 128, 95, 96, 137, 95, 96, 137, 128, 95, 136, 137, 95, 136, 137, 128, 96, 97, 138, 96, 97, 138, 128, 96, 137, 138, 96, 137, 138, 128, 97, 98, 139, 97, 98, 139, 128, 97, 138, 139, 97, 138, 139, 128, 98, 99, 140, 98, 99, 140, 128, 98, 139, 140, 98, 139, 140, 128, 99, 100, 141, 99, 100, 141, 128, 99, 140, 141, 99, 140, 141, 128, 100, 101, 142, 100, 101, 142, 128, 100, 141, 142, 100, 141, 142, 128, 101, 102, 143, 101, 102, 143, 128, 101, 142, 143, 101, 142, 143, 128, 102, 103, 144, 102, 103, 144, 128, 102, 143, 144, 102, 143, 144, 128, 103, 104, 145, 103, 104, 145, 128, 103, 144, 145, 103, 144, 145, 128, 104, 105, 146, 104, 105, 146, 128, 104, 145, 146, 104, 145, 146, 128, 105, 106, 147, 105, 106, 147, 128, 105, 146, 147, 105, 146, 147, 128, 106, 107, 148, 106, 107, 148, 128, 106, 147, 148, 106, 147, 148, 128, 107, 108, 149, 107, 108, 149, 128, 107, 148, 149, 107, 148, 149, 128, 108, 109, 150, 108, 109, 150, 128, 108, 149, 150, 108, 149, 150, 128, 109, 110, 151, 109, 110, 151, 128, 109, 150, 151, 109, 150, 151, 128, 110, 111, 152, 110, 111, 152, 128, 110, 151, 152, 110, 151, 152, 128, 111, 112, 153, 111, 112, 153, 128, 111, 152, 153, 111, 152, 153, 128, 112, 113, 154, 112, 113, 154, 128, 112, 153, 154, 112, 153, 154, 128, 113, 114, 155, 113, 114, 155, 128, 113, 154, 155, 113, 154, 155, 128, 114, 115, 156, 114, 115, 156, 128, 114, 155, 156, 114, 155, 156, 128, 115, 116, 157, 115, 116, 157, 128, 115, 156, 157, 115, 156, 157, 128, 116, 117, 158, 116, 117, 158, 128, 116, 157, 158, 116, 157, 158, 128, 117, 118, 159, 117, 118, 159, 128, 117, 158, 159, 117, 158, 159, 128, 118, 119, 160, 118, 119, 160, 128, 118, 159, 160, 118, 159, 160, 128, 119, 120, 161, 119, 120, 161, 128, 119, 160, 161, 119, 160, 161, 128, 120, 121, 162, 120, 121, 162, 128, 120, 161, 162, 120, 161, 162, 128, 121, 122, 163, 121, 122, 163, 128, 121, 162, 163, 121, 162, 163, 128, 123, 124, 165, 123, 124, 165, 128, 123, 164, 165, 123, 164, 165, 128, 124, 125, 166, 124, 125, 166, 128, 124, 165, 166, 124, 165, 166, 128, 125, 126, 167, 125, 126, 167, 128, 125, 166, 167, 125, 166, 167, 128, 126, 127, 168, 126, 127, 168, 128, 126, 167, 168, 126, 167, 168, 128, 127, 128, 169, 127, 128, 169, 128, 127, 168, 169, 127, 168, 169, 128, 128, 129, 170, 128, 129, 170, 128, 128, 169, 170, 128, 169, 170, 128, 129, 130, 171, 129, 130, 171, 128, 129, 170, 171, 129, 170, 171, 128, 130, 131, 172, 130, 131, 172, 128, 130, 171, 172, 130, 171, 172, 128, 131, 132, 173, 131, 132, 173, 128, 131, 172, 173, 131, 172, 173, 128, 132, 133, 174, 132, 133, 174, 128, 132, 173, 174, 132, 173, 174, 128, 133, 134, 175, 133, 134, 175, 128, 133, 174, 175, 133, 174, 175, 128, 134, 135, 176, 134, 135, 176, 128, 134, 175, 176, 134, 175, 176, 128, 135, 136, 177, 135, 136, 177, 128, 135, 176, 177, 135, 176, 177, 128, 136, 137, 178, 136, 137, 178, 128, 136, 177, 178, 136, 177, 178, 128, 137, 138, 179, 137, 138, 179, 128, 137, 178, 179, 137, 178, 179, 128, 138, 139, 180, 138, 139, 180, 128, 138, 179, 180, 138, 179, 180, 128, 139, 140, 181, 139, 140, 181, 128, 139, 180, 181, 139, 180, 181, 128, 140, 141, 182, 140, 141, 182, 128, 140, 181, 182, 140, 181, 182, 128, 141, 142, 183, 141, 142, 183, 128, 141, 182, 183, 141, 182, 183, 128, 142, 143, 184, 142, 143, 184, 128, 142, 183, 184, 142, 183, 184, 128, 143, 144, 185, 143, 144, 185, 128, 143, 184, 185, 143, 184, 185, 128, 144, 145, 186, 144, 145, 186, 128, 144, 185, 186, 144, 185, 186, 128, 145, 146, 187, 145, 146, 187, 128, 145, 186, 187, 145, 186, 187, 128, 146, 147, 188, 146, 147, 188, 128, 146, 187, 188, 146, 187, 188, 128, 147, 148, 189, 147, 148, 189, 128, 147, 188, 189, 147, 188, 189, 128, 148, 149, 190, 148, 149, 190, 128, 148, 189, 190, 148, 189, 190, 128, 149, 150, 191, 149, 150, 191, 128, 149, 190, 191, 149, 190, 191, 128, 150, 151, 192, 150, 151, 192, 128, 150, 191, 192, 150, 191, 192, 128, 151, 152, 193, 151, 152, 193, 128, 151, 192, 193, 151, 192, 193, 128, 152, 153, 194, 152, 153, 194, 128, 152, 193, 194, 152, 193, 194, 128, 153, 154, 195, 153, 154, 195, 128, 153, 194, 195, 153, 194, 195, 128, 154, 155, 196, 154, 155, 196, 128, 154, 195, 196, 154, 195, 196, 128, 155, 156, 197, 155, 156, 197, 128, 155, 196, 197, 155, 196, 197, 128, 156, 157, 198, 156, 157, 198, 128, 156, 197, 198, 156, 197, 198, 128, 157, 158, 199, 157, 158, 199, 128, 157, 198, 199, 157, 198, 199, 128, 158, 159, 200, 158, 159, 200, 128, 158, 199, 200, 158, 199, 200, 128, 159, 160, 201, 159, 160, 201, 128, 159, 200, 201, 159, 200, 201, 128, 160, 161, 202, 160, 161, 202, 128, 160, 201, 202, 160, 201, 202, 128, 161, 162, 203, 161, 162, 203, 128, 161, 202, 203, 161, 202, 203, 128, 162, 163, 204, 162, 163, 204, 128, 162, 203, 204, 162, 203, 204, 128, 164, 165, 206, 164, 165, 206, 128, 164, 205, 206, 164, 205, 206, 128, 165, 166, 207, 165, 166, 207, 128, 165, 206, 207, 165, 206, 207, 128, 166, 167, 208, 166, 167, 208, 128, 166, 207, 208, 166, 207, 208, 128, 167, 168, 209, 167, 168, 209, 128, 167, 208, 209, 167, 208, 209, 128, 168, 169, 210, 168, 169, 210, 128, 168, 209, 210, 168, 209, 210, 128, 169, 170, 211, 169, 170, 211, 128, 169, 210, 211, 169, 210, 211, 128, 170, 171, 212, 170, 171, 212, 128, 170, 211, 212, 170, 211, 212, 128, 171, 172, 213, 171, 172, 213, 128, 171, 212, 213, 171, 212, 213, 128, 172, 173, 214, 172, 173, 214, 128, 172, 213, 214, 172, 213, 214, 128, 173, 174, 215, 173, 174, 215, 128, 173, 214, 215, 173, 214, 215, 128, 174, 175, 216, 174, 175, 216, 128, 174, 215, 216, 174, 215, 216, 128, 175, 176, 217, 175, 176, 217, 128, 175, 216, 217, 175, 216, 217, 128, 176, 177, 218, 176, 177, 218, 128, 176, 217, 218, 176, 217, 218, 128, 177, 178, 219, 177, 178, 219, 128, 177, 218, 219, 177, 218, 219, 128, 178, 179, 220, 178, 179, 220, 128, 178, 219, 220, 178, 219, 220, 128, 179, 180, 221, 179, 180, 221, 128, 179, 220, 221, 179, 220, 221, 128, 180, 181, 222, 180, 181, 222, 128, 180, 221, 222, 180, 221, 222, 128, 181, 182, 223, 181, 182, 223, 128, 181, 222, 223, 181, 222, 223, 128, 182, 183, 224, 182, 183, 224, 128, 182, 223, 224, 182, 223, 224, 128, 183, 184, 225, 183, 184, 225, 128, 183, 224, 225, 183, 224, 225, 128, 184, 185, 226, 184, 185, 226, 128, 184, 225, 226, 184, 225, 226, 128, 185, 186, 227, 185, 186, 227, 128, 185, 226, 227, 185, 226, 227, 128, 186, 187, 228, 186, 187, 228, 128, 186, 227, 228, 186, 227, 228, 128, 187, 188, 229, 187, 188, 229, 128, 187, 228, 229, 187, 228, 229, 128, 188, 189, 230, 188, 189, 230, 128, 188, 229, 230, 188, 229, 230, 128, 189, 190, 231, 189, 190, 231, 128, 189, 230, 231, 189, 230, 231, 128, 190, 191, 232, 190, 191, 232, 128, 190, 231, 232, 190, 231, 232, 128, 191, 192, 233, 191, 192, 233, 128, 191, 232, 233, 191, 232, 233, 128, 192, 193, 234, 192, 193, 234, 128, 192, 233, 234, 192, 233, 234, 128, 193, 194, 235, 193, 194, 235, 128, 193, 234, 235, 193, 234, 235, 128, 194, 195, 236, 194, 195, 236, 128, 194, 235, 236, 194, 235, 236, 128, 195, 196, 237, 195, 196, 237, 128, 195, 236, 237, 195, 236, 237, 128, 196, 197, 238, 196, 197, 238, 128, 196, 237, 238, 196, 237, 238, 128, 197, 198, 239, 197, 198, 239, 128, 197, 238, 239, 197, 238, 239, 128, 198, 199, 240, 198, 199, 240, 128, 198, 239, 240, 198, 239, 240, 128, 199, 200, 241, 199, 200, 241, 128, 199, 240, 241, 199, 240, 241, 128, 200, 201, 242, 200, 201, 242, 128, 200, 241, 242, 200, 241, 242, 128, 201, 202, 243, 201, 202, 243, 128, 201, 242, 243, 201, 242, 243, 128, 202, 203, 244, 202, 203, 244, 128, 202, 243, 244, 202, 243, 244, 128, 203, 204, 245, 203, 204, 245, 128, 203, 244, 245, 203, 244, 245, 128, 205, 206, 247, 205, 206, 247, 128, 205, 246, 247, 205, 246, 247, 128, 206, 207, 248, 206, 207, 248, 128, 206, 247, 248, 206, 247, 248, 128, 207, 208, 249, 207, 208, 249, 128, 207, 248, 249, 207, 248, 249, 128, 208, 209, 250, 208, 209, 250, 128, 208, 249, 250, 208, 249, 250, 128, 209, 210, 251, 209, 210, 251, 128, 209, 250, 251, 209, 250, 251, 128, 210, 211, 252, 210, 211, 252, 128, 210, 251, 252, 210, 251, 252, 128, 211, 212, 253, 211, 212, 253, 128, 211, 252, 253, 211, 252, 253, 128, 212, 213, 254, 212, 213, 254, 128, 212, 253, 254, 212, 253, 254, 128, 213, 214, 255, 213, 214, 255, 128, 213, 254, 255, 213, 254, 255, 128, 214, 215, 256, 214, 215, 256, 128, 214, 255, 256, 214, 255, 256, 128, 215, 216, 257, 215, 216, 257, 128, 215, 256, 257, 215, 256, 257, 128, 216, 217, 258, 216, 217, 258, 128, 216, 257, 258, 216, 257, 258, 128, 217, 218, 259, 217, 218, 259, 128, 217, 258, 259, 217, 258, 259, 128, 218, 219, 260, 218, 219, 260, 128, 218, 259, 260, 218, 259, 260, 128, 219, 220, 261, 219, 220, 261, 128, 219, 260, 261, 219, 260, 261, 128, 220, 221, 262, 220, 221, 262, 128, 220, 261, 262, 220, 261, 262, 128, 221, 222, 263, 221, 222, 263, 128, 221, 262, 263, 221, 262, 263, 128, 222, 223, 264, 222, 223, 264, 128, 222, 263, 264, 222, 263, 264, 128, 223, 224, 265, 223, 224, 265, 128, 223, 264, 265, 223, 264, 265, 128, 224, 225, 266, 224, 225, 266, 128, 224, 265, 266, 224, 265, 266, 128, 225, 226, 267, 225, 226, 267, 128, 225, 266, 267, 225, 266, 267, 128, 226, 227, 268, 226, 227, 268, 128, 226, 267, 268, 226, 267, 268, 128, 227, 228, 269, 227, 228, 269, 128, 227, 268, 269, 227, 268, 269, 128, 228, 229, 270, 228, 229, 270, 128, 228, 269, 270, 228, 269, 270, 128, 229, 230, 271, 229, 230, 271, 128, 229, 270, 271, 229, 270, 271, 128, 230, 231, 272, 230, 231, 272, 128, 230, 271, 272, 230, 271, 272, 128, 231, 232, 273, 231, 232, 273, 128, 231, 272, 273, 231, 272, 273, 128, 232, 233, 274, 232, 233, 274, 128, 232, 273, 274, 232, 273, 274, 128, 233, 234, 275, 233, 234, 275, 128, 233, 274, 275, 233, 274, 275, 128, 234, 235, 276, 234, 235, 276, 128, 234, 275, 276, 234, 275, 276, 128, 235, 236, 277, 235, 236, 277, 128, 235, 276, 277, 235, 276, 277, 128, 236, 237, 278, 236, 237, 278, 128, 236, 277, 278, 236, 277, 278, 128, 237, 238, 279, 237, 238, 279, 128, 237, 278, 279, 237, 278, 279, 128, 238, 239, 280, 238, 239, 280, 128, 238, 279, 280, 238, 279, 280, 128, 239, 240, 281, 239, 240, 281, 128, 239, 280, 281, 239, 280, 281, 128, 240, 241, 282, 240, 241, 282, 128, 240, 281, 282, 240, 281, 282, 128, 241, 242, 283, 241, 242, 283, 128, 241, 282, 283, 241, 282, 283, 128, 242, 243, 284, 242, 243, 284, 128, 242, 283, 284, 242, 283, 284, 128, 243, 244, 285, 243, 244, 285, 128, 243, 284, 285, 243, 284, 285, 128, 244, 245, 286, 244, 245, 286, 128, 244, 285, 286, 244, 285, 286, 128, 246, 247, 288, 246, 247, 288, 128, 246, 287, 288, 246, 287, 288, 128, 247, 248, 289, 247, 248, 289, 128, 247, 288, 289, 247, 288, 289, 128, 248, 249, 290, 248, 249, 290, 128, 248, 289, 290, 248, 289, 290, 128, 249, 250, 291, 249, 250, 291, 128, 249, 290, 291, 249, 290, 291, 128, 250, 251, 292, 250, 251, 292, 128, 250, 291, 292, 250, 291, 292, 128, 251, 252, 293, 251, 252, 293, 128, 251, 292, 293, 251, 292, 293, 128, 252, 253, 294, 252, 253, 294, 128, 252, 293, 294, 252, 293, 294, 128, 253, 254, 295, 253, 254, 295, 128, 253, 294, 295, 253, 294, 295, 128, 254, 255, 296, 254, 255, 296, 128, 254, 295, 296, 254, 295, 296, 128, 255, 256, 297, 255, 256, 297, 128, 255, 296, 297, 255, 296, 297, 128, 256, 257, 298, 256, 257, 298, 128, 256, 297, 298, 256, 297, 298, 128, 257, 258, 299, 257, 258, 299, 128, 257, 298, 299, 257, 298, 299, 128, 258, 259, 300, 258, 259, 300, 128, 258, 299, 300, 258, 299, 300, 128, 259, 260, 301, 259, 260, 301, 128, 259, 300, 301, 259, 300, 301, 128, 260, 261, 302, 260, 261, 302, 128, 260, 301, 302, 260, 301, 302, 128, 261, 262, 303, 261, 262, 303, 128, 261, 302, 303, 261, 302, 303, 128, 262, 263, 304, 262, 263, 304, 128, 262, 303, 304, 262, 303, 304, 128, 263, 264, 305, 263, 264, 305, 128, 263, 304, 305, 263, 304, 305, 128, 264, 265, 306, 264, 265, 306, 128, 264, 305, 306, 264, 305, 306, 128, 265, 266, 307, 265, 266, 307, 128, 265, 306, 307, 265, 306, 307, 128, 266, 267, 308, 266, 267, 308, 128, 266, 307, 308, 266, 307, 308, 128, 267, 268, 309, 267, 268, 309, 128, 267, 308, 309, 267, 308, 309, 128, 268, 269, 310, 268, 269, 310, 128, 268, 309, 310, 268, 309, 310, 128, 269, 270, 311, 269, 270, 311, 128, 269, 310, 311, 269, 310, 311, 128, 270, 271, 312, 270, 271, 312, 128, 270, 311, 312, 270, 311, 312, 128, 271, 272, 313, 271, 272, 313, 128, 271, 312, 313, 271, 312, 313, 128, 272, 273, 314, 272, 273, 314, 128, 272, 313, 314, 272, 313, 314, 128, 273, 274, 315, 273, 274, 315, 128, 273, 314, 315, 273, 314, 315, 128, 274, 275, 316, 274, 275, 316, 128, 274, 315, 316, 274, 315, 316, 128, 275, 276, 317, 275, 276, 317, 128, 275, 316, 317, 275, 316, 317, 128, 276, 277, 318, 276, 277, 318, 128, 276, 317, 318, 276, 317, 318, 128, 277, 278, 319, 277, 278, 319, 128, 277, 318, 319, 277, 318, 319, 128, 278, 279, 320, 278, 279, 320, 128, 278, 319, 320, 278, 319, 320, 128, 279, 280, 321, 279, 280, 321, 128, 279, 320, 321, 279, 320, 321, 128, 280, 281, 322, 280, 281, 322, 128, 280, 321, 322, 280, 321, 322, 128, 281, 282, 323, 281, 282, 323, 128, 281, 322, 323, 281, 322, 323, 128, 282, 283, 324, 282, 283, 324, 128, 282, 323, 324, 282, 323, 324, 128, 283, 284, 325, 283, 284, 325, 128, 283, 324, 325, 283, 324, 325, 128, 284, 285, 326, 284, 285, 326, 128, 284, 325, 326, 284, 325, 326, 128, 285, 286, 327, 285, 286, 327, 128, 285, 326, 327, 285, 326, 327, 128, 287, 288, 329, 287, 288, 329, 128, 287, 328, 329, 287, 328, 329, 128, 288, 289, 330, 288, 289, 330, 128, 288, 329, 330, 288, 329, 330, 128, 289, 290, 331, 289, 290, 331, 128, 289, 330, 331, 289, 330, 331, 128, 290, 291, 332, 290, 291, 332, 128, 290, 331, 332, 290, 331, 332, 128, 291, 292, 333, 291, 292, 333, 128, 291, 332, 333, 291, 332, 333, 128, 292, 293, 334, 292, 293, 334, 128, 292, 333, 334, 292, 333, 334, 128, 293, 294, 335, 293, 294, 335, 128, 293, 334, 335, 293, 334, 335, 128, 294, 295, 336, 294, 295, 336, 128, 294, 335, 336, 294, 335, 336, 128, 295, 296, 337, 295, 296, 337, 128, 295, 336, 337, 295, 336, 337, 128, 296, 297, 338, 296, 297, 338, 128, 296, 337, 338, 296, 337, 338, 128, 297, 298, 339, 297, 298, 339, 128, 297, 338, 339, 297, 338, 339, 128, 298, 299, 340, 298, 299, 340, 128, 298, 339, 340, 298, 339, 340, 128, 299, 300, 341, 299, 300, 341, 128, 299, 340, 341, 299, 340, 341, 128, 300, 301, 342, 300, 301, 342, 128, 300, 341, 342, 300, 341, 342, 128, 301, 302, 343, 301, 302, 343, 128, 301, 342, 343, 301, 342, 343, 128, 302, 303, 344, 302, 303, 344, 128, 302, 343, 344, 302, 343, 344, 128, 303, 304, 345, 303, 304, 345, 128, 303, 344, 345, 303, 344, 345, 128, 304, 305, 346, 304, 305, 346, 128, 304, 345, 346, 304, 345, 346, 128, 305, 306, 347, 305, 306, 347, 128, 305, 346, 347, 305, 346, 347, 128, 306, 307, 348, 306, 307, 348, 128, 306, 347, 348, 306, 347, 348, 128, 307, 308, 349, 307, 308, 349, 128, 307, 348, 349, 307, 348, 349, 128, 308, 309, 350, 308, 309, 350, 128, 308, 349, 350, 308, 349, 350, 128, 309, 310, 351, 309, 310, 351, 128, 309, 350, 351, 309, 350, 351, 128, 310, 311, 352, 310, 311, 352, 128, 310, 351, 352, 310, 351, 352, 128, 311, 312, 353, 311, 312, 353, 128, 311, 352, 353, 311, 352, 353, 128, 312, 313, 354, 312, 313, 354, 128, 312, 353, 354, 312, 353, 354, 128, 313, 314, 355, 313, 314, 355, 128, 313, 354, 355, 313, 354, 355, 128, 314, 315, 356, 314, 315, 356, 128, 314, 355, 356, 314, 355, 356, 128, 315, 316, 357, 315, 316, 357, 128, 315, 356, 357, 315, 356, 357, 128, 316, 317, 358, 316, 317, 358, 128, 316, 357, 358, 316, 357, 358, 128, 317, 318, 359, 317, 318, 359, 128, 317, 358, 359, 317, 358, 359, 128, 318, 319, 360, 318, 319, 360, 128, 318, 359, 360, 318, 359, 360, 128, 319, 320, 361, 319, 320, 361, 128, 319, 360, 361, 319, 360, 361, 128, 320, 321, 362, 320, 321, 362, 128, 320, 361, 362, 320, 361, 362, 128, 321, 322, 363, 321, 322, 363, 128, 321, 362, 363, 321, 362, 363, 128, 322, 323, 364, 322, 323, 364, 128, 322, 363, 364, 322, 363, 364, 128, 323, 324, 365, 323, 324, 365, 128, 323, 364, 365, 323, 364, 365, 128, 324, 325, 366, 324, 325, 366, 128, 324, 365, 366, 324, 365, 366, 128, 325, 326, 367, 325, 326, 367, 128, 325, 366, 367, 325, 366, 367, 128, 326, 327, 368, 326, 327, 368, 128, 326, 367, 368, 326, 367, 368, 128, 328, 329, 370, 328, 329, 370, 128, 328, 369, 370, 328, 369, 370, 128, 329, 330, 371, 329, 330, 371, 128, 329, 370, 371, 329, 370, 371, 128, 330, 331, 372, 330, 331, 372, 128, 330, 371, 372, 330, 371, 372, 128, 331, 332, 373, 331, 332, 373, 128, 331, 372, 373, 331, 372, 373, 128, 332, 333, 374, 332, 333, 374, 128, 332, 373, 374, 332, 373, 374, 128, 333, 334, 375, 333, 334, 375, 128, 333, 374, 375, 333, 374, 375, 128, 334, 335, 376, 334, 335, 376, 128, 334, 375, 376, 334, 375, 376, 128, 335, 336, 377, 335, 336, 377, 128, 335, 376, 377, 335, 376, 377, 128, 336, 337, 378, 336, 337, 378, 128, 336, 377, 378, 336, 377, 378, 128, 337, 338, 379, 337, 338, 379, 128, 337, 378, 379, 337, 378, 379, 128, 338, 339, 380, 338, 339, 380, 128, 338, 379, 380, 338, 379, 380, 128, 339, 340, 381, 339, 340, 381, 128, 339, 380, 381, 339, 380, 381, 128, 340, 341, 382, 340, 341, 382, 128, 340, 381, 382, 340, 381, 382, 128, 341, 342, 383, 341, 342, 383, 128, 341, 382, 383, 341, 382, 383, 128, 342, 343, 384, 342, 343, 384, 128, 342, 383, 384, 342, 383, 384, 128, 343, 344, 385, 343, 344, 385, 128, 343, 384, 385, 343, 384, 385, 128, 344, 345, 386, 344, 345, 386, 128, 344, 385, 386, 344, 385, 386, 128, 345, 346, 387, 345, 346, 387, 128, 345, 386, 387, 345, 386, 387, 128, 346, 347, 388, 346, 347, 388, 128, 346, 387, 388, 346, 387, 388, 128, 347, 348, 389, 347, 348, 389, 128, 347, 388, 389, 347, 388, 389, 128, 348, 349, 390, 348, 349, 390, 128, 348, 389, 390, 348, 389, 390, 128, 349, 350, 391, 349, 350, 391, 128, 349, 390, 391, 349, 390, 391, 128, 350, 351, 392, 350, 351, 392, 128, 350, 391, 392, 350, 391, 392, 128, 351, 352, 393, 351, 352, 393, 128, 351, 392, 393, 351, 392, 393, 128, 352, 353, 394, 352, 353, 394, 128, 352, 393, 394, 352, 393, 394, 128, 353, 354, 395, 353, 354, 395, 128, 353, 394, 395, 353, 394, 395, 128, 354, 355, 396, 354, 355, 396, 128, 354, 395, 396, 354, 395, 396, 128, 355, 356, 397, 355, 356, 397, 128, 355, 396, 397, 355, 396, 397, 128, 356, 357, 398, 356, 357, 398, 128, 356, 397, 398, 356, 397, 398, 128, 357, 358, 399, 357, 358, 399, 128, 357, 398, 399, 357, 398, 399, 128, 358, 359, 400, 358, 359, 400, 128, 358, 399, 400, 358, 399, 400, 128, 359, 360, 401, 359, 360, 401, 128, 359, 400, 401, 359, 400, 401, 128, 360, 361, 402, 360, 361, 402, 128, 360, 401, 402, 360, 401, 402, 128, 361, 362, 403, 361, 362, 403, 128, 361, 402, 403, 361, 402, 403, 128, 362, 363, 404, 362, 363, 404, 128, 362, 403, 404, 362, 403, 404, 128, 363, 364, 405, 363, 364, 405, 128, 363, 404, 405, 363, 404, 405, 128, 364, 365, 406, 364, 365, 406, 128, 364, 405, 406, 364, 405, 406, 128, 365, 366, 407, 365, 366, 407, 128, 365, 406, 407, 365, 406, 407, 128, 366, 367, 408, 366, 367, 408, 128, 366, 407, 408, 366, 407, 408, 128, 367, 368, 409, 367, 368, 409, 128, 367, 408, 409, 367, 408, 409, 128, 369, 370, 411, 369, 370, 411, 128, 369, 410, 411, 369, 410, 411, 128, 370, 371, 412, 370, 371, 412, 128, 370, 411, 412, 370, 411, 412, 128, 371, 372, 413, 371, 372, 413, 128, 371, 412, 413, 371, 412, 413, 128, 372, 373, 414, 372, 373, 414, 128, 372, 413, 414, 372, 413, 414, 128, 373, 374, 415, 373, 374, 415, 128, 373, 414, 415, 373, 414, 415, 128, 374, 375, 416, 374, 375, 416, 128, 374, 415, 416, 374, 415, 416, 128, 375, 376, 417, 375, 376, 417, 128, 375, 416, 417, 375, 416, 417, 128, 376, 377, 418, 376, 377, 418, 128, 376, 417, 418, 376, 417, 418, 128, 377, 378, 419, 377, 378, 419, 128, 377, 418, 419, 377, 418, 419, 128, 378, 379, 420, 378, 379, 420, 128, 378, 419, 420, 378, 419, 420, 128, 379, 380, 421, 379, 380, 421, 128, 379, 420, 421, 379, 420, 421, 128, 380, 381, 422, 380, 381, 422, 128, 380, 421, 422, 380, 421, 422, 128, 381, 382, 423, 381, 382, 423, 128, 381, 422, 423, 381, 422, 423, 128, 382, 383, 424, 382, 383, 424, 128, 382, 423, 424, 382, 423, 424, 128, 383, 384, 425, 383, 384, 425, 128, 383, 424, 425, 383, 424, 425, 128, 384, 385, 426, 384, 385, 426, 128, 384, 425, 426, 384, 425, 426, 128, 385, 386, 427, 385, 386, 427, 128, 385, 426, 427, 385, 426, 427, 128, 386, 387, 428, 386, 387, 428, 128, 386, 427, 428, 386, 427, 428, 128, 387, 388, 429, 387, 388, 429, 128, 387, 428, 429, 387, 428, 429, 128, 388, 389, 430, 388, 389, 430, 128, 388, 429, 430, 388, 429, 430, 128, 389, 390, 431, 389, 390, 431, 128, 389, 430, 431, 389, 430, 431, 128, 390, 391, 432, 390, 391, 432, 128, 390, 431, 432, 390, 431, 432, 128, 391, 392, 433, 391, 392, 433, 128, 391, 432, 433, 391, 432, 433, 128, 392, 393, 434, 392, 393, 434, 128, 392, 433, 434, 392, 433, 434, 128, 393, 394, 435, 393, 394, 435, 128, 393, 434, 435, 393, 434, 435, 128, 394, 395, 436, 394, 395, 436, 128, 394, 435, 436, 394, 435, 436, 128, 395, 396, 437, 395, 396, 437, 128, 395, 436, 437, 395, 436, 437, 128, 396, 397, 438, 396, 397, 438, 128, 396, 437, 438, 396, 437, 438, 128, 397, 398, 439, 397, 398, 439, 128, 397, 438, 439, 397, 438, 439, 128, 398, 399, 440, 398, 399, 440, 128, 398, 439, 440, 398, 439, 440, 128, 399, 400, 441, 399, 400, 441, 128, 399, 440, 441, 399, 440, 441, 128, 400, 401, 442, 400, 401, 442, 128, 400, 441, 442, 400, 441, 442, 128, 401, 402, 443, 401, 402, 443, 128, 401, 442, 443, 401, 442, 443, 128, 402, 403, 444, 402, 403, 444, 128, 402, 443, 444, 402, 443, 444, 128, 403, 404, 445, 403, 404, 445, 128, 403, 444, 445, 403, 444, 445, 128, 404, 405, 446, 404, 405, 446, 128, 404, 445, 446, 404, 445, 446, 128, 405, 406, 447, 405, 406, 447, 128, 405, 446, 447, 405, 446, 447, 128, 406, 407, 448, 406, 407, 448, 128, 406, 447, 448, 406, 447, 448, 128, 407, 408, 449, 407, 408, 449, 128, 407, 448, 449, 407, 448, 449, 128, 408, 409, 450, 408, 409, 450, 128, 408, 449, 450, 408, 449, 450, 128, 410, 411, 452, 410, 411, 452, 128, 410, 451, 452, 410, 451, 452, 128, 411, 412, 453, 411, 412, 453, 128, 411, 452, 453, 411, 452, 453, 128, 412, 413, 454, 412, 413, 454, 128, 412, 453, 454, 412, 453, 454, 128, 413, 414, 455, 413, 414, 455, 128, 413, 454, 455, 413, 454, 455, 128, 414, 415, 456, 414, 415, 456, 128, 414, 455, 456, 414, 455, 456, 128, 415, 416, 457, 415, 416, 457, 128, 415, 456, 457, 415, 456, 457, 128, 416, 417, 458, 416, 417, 458, 128, 416, 457, 458, 416, 457, 458, 128, 417, 418, 459, 417, 418, 459, 128, 417, 458, 459, 417, 458, 459, 128, 418, 419, 460, 418, 419, 460, 128, 418, 459, 460, 418, 459, 460, 128, 419, 420, 461, 419, 420, 461, 128, 419, 460, 461, 419, 460, 461, 128, 420, 421, 462, 420, 421, 462, 128, 420, 461, 462, 420, 461, 462, 128, 421, 422, 463, 421, 422, 463, 128, 421, 462, 463, 421, 462, 463, 128, 422, 423, 464, 422, 423, 464, 128, 422, 463, 464, 422, 463, 464, 128, 423, 424, 465, 423, 424, 465, 128, 423, 464, 465, 423, 464, 465, 128, 424, 425, 466, 424, 425, 466, 128, 424, 465, 466, 424, 465, 466, 128, 425, 426, 467, 425, 426, 467, 128, 425, 466, 467, 425, 466, 467, 128, 426, 427, 468, 426, 427, 468, 128, 426, 467, 468, 426, 467, 468, 128, 427, 428, 469, 427, 428, 469, 128, 427, 468, 469, 427, 468, 469, 128, 428, 429, 470, 428, 429, 470, 128, 428, 469, 470, 428, 469, 470, 128, 429, 430, 471, 429, 430, 471, 128, 429, 470, 471, 429, 470, 471, 128, 430, 431, 472, 430, 431, 472, 128, 430, 471, 472, 430, 471, 472, 128, 431, 432, 473, 431, 432, 473, 128, 431, 472, 473, 431, 472, 473, 128, 432, 433, 474, 432, 433, 474, 128, 432, 473, 474, 432, 473, 474, 128, 433, 434, 475, 433, 434, 475, 128, 433, 474, 475, 433, 474, 475, 128, 434, 435, 476, 434, 435, 476, 128, 434, 475, 476, 434, 475, 476, 128, 435, 436, 477, 435, 436, 477, 128, 435, 476, 477, 435, 476, 477, 128, 436, 437, 478, 436, 437, 478, 128, 436, 477, 478, 436, 477, 478, 128, 437, 438, 479, 437, 438, 479, 128, 437, 478, 479, 437, 478, 479, 128, 438, 439, 480, 438, 439, 480, 128, 438, 479, 480, 438, 479, 480, 128, 439, 440, 481, 439, 440, 481, 128, 439, 480, 481, 439, 480, 481, 128, 440, 441, 482, 440, 441, 482, 128, 440, 481, 482, 440, 481, 482, 128, 441, 442, 483, 441, 442, 483, 128, 441, 482, 483, 441, 482, 483, 128, 442, 443, 484, 442, 443, 484, 128, 442, 483, 484, 442, 483, 484, 128, 443, 444, 485, 443, 444, 485, 128, 443, 484, 485, 443, 484, 485, 128, 444, 445, 486, 444, 445, 486, 128, 444, 485, 486, 444, 485, 486, 128, 445, 446, 487, 445, 446, 487, 128, 445, 486, 487, 445, 486, 487, 128, 446, 447, 488, 446, 447, 488, 128, 446, 487, 488, 446, 487, 488, 128, 447, 448, 489, 447, 448, 489, 128, 447, 488, 489, 447, 488, 489, 128, 448, 449, 490, 448, 449, 490, 128, 448, 489, 490, 448, 489, 490, 128, 449, 450, 491, 449, 450, 491, 128, 449, 490, 491, 449, 490, 491, 128, 451, 452, 493, 451, 452, 493, 128, 451, 492, 493, 451, 492, 493, 128, 452, 453, 494, 452, 453, 494, 128, 452, 493, 494, 452, 493, 494, 128, 453, 454, 495, 453, 454, 495, 128, 453, 494, 495, 453, 494, 495, 128, 454, 455, 496, 454, 455, 496, 128, 454, 495, 496, 454, 495, 496, 128, 455, 456, 497, 455, 456, 497, 128, 455, 496, 497, 455, 496, 497, 128, 456, 457, 498, 456, 457, 498, 128, 456, 497, 498, 456, 497, 498, 128, 457, 458, 499, 457, 458, 499, 128, 457, 498, 499, 457, 498, 499, 128, 458, 459, 500, 458, 459, 500, 128, 458, 499, 500, 458, 499, 500, 128, 459, 460, 501, 459, 460, 501, 128, 459, 500, 501, 459, 500, 501, 128, 460, 461, 502, 460, 461, 502, 128, 460, 501, 502, 460, 501, 502, 128, 461, 462, 503, 461, 462, 503, 128, 461, 502, 503, 461, 502, 503, 128, 462, 463, 504, 462, 463, 504, 128, 462, 503, 504, 462, 503, 504, 128, 463, 464, 505, 463, 464, 505, 128, 463, 504, 505, 463, 504, 505, 128, 464, 465, 506, 464, 465, 506, 128, 464, 505, 506, 464, 505, 506, 128, 465, 466, 507, 465, 466, 507, 128, 465, 506, 507, 465, 506, 507, 128, 466, 467, 508, 466, 467, 508, 128, 466, 507, 508, 466, 507, 508, 128, 467, 468, 509, 467, 468, 509, 128, 467, 508, 509, 467, 508, 509, 128, 468, 469, 510, 468, 469, 510, 128, 468, 509, 510, 468, 509, 510, 128, 469, 470, 511, 469, 470, 511, 128, 469, 510, 511, 469, 510, 511, 128, 470, 471, 512, 470, 471, 512, 128, 470, 511, 512, 470, 511, 512, 128, 471, 472, 513, 471, 472, 513, 128, 471, 512, 513, 471, 512, 513, 128, 472, 473, 514, 472, 473, 514, 128, 472, 513, 514, 472, 513, 514, 128, 473, 474, 515, 473, 474, 515, 128, 473, 514, 515, 473, 514, 515, 128, 474, 475, 516, 474, 475, 516, 128, 474, 515, 516, 474, 515, 516, 128, 475, 476, 517, 475, 476, 517, 128, 475, 516, 517, 475, 516, 517, 128, 476, 477, 518, 476, 477, 518, 128, 476, 517, 518, 476, 517, 518, 128, 477, 478, 519, 477, 478, 519, 128, 477, 518, 519, 477, 518, 519, 128, 478, 479, 520, 478, 479, 520, 128, 478, 519, 520, 478, 519, 520, 128, 479, 480, 521, 479, 480, 521, 128, 479, 520, 521, 479, 520, 521, 128, 480, 481, 522, 480, 481, 522, 128, 480, 521, 522, 480, 521, 522, 128, 481, 482, 523, 481, 482, 523, 128, 481, 522, 523, 481, 522, 523, 128, 482, 483, 524, 482, 483, 524, 128, 482, 523, 524, 482, 523, 524, 128, 483, 484, 525, 483, 484, 525, 128, 483, 524, 525, 483, 524, 525, 128, 484, 485, 526, 484, 485, 526, 128, 484, 525, 526, 484, 525, 526, 128, 485, 486, 527, 485, 486, 527, 128, 485, 526, 527, 485, 526, 527, 128, 486, 487, 528, 486, 487, 528, 128, 486, 527, 528, 486, 527, 528, 128, 487, 488, 529, 487, 488, 529, 128, 487, 528, 529, 487, 528, 529, 128, 488, 489, 530, 488, 489, 530, 128, 488, 529, 530, 488, 529, 530, 128, 489, 490, 531, 489, 490, 531, 128, 489, 530, 531, 489, 530, 531, 128, 490, 491, 532, 490, 491, 532, 128, 490, 531, 532, 490, 531, 532, 128, 492, 493, 534, 492, 493, 534, 128, 492, 533, 534, 492, 533, 534, 128, 493, 494, 535, 493, 494, 535, 128, 493, 534, 535, 493, 534, 535, 128, 494, 495, 536, 494, 495, 536, 128, 494, 535, 536, 494, 535, 536, 128, 495, 496, 537, 495, 496, 537, 128, 495, 536, 537, 495, 536, 537, 128, 496, 497, 538, 496, 497, 538, 128, 496, 537, 538, 496, 537, 538, 128, 497, 498, 539, 497, 498, 539, 128, 497, 538, 539, 497, 538, 539, 128, 498, 499, 540, 498, 499, 540, 128, 498, 539, 540, 498, 539, 540, 128, 499, 500, 541, 499, 500, 541, 128, 499, 540, 541, 499, 540, 541, 128, 500, 501, 542, 500, 501, 542, 128, 500, 541, 542, 500, 541, 542, 128, 501, 502, 543, 501, 502, 543, 128, 501, 542, 543, 501, 542, 543, 128, 502, 503, 544, 502, 503, 544, 128, 502, 543, 544, 502, 543, 544, 128, 503, 504, 545, 503, 504, 545, 128, 503, 544, 545, 503, 544, 545, 128, 504, 505, 546, 504, 505, 546, 128, 504, 545, 546, 504, 545, 546, 128, 505, 506, 547, 505, 506, 547, 128, 505, 546, 547, 505, 546, 547, 128, 506, 507, 548, 506, 507, 548, 128, 506, 547, 548, 506, 547, 548, 128, 507, 508, 549, 507, 508, 549, 128, 507, 548, 549, 507, 548, 549, 128, 508, 509, 550, 508, 509, 550, 128, 508, 549, 550, 508, 549, 550, 128, 509, 510, 551, 509, 510, 551, 128, 509, 550, 551, 509, 550, 551, 128, 510, 511, 552, 510, 511, 552, 128, 510, 551, 552, 510, 551, 552, 128, 511, 512, 553, 511, 512, 553, 128, 511, 552, 553, 511, 552, 553, 128, 512, 513, 554, 512, 513, 554, 128, 512, 553, 554, 512, 553, 554, 128, 513, 514, 555, 513, 514, 555, 128, 513, 554, 555, 513, 554, 555, 128, 514, 515, 556, 514, 515, 556, 128, 514, 555, 556, 514, 555, 556, 128, 515, 516, 557, 515, 516, 557, 128, 515, 556, 557, 515, 556, 557, 128, 516, 517, 558, 516, 517, 558, 128, 516, 557, 558, 516, 557, 558, 128, 517, 518, 559, 517, 518, 559, 128, 517, 558, 559, 517, 558, 559, 128, 518, 519, 560, 518, 519, 560, 128, 518, 559, 560, 518, 559, 560, 128, 519, 520, 561, 519, 520, 561, 128, 519, 560, 561, 519, 560, 561, 128, 520, 521, 562, 520, 521, 562, 128, 520, 561, 562, 520, 561, 562, 128, 521, 522, 563, 521, 522, 563, 128, 521, 562, 563, 521, 562, 563, 128, 522, 523, 564, 522, 523, 564, 128, 522, 563, 564, 522, 563, 564, 128, 523, 524, 565, 523, 524, 565, 128, 523, 564, 565, 523, 564, 565, 128, 524, 525, 566, 524, 525, 566, 128, 524, 565, 566, 524, 565, 566, 128, 525, 526, 567, 525, 526, 567, 128, 525, 566, 567, 525, 566, 567, 128, 526, 527, 568, 526, 527, 568, 128, 526, 567, 568, 526, 567, 568, 128, 527, 528, 569, 527, 528, 569, 128, 527, 568, 569, 527, 568, 569, 128, 528, 529, 570, 528, 529, 570, 128, 528, 569, 570, 528, 569, 570, 128, 529, 530, 571, 529, 530, 571, 128, 529, 570, 571, 529, 570, 571, 128, 530, 531, 572, 530, 531, 572, 128, 530, 571, 572, 530, 571, 572, 128, 531, 532, 573, 531, 532, 573, 128, 531, 572, 573, 531, 572, 573, 128, 533, 534, 575, 533, 534, 575, 128, 533, 574, 575, 533, 574, 575, 128, 534, 535, 576, 534, 535, 576, 128, 534, 575, 576, 534, 575, 576, 128, 535, 536, 577, 535, 536, 577, 128, 535, 576, 577, 535, 576, 577, 128, 536, 537, 578, 536, 537, 578, 128, 536, 577, 578, 536, 577, 578, 128, 537, 538, 579, 537, 538, 579, 128, 537, 578, 579, 537, 578, 579, 128, 538, 539, 580, 538, 539, 580, 128, 538, 579, 580, 538, 579, 580, 128, 539, 540, 581, 539, 540, 581, 128, 539, 580, 581, 539, 580, 581, 128, 540, 541, 582, 540, 541, 582, 128, 540, 581, 582, 540, 581, 582, 128, 541, 542, 583, 541, 542, 583, 128, 541, 582, 583, 541, 582, 583, 128, 542, 543, 584, 542, 543, 584, 128, 542, 583, 584, 542, 583, 584, 128, 543, 544, 585, 543, 544, 585, 128, 543, 584, 585, 543, 584, 585, 128, 544, 545, 586, 544, 545, 586, 128, 544, 585, 586, 544, 585, 586, 128, 545, 546, 587, 545, 546, 587, 128, 545, 586, 587, 545, 586, 587, 128, 546, 547, 588, 546, 547, 588, 128, 546, 587, 588, 546, 587, 588, 128, 547, 548, 589, 547, 548, 589, 128, 547, 588, 589, 547, 588, 589, 128, 548, 549, 590, 548, 549, 590, 128, 548, 589, 590, 548, 589, 590, 128, 549, 550, 591, 549, 550, 591, 128, 549, 590, 591, 549, 590, 591, 128, 550, 551, 592, 550, 551, 592, 128, 550, 591, 592, 550, 591, 592, 128, 551, 552, 593, 551, 552, 593, 128, 551, 592, 593, 551, 592, 593, 128, 552, 553, 594, 552, 553, 594, 128, 552, 593, 594, 552, 593, 594, 128, 553, 554, 595, 553, 554, 595, 128, 553, 594, 595, 553, 594, 595, 128, 554, 555, 596, 554, 555, 596, 128, 554, 595, 596, 554, 595, 596, 128, 555, 556, 597, 555, 556, 597, 128, 555, 596, 597, 555, 596, 597, 128, 556, 557, 598, 556, 557, 598, 128, 556, 597, 598, 556, 597, 598, 128, 557, 558, 599, 557, 558, 599, 128, 557, 598, 599, 557, 598, 599, 128, 558, 559, 600, 558, 559, 600, 128, 558, 599, 600, 558, 599, 600, 128, 559, 560, 601, 559, 560, 601, 128, 559, 600, 601, 559, 600, 601, 128, 560, 561, 602, 560, 561, 602, 128, 560, 601, 602, 560, 601, 602, 128, 561, 562, 603, 561, 562, 603, 128, 561, 602, 603, 561, 602, 603, 128, 562, 563, 604, 562, 563, 604, 128, 562, 603, 604, 562, 603, 604, 128, 563, 564, 605, 563, 564, 605, 128, 563, 604, 605, 563, 604, 605, 128, 564, 565, 606, 564, 565, 606, 128, 564, 605, 606, 564, 605, 606, 128, 565, 566, 607, 565, 566, 607, 128, 565, 606, 607, 565, 606, 607, 128, 566, 567, 608, 566, 567, 608, 128, 566, 607, 608, 566, 607, 608, 128, 567, 568, 609, 567, 568, 609, 128, 567, 608, 609, 567, 608, 609, 128, 568, 569, 610, 568, 569, 610, 128, 568, 609, 610, 568, 609, 610, 128, 569, 570, 611, 569, 570, 611, 128, 569, 610, 611, 569, 610, 611, 128, 570, 571, 612, 570, 571, 612, 128, 570, 611, 612, 570, 611, 612, 128, 571, 572, 613, 571, 572, 613, 128, 571, 612, 613, 571, 612, 613, 128, 572, 573, 614, 572, 573, 614, 128, 572, 613, 614, 572, 613, 614, 128, 574, 575, 616, 574, 575, 616, 128, 574, 615, 616, 574, 615, 616, 128, 575, 576, 617, 575, 576, 617, 128, 575, 616, 617, 575, 616, 617, 128, 576, 577, 618, 576, 577, 618, 128, 576, 617, 618, 576, 617, 618, 128, 577, 578, 619, 577, 578, 619, 128, 577, 618, 619, 577, 618, 619, 128, 578, 579, 620, 578, 579, 620, 128, 578, 619, 620, 578, 619, 620, 128, 579, 580, 621, 579, 580, 621, 128, 579, 620, 621, 579, 620, 621, 128, 580, 581, 622, 580, 581, 622, 128, 580, 621, 622, 580, 621, 622, 128, 581, 582, 623, 581, 582, 623, 128, 581, 622, 623, 581, 622, 623, 128, 582, 583, 624, 582, 583, 624, 128, 582, 623, 624, 582, 623, 624, 128, 583, 584, 625, 583, 584, 625, 128, 583, 624, 625, 583, 624, 625, 128, 584, 585, 626, 584, 585, 626, 128, 584, 625, 626, 584, 625, 626, 128, 585, 586, 627, 585, 586, 627, 128, 585, 626, 627, 585, 626, 627, 128, 586, 587, 628, 586, 587, 628, 128, 586, 627, 628, 586, 627, 628, 128, 587, 588, 629, 587, 588, 629, 128, 587, 628, 629, 587, 628, 629, 128, 588, 589, 630, 588, 589, 630, 128, 588, 629, 630, 588, 629, 630, 128, 589, 590, 631, 589, 590, 631, 128, 589, 630, 631, 589, 630, 631, 128, 590, 591, 632, 590, 591, 632, 128, 590, 631, 632, 590, 631, 632, 128, 591, 592, 633, 591, 592, 633, 128, 591, 632, 633, 591, 632, 633, 128, 592, 593, 634, 592, 593, 634, 128, 592, 633, 634, 592, 633, 634, 128, 593, 594, 635, 593, 594, 635, 128, 593, 634, 635, 593, 634, 635, 128, 594, 595, 636, 594, 595, 636, 128, 594, 635, 636, 594, 635, 636, 128, 595, 596, 637, 595, 596, 637, 128, 595, 636, 637, 595, 636, 637, 128, 596, 597, 638, 596, 597, 638, 128, 596, 637, 638, 596, 637, 638, 128, 597, 598, 639, 597, 598, 639, 128, 597, 638, 639, 597, 638, 639, 128, 598, 599, 640, 598, 599, 640, 128, 598, 639, 640, 598, 639, 640, 128, 599, 600, 641, 599, 600, 641, 128, 599, 640, 641, 599, 640, 641, 128, 600, 601, 642, 600, 601, 642, 128, 600, 641, 642, 600, 641, 642, 128, 601, 602, 643, 601, 602, 643, 128, 601, 642, 643, 601, 642, 643, 128, 602, 603, 644, 602, 603, 644, 128, 602, 643, 644, 602, 643, 644, 128, 603, 604, 645, 603, 604, 645, 128, 603, 644, 645, 603, 644, 645, 128, 604, 605, 646, 604, 605, 646, 128, 604, 645, 646, 604, 645, 646, 128, 605, 606, 647, 605, 606, 647, 128, 605, 646, 647, 605, 646, 647, 128, 606, 607, 648, 606, 607, 648, 128, 606, 647, 648, 606, 647, 648, 128, 607, 608, 649, 607, 608, 649, 128, 607, 648, 649, 607, 648, 649, 128, 608, 609, 650, 608, 609, 650, 128, 608, 649, 650, 608, 649, 650, 128, 609, 610, 651, 609, 610, 651, 128, 609, 650, 651, 609, 650, 651, 128, 610, 611, 652, 610, 611, 652, 128, 610, 651, 652, 610, 651, 652, 128, 611, 612, 653, 611, 612, 653, 128, 611, 652, 653, 611, 652, 653, 128, 612, 613, 654, 612, 613, 654, 128, 612, 653, 654, 612, 653, 654, 128, 613, 614, 655, 613, 614, 655, 128, 613, 654, 655, 613, 654, 655, 128, 615, 616, 657, 615, 616, 657, 128, 615, 656, 657, 615, 656, 657, 128, 616, 617, 658, 616, 617, 658, 128, 616, 657, 658, 616, 657, 658, 128, 617, 618, 659, 617, 618, 659, 128, 617, 658, 659, 617, 658, 659, 128, 618, 619, 660, 618, 619, 660, 128, 618, 659, 660, 618, 659, 660, 128, 619, 620, 661, 619, 620, 661, 128, 619, 660, 661, 619, 660, 661, 128, 620, 621, 662, 620, 621, 662, 128, 620, 661, 662, 620, 661, 662, 128, 621, 622, 663, 621, 622, 663, 128, 621, 662, 663, 621, 662, 663, 128, 622, 623, 664, 622, 623, 664, 128, 622, 663, 664, 622, 663, 664, 128, 623, 624, 665, 623, 624, 665, 128, 623, 664, 665, 623, 664, 665, 128, 624, 625, 666, 624, 625, 666, 128, 624, 665, 666, 624, 665, 666, 128, 625, 626, 667, 625, 626, 667, 128, 625, 666, 667, 625, 666, 667, 128, 626, 627, 668, 626, 627, 668, 128, 626, 667, 668, 626, 667, 668, 128, 627, 628, 669, 627, 628, 669, 128, 627, 668, 669, 627, 668, 669, 128, 628, 629, 670, 628, 629, 670, 128, 628, 669, 670, 628, 669, 670, 128, 629, 630, 671, 629, 630, 671, 128, 629, 670, 671, 629, 670, 671, 128, 630, 631, 672, 630, 631, 672, 128, 630, 671, 672, 630, 671, 672, 128, 631, 632, 673, 631, 632, 673, 128, 631, 672, 673, 631, 672, 673, 128, 632, 633, 674, 632, 633, 674, 128, 632, 673, 674, 632, 673, 674, 128, 633, 634, 675, 633, 634, 675, 128, 633, 674, 675, 633, 674, 675, 128, 634, 635, 676, 634, 635, 676, 128, 634, 675, 676, 634, 675, 676, 128, 635, 636, 677, 635, 636, 677, 128, 635, 676, 677, 635, 676, 677, 128, 636, 637, 678, 636, 637, 678, 128, 636, 677, 678, 636, 677, 678, 128, 637, 638, 679, 637, 638, 679, 128, 637, 678, 679, 637, 678, 679, 128, 638, 639, 680, 638, 639, 680, 128, 638, 679, 680, 638, 679, 680, 128, 639, 640, 681, 639, 640, 681, 128, 639, 680, 681, 639, 680, 681, 128, 640, 641, 682, 640, 641, 682, 128, 640, 681, 682, 640, 681, 682, 128, 641, 642, 683, 641, 642, 683, 128, 641, 682, 683, 641, 682, 683, 128, 642, 643, 684, 642, 643, 684, 128, 642, 683, 684, 642, 683, 684, 128, 643, 644, 685, 643, 644, 685, 128, 643, 684, 685, 643, 684, 685, 128, 644, 645, 686, 644, 645, 686, 128, 644, 685, 686, 644, 685, 686, 128, 645, 646, 687, 645, 646, 687, 128, 645, 686, 687, 645, 686, 687, 128, 646, 647, 688, 646, 647, 688, 128, 646, 687, 688, 646, 687, 688, 128, 647, 648, 689, 647, 648, 689, 128, 647, 688, 689, 647, 688, 689, 128, 648, 649, 690, 648, 649, 690, 128, 648, 689, 690, 648, 689, 690, 128, 649, 650, 691, 649, 650, 691, 128, 649, 690, 691, 649, 690, 691, 128, 650, 651, 692, 650, 651, 692, 128, 650, 691, 692, 650, 691, 692, 128, 651, 652, 693, 651, 652, 693, 128, 651, 692, 693, 651, 692, 693, 128, 652, 653, 694, 652, 653, 694, 128, 652, 693, 694, 652, 693, 694, 128, 653, 654, 695, 653, 654, 695, 128, 653, 694, 695, 653, 694, 695, 128, 654, 655, 696, 654, 655, 696, 128, 654, 695, 696, 654, 695, 696, 128, 656, 657, 698, 656, 657, 698, 128, 656, 697, 698, 656, 697, 698, 128, 657, 658, 699, 657, 658, 699, 128, 657, 698, 699, 657, 698, 699, 128, 658, 659, 700, 658, 659, 700, 128, 658, 699, 700, 658, 699, 700, 128, 659, 660, 701, 659, 660, 701, 128, 659, 700, 701, 659, 700, 701, 128, 660, 661, 702, 660, 661, 702, 128, 660, 701, 702, 660, 701, 702, 128, 661, 662, 703, 661, 662, 703, 128, 661, 702, 703, 661, 702, 703, 128, 662, 663, 704, 662, 663, 704, 128, 662, 703, 704, 662, 703, 704, 128, 663, 664, 705, 663, 664, 705, 128, 663, 704, 705, 663, 704, 705, 128, 664, 665, 706, 664, 665, 706, 128, 664, 705, 706, 664, 705, 706, 128, 665, 666, 707, 665, 666, 707, 128, 665, 706, 707, 665, 706, 707, 128, 666, 667, 708, 666, 667, 708, 128, 666, 707, 708, 666, 707, 708, 128, 667, 668, 709, 667, 668, 709, 128, 667, 708, 709, 667, 708, 709, 128, 668, 669, 710, 668, 669, 710, 128, 668, 709, 710, 668, 709, 710, 128, 669, 670, 711, 669, 670, 711, 128, 669, 710, 711, 669, 710, 711, 128, 670, 671, 712, 670, 671, 712, 128, 670, 711, 712, 670, 711, 712, 128, 671, 672, 713, 671, 672, 713, 128, 671, 712, 713, 671, 712, 713, 128, 672, 673, 714, 672, 673, 714, 128, 672, 713, 714, 672, 713, 714, 128, 673, 674, 715, 673, 674, 715, 128, 673, 714, 715, 673, 714, 715, 128, 674, 675, 716, 674, 675, 716, 128, 674, 715, 716, 674, 715, 716, 128, 675, 676, 717, 675, 676, 717, 128, 675, 716, 717, 675, 716, 717, 128, 676, 677, 718, 676, 677, 718, 128, 676, 717, 718, 676, 717, 718, 128, 677, 678, 719, 677, 678, 719, 128, 677, 718, 719, 677, 718, 719, 128, 678, 679, 720, 678, 679, 720, 128, 678, 719, 720, 678, 719, 720, 128, 679, 680, 721, 679, 680, 721, 128, 679, 720, 721, 679, 720, 721, 128, 680, 681, 722, 680, 681, 722, 128, 680, 721, 722, 680, 721, 722, 128, 681, 682, 723, 681, 682, 723, 128, 681, 722, 723, 681, 722, 723, 128, 682, 683, 724, 682, 683, 724, 128, 682, 723, 724, 682, 723, 724, 128, 683, 684, 725, 683, 684, 725, 128, 683, 724, 725, 683, 724, 725, 128, 684, 685, 726, 684, 685, 726, 128, 684, 725, 726, 684, 725, 726, 128, 685, 686, 727, 685, 686, 727, 128, 685, 726, 727, 685, 726, 727, 128, 686, 687, 728, 686, 687, 728, 128, 686, 727, 728, 686, 727, 728, 128, 687, 688, 729, 687, 688, 729, 128, 687, 728, 729, 687, 728, 729, 128, 688, 689, 730, 688, 689, 730, 128, 688, 729, 730, 688, 729, 730, 128, 689, 690, 731, 689, 690, 731, 128, 689, 730, 731, 689, 730, 731, 128, 690, 691, 732, 690, 691, 732, 128, 690, 731, 732, 690, 731, 732, 128, 691, 692, 733, 691, 692, 733, 128, 691, 732, 733, 691, 732, 733, 128, 692, 693, 734, 692, 693, 734, 128, 692, 733, 734, 692, 733, 734, 128, 693, 694, 735, 693, 694, 735, 128, 693, 734, 735, 693, 734, 735, 128, 694, 695, 736, 694, 695, 736, 128, 694, 735, 736, 694, 735, 736, 128, 695, 696, 737, 695, 696, 737, 128, 695, 736, 737, 695, 736, 737, 128, 697, 698, 739, 697, 698, 739, 128, 697, 738, 739, 697, 738, 739, 128, 698, 699, 740, 698, 699, 740, 128, 698, 739, 740, 698, 739, 740, 128, 699, 700, 741, 699, 700, 741, 128, 699, 740, 741, 699, 740, 741, 128, 700, 701, 742, 700, 701, 742, 128, 700, 741, 742, 700, 741, 742, 128, 701, 702, 743, 701, 702, 743, 128, 701, 742, 743, 701, 742, 743, 128, 702, 703, 744, 702, 703, 744, 128, 702, 743, 744, 702, 743, 744, 128, 703, 704, 745, 703, 704, 745, 128, 703, 744, 745, 703, 744, 745, 128, 704, 705, 746, 704, 705, 746, 128, 704, 745, 746, 704, 745, 746, 128, 705, 706, 747, 705, 706, 747, 128, 705, 746, 747, 705, 746, 747, 128, 706, 707, 748, 706, 707, 748, 128, 706, 747, 748, 706, 747, 748, 128, 707, 708, 749, 707, 708, 749, 128, 707, 748, 749, 707, 748, 749, 128, 708, 709, 750, 708, 709, 750, 128, 708, 749, 750, 708, 749, 750, 128, 709, 710, 751, 709, 710, 751, 128, 709, 750, 751, 709, 750, 751, 128, 710, 711, 752, 710, 711, 752, 128, 710, 751, 752, 710, 751, 752, 128, 711, 712, 753, 711, 712, 753, 128, 711, 752, 753, 711, 752, 753, 128, 712, 713, 754, 712, 713, 754, 128, 712, 753, 754, 712, 753, 754, 128, 713, 714, 755, 713, 714, 755, 128, 713, 754, 755, 713, 754, 755, 128, 714, 715, 756, 714, 715, 756, 128, 714, 755, 756, 714, 755, 756, 128, 715, 716, 757, 715, 716, 757, 128, 715, 756, 757, 715, 756, 757, 128, 716, 717, 758, 716, 717, 758, 128, 716, 757, 758, 716, 757, 758, 128, 717, 718, 759, 717, 718, 759, 128, 717, 758, 759, 717, 758, 759, 128, 718, 719, 760, 718, 719, 760, 128, 718, 759, 760, 718, 759, 760, 128, 719, 720, 761, 719, 720, 761, 128, 719, 760, 761, 719, 760, 761, 128, 720, 721, 762, 720, 721, 762, 128, 720, 761, 762, 720, 761, 762, 128, 721, 722, 763, 721, 722, 763, 128, 721, 762, 763, 721, 762, 763, 128, 722, 723, 764, 722, 723, 764, 128, 722, 763, 764, 722, 763, 764, 128, 723, 724, 765, 723, 724, 765, 128, 723, 764, 765, 723, 764, 765, 128, 724, 725, 766, 724, 725, 766, 128, 724, 765, 766, 724, 765, 766, 128, 725, 726, 767, 725, 726, 767, 128, 725, 766, 767, 725, 766, 767, 128, 726, 727, 768, 726, 727, 768, 128, 726, 767, 768, 726, 767, 768, 128, 727, 728, 769, 727, 728, 769, 128, 727, 768, 769, 727, 768, 769, 128, 728, 729, 770, 728, 729, 770, 128, 728, 769, 770, 728, 769, 770, 128, 729, 730, 771, 729, 730, 771, 128, 729, 770, 771, 729, 770, 771, 128, 730, 731, 772, 730, 731, 772, 128, 730, 771, 772, 730, 771, 772, 128, 731, 732, 773, 731, 732, 773, 128, 731, 772, 773, 731, 772, 773, 128, 732, 733, 774, 732, 733, 774, 128, 732, 773, 774, 732, 773, 774, 128, 733, 734, 775, 733, 734, 775, 128, 733, 774, 775, 733, 774, 775, 128, 734, 735, 776, 734, 735, 776, 128, 734, 775, 776, 734, 775, 776, 128, 735, 736, 777, 735, 736, 777, 128, 735, 776, 777, 735, 776, 777, 128, 736, 737, 778, 736, 737, 778, 128, 736, 777, 778, 736, 777, 778, 128, 738, 739, 780, 738, 739, 780, 128, 738, 779, 780, 738, 779, 780, 128, 739, 740, 781, 739, 740, 781, 128, 739, 780, 781, 739, 780, 781, 128, 740, 741, 782, 740, 741, 782, 128, 740, 781, 782, 740, 781, 782, 128, 741, 742, 783, 741, 742, 783, 128, 741, 782, 783, 741, 782, 783, 128, 742, 743, 784, 742, 743, 784, 128, 742, 783, 784, 742, 783, 784, 128, 743, 744, 785, 743, 744, 785, 128, 743, 784, 785, 743, 784, 785, 128, 744, 745, 786, 744, 745, 786, 128, 744, 785, 786, 744, 785, 786, 128, 745, 746, 787, 745, 746, 787, 128, 745, 786, 787, 745, 786, 787, 128, 746, 747, 788, 746, 747, 788, 128, 746, 787, 788, 746, 787, 788, 128, 747, 748, 789, 747, 748, 789, 128, 747, 788, 789, 747, 788, 789, 128, 748, 749, 790, 748, 749, 790, 128, 748, 789, 790, 748, 789, 790, 128, 749, 750, 791, 749, 750, 791, 128, 749, 790, 791, 749, 790, 791, 128, 750, 751, 792, 750, 751, 792, 128, 750, 791, 792, 750, 791, 792, 128, 751, 752, 793, 751, 752, 793, 128, 751, 792, 793, 751, 792, 793, 128, 752, 753, 794, 752, 753, 794, 128, 752, 793, 794, 752, 793, 794, 128, 753, 754, 795, 753, 754, 795, 128, 753, 794, 795, 753, 794, 795, 128, 754, 755, 796, 754, 755, 796, 128, 754, 795, 796, 754, 795, 796, 128, 755, 756, 797, 755, 756, 797, 128, 755, 796, 797, 755, 796, 797, 128, 756, 757, 798, 756, 757, 798, 128, 756, 797, 798, 756, 797, 798, 128, 757, 758, 799, 757, 758, 799, 128, 757, 798, 799, 757, 798, 799, 128, 758, 759, 800, 758, 759, 800, 128, 758, 799, 800, 758, 799, 800, 128, 759, 760, 801, 759, 760, 801, 128, 759, 800, 801, 759, 800, 801, 128, 760, 761, 802, 760, 761, 802, 128, 760, 801, 802, 760, 801, 802, 128, 761, 762, 803, 761, 762, 803, 128, 761, 802, 803, 761, 802, 803, 128, 762, 763, 804, 762, 763, 804, 128, 762, 803, 804, 762, 803, 804, 128, 763, 764, 805, 763, 764, 805, 128, 763, 804, 805, 763, 804, 805, 128, 764, 765, 806, 764, 765, 806, 128, 764, 805, 806, 764, 805, 806, 128, 765, 766, 807, 765, 766, 807, 128, 765, 806, 807, 765, 806, 807, 128, 766, 767, 808, 766, 767, 808, 128, 766, 807, 808, 766, 807, 808, 128, 767, 768, 809, 767, 768, 809, 128, 767, 808, 809, 767, 808, 809, 128, 768, 769, 810, 768, 769, 810, 128, 768, 809, 810, 768, 809, 810, 128, 769, 770, 811, 769, 770, 811, 128, 769, 810, 811, 769, 810, 811, 128, 770, 771, 812, 770, 771, 812, 128, 770, 811, 812, 770, 811, 812, 128, 771, 772, 813, 771, 772, 813, 128, 771, 812, 813, 771, 812, 813, 128, 772, 773, 814, 772, 773, 814, 128, 772, 813, 814, 772, 813, 814, 128, 773, 774, 815, 773, 774, 815, 128, 773, 814, 815, 773, 814, 815, 128, 774, 775, 816, 774, 775, 816, 128, 774, 815, 816, 774, 815, 816, 128, 775, 776, 817, 775, 776, 817, 128, 775, 816, 817, 775, 816, 817, 128, 776, 777, 818, 776, 777, 818, 128, 776, 817, 818, 776, 817, 818, 128, 777, 778, 819, 777, 778, 819, 128, 777, 818, 819, 777, 818, 819, 128, 779, 780, 821, 779, 780, 821, 128, 779, 820, 821, 779, 820, 821, 128, 780, 781, 822, 780, 781, 822, 128, 780, 821, 822, 780, 821, 822, 128, 781, 782, 823, 781, 782, 823, 128, 781, 822, 823, 781, 822, 823, 128, 782, 783, 824, 782, 783, 824, 128, 782, 823, 824, 782, 823, 824, 128, 783, 784, 825, 783, 784, 825, 128, 783, 824, 825, 783, 824, 825, 128, 784, 785, 826, 784, 785, 826, 128, 784, 825, 826, 784, 825, 826, 128, 785, 786, 827, 785, 786, 827, 128, 785, 826, 827, 785, 826, 827, 128, 786, 787, 828, 786, 787, 828, 128, 786, 827, 828, 786, 827, 828, 128, 787, 788, 829, 787, 788, 829, 128, 787, 828, 829, 787, 828, 829, 128, 788, 789, 830, 788, 789, 830, 128, 788, 829, 830, 788, 829, 830, 128, 789, 790, 831, 789, 790, 831, 128, 789, 830, 831, 789, 830, 831, 128, 790, 791, 832, 790, 791, 832, 128, 790, 831, 832, 790, 831, 832, 128, 791, 792, 833, 791, 792, 833, 128, 791, 832, 833, 791, 832, 833, 128, 792, 793, 834, 792, 793, 834, 128, 792, 833, 834, 792, 833, 834, 128, 793, 794, 835, 793, 794, 835, 128, 793, 834, 835, 793, 834, 835, 128, 794, 795, 836, 794, 795, 836, 128, 794, 835, 836, 794, 835, 836, 128, 795, 796, 837, 795, 796, 837, 128, 795, 836, 837, 795, 836, 837, 128, 796, 797, 838, 796, 797, 838, 128, 796, 837, 838, 796, 837, 838, 128, 797, 798, 839, 797, 798, 839, 128, 797, 838, 839, 797, 838, 839, 128, 798, 799, 840, 798, 799, 840, 128, 798, 839, 840, 798, 839, 840, 128, 799, 800, 841, 799, 800, 841, 128, 799, 840, 841, 799, 840, 841, 128, 800, 801, 842, 800, 801, 842, 128, 800, 841, 842, 800, 841, 842, 128, 801, 802, 843, 801, 802, 843, 128, 801, 842, 843, 801, 842, 843, 128, 802, 803, 844, 802, 803, 844, 128, 802, 843, 844, 802, 843, 844, 128, 803, 804, 845, 803, 804, 845, 128, 803, 844, 845, 803, 844, 845, 128, 804, 805, 846, 804, 805, 846, 128, 804, 845, 846, 804, 845, 846, 128, 805, 806, 847, 805, 806, 847, 128, 805, 846, 847, 805, 846, 847, 128, 806, 807, 848, 806, 807, 848, 128, 806, 847, 848, 806, 847, 848, 128, 807, 808, 849, 807, 808, 849, 128, 807, 848, 849, 807, 848, 849, 128, 808, 809, 850, 808, 809, 850, 128, 808, 849, 850, 808, 849, 850, 128, 809, 810, 851, 809, 810, 851, 128, 809, 850, 851, 809, 850, 851, 128, 810, 811, 852, 810, 811, 852, 128, 810, 851, 852, 810, 851, 852, 128, 811, 812, 853, 811, 812, 853, 128, 811, 852, 853, 811, 852, 853, 128, 812, 813, 854, 812, 813, 854, 128, 812, 853, 854, 812, 853, 854, 128, 813, 814, 855, 813, 814, 855, 128, 813, 854, 855, 813, 854, 855, 128, 814, 815, 856, 814, 815, 856, 128, 814, 855, 856, 814, 855, 856, 128, 815, 816, 857, 815, 816, 857, 128, 815, 856, 857, 815, 856, 857, 128, 816, 817, 858, 816, 817, 858, 128, 816, 857, 858, 816, 857, 858, 128, 817, 818, 859, 817, 818, 859, 128, 817, 858, 859, 817, 858, 859, 128, 818, 819, 860, 818, 819, 860, 128, 818, 859, 860, 818, 859, 860, 128, 820, 821, 862, 820, 821, 862, 128, 820, 861, 862, 820, 861, 862, 128, 821, 822, 863, 821, 822, 863, 128, 821, 862, 863, 821, 862, 863, 128, 822, 823, 864, 822, 823, 864, 128, 822, 863, 864, 822, 863, 864, 128, 823, 824, 865, 823, 824, 865, 128, 823, 864, 865, 823, 864, 865, 128, 824, 825, 866, 824, 825, 866, 128, 824, 865, 866, 824, 865, 866, 128, 825, 826, 867, 825, 826, 867, 128, 825, 866, 867, 825, 866, 867, 128, 826, 827, 868, 826, 827, 868, 128, 826, 867, 868, 826, 867, 868, 128, 827, 828, 869, 827, 828, 869, 128, 827, 868, 869, 827, 868, 869, 128, 828, 829, 870, 828, 829, 870, 128, 828, 869, 870, 828, 869, 870, 128, 829, 830, 871, 829, 830, 871, 128, 829, 870, 871, 829, 870, 871, 128, 830, 831, 872, 830, 831, 872, 128, 830, 871, 872, 830, 871, 872, 128, 831, 832, 873, 831, 832, 873, 128, 831, 872, 873, 831, 872, 873, 128, 832, 833, 874, 832, 833, 874, 128, 832, 873, 874, 832, 873, 874, 128, 833, 834, 875, 833, 834, 875, 128, 833, 874, 875, 833, 874, 875, 128, 834, 835, 876, 834, 835, 876, 128, 834, 875, 876, 834, 875, 876, 128, 835, 836, 877, 835, 836, 877, 128, 835, 876, 877, 835, 876, 877, 128, 836, 837, 878, 836, 837, 878, 128, 836, 877, 878, 836, 877, 878, 128, 837, 838, 879, 837, 838, 879, 128, 837, 878, 879, 837, 878, 879, 128, 838, 839, 880, 838, 839, 880, 128, 838, 879, 880, 838, 879, 880, 128, 839, 840, 881, 839, 840, 881, 128, 839, 880, 881, 839, 880, 881, 128, 840, 841, 882, 840, 841, 882, 128, 840, 881, 882, 840, 881, 882, 128, 841, 842, 883, 841, 842, 883, 128, 841, 882, 883, 841, 882, 883, 128, 842, 843, 884, 842, 843, 884, 128, 842, 883, 884, 842, 883, 884, 128, 843, 844, 885, 843, 844, 885, 128, 843, 884, 885, 843, 884, 885, 128, 844, 845, 886, 844, 845, 886, 128, 844, 885, 886, 844, 885, 886, 128, 845, 846, 887, 845, 846, 887, 128, 845, 886, 887, 845, 886, 887, 128, 846, 847, 888, 846, 847, 888, 128, 846, 887, 888, 846, 887, 888, 128, 847, 848, 889, 847, 848, 889, 128, 847, 888, 889, 847, 888, 889, 128, 848, 849, 890, 848, 849, 890, 128, 848, 889, 890, 848, 889, 890, 128, 849, 850, 891, 849, 850, 891, 128, 849, 890, 891, 849, 890, 891, 128, 850, 851, 892, 850, 851, 892, 128, 850, 891, 892, 850, 891, 892, 128, 851, 852, 893, 851, 852, 893, 128, 851, 892, 893, 851, 892, 893, 128, 852, 853, 894, 852, 853, 894, 128, 852, 893, 894, 852, 893, 894, 128, 853, 854, 895, 853, 854, 895, 128, 853, 894, 895, 853, 894, 895, 128, 854, 855, 896, 854, 855, 896, 128, 854, 895, 896, 854, 895, 896, 128, 855, 856, 897, 855, 856, 897, 128, 855, 896, 897, 855, 896, 897, 128, 856, 857, 898, 856, 857, 898, 128, 856, 897, 898, 856, 897, 898, 128, 857, 858, 899, 857, 858, 899, 128, 857, 898, 899, 857, 898, 899, 128, 858, 859, 900, 858, 859, 900, 128, 858, 899, 900, 858, 899, 900, 128, 859, 860, 901, 859, 860, 901, 128, 859, 900, 901, 859, 900, 901, 128, 861, 862, 903, 861, 862, 903, 128, 861, 902, 903, 861, 902, 903, 128, 862, 863, 904, 862, 863, 904, 128, 862, 903, 904, 862, 903, 904, 128, 863, 864, 905, 863, 864, 905, 128, 863, 904, 905, 863, 904, 905, 128, 864, 865, 906, 864, 865, 906, 128, 864, 905, 906, 864, 905, 906, 128, 865, 866, 907, 865, 866, 907, 128, 865, 906, 907, 865, 906, 907, 128, 866, 867, 908, 866, 867, 908, 128, 866, 907, 908, 866, 907, 908, 128, 867, 868, 909, 867, 868, 909, 128, 867, 908, 909, 867, 908, 909, 128, 868, 869, 910, 868, 869, 910, 128, 868, 909, 910, 868, 909, 910, 128, 869, 870, 911, 869, 870, 911, 128, 869, 910, 911, 869, 910, 911, 128, 870, 871, 912, 870, 871, 912, 128, 870, 911, 912, 870, 911, 912, 128, 871, 872, 913, 871, 872, 913, 128, 871, 912, 913, 871, 912, 913, 128, 872, 873, 914, 872, 873, 914, 128, 872, 913, 914, 872, 913, 914, 128, 873, 874, 915, 873, 874, 915, 128, 873, 914, 915, 873, 914, 915, 128, 874, 875, 916, 874, 875, 916, 128, 874, 915, 916, 874, 915, 916, 128, 875, 876, 917, 875, 876, 917, 128, 875, 916, 917, 875, 916, 917, 128, 876, 877, 918, 876, 877, 918, 128, 876, 917, 918, 876, 917, 918, 128, 877, 878, 919, 877, 878, 919, 128, 877, 918, 919, 877, 918, 919, 128, 878, 879, 920, 878, 879, 920, 128, 878, 919, 920, 878, 919, 920, 128, 879, 880, 921, 879, 880, 921, 128, 879, 920, 921, 879, 920, 921, 128, 880, 881, 922, 880, 881, 922, 128, 880, 921, 922, 880, 921, 922, 128, 881, 882, 923, 881, 882, 923, 128, 881, 922, 923, 881, 922, 923, 128, 882, 883, 924, 882, 883, 924, 128, 882, 923, 924, 882, 923, 924, 128, 883, 884, 925, 883, 884, 925, 128, 883, 924, 925, 883, 924, 925, 128, 884, 885, 926, 884, 885, 926, 128, 884, 925, 926, 884, 925, 926, 128, 885, 886, 927, 885, 886, 927, 128, 885, 926, 927, 885, 926, 927, 128, 886, 887, 928, 886, 887, 928, 128, 886, 927, 928, 886, 927, 928, 128, 887, 888, 929, 887, 888, 929, 128, 887, 928, 929, 887, 928, 929, 128, 888, 889, 930, 888, 889, 930, 128, 888, 929, 930, 888, 929, 930, 128, 889, 890, 931, 889, 890, 931, 128, 889, 930, 931, 889, 930, 931, 128, 890, 891, 932, 890, 891, 932, 128, 890, 931, 932, 890, 931, 932, 128, 891, 892, 933, 891, 892, 933, 128, 891, 932, 933, 891, 932, 933, 128, 892, 893, 934, 892, 893, 934, 128, 892, 933, 934, 892, 933, 934, 128, 893, 894, 935, 893, 894, 935, 128, 893, 934, 935, 893, 934, 935, 128, 894, 895, 936, 894, 895, 936, 128, 894, 935, 936, 894, 935, 936, 128, 895, 896, 937, 895, 896, 937, 128, 895, 936, 937, 895, 936, 937, 128, 896, 897, 938, 896, 897, 938, 128, 896, 937, 938, 896, 937, 938, 128, 897, 898, 939, 897, 898, 939, 128, 897, 938, 939, 897, 938, 939, 128, 898, 899, 940, 898, 899, 940, 128, 898, 939, 940, 898, 939, 940, 128, 899, 900, 941, 899, 900, 941, 128, 899, 940, 941, 899, 940, 941, 128, 900, 901, 942, 900, 901, 942, 128, 900, 941, 942, 900, 941, 942, 128, 902, 903, 944, 902, 903, 944, 128, 902, 943, 944, 902, 943, 944, 128, 903, 904, 945, 903, 904, 945, 128, 903, 944, 945, 903, 944, 945, 128, 904, 905, 946, 904, 905, 946, 128, 904, 945, 946, 904, 945, 946, 128, 905, 906, 947, 905, 906, 947, 128, 905, 946, 947, 905, 946, 947, 128, 906, 907, 948, 906, 907, 948, 128, 906, 947, 948, 906, 947, 948, 128, 907, 908, 949, 907, 908, 949, 128, 907, 948, 949, 907, 948, 949, 128, 908, 909, 950, 908, 909, 950, 128, 908, 949, 950, 908, 949, 950, 128, 909, 910, 951, 909, 910, 951, 128, 909, 950, 951, 909, 950, 951, 128, 910, 911, 952, 910, 911, 952, 128, 910, 951, 952, 910, 951, 952, 128, 911, 912, 953, 911, 912, 953, 128, 911, 952, 953, 911, 952, 953, 128, 912, 913, 954, 912, 913, 954, 128, 912, 953, 954, 912, 953, 954, 128, 913, 914, 955, 913, 914, 955, 128, 913, 954, 955, 913, 954, 955, 128, 914, 915, 956, 914, 915, 956, 128, 914, 955, 956, 914, 955, 956, 128, 915, 916, 957, 915, 916, 957, 128, 915, 956, 957, 915, 956, 957, 128, 916, 917, 958, 916, 917, 958, 128, 916, 957, 958, 916, 957, 958, 128, 917, 918, 959, 917, 918, 959, 128, 917, 958, 959, 917, 958, 959, 128, 918, 919, 960, 918, 919, 960, 128, 918, 959, 960, 918, 959, 960, 128, 919, 920, 961, 919, 920, 961, 128, 919, 960, 961, 919, 960, 961, 128, 920, 921, 962, 920, 921, 962, 128, 920, 961, 962, 920, 961, 962, 128, 921, 922, 963, 921, 922, 963, 128, 921, 962, 963, 921, 962, 963, 128, 922, 923, 964, 922, 923, 964, 128, 922, 963, 964, 922, 963, 964, 128, 923, 924, 965, 923, 924, 965, 128, 923, 964, 965, 923, 964, 965, 128, 924, 925, 966, 924, 925, 966, 128, 924, 965, 966, 924, 965, 966, 128, 925, 926, 967, 925, 926, 967, 128, 925, 966, 967, 925, 966, 967, 128, 926, 927, 968, 926, 927, 968, 128, 926, 967, 968, 926, 967, 968, 128, 927, 928, 969, 927, 928, 969, 128, 927, 968, 969, 927, 968, 969, 128, 928, 929, 970, 928, 929, 970, 128, 928, 969, 970, 928, 969, 970, 128, 929, 930, 971, 929, 930, 971, 128, 929, 970, 971, 929, 970, 971, 128, 930, 931, 972, 930, 931, 972, 128, 930, 971, 972, 930, 971, 972, 128, 931, 932, 973, 931, 932, 973, 128, 931, 972, 973, 931, 972, 973, 128, 932, 933, 974, 932, 933, 974, 128, 932, 973, 974, 932, 973, 974, 128, 933, 934, 975, 933, 934, 975, 128, 933, 974, 975, 933, 974, 975, 128, 934, 935, 976, 934, 935, 976, 128, 934, 975, 976, 934, 975, 976, 128, 935, 936, 977, 935, 936, 977, 128, 935, 976, 977, 935, 976, 977, 128, 936, 937, 978, 936, 937, 978, 128, 936, 977, 978, 936, 977, 978, 128, 937, 938, 979, 937, 938, 979, 128, 937, 978, 979, 937, 978, 979, 128, 938, 939, 980, 938, 939, 980, 128, 938, 979, 980, 938, 979, 980, 128, 939, 940, 981, 939, 940, 981, 128, 939, 980, 981, 939, 980, 981, 128, 940, 941, 982, 940, 941, 982, 128, 940, 981, 982, 940, 981, 982, 128, 941, 942, 983, 941, 942, 983, 128, 941, 982, 983, 941, 982, 983, 128, 943, 944, 985, 943, 944, 985, 128, 943, 984, 985, 943, 984, 985, 128, 944, 945, 986, 944, 945, 986, 128, 944, 985, 986, 944, 985, 986, 128, 945, 946, 987, 945, 946, 987, 128, 945, 986, 987, 945, 986, 987, 128, 946, 947, 988, 946, 947, 988, 128, 946, 987, 988, 946, 987, 988, 128, 947, 948, 989, 947, 948, 989, 128, 947, 988, 989, 947, 988, 989, 128, 948, 949, 990, 948, 949, 990, 128, 948, 989, 990, 948, 989, 990, 128, 949, 950, 991, 949, 950, 991, 128, 949, 990, 991, 949, 990, 991, 128, 950, 951, 992, 950, 951, 992, 128, 950, 991, 992, 950, 991, 992, 128, 951, 952, 993, 951, 952, 993, 128, 951, 992, 993, 951, 992, 993, 128, 952, 953, 994, 952, 953, 994, 128, 952, 993, 994, 952, 993, 994, 128, 953, 954, 995, 953, 954, 995, 128, 953, 994, 995, 953, 994, 995, 128, 954, 955, 996, 954, 955, 996, 128, 954, 995, 996, 954, 995, 996, 128, 955, 956, 997, 955, 956, 997, 128, 955, 996, 997, 955, 996, 997, 128, 956, 957, 998, 956, 957, 998, 128, 956, 997, 998, 956, 997, 998, 128, 957, 958, 999, 957, 958, 999, 128, 957, 998, 999, 957, 998, 999, 128, 958, 959, 1000, 958, 959, 1000, 128, 958, 999, 1000, 958, 999, 1000, 128, 959, 960, 1001, 959, 960, 1001, 128, 959, 1000, 1001, 959, 1000, 1001, 128, 960, 961, 1002, 960, 961, 1002, 128, 960, 1001, 1002, 960, 1001, 1002, 128, 961, 962, 1003, 961, 962, 1003, 128, 961, 1002, 1003, 961, 1002, 1003, 128, 962, 963, 1004, 962, 963, 1004, 128, 962, 1003, 1004, 962, 1003, 1004, 128, 963, 964, 1005, 963, 964, 1005, 128, 963, 1004, 1005, 963, 1004, 1005, 128, 964, 965, 1006, 964, 965, 1006, 128, 964, 1005, 1006, 964, 1005, 1006, 128, 965, 966, 1007, 965, 966, 1007, 128, 965, 1006, 1007, 965, 1006, 1007, 128, 966, 967, 1008, 966, 967, 1008, 128, 966, 1007, 1008, 966, 1007, 1008, 128, 967, 968, 1009, 967, 968, 1009, 128, 967, 1008, 1009, 967, 1008, 1009, 128, 968, 969, 1010, 968, 969, 1010, 128, 968, 1009, 1010, 968, 1009, 1010, 128, 969, 970, 1011, 969, 970, 1011, 128, 969, 1010, 1011, 969, 1010, 1011, 128, 970, 971, 1012, 970, 971, 1012, 128, 970, 1011, 1012, 970, 1011, 1012, 128, 971, 972, 1013, 971, 972, 1013, 128, 971, 1012, 1013, 971, 1012, 1013, 128, 972, 973, 1014, 972, 973, 1014, 128, 972, 1013, 1014, 972, 1013, 1014, 128, 973, 974, 1015, 973, 974, 1015, 128, 973, 1014, 1015, 973, 1014, 1015, 128, 974, 975, 1016, 974, 975, 1016, 128, 974, 1015, 1016, 974, 1015, 1016, 128, 975, 976, 1017, 975, 976, 1017, 128, 975, 1016, 1017, 975, 1016, 1017, 128, 976, 977, 1018, 976, 977, 1018, 128, 976, 1017, 1018, 976, 1017, 1018, 128, 977, 978, 1019, 977, 978, 1019, 128, 977, 1018, 1019, 977, 1018, 1019, 128, 978, 979, 1020, 978, 979, 1020, 128, 978, 1019, 1020, 978, 1019, 1020, 128, 979, 980, 1021, 979, 980, 1021, 128, 979, 1020, 1021, 979, 1020, 1021, 128, 980, 981, 1022, 980, 981, 1022, 128, 980, 1021, 1022, 980, 1021, 1022, 128, 981, 982, 1023, 981, 982, 1023, 128, 981, 1022, 1023, 981, 1022, 1023, 128, 982, 983, 1024, 982, 983, 1024, 128, 982, 1023, 1024, 982, 1023, 1024, 128, 984, 985, 1026, 984, 985, 1026, 128, 984, 1025, 1026, 984, 1025, 1026, 128, 985, 986, 1027, 985, 986, 1027, 128, 985, 1026, 1027, 985, 1026, 1027, 128, 986, 987, 1028, 986, 987, 1028, 128, 986, 1027, 1028, 986, 1027, 1028, 128, 987, 988, 1029, 987, 988, 1029, 128, 987, 1028, 1029, 987, 1028, 1029, 128, 988, 989, 1030, 988, 989, 1030, 128, 988, 1029, 1030, 988, 1029, 1030, 128, 989, 990, 1031, 989, 990, 1031, 128, 989, 1030, 1031, 989, 1030, 1031, 128, 990, 991, 1032, 990, 991, 1032, 128, 990, 1031, 1032, 990, 1031, 1032, 128, 991, 992, 1033, 991, 992, 1033, 128, 991, 1032, 1033, 991, 1032, 1033, 128, 992, 993, 1034, 992, 993, 1034, 128, 992, 1033, 1034, 992, 1033, 1034, 128, 993, 994, 1035, 993, 994, 1035, 128, 993, 1034, 1035, 993, 1034, 1035, 128, 994, 995, 1036, 994, 995, 1036, 128, 994, 1035, 1036, 994, 1035, 1036, 128, 995, 996, 1037, 995, 996, 1037, 128, 995, 1036, 1037, 995, 1036, 1037, 128, 996, 997, 1038, 996, 997, 1038, 128, 996, 1037, 1038, 996, 1037, 1038, 128, 997, 998, 1039, 997, 998, 1039, 128, 997, 1038, 1039, 997, 1038, 1039, 128, 998, 999, 1040, 998, 999, 1040, 128, 998, 1039, 1040, 998, 1039, 1040, 128, 999, 1000, 1041, 999, 1000, 1041, 128, 999, 1040, 1041, 999, 1040, 1041, 128, 1000, 1001, 1042, 1000, 1001, 1042, 128, 1000, 1041, 1042, 1000, 1041, 1042, 128, 1001, 1002, 1043, 1001, 1002, 1043, 128, 1001, 1042, 1043, 1001, 1042, 1043, 128, 1002, 1003, 1044, 1002, 1003, 1044, 128, 1002, 1043, 1044, 1002, 1043, 1044, 128, 1003, 1004, 1045, 1003, 1004, 1045, 128, 1003, 1044, 1045, 1003, 1044, 1045, 128, 1004, 1005, 1046, 1004, 1005, 1046, 128, 1004, 1045, 1046, 1004, 1045, 1046, 128, 1005, 1006, 1047, 1005, 1006, 1047, 128, 1005, 1046, 1047, 1005, 1046, 1047, 128, 1006, 1007, 1048, 1006, 1007, 1048, 128, 1006, 1047, 1048, 1006, 1047, 1048, 128, 1007, 1008, 1049, 1007, 1008, 1049, 128, 1007, 1048, 1049, 1007, 1048, 1049, 128, 1008, 1009, 1050, 1008, 1009, 1050, 128, 1008, 1049, 1050, 1008, 1049, 1050, 128, 1009, 1010, 1051, 1009, 1010, 1051, 128, 1009, 1050, 1051, 1009, 1050, 1051, 128, 1010, 1011, 1052, 1010, 1011, 1052, 128, 1010, 1051, 1052, 1010, 1051, 1052, 128, 1011, 1012, 1053, 1011, 1012, 1053, 128, 1011, 1052, 1053, 1011, 1052, 1053, 128, 1012, 1013, 1054, 1012, 1013, 1054, 128, 1012, 1053, 1054, 1012, 1053, 1054, 128, 1013, 1014, 1055, 1013, 1014, 1055, 128, 1013, 1054, 1055, 1013, 1054, 1055, 128, 1014, 1015, 1056, 1014, 1015, 1056, 128, 1014, 1055, 1056, 1014, 1055, 1056, 128, 1015, 1016, 1057, 1015, 1016, 1057, 128, 1015, 1056, 1057, 1015, 1056, 1057, 128, 1016, 1017, 1058, 1016, 1017, 1058, 128, 1016, 1057, 1058, 1016, 1057, 1058, 128, 1017, 1018, 1059, 1017, 1018, 1059, 128, 1017, 1058, 1059, 1017, 1058, 1059, 128, 1018, 1019, 1060, 1018, 1019, 1060, 128, 1018, 1059, 1060, 1018, 1059, 1060, 128, 1019, 1020, 1061, 1019, 1020, 1061, 128, 1019, 1060, 1061, 1019, 1060, 1061, 128, 1020, 1021, 1062, 1020, 1021, 1062, 128, 1020, 1061, 1062, 1020, 1061, 1062, 128, 1021, 1022, 1063, 1021, 1022, 1063, 128, 1021, 1062, 1063, 1021, 1062, 1063, 128, 1022, 1023, 1064, 1022, 1023, 1064, 128, 1022, 1063, 1064, 1022, 1063, 1064, 128, 1023, 1024, 1065, 1023, 1024, 1065, 128, 1023, 1064, 1065, 1023, 1064, 1065, 128, 1025, 1026, 1067, 1025, 1026, 1067, 128, 1025, 1066, 1067, 1025, 1066, 1067, 128, 1026, 1027, 1068, 1026, 1027, 1068, 128, 1026, 1067, 1068, 1026, 1067, 1068, 128, 1027, 1028, 1069, 1027, 1028, 1069, 128, 1027, 1068, 1069, 1027, 1068, 1069, 128, 1028, 1029, 1070, 1028, 1029, 1070, 128, 1028, 1069, 1070, 1028, 1069, 1070, 128, 1029, 1030, 1071, 1029, 1030, 1071, 128, 1029, 1070, 1071, 1029, 1070, 1071, 128, 1030, 1031, 1072, 1030, 1031, 1072, 128, 1030, 1071, 1072, 1030, 1071, 1072, 128, 1031, 1032, 1073, 1031, 1032, 1073, 128, 1031, 1072, 1073, 1031, 1072, 1073, 128, 1032, 1033, 1074, 1032, 1033, 1074, 128, 1032, 1073, 1074, 1032, 1073, 1074, 128, 1033, 1034, 1075, 1033, 1034, 1075, 128, 1033, 1074, 1075, 1033, 1074, 1075, 128, 1034, 1035, 1076, 1034, 1035, 1076, 128, 1034, 1075, 1076, 1034, 1075, 1076, 128, 1035, 1036, 1077, 1035, 1036, 1077, 128, 1035, 1076, 1077, 1035, 1076, 1077, 128, 1036, 1037, 1078, 1036, 1037, 1078, 128, 1036, 1077, 1078, 1036, 1077, 1078, 128, 1037, 1038, 1079, 1037, 1038, 1079, 128, 1037, 1078, 1079, 1037, 1078, 1079, 128, 1038, 1039, 1080, 1038, 1039, 1080, 128, 1038, 1079, 1080, 1038, 1079, 1080, 128, 1039, 1040, 1081, 1039, 1040, 1081, 128, 1039, 1080, 1081, 1039, 1080, 1081, 128, 1040, 1041, 1082, 1040, 1041, 1082, 128, 1040, 1081, 1082, 1040, 1081, 1082, 128, 1041, 1042, 1083, 1041, 1042, 1083, 128, 1041, 1082, 1083, 1041, 1082, 1083, 128, 1042, 1043, 1084, 1042, 1043, 1084, 128, 1042, 1083, 1084, 1042, 1083, 1084, 128, 1043, 1044, 1085, 1043, 1044, 1085, 128, 1043, 1084, 1085, 1043, 1084, 1085, 128, 1044, 1045, 1086, 1044, 1045, 1086, 128, 1044, 1085, 1086, 1044, 1085, 1086, 128, 1045, 1046, 1087, 1045, 1046, 1087, 128, 1045, 1086, 1087, 1045, 1086, 1087, 128, 1046, 1047, 1088, 1046, 1047, 1088, 128, 1046, 1087, 1088, 1046, 1087, 1088, 128, 1047, 1048, 1089, 1047, 1048, 1089, 128, 1047, 1088, 1089, 1047, 1088, 1089, 128, 1048, 1049, 1090, 1048, 1049, 1090, 128, 1048, 1089, 1090, 1048, 1089, 1090, 128, 1049, 1050, 1091, 1049, 1050, 1091, 128, 1049, 1090, 1091, 1049, 1090, 1091, 128, 1050, 1051, 1092, 1050, 1051, 1092, 128, 1050, 1091, 1092, 1050, 1091, 1092, 128, 1051, 1052, 1093, 1051, 1052, 1093, 128, 1051, 1092, 1093, 1051, 1092, 1093, 128, 1052, 1053, 1094, 1052, 1053, 1094, 128, 1052, 1093, 1094, 1052, 1093, 1094, 128, 1053, 1054, 1095, 1053, 1054, 1095, 128, 1053, 1094, 1095, 1053, 1094, 1095, 128, 1054, 1055, 1096, 1054, 1055, 1096, 128, 1054, 1095, 1096, 1054, 1095, 1096, 128, 1055, 1056, 1097, 1055, 1056, 1097, 128, 1055, 1096, 1097, 1055, 1096, 1097, 128, 1056, 1057, 1098, 1056, 1057, 1098, 128, 1056, 1097, 1098, 1056, 1097, 1098, 128, 1057, 1058, 1099, 1057, 1058, 1099, 128, 1057, 1098, 1099, 1057, 1098, 1099, 128, 1058, 1059, 1100, 1058, 1059, 1100, 128, 1058, 1099, 1100, 1058, 1099, 1100, 128, 1059, 1060, 1101, 1059, 1060, 1101, 128, 1059, 1100, 1101, 1059, 1100, 1101, 128, 1060, 1061, 1102, 1060, 1061, 1102, 128, 1060, 1101, 1102, 1060, 1101, 1102, 128, 1061, 1062, 1103, 1061, 1062, 1103, 128, 1061, 1102, 1103, 1061, 1102, 1103, 128, 1062, 1063, 1104, 1062, 1063, 1104, 128, 1062, 1103, 1104, 1062, 1103, 1104, 128, 1063, 1064, 1105, 1063, 1064, 1105, 128, 1063, 1104, 1105, 1063, 1104, 1105, 128, 1064, 1065, 1106, 1064, 1065, 1106, 128, 1064, 1105, 1106, 1064, 1105, 1106, 128, 1066, 1067, 1108, 1066, 1067, 1108, 128, 1066, 1107, 1108, 1066, 1107, 1108, 128, 1067, 1068, 1109, 1067, 1068, 1109, 128, 1067, 1108, 1109, 1067, 1108, 1109, 128, 1068, 1069, 1110, 1068, 1069, 1110, 128, 1068, 1109, 1110, 1068, 1109, 1110, 128, 1069, 1070, 1111, 1069, 1070, 1111, 128, 1069, 1110, 1111, 1069, 1110, 1111, 128, 1070, 1071, 1112, 1070, 1071, 1112, 128, 1070, 1111, 1112, 1070, 1111, 1112, 128, 1071, 1072, 1113, 1071, 1072, 1113, 128, 1071, 1112, 1113, 1071, 1112, 1113, 128, 1072, 1073, 1114, 1072, 1073, 1114, 128, 1072, 1113, 1114, 1072, 1113, 1114, 128, 1073, 1074, 1115, 1073, 1074, 1115, 128, 1073, 1114, 1115, 1073, 1114, 1115, 128, 1074, 1075, 1116, 1074, 1075, 1116, 128, 1074, 1115, 1116, 1074, 1115, 1116, 128, 1075, 1076, 1117, 1075, 1076, 1117, 128, 1075, 1116, 1117, 1075, 1116, 1117, 128, 1076, 1077, 1118, 1076, 1077, 1118, 128, 1076, 1117, 1118, 1076, 1117, 1118, 128, 1077, 1078, 1119, 1077, 1078, 1119, 128, 1077, 1118, 1119, 1077, 1118, 1119, 128, 1078, 1079, 1120, 1078, 1079, 1120, 128, 1078, 1119, 1120, 1078, 1119, 1120, 128, 1079, 1080, 1121, 1079, 1080, 1121, 128, 1079, 1120, 1121, 1079, 1120, 1121, 128, 1080, 1081, 1122, 1080, 1081, 1122, 128, 1080, 1121, 1122, 1080, 1121, 1122, 128, 1081, 1082, 1123, 1081, 1082, 1123, 128, 1081, 1122, 1123, 1081, 1122, 1123, 128, 1082, 1083, 1124, 1082, 1083, 1124, 128, 1082, 1123, 1124, 1082, 1123, 1124, 128, 1083, 1084, 1125, 1083, 1084, 1125, 128, 1083, 1124, 1125, 1083, 1124, 1125, 128, 1084, 1085, 1126, 1084, 1085, 1126, 128, 1084, 1125, 1126, 1084, 1125, 1126, 128, 1085, 1086, 1127, 1085, 1086, 1127, 128, 1085, 1126, 1127, 1085, 1126, 1127, 128, 1086, 1087, 1128, 1086, 1087, 1128, 128, 1086, 1127, 1128, 1086, 1127, 1128, 128, 1087, 1088, 1129, 1087, 1088, 1129, 128, 1087, 1128, 1129, 1087, 1128, 1129, 128, 1088, 1089, 1130, 1088, 1089, 1130, 128, 1088, 1129, 1130, 1088, 1129, 1130, 128, 1089, 1090, 1131, 1089, 1090, 1131, 128, 1089, 1130, 1131, 1089, 1130, 1131, 128, 1090, 1091, 1132, 1090, 1091, 1132, 128, 1090, 1131, 1132, 1090, 1131, 1132, 128, 1091, 1092, 1133, 1091, 1092, 1133, 128, 1091, 1132, 1133, 1091, 1132, 1133, 128, 1092, 1093, 1134, 1092, 1093, 1134, 128, 1092, 1133, 1134, 1092, 1133, 1134, 128, 1093, 1094, 1135, 1093, 1094, 1135, 128, 1093, 1134, 1135, 1093, 1134, 1135, 128, 1094, 1095, 1136, 1094, 1095, 1136, 128, 1094, 1135, 1136, 1094, 1135, 1136, 128, 1095, 1096, 1137, 1095, 1096, 1137, 128, 1095, 1136, 1137, 1095, 1136, 1137, 128, 1096, 1097, 1138, 1096, 1097, 1138, 128, 1096, 1137, 1138, 1096, 1137, 1138, 128, 1097, 1098, 1139, 1097, 1098, 1139, 128, 1097, 1138, 1139, 1097, 1138, 1139, 128, 1098, 1099, 1140, 1098, 1099, 1140, 128, 1098, 1139, 1140, 1098, 1139, 1140, 128, 1099, 1100, 1141, 1099, 1100, 1141, 128, 1099, 1140, 1141, 1099, 1140, 1141, 128, 1100, 1101, 1142, 1100, 1101, 1142, 128, 1100, 1141, 1142, 1100, 1141, 1142, 128, 1101, 1102, 1143, 1101, 1102, 1143, 128, 1101, 1142, 1143, 1101, 1142, 1143, 128, 1102, 1103, 1144, 1102, 1103, 1144, 128, 1102, 1143, 1144, 1102, 1143, 1144, 128, 1103, 1104, 1145, 1103, 1104, 1145, 128, 1103, 1144, 1145, 1103, 1144, 1145, 128, 1104, 1105, 1146, 1104, 1105, 1146, 128, 1104, 1145, 1146, 1104, 1145, 1146, 128, 1105, 1106, 1147, 1105, 1106, 1147, 128, 1105, 1146, 1147, 1105, 1146, 1147, 128, 1107, 1108, 1149, 1107, 1108, 1149, 128, 1107, 1148, 1149, 1107, 1148, 1149, 128, 1108, 1109, 1150, 1108, 1109, 1150, 128, 1108, 1149, 1150, 1108, 1149, 1150, 128, 1109, 1110, 1151, 1109, 1110, 1151, 128, 1109, 1150, 1151, 1109, 1150, 1151, 128, 1110, 1111, 1152, 1110, 1111, 1152, 128, 1110, 1151, 1152, 1110, 1151, 1152, 128, 1111, 1112, 1153, 1111, 1112, 1153, 128, 1111, 1152, 1153, 1111, 1152, 1153, 128, 1112, 1113, 1154, 1112, 1113, 1154, 128, 1112, 1153, 1154, 1112, 1153, 1154, 128, 1113, 1114, 1155, 1113, 1114, 1155, 128, 1113, 1154, 1155, 1113, 1154, 1155, 128, 1114, 1115, 1156, 1114, 1115, 1156, 128, 1114, 1155, 1156, 1114, 1155, 1156, 128, 1115, 1116, 1157, 1115, 1116, 1157, 128, 1115, 1156, 1157, 1115, 1156, 1157, 128, 1116, 1117, 1158, 1116, 1117, 1158, 128, 1116, 1157, 1158, 1116, 1157, 1158, 128, 1117, 1118, 1159, 1117, 1118, 1159, 128, 1117, 1158, 1159, 1117, 1158, 1159, 128, 1118, 1119, 1160, 1118, 1119, 1160, 128, 1118, 1159, 1160, 1118, 1159, 1160, 128, 1119, 1120, 1161, 1119, 1120, 1161, 128, 1119, 1160, 1161, 1119, 1160, 1161, 128, 1120, 1121, 1162, 1120, 1121, 1162, 128, 1120, 1161, 1162, 1120, 1161, 1162, 128, 1121, 1122, 1163, 1121, 1122, 1163, 128, 1121, 1162, 1163, 1121, 1162, 1163, 128, 1122, 1123, 1164, 1122, 1123, 1164, 128, 1122, 1163, 1164, 1122, 1163, 1164, 128, 1123, 1124, 1165, 1123, 1124, 1165, 128, 1123, 1164, 1165, 1123, 1164, 1165, 128, 1124, 1125, 1166, 1124, 1125, 1166, 128, 1124, 1165, 1166, 1124, 1165, 1166, 128, 1125, 1126, 1167, 1125, 1126, 1167, 128, 1125, 1166, 1167, 1125, 1166, 1167, 128, 1126, 1127, 1168, 1126, 1127, 1168, 128, 1126, 1167, 1168, 1126, 1167, 1168, 128, 1127, 1128, 1169, 1127, 1128, 1169, 128, 1127, 1168, 1169, 1127, 1168, 1169, 128, 1128, 1129, 1170, 1128, 1129, 1170, 128, 1128, 1169, 1170, 1128, 1169, 1170, 128, 1129, 1130, 1171, 1129, 1130, 1171, 128, 1129, 1170, 1171, 1129, 1170, 1171, 128, 1130, 1131, 1172, 1130, 1131, 1172, 128, 1130, 1171, 1172, 1130, 1171, 1172, 128, 1131, 1132, 1173, 1131, 1132, 1173, 128, 1131, 1172, 1173, 1131, 1172, 1173, 128, 1132, 1133, 1174, 1132, 1133, 1174, 128, 1132, 1173, 1174, 1132, 1173, 1174, 128, 1133, 1134, 1175, 1133, 1134, 1175, 128, 1133, 1174, 1175, 1133, 1174, 1175, 128, 1134, 1135, 1176, 1134, 1135, 1176, 128, 1134, 1175, 1176, 1134, 1175, 1176, 128, 1135, 1136, 1177, 1135, 1136, 1177, 128, 1135, 1176, 1177, 1135, 1176, 1177, 128, 1136, 1137, 1178, 1136, 1137, 1178, 128, 1136, 1177, 1178, 1136, 1177, 1178, 128, 1137, 1138, 1179, 1137, 1138, 1179, 128, 1137, 1178, 1179, 1137, 1178, 1179, 128, 1138, 1139, 1180, 1138, 1139, 1180, 128, 1138, 1179, 1180, 1138, 1179, 1180, 128, 1139, 1140, 1181, 1139, 1140, 1181, 128, 1139, 1180, 1181, 1139, 1180, 1181, 128, 1140, 1141, 1182, 1140, 1141, 1182, 128, 1140, 1181, 1182, 1140, 1181, 1182, 128, 1141, 1142, 1183, 1141, 1142, 1183, 128, 1141, 1182, 1183, 1141, 1182, 1183, 128, 1142, 1143, 1184, 1142, 1143, 1184, 128, 1142, 1183, 1184, 1142, 1183, 1184, 128, 1143, 1144, 1185, 1143, 1144, 1185, 128, 1143, 1184, 1185, 1143, 1184, 1185, 128, 1144, 1145, 1186, 1144, 1145, 1186, 128, 1144, 1185, 1186, 1144, 1185, 1186, 128, 1145, 1146, 1187, 1145, 1146, 1187, 128, 1145, 1186, 1187, 1145, 1186, 1187, 128, 1146, 1147, 1188, 1146, 1147, 1188, 128, 1146, 1187, 1188, 1146, 1187, 1188, 128, 1148, 1149, 1190, 1148, 1149, 1190, 128, 1148, 1189, 1190, 1148, 1189, 1190, 128, 1149, 1150, 1191, 1149, 1150, 1191, 128, 1149, 1190, 1191, 1149, 1190, 1191, 128, 1150, 1151, 1192, 1150, 1151, 1192, 128, 1150, 1191, 1192, 1150, 1191, 1192, 128, 1151, 1152, 1193, 1151, 1152, 1193, 128, 1151, 1192, 1193, 1151, 1192, 1193, 128, 1152, 1153, 1194, 1152, 1153, 1194, 128, 1152, 1193, 1194, 1152, 1193, 1194, 128, 1153, 1154, 1195, 1153, 1154, 1195, 128, 1153, 1194, 1195, 1153, 1194, 1195, 128, 1154, 1155, 1196, 1154, 1155, 1196, 128, 1154, 1195, 1196, 1154, 1195, 1196, 128, 1155, 1156, 1197, 1155, 1156, 1197, 128, 1155, 1196, 1197, 1155, 1196, 1197, 128, 1156, 1157, 1198, 1156, 1157, 1198, 128, 1156, 1197, 1198, 1156, 1197, 1198, 128, 1157, 1158, 1199, 1157, 1158, 1199, 128, 1157, 1198, 1199, 1157, 1198, 1199, 128, 1158, 1159, 1200, 1158, 1159, 1200, 128, 1158, 1199, 1200, 1158, 1199, 1200, 128, 1159, 1160, 1201, 1159, 1160, 1201, 128, 1159, 1200, 1201, 1159, 1200, 1201, 128, 1160, 1161, 1202, 1160, 1161, 1202, 128, 1160, 1201, 1202, 1160, 1201, 1202, 128, 1161, 1162, 1203, 1161, 1162, 1203, 128, 1161, 1202, 1203, 1161, 1202, 1203, 128, 1162, 1163, 1204, 1162, 1163, 1204, 128, 1162, 1203, 1204, 1162, 1203, 1204, 128, 1163, 1164, 1205, 1163, 1164, 1205, 128, 1163, 1204, 1205, 1163, 1204, 1205, 128, 1164, 1165, 1206, 1164, 1165, 1206, 128, 1164, 1205, 1206, 1164, 1205, 1206, 128, 1165, 1166, 1207, 1165, 1166, 1207, 128, 1165, 1206, 1207, 1165, 1206, 1207, 128, 1166, 1167, 1208, 1166, 1167, 1208, 128, 1166, 1207, 1208, 1166, 1207, 1208, 128, 1167, 1168, 1209, 1167, 1168, 1209, 128, 1167, 1208, 1209, 1167, 1208, 1209, 128, 1168, 1169, 1210, 1168, 1169, 1210, 128, 1168, 1209, 1210, 1168, 1209, 1210, 128, 1169, 1170, 1211, 1169, 1170, 1211, 128, 1169, 1210, 1211, 1169, 1210, 1211, 128, 1170, 1171, 1212, 1170, 1171, 1212, 128, 1170, 1211, 1212, 1170, 1211, 1212, 128, 1171, 1172, 1213, 1171, 1172, 1213, 128, 1171, 1212, 1213, 1171, 1212, 1213, 128, 1172, 1173, 1214, 1172, 1173, 1214, 128, 1172, 1213, 1214, 1172, 1213, 1214, 128, 1173, 1174, 1215, 1173, 1174, 1215, 128, 1173, 1214, 1215, 1173, 1214, 1215, 128, 1174, 1175, 1216, 1174, 1175, 1216, 128, 1174, 1215, 1216, 1174, 1215, 1216, 128, 1175, 1176, 1217, 1175, 1176, 1217, 128, 1175, 1216, 1217, 1175, 1216, 1217, 128, 1176, 1177, 1218, 1176, 1177, 1218, 128, 1176, 1217, 1218, 1176, 1217, 1218, 128, 1177, 1178, 1219, 1177, 1178, 1219, 128, 1177, 1218, 1219, 1177, 1218, 1219, 128, 1178, 1179, 1220, 1178, 1179, 1220, 128, 1178, 1219, 1220, 1178, 1219, 1220, 128, 1179, 1180, 1221, 1179, 1180, 1221, 128, 1179, 1220, 1221, 1179, 1220, 1221, 128, 1180, 1181, 1222, 1180, 1181, 1222, 128, 1180, 1221, 1222, 1180, 1221, 1222, 128, 1181, 1182, 1223, 1181, 1182, 1223, 128, 1181, 1222, 1223, 1181, 1222, 1223, 128, 1182, 1183, 1224, 1182, 1183, 1224, 128, 1182, 1223, 1224, 1182, 1223, 1224, 128, 1183, 1184, 1225, 1183, 1184, 1225, 128, 1183, 1224, 1225, 1183, 1224, 1225, 128, 1184, 1185, 1226, 1184, 1185, 1226, 128, 1184, 1225, 1226, 1184, 1225, 1226, 128, 1185, 1186, 1227, 1185, 1186, 1227, 128, 1185, 1226, 1227, 1185, 1226, 1227, 128, 1186, 1187, 1228, 1186, 1187, 1228, 128, 1186, 1227, 1228, 1186, 1227, 1228, 128, 1187, 1188, 1229, 1187, 1188, 1229, 128, 1187, 1228, 1229, 1187, 1228, 1229, 128, 1189, 1190, 1231, 1189, 1190, 1231, 128, 1189, 1230, 1231, 1189, 1230, 1231, 128, 1190, 1191, 1232, 1190, 1191, 1232, 128, 1190, 1231, 1232, 1190, 1231, 1232, 128, 1191, 1192, 1233, 1191, 1192, 1233, 128, 1191, 1232, 1233, 1191, 1232, 1233, 128, 1192, 1193, 1234, 1192, 1193, 1234, 128, 1192, 1233, 1234, 1192, 1233, 1234, 128, 1193, 1194, 1235, 1193, 1194, 1235, 128, 1193, 1234, 1235, 1193, 1234, 1235, 128, 1194, 1195, 1236, 1194, 1195, 1236, 128, 1194, 1235, 1236, 1194, 1235, 1236, 128, 1195, 1196, 1237, 1195, 1196, 1237, 128, 1195, 1236, 1237, 1195, 1236, 1237, 128, 1196, 1197, 1238, 1196, 1197, 1238, 128, 1196, 1237, 1238, 1196, 1237, 1238, 128, 1197, 1198, 1239, 1197, 1198, 1239, 128, 1197, 1238, 1239, 1197, 1238, 1239, 128, 1198, 1199, 1240, 1198, 1199, 1240, 128, 1198, 1239, 1240, 1198, 1239, 1240, 128, 1199, 1200, 1241, 1199, 1200, 1241, 128, 1199, 1240, 1241, 1199, 1240, 1241, 128, 1200, 1201, 1242, 1200, 1201, 1242, 128, 1200, 1241, 1242, 1200, 1241, 1242, 128, 1201, 1202, 1243, 1201, 1202, 1243, 128, 1201, 1242, 1243, 1201, 1242, 1243, 128, 1202, 1203, 1244, 1202, 1203, 1244, 128, 1202, 1243, 1244, 1202, 1243, 1244, 128, 1203, 1204, 1245, 1203, 1204, 1245, 128, 1203, 1244, 1245, 1203, 1244, 1245, 128, 1204, 1205, 1246, 1204, 1205, 1246, 128, 1204, 1245, 1246, 1204, 1245, 1246, 128, 1205, 1206, 1247, 1205, 1206, 1247, 128, 1205, 1246, 1247, 1205, 1246, 1247, 128, 1206, 1207, 1248, 1206, 1207, 1248, 128, 1206, 1247, 1248, 1206, 1247, 1248, 128, 1207, 1208, 1249, 1207, 1208, 1249, 128, 1207, 1248, 1249, 1207, 1248, 1249, 128, 1208, 1209, 1250, 1208, 1209, 1250, 128, 1208, 1249, 1250, 1208, 1249, 1250, 128, 1209, 1210, 1251, 1209, 1210, 1251, 128, 1209, 1250, 1251, 1209, 1250, 1251, 128, 1210, 1211, 1252, 1210, 1211, 1252, 128, 1210, 1251, 1252, 1210, 1251, 1252, 128, 1211, 1212, 1253, 1211, 1212, 1253, 128, 1211, 1252, 1253, 1211, 1252, 1253, 128, 1212, 1213, 1254, 1212, 1213, 1254, 128, 1212, 1253, 1254, 1212, 1253, 1254, 128, 1213, 1214, 1255, 1213, 1214, 1255, 128, 1213, 1254, 1255, 1213, 1254, 1255, 128, 1214, 1215, 1256, 1214, 1215, 1256, 128, 1214, 1255, 1256, 1214, 1255, 1256, 128, 1215, 1216, 1257, 1215, 1216, 1257, 128, 1215, 1256, 1257, 1215, 1256, 1257, 128, 1216, 1217, 1258, 1216, 1217, 1258, 128, 1216, 1257, 1258, 1216, 1257, 1258, 128, 1217, 1218, 1259, 1217, 1218, 1259, 128, 1217, 1258, 1259, 1217, 1258, 1259, 128, 1218, 1219, 1260, 1218, 1219, 1260, 128, 1218, 1259, 1260, 1218, 1259, 1260, 128, 1219, 1220, 1261, 1219, 1220, 1261, 128, 1219, 1260, 1261, 1219, 1260, 1261, 128, 1220, 1221, 1262, 1220, 1221, 1262, 128, 1220, 1261, 1262, 1220, 1261, 1262, 128, 1221, 1222, 1263, 1221, 1222, 1263, 128, 1221, 1262, 1263, 1221, 1262, 1263, 128, 1222, 1223, 1264, 1222, 1223, 1264, 128, 1222, 1263, 1264, 1222, 1263, 1264, 128, 1223, 1224, 1265, 1223, 1224, 1265, 128, 1223, 1264, 1265, 1223, 1264, 1265, 128, 1224, 1225, 1266, 1224, 1225, 1266, 128, 1224, 1265, 1266, 1224, 1265, 1266, 128, 1225, 1226, 1267, 1225, 1226, 1267, 128, 1225, 1266, 1267, 1225, 1266, 1267, 128, 1226, 1227, 1268, 1226, 1227, 1268, 128, 1226, 1267, 1268, 1226, 1267, 1268, 128, 1227, 1228, 1269, 1227, 1228, 1269, 128, 1227, 1268, 1269, 1227, 1268, 1269, 128, 1228, 1229, 1270, 1228, 1229, 1270, 128, 1228, 1269, 1270, 1228, 1269, 1270, 128, 1230, 1231, 1272, 1230, 1231, 1272, 128, 1230, 1271, 1272, 1230, 1271, 1272, 128, 1231, 1232, 1273, 1231, 1232, 1273, 128, 1231, 1272, 1273, 1231, 1272, 1273, 128, 1232, 1233, 1274, 1232, 1233, 1274, 128, 1232, 1273, 1274, 1232, 1273, 1274, 128, 1233, 1234, 1275, 1233, 1234, 1275, 128, 1233, 1274, 1275, 1233, 1274, 1275, 128, 1234, 1235, 1276, 1234, 1235, 1276, 128, 1234, 1275, 1276, 1234, 1275, 1276, 128, 1235, 1236, 1277, 1235, 1236, 1277, 128, 1235, 1276, 1277, 1235, 1276, 1277, 128, 1236, 1237, 1278, 1236, 1237, 1278, 128, 1236, 1277, 1278, 1236, 1277, 1278, 128, 1237, 1238, 1279, 1237, 1238, 1279, 128, 1237, 1278, 1279, 1237, 1278, 1279, 128, 1238, 1239, 1280, 1238, 1239, 1280, 128, 1238, 1279, 1280, 1238, 1279, 1280, 128, 1239, 1240, 1281, 1239, 1240, 1281, 128, 1239, 1280, 1281, 1239, 1280, 1281, 128, 1240, 1241, 1282, 1240, 1241, 1282, 128, 1240, 1281, 1282, 1240, 1281, 1282, 128, 1241, 1242, 1283, 1241, 1242, 1283, 128, 1241, 1282, 1283, 1241, 1282, 1283, 128, 1242, 1243, 1284, 1242, 1243, 1284, 128, 1242, 1283, 1284, 1242, 1283, 1284, 128, 1243, 1244, 1285, 1243, 1244, 1285, 128, 1243, 1284, 1285, 1243, 1284, 1285, 128, 1244, 1245, 1286, 1244, 1245, 1286, 128, 1244, 1285, 1286, 1244, 1285, 1286, 128, 1245, 1246, 1287, 1245, 1246, 1287, 128, 1245, 1286, 1287, 1245, 1286, 1287, 128, 1246, 1247, 1288, 1246, 1247, 1288, 128, 1246, 1287, 1288, 1246, 1287, 1288, 128, 1247, 1248, 1289, 1247, 1248, 1289, 128, 1247, 1288, 1289, 1247, 1288, 1289, 128, 1248, 1249, 1290, 1248, 1249, 1290, 128, 1248, 1289, 1290, 1248, 1289, 1290, 128, 1249, 1250, 1291, 1249, 1250, 1291, 128, 1249, 1290, 1291, 1249, 1290, 1291, 128, 1250, 1251, 1292, 1250, 1251, 1292, 128, 1250, 1291, 1292, 1250, 1291, 1292, 128, 1251, 1252, 1293, 1251, 1252, 1293, 128, 1251, 1292, 1293, 1251, 1292, 1293, 128, 1252, 1253, 1294, 1252, 1253, 1294, 128, 1252, 1293, 1294, 1252, 1293, 1294, 128, 1253, 1254, 1295, 1253, 1254, 1295, 128, 1253, 1294, 1295, 1253, 1294, 1295, 128, 1254, 1255, 1296, 1254, 1255, 1296, 128, 1254, 1295, 1296, 1254, 1295, 1296, 128, 1255, 1256, 1297, 1255, 1256, 1297, 128, 1255, 1296, 1297, 1255, 1296, 1297, 128, 1256, 1257, 1298, 1256, 1257, 1298, 128, 1256, 1297, 1298, 1256, 1297, 1298, 128, 1257, 1258, 1299, 1257, 1258, 1299, 128, 1257, 1298, 1299, 1257, 1298, 1299, 128, 1258, 1259, 1300, 1258, 1259, 1300, 128, 1258, 1299, 1300, 1258, 1299, 1300, 128, 1259, 1260, 1301, 1259, 1260, 1301, 128, 1259, 1300, 1301, 1259, 1300, 1301, 128, 1260, 1261, 1302, 1260, 1261, 1302, 128, 1260, 1301, 1302, 1260, 1301, 1302, 128, 1261, 1262, 1303, 1261, 1262, 1303, 128, 1261, 1302, 1303, 1261, 1302, 1303, 128, 1262, 1263, 1304, 1262, 1263, 1304, 128, 1262, 1303, 1304, 1262, 1303, 1304, 128, 1263, 1264, 1305, 1263, 1264, 1305, 128, 1263, 1304, 1305, 1263, 1304, 1305, 128, 1264, 1265, 1306, 1264, 1265, 1306, 128, 1264, 1305, 1306, 1264, 1305, 1306, 128, 1265, 1266, 1307, 1265, 1266, 1307, 128, 1265, 1306, 1307, 1265, 1306, 1307, 128, 1266, 1267, 1308, 1266, 1267, 1308, 128, 1266, 1307, 1308, 1266, 1307, 1308, 128, 1267, 1268, 1309, 1267, 1268, 1309, 128, 1267, 1308, 1309, 1267, 1308, 1309, 128, 1268, 1269, 1310, 1268, 1269, 1310, 128, 1268, 1309, 1310, 1268, 1309, 1310, 128, 1269, 1270, 1311, 1269, 1270, 1311, 128, 1269, 1310, 1311, 1269, 1310, 1311, 128, 1271, 1272, 1313, 1271, 1272, 1313, 128, 1271, 1312, 1313, 1271, 1312, 1313, 128, 1272, 1273, 1314, 1272, 1273, 1314, 128, 1272, 1313, 1314, 1272, 1313, 1314, 128, 1273, 1274, 1315, 1273, 1274, 1315, 128, 1273, 1314, 1315, 1273, 1314, 1315, 128, 1274, 1275, 1316, 1274, 1275, 1316, 128, 1274, 1315, 1316, 1274, 1315, 1316, 128, 1275, 1276, 1317, 1275, 1276, 1317, 128, 1275, 1316, 1317, 1275, 1316, 1317, 128, 1276, 1277, 1318, 1276, 1277, 1318, 128, 1276, 1317, 1318, 1276, 1317, 1318, 128, 1277, 1278, 1319, 1277, 1278, 1319, 128, 1277, 1318, 1319, 1277, 1318, 1319, 128, 1278, 1279, 1320, 1278, 1279, 1320, 128, 1278, 1319, 1320, 1278, 1319, 1320, 128, 1279, 1280, 1321, 1279, 1280, 1321, 128, 1279, 1320, 1321, 1279, 1320, 1321, 128, 1280, 1281, 1322, 1280, 1281, 1322, 128, 1280, 1321, 1322, 1280, 1321, 1322, 128, 1281, 1282, 1323, 1281, 1282, 1323, 128, 1281, 1322, 1323, 1281, 1322, 1323, 128, 1282, 1283, 1324, 1282, 1283, 1324, 128, 1282, 1323, 1324, 1282, 1323, 1324, 128, 1283, 1284, 1325, 1283, 1284, 1325, 128, 1283, 1324, 1325, 1283, 1324, 1325, 128, 1284, 1285, 1326, 1284, 1285, 1326, 128, 1284, 1325, 1326, 1284, 1325, 1326, 128, 1285, 1286, 1327, 1285, 1286, 1327, 128, 1285, 1326, 1327, 1285, 1326, 1327, 128, 1286, 1287, 1328, 1286, 1287, 1328, 128, 1286, 1327, 1328, 1286, 1327, 1328, 128, 1287, 1288, 1329, 1287, 1288, 1329, 128, 1287, 1328, 1329, 1287, 1328, 1329, 128, 1288, 1289, 1330, 1288, 1289, 1330, 128, 1288, 1329, 1330, 1288, 1329, 1330, 128, 1289, 1290, 1331, 1289, 1290, 1331, 128, 1289, 1330, 1331, 1289, 1330, 1331, 128, 1290, 1291, 1332, 1290, 1291, 1332, 128, 1290, 1331, 1332, 1290, 1331, 1332, 128, 1291, 1292, 1333, 1291, 1292, 1333, 128, 1291, 1332, 1333, 1291, 1332, 1333, 128, 1292, 1293, 1334, 1292, 1293, 1334, 128, 1292, 1333, 1334, 1292, 1333, 1334, 128, 1293, 1294, 1335, 1293, 1294, 1335, 128, 1293, 1334, 1335, 1293, 1334, 1335, 128, 1294, 1295, 1336, 1294, 1295, 1336, 128, 1294, 1335, 1336, 1294, 1335, 1336, 128, 1295, 1296, 1337, 1295, 1296, 1337, 128, 1295, 1336, 1337, 1295, 1336, 1337, 128, 1296, 1297, 1338, 1296, 1297, 1338, 128, 1296, 1337, 1338, 1296, 1337, 1338, 128, 1297, 1298, 1339, 1297, 1298, 1339, 128, 1297, 1338, 1339, 1297, 1338, 1339, 128, 1298, 1299, 1340, 1298, 1299, 1340, 128, 1298, 1339, 1340, 1298, 1339, 1340, 128, 1299, 1300, 1341, 1299, 1300, 1341, 128, 1299, 1340, 1341, 1299, 1340, 1341, 128, 1300, 1301, 1342, 1300, 1301, 1342, 128, 1300, 1341, 1342, 1300, 1341, 1342, 128, 1301, 1302, 1343, 1301, 1302, 1343, 128, 1301, 1342, 1343, 1301, 1342, 1343, 128, 1302, 1303, 1344, 1302, 1303, 1344, 128, 1302, 1343, 1344, 1302, 1343, 1344, 128, 1303, 1304, 1345, 1303, 1304, 1345, 128, 1303, 1344, 1345, 1303, 1344, 1345, 128, 1304, 1305, 1346, 1304, 1305, 1346, 128, 1304, 1345, 1346, 1304, 1345, 1346, 128, 1305, 1306, 1347, 1305, 1306, 1347, 128, 1305, 1346, 1347, 1305, 1346, 1347, 128, 1306, 1307, 1348, 1306, 1307, 1348, 128, 1306, 1347, 1348, 1306, 1347, 1348, 128, 1307, 1308, 1349, 1307, 1308, 1349, 128, 1307, 1348, 1349, 1307, 1348, 1349, 128, 1308, 1309, 1350, 1308, 1309, 1350, 128, 1308, 1349, 1350, 1308, 1349, 1350, 128, 1309, 1310, 1351, 1309, 1310, 1351, 128, 1309, 1350, 1351, 1309, 1350, 1351, 128, 1310, 1311, 1352, 1310, 1311, 1352, 128, 1310, 1351, 1352, 1310, 1351, 1352, 128, 1312, 1313, 1354, 1312, 1313, 1354, 128, 1312, 1353, 1354, 1312, 1353, 1354, 128, 1313, 1314, 1355, 1313, 1314, 1355, 128, 1313, 1354, 1355, 1313, 1354, 1355, 128, 1314, 1315, 1356, 1314, 1315, 1356, 128, 1314, 1355, 1356, 1314, 1355, 1356, 128, 1315, 1316, 1357, 1315, 1316, 1357, 128, 1315, 1356, 1357, 1315, 1356, 1357, 128, 1316, 1317, 1358, 1316, 1317, 1358, 128, 1316, 1357, 1358, 1316, 1357, 1358, 128, 1317, 1318, 1359, 1317, 1318, 1359, 128, 1317, 1358, 1359, 1317, 1358, 1359, 128, 1318, 1319, 1360, 1318, 1319, 1360, 128, 1318, 1359, 1360, 1318, 1359, 1360, 128, 1319, 1320, 1361, 1319, 1320, 1361, 128, 1319, 1360, 1361, 1319, 1360, 1361, 128, 1320, 1321, 1362, 1320, 1321, 1362, 128, 1320, 1361, 1362, 1320, 1361, 1362, 128, 1321, 1322, 1363, 1321, 1322, 1363, 128, 1321, 1362, 1363, 1321, 1362, 1363, 128, 1322, 1323, 1364, 1322, 1323, 1364, 128, 1322, 1363, 1364, 1322, 1363, 1364, 128, 1323, 1324, 1365, 1323, 1324, 1365, 128, 1323, 1364, 1365, 1323, 1364, 1365, 128, 1324, 1325, 1366, 1324, 1325, 1366, 128, 1324, 1365, 1366, 1324, 1365, 1366, 128, 1325, 1326, 1367, 1325, 1326, 1367, 128, 1325, 1366, 1367, 1325, 1366, 1367, 128, 1326, 1327, 1368, 1326, 1327, 1368, 128, 1326, 1367, 1368, 1326, 1367, 1368, 128, 1327, 1328, 1369, 1327, 1328, 1369, 128, 1327, 1368, 1369, 1327, 1368, 1369, 128, 1328, 1329, 1370, 1328, 1329, 1370, 128, 1328, 1369, 1370, 1328, 1369, 1370, 128, 1329, 1330, 1371, 1329, 1330, 1371, 128, 1329, 1370, 1371, 1329, 1370, 1371, 128, 1330, 1331, 1372, 1330, 1331, 1372, 128, 1330, 1371, 1372, 1330, 1371, 1372, 128, 1331, 1332, 1373, 1331, 1332, 1373, 128, 1331, 1372, 1373, 1331, 1372, 1373, 128, 1332, 1333, 1374, 1332, 1333, 1374, 128, 1332, 1373, 1374, 1332, 1373, 1374, 128, 1333, 1334, 1375, 1333, 1334, 1375, 128, 1333, 1374, 1375, 1333, 1374, 1375, 128, 1334, 1335, 1376, 1334, 1335, 1376, 128, 1334, 1375, 1376, 1334, 1375, 1376, 128, 1335, 1336, 1377, 1335, 1336, 1377, 128, 1335, 1376, 1377, 1335, 1376, 1377, 128, 1336, 1337, 1378, 1336, 1337, 1378, 128, 1336, 1377, 1378, 1336, 1377, 1378, 128, 1337, 1338, 1379, 1337, 1338, 1379, 128, 1337, 1378, 1379, 1337, 1378, 1379, 128, 1338, 1339, 1380, 1338, 1339, 1380, 128, 1338, 1379, 1380, 1338, 1379, 1380, 128, 1339, 1340, 1381, 1339, 1340, 1381, 128, 1339, 1380, 1381, 1339, 1380, 1381, 128, 1340, 1341, 1382, 1340, 1341, 1382, 128, 1340, 1381, 1382, 1340, 1381, 1382, 128, 1341, 1342, 1383, 1341, 1342, 1383, 128, 1341, 1382, 1383, 1341, 1382, 1383, 128, 1342, 1343, 1384, 1342, 1343, 1384, 128, 1342, 1383, 1384, 1342, 1383, 1384, 128, 1343, 1344, 1385, 1343, 1344, 1385, 128, 1343, 1384, 1385, 1343, 1384, 1385, 128, 1344, 1345, 1386, 1344, 1345, 1386, 128, 1344, 1385, 1386, 1344, 1385, 1386, 128, 1345, 1346, 1387, 1345, 1346, 1387, 128, 1345, 1386, 1387, 1345, 1386, 1387, 128, 1346, 1347, 1388, 1346, 1347, 1388, 128, 1346, 1387, 1388, 1346, 1387, 1388, 128, 1347, 1348, 1389, 1347, 1348, 1389, 128, 1347, 1388, 1389, 1347, 1388, 1389, 128, 1348, 1349, 1390, 1348, 1349, 1390, 128, 1348, 1389, 1390, 1348, 1389, 1390, 128, 1349, 1350, 1391, 1349, 1350, 1391, 128, 1349, 1390, 1391, 1349, 1390, 1391, 128, 1350, 1351, 1392, 1350, 1351, 1392, 128, 1350, 1391, 1392, 1350, 1391, 1392, 128, 1351, 1352, 1393, 1351, 1352, 1393, 128, 1351, 1392, 1393, 1351, 1392, 1393, 128, 1353, 1354, 1395, 1353, 1354, 1395, 128, 1353, 1394, 1395, 1353, 1394, 1395, 128, 1354, 1355, 1396, 1354, 1355, 1396, 128, 1354, 1395, 1396, 1354, 1395, 1396, 128, 1355, 1356, 1397, 1355, 1356, 1397, 128, 1355, 1396, 1397, 1355, 1396, 1397, 128, 1356, 1357, 1398, 1356, 1357, 1398, 128, 1356, 1397, 1398, 1356, 1397, 1398, 128, 1357, 1358, 1399, 1357, 1358, 1399, 128, 1357, 1398, 1399, 1357, 1398, 1399, 128, 1358, 1359, 1400, 1358, 1359, 1400, 128, 1358, 1399, 1400, 1358, 1399, 1400, 128, 1359, 1360, 1401, 1359, 1360, 1401, 128, 1359, 1400, 1401, 1359, 1400, 1401, 128, 1360, 1361, 1402, 1360, 1361, 1402, 128, 1360, 1401, 1402, 1360, 1401, 1402, 128, 1361, 1362, 1403, 1361, 1362, 1403, 128, 1361, 1402, 1403, 1361, 1402, 1403, 128, 1362, 1363, 1404, 1362, 1363, 1404, 128, 1362, 1403, 1404, 1362, 1403, 1404, 128, 1363, 1364, 1405, 1363, 1364, 1405, 128, 1363, 1404, 1405, 1363, 1404, 1405, 128, 1364, 1365, 1406, 1364, 1365, 1406, 128, 1364, 1405, 1406, 1364, 1405, 1406, 128, 1365, 1366, 1407, 1365, 1366, 1407, 128, 1365, 1406, 1407, 1365, 1406, 1407, 128, 1366, 1367, 1408, 1366, 1367, 1408, 128, 1366, 1407, 1408, 1366, 1407, 1408, 128, 1367, 1368, 1409, 1367, 1368, 1409, 128, 1367, 1408, 1409, 1367, 1408, 1409, 128, 1368, 1369, 1410, 1368, 1369, 1410, 128, 1368, 1409, 1410, 1368, 1409, 1410, 128, 1369, 1370, 1411, 1369, 1370, 1411, 128, 1369, 1410, 1411, 1369, 1410, 1411, 128, 1370, 1371, 1412, 1370, 1371, 1412, 128, 1370, 1411, 1412, 1370, 1411, 1412, 128, 1371, 1372, 1413, 1371, 1372, 1413, 128, 1371, 1412, 1413, 1371, 1412, 1413, 128, 1372, 1373, 1414, 1372, 1373, 1414, 128, 1372, 1413, 1414, 1372, 1413, 1414, 128, 1373, 1374, 1415, 1373, 1374, 1415, 128, 1373, 1414, 1415, 1373, 1414, 1415, 128, 1374, 1375, 1416, 1374, 1375, 1416, 128, 1374, 1415, 1416, 1374, 1415, 1416, 128, 1375, 1376, 1417, 1375, 1376, 1417, 128, 1375, 1416, 1417, 1375, 1416, 1417, 128, 1376, 1377, 1418, 1376, 1377, 1418, 128, 1376, 1417, 1418, 1376, 1417, 1418, 128, 1377, 1378, 1419, 1377, 1378, 1419, 128, 1377, 1418, 1419, 1377, 1418, 1419, 128, 1378, 1379, 1420, 1378, 1379, 1420, 128, 1378, 1419, 1420, 1378, 1419, 1420, 128, 1379, 1380, 1421, 1379, 1380, 1421, 128, 1379, 1420, 1421, 1379, 1420, 1421, 128, 1380, 1381, 1422, 1380, 1381, 1422, 128, 1380, 1421, 1422, 1380, 1421, 1422, 128, 1381, 1382, 1423, 1381, 1382, 1423, 128, 1381, 1422, 1423, 1381, 1422, 1423, 128, 1382, 1383, 1424, 1382, 1383, 1424, 128, 1382, 1423, 1424, 1382, 1423, 1424, 128, 1383, 1384, 1425, 1383, 1384, 1425, 128, 1383, 1424, 1425, 1383, 1424, 1425, 128, 1384, 1385, 1426, 1384, 1385, 1426, 128, 1384, 1425, 1426, 1384, 1425, 1426, 128, 1385, 1386, 1427, 1385, 1386, 1427, 128, 1385, 1426, 1427, 1385, 1426, 1427, 128, 1386, 1387, 1428, 1386, 1387, 1428, 128, 1386, 1427, 1428, 1386, 1427, 1428, 128, 1387, 1388, 1429, 1387, 1388, 1429, 128, 1387, 1428, 1429, 1387, 1428, 1429, 128, 1388, 1389, 1430, 1388, 1389, 1430, 128, 1388, 1429, 1430, 1388, 1429, 1430, 128, 1389, 1390, 1431, 1389, 1390, 1431, 128, 1389, 1430, 1431, 1389, 1430, 1431, 128, 1390, 1391, 1432, 1390, 1391, 1432, 128, 1390, 1431, 1432, 1390, 1431, 1432, 128, 1391, 1392, 1433, 1391, 1392, 1433, 128, 1391, 1432, 1433, 1391, 1432, 1433, 128, 1392, 1393, 1434, 1392, 1393, 1434, 128, 1392, 1433, 1434, 1392, 1433, 1434, 128, 1394, 1395, 1436, 1394, 1395, 1436, 128, 1394, 1435, 1436, 1394, 1435, 1436, 128, 1395, 1396, 1437, 1395, 1396, 1437, 128, 1395, 1436, 1437, 1395, 1436, 1437, 128, 1396, 1397, 1438, 1396, 1397, 1438, 128, 1396, 1437, 1438, 1396, 1437, 1438, 128, 1397, 1398, 1439, 1397, 1398, 1439, 128, 1397, 1438, 1439, 1397, 1438, 1439, 128, 1398, 1399, 1440, 1398, 1399, 1440, 128, 1398, 1439, 1440, 1398, 1439, 1440, 128, 1399, 1400, 1441, 1399, 1400, 1441, 128, 1399, 1440, 1441, 1399, 1440, 1441, 128, 1400, 1401, 1442, 1400, 1401, 1442, 128, 1400, 1441, 1442, 1400, 1441, 1442, 128, 1401, 1402, 1443, 1401, 1402, 1443, 128, 1401, 1442, 1443, 1401, 1442, 1443, 128, 1402, 1403, 1444, 1402, 1403, 1444, 128, 1402, 1443, 1444, 1402, 1443, 1444, 128, 1403, 1404, 1445, 1403, 1404, 1445, 128, 1403, 1444, 1445, 1403, 1444, 1445, 128, 1404, 1405, 1446, 1404, 1405, 1446, 128, 1404, 1445, 1446, 1404, 1445, 1446, 128, 1405, 1406, 1447, 1405, 1406, 1447, 128, 1405, 1446, 1447, 1405, 1446, 1447, 128, 1406, 1407, 1448, 1406, 1407, 1448, 128, 1406, 1447, 1448, 1406, 1447, 1448, 128, 1407, 1408, 1449, 1407, 1408, 1449, 128, 1407, 1448, 1449, 1407, 1448, 1449, 128, 1408, 1409, 1450, 1408, 1409, 1450, 128, 1408, 1449, 1450, 1408, 1449, 1450, 128, 1409, 1410, 1451, 1409, 1410, 1451, 128, 1409, 1450, 1451, 1409, 1450, 1451, 128, 1410, 1411, 1452, 1410, 1411, 1452, 128, 1410, 1451, 1452, 1410, 1451, 1452, 128, 1411, 1412, 1453, 1411, 1412, 1453, 128, 1411, 1452, 1453, 1411, 1452, 1453, 128, 1412, 1413, 1454, 1412, 1413, 1454, 128, 1412, 1453, 1454, 1412, 1453, 1454, 128, 1413, 1414, 1455, 1413, 1414, 1455, 128, 1413, 1454, 1455, 1413, 1454, 1455, 128, 1414, 1415, 1456, 1414, 1415, 1456, 128, 1414, 1455, 1456, 1414, 1455, 1456, 128, 1415, 1416, 1457, 1415, 1416, 1457, 128, 1415, 1456, 1457, 1415, 1456, 1457, 128, 1416, 1417, 1458, 1416, 1417, 1458, 128, 1416, 1457, 1458, 1416, 1457, 1458, 128, 1417, 1418, 1459, 1417, 1418, 1459, 128, 1417, 1458, 1459, 1417, 1458, 1459, 128, 1418, 1419, 1460, 1418, 1419, 1460, 128, 1418, 1459, 1460, 1418, 1459, 1460, 128, 1419, 1420, 1461, 1419, 1420, 1461, 128, 1419, 1460, 1461, 1419, 1460, 1461, 128, 1420, 1421, 1462, 1420, 1421, 1462, 128, 1420, 1461, 1462, 1420, 1461, 1462, 128, 1421, 1422, 1463, 1421, 1422, 1463, 128, 1421, 1462, 1463, 1421, 1462, 1463, 128, 1422, 1423, 1464, 1422, 1423, 1464, 128, 1422, 1463, 1464, 1422, 1463, 1464, 128, 1423, 1424, 1465, 1423, 1424, 1465, 128, 1423, 1464, 1465, 1423, 1464, 1465, 128, 1424, 1425, 1466, 1424, 1425, 1466, 128, 1424, 1465, 1466, 1424, 1465, 1466, 128, 1425, 1426, 1467, 1425, 1426, 1467, 128, 1425, 1466, 1467, 1425, 1466, 1467, 128, 1426, 1427, 1468, 1426, 1427, 1468, 128, 1426, 1467, 1468, 1426, 1467, 1468, 128, 1427, 1428, 1469, 1427, 1428, 1469, 128, 1427, 1468, 1469, 1427, 1468, 1469, 128, 1428, 1429, 1470, 1428, 1429, 1470, 128, 1428, 1469, 1470, 1428, 1469, 1470, 128, 1429, 1430, 1471, 1429, 1430, 1471, 128, 1429, 1470, 1471, 1429, 1470, 1471, 128, 1430, 1431, 1472, 1430, 1431, 1472, 128, 1430, 1471, 1472, 1430, 1471, 1472, 128, 1431, 1432, 1473, 1431, 1432, 1473, 128, 1431, 1472, 1473, 1431, 1472, 1473, 128, 1432, 1433, 1474, 1432, 1433, 1474, 128, 1432, 1473, 1474, 1432, 1473, 1474, 128, 1433, 1434, 1475, 1433, 1434, 1475, 128, 1433, 1474, 1475, 1433, 1474, 1475, 128, 1435, 1436, 1477, 1435, 1436, 1477, 128, 1435, 1476, 1477, 1435, 1476, 1477, 128, 1436, 1437, 1478, 1436, 1437, 1478, 128, 1436, 1477, 1478, 1436, 1477, 1478, 128, 1437, 1438, 1479, 1437, 1438, 1479, 128, 1437, 1478, 1479, 1437, 1478, 1479, 128, 1438, 1439, 1480, 1438, 1439, 1480, 128, 1438, 1479, 1480, 1438, 1479, 1480, 128, 1439, 1440, 1481, 1439, 1440, 1481, 128, 1439, 1480, 1481, 1439, 1480, 1481, 128, 1440, 1441, 1482, 1440, 1441, 1482, 128, 1440, 1481, 1482, 1440, 1481, 1482, 128, 1441, 1442, 1483, 1441, 1442, 1483, 128, 1441, 1482, 1483, 1441, 1482, 1483, 128, 1442, 1443, 1484, 1442, 1443, 1484, 128, 1442, 1483, 1484, 1442, 1483, 1484, 128, 1443, 1444, 1485, 1443, 1444, 1485, 128, 1443, 1484, 1485, 1443, 1484, 1485, 128, 1444, 1445, 1486, 1444, 1445, 1486, 128, 1444, 1485, 1486, 1444, 1485, 1486, 128, 1445, 1446, 1487, 1445, 1446, 1487, 128, 1445, 1486, 1487, 1445, 1486, 1487, 128, 1446, 1447, 1488, 1446, 1447, 1488, 128, 1446, 1487, 1488, 1446, 1487, 1488, 128, 1447, 1448, 1489, 1447, 1448, 1489, 128, 1447, 1488, 1489, 1447, 1488, 1489, 128, 1448, 1449, 1490, 1448, 1449, 1490, 128, 1448, 1489, 1490, 1448, 1489, 1490, 128, 1449, 1450, 1491, 1449, 1450, 1491, 128, 1449, 1490, 1491, 1449, 1490, 1491, 128, 1450, 1451, 1492, 1450, 1451, 1492, 128, 1450, 1491, 1492, 1450, 1491, 1492, 128, 1451, 1452, 1493, 1451, 1452, 1493, 128, 1451, 1492, 1493, 1451, 1492, 1493, 128, 1452, 1453, 1494, 1452, 1453, 1494, 128, 1452, 1493, 1494, 1452, 1493, 1494, 128, 1453, 1454, 1495, 1453, 1454, 1495, 128, 1453, 1494, 1495, 1453, 1494, 1495, 128, 1454, 1455, 1496, 1454, 1455, 1496, 128, 1454, 1495, 1496, 1454, 1495, 1496, 128, 1455, 1456, 1497, 1455, 1456, 1497, 128, 1455, 1496, 1497, 1455, 1496, 1497, 128, 1456, 1457, 1498, 1456, 1457, 1498, 128, 1456, 1497, 1498, 1456, 1497, 1498, 128, 1457, 1458, 1499, 1457, 1458, 1499, 128, 1457, 1498, 1499, 1457, 1498, 1499, 128, 1458, 1459, 1500, 1458, 1459, 1500, 128, 1458, 1499, 1500, 1458, 1499, 1500, 128, 1459, 1460, 1501, 1459, 1460, 1501, 128, 1459, 1500, 1501, 1459, 1500, 1501, 128, 1460, 1461, 1502, 1460, 1461, 1502, 128, 1460, 1501, 1502, 1460, 1501, 1502, 128, 1461, 1462, 1503, 1461, 1462, 1503, 128, 1461, 1502, 1503, 1461, 1502, 1503, 128, 1462, 1463, 1504, 1462, 1463, 1504, 128, 1462, 1503, 1504, 1462, 1503, 1504, 128, 1463, 1464, 1505, 1463, 1464, 1505, 128, 1463, 1504, 1505, 1463, 1504, 1505, 128, 1464, 1465, 1506, 1464, 1465, 1506, 128, 1464, 1505, 1506, 1464, 1505, 1506, 128, 1465, 1466, 1507, 1465, 1466, 1507, 128, 1465, 1506, 1507, 1465, 1506, 1507, 128, 1466, 1467, 1508, 1466, 1467, 1508, 128, 1466, 1507, 1508, 1466, 1507, 1508, 128, 1467, 1468, 1509, 1467, 1468, 1509, 128, 1467, 1508, 1509, 1467, 1508, 1509, 128, 1468, 1469, 1510, 1468, 1469, 1510, 128, 1468, 1509, 1510, 1468, 1509, 1510, 128, 1469, 1470, 1511, 1469, 1470, 1511, 128, 1469, 1510, 1511, 1469, 1510, 1511, 128, 1470, 1471, 1512, 1470, 1471, 1512, 128, 1470, 1511, 1512, 1470, 1511, 1512, 128, 1471, 1472, 1513, 1471, 1472, 1513, 128, 1471, 1512, 1513, 1471, 1512, 1513, 128, 1472, 1473, 1514, 1472, 1473, 1514, 128, 1472, 1513, 1514, 1472, 1513, 1514, 128, 1473, 1474, 1515, 1473, 1474, 1515, 128, 1473, 1514, 1515, 1473, 1514, 1515, 128, 1474, 1475, 1516, 1474, 1475, 1516, 128, 1474, 1515, 1516, 1474, 1515, 1516, 128, 1476, 1477, 1518, 1476, 1477, 1518, 128, 1476, 1517, 1518, 1476, 1517, 1518, 128, 1477, 1478, 1519, 1477, 1478, 1519, 128, 1477, 1518, 1519, 1477, 1518, 1519, 128, 1478, 1479, 1520, 1478, 1479, 1520, 128, 1478, 1519, 1520, 1478, 1519, 1520, 128, 1479, 1480, 1521, 1479, 1480, 1521, 128, 1479, 1520, 1521, 1479, 1520, 1521, 128, 1480, 1481, 1522, 1480, 1481, 1522, 128, 1480, 1521, 1522, 1480, 1521, 1522, 128, 1481, 1482, 1523, 1481, 1482, 1523, 128, 1481, 1522, 1523, 1481, 1522, 1523, 128, 1482, 1483, 1524, 1482, 1483, 1524, 128, 1482, 1523, 1524, 1482, 1523, 1524, 128, 1483, 1484, 1525, 1483, 1484, 1525, 128, 1483, 1524, 1525, 1483, 1524, 1525, 128, 1484, 1485, 1526, 1484, 1485, 1526, 128, 1484, 1525, 1526, 1484, 1525, 1526, 128, 1485, 1486, 1527, 1485, 1486, 1527, 128, 1485, 1526, 1527, 1485, 1526, 1527, 128, 1486, 1487, 1528, 1486, 1487, 1528, 128, 1486, 1527, 1528, 1486, 1527, 1528, 128, 1487, 1488, 1529, 1487, 1488, 1529, 128, 1487, 1528, 1529, 1487, 1528, 1529, 128, 1488, 1489, 1530, 1488, 1489, 1530, 128, 1488, 1529, 1530, 1488, 1529, 1530, 128, 1489, 1490, 1531, 1489, 1490, 1531, 128, 1489, 1530, 1531, 1489, 1530, 1531, 128, 1490, 1491, 1532, 1490, 1491, 1532, 128, 1490, 1531, 1532, 1490, 1531, 1532, 128, 1491, 1492, 1533, 1491, 1492, 1533, 128, 1491, 1532, 1533, 1491, 1532, 1533, 128, 1492, 1493, 1534, 1492, 1493, 1534, 128, 1492, 1533, 1534, 1492, 1533, 1534, 128, 1493, 1494, 1535, 1493, 1494, 1535, 128, 1493, 1534, 1535, 1493, 1534, 1535, 128, 1494, 1495, 1536, 1494, 1495, 1536, 128, 1494, 1535, 1536, 1494, 1535, 1536, 128, 1495, 1496, 1537, 1495, 1496, 1537, 128, 1495, 1536, 1537, 1495, 1536, 1537, 128, 1496, 1497, 1538, 1496, 1497, 1538, 128, 1496, 1537, 1538, 1496, 1537, 1538, 128, 1497, 1498, 1539, 1497, 1498, 1539, 128, 1497, 1538, 1539, 1497, 1538, 1539, 128, 1498, 1499, 1540, 1498, 1499, 1540, 128, 1498, 1539, 1540, 1498, 1539, 1540, 128, 1499, 1500, 1541, 1499, 1500, 1541, 128, 1499, 1540, 1541, 1499, 1540, 1541, 128, 1500, 1501, 1542, 1500, 1501, 1542, 128, 1500, 1541, 1542, 1500, 1541, 1542, 128, 1501, 1502, 1543, 1501, 1502, 1543, 128, 1501, 1542, 1543, 1501, 1542, 1543, 128, 1502, 1503, 1544, 1502, 1503, 1544, 128, 1502, 1543, 1544, 1502, 1543, 1544, 128, 1503, 1504, 1545, 1503, 1504, 1545, 128, 1503, 1544, 1545, 1503, 1544, 1545, 128, 1504, 1505, 1546, 1504, 1505, 1546, 128, 1504, 1545, 1546, 1504, 1545, 1546, 128, 1505, 1506, 1547, 1505, 1506, 1547, 128, 1505, 1546, 1547, 1505, 1546, 1547, 128, 1506, 1507, 1548, 1506, 1507, 1548, 128, 1506, 1547, 1548, 1506, 1547, 1548, 128, 1507, 1508, 1549, 1507, 1508, 1549, 128, 1507, 1548, 1549, 1507, 1548, 1549, 128, 1508, 1509, 1550, 1508, 1509, 1550, 128, 1508, 1549, 1550, 1508, 1549, 1550, 128, 1509, 1510, 1551, 1509, 1510, 1551, 128, 1509, 1550, 1551, 1509, 1550, 1551, 128, 1510, 1511, 1552, 1510, 1511, 1552, 128, 1510, 1551, 1552, 1510, 1551, 1552, 128, 1511, 1512, 1553, 1511, 1512, 1553, 128, 1511, 1552, 1553, 1511, 1552, 1553, 128, 1512, 1513, 1554, 1512, 1513, 1554, 128, 1512, 1553, 1554, 1512, 1553, 1554, 128, 1513, 1514, 1555, 1513, 1514, 1555, 128, 1513, 1554, 1555, 1513, 1554, 1555, 128, 1514, 1515, 1556, 1514, 1515, 1556, 128, 1514, 1555, 1556, 1514, 1555, 1556, 128, 1515, 1516, 1557, 1515, 1516, 1557, 128, 1515, 1556, 1557, 1515, 1556, 1557, 128, 1517, 1518, 1559, 1517, 1518, 1559, 128, 1517, 1558, 1559, 1517, 1558, 1559, 128, 1518, 1519, 1560, 1518, 1519, 1560, 128, 1518, 1559, 1560, 1518, 1559, 1560, 128, 1519, 1520, 1561, 1519, 1520, 1561, 128, 1519, 1560, 1561, 1519, 1560, 1561, 128, 1520, 1521, 1562, 1520, 1521, 1562, 128, 1520, 1561, 1562, 1520, 1561, 1562, 128, 1521, 1522, 1563, 1521, 1522, 1563, 128, 1521, 1562, 1563, 1521, 1562, 1563, 128, 1522, 1523, 1564, 1522, 1523, 1564, 128, 1522, 1563, 1564, 1522, 1563, 1564, 128, 1523, 1524, 1565, 1523, 1524, 1565, 128, 1523, 1564, 1565, 1523, 1564, 1565, 128, 1524, 1525, 1566, 1524, 1525, 1566, 128, 1524, 1565, 1566, 1524, 1565, 1566, 128, 1525, 1526, 1567, 1525, 1526, 1567, 128, 1525, 1566, 1567, 1525, 1566, 1567, 128, 1526, 1527, 1568, 1526, 1527, 1568, 128, 1526, 1567, 1568, 1526, 1567, 1568, 128, 1527, 1528, 1569, 1527, 1528, 1569, 128, 1527, 1568, 1569, 1527, 1568, 1569, 128, 1528, 1529, 1570, 1528, 1529, 1570, 128, 1528, 1569, 1570, 1528, 1569, 1570, 128, 1529, 1530, 1571, 1529, 1530, 1571, 128, 1529, 1570, 1571, 1529, 1570, 1571, 128, 1530, 1531, 1572, 1530, 1531, 1572, 128, 1530, 1571, 1572, 1530, 1571, 1572, 128, 1531, 1532, 1573, 1531, 1532, 1573, 128, 1531, 1572, 1573, 1531, 1572, 1573, 128, 1532, 1533, 1574, 1532, 1533, 1574, 128, 1532, 1573, 1574, 1532, 1573, 1574, 128, 1533, 1534, 1575, 1533, 1534, 1575, 128, 1533, 1574, 1575, 1533, 1574, 1575, 128, 1534, 1535, 1576, 1534, 1535, 1576, 128, 1534, 1575, 1576, 1534, 1575, 1576, 128, 1535, 1536, 1577, 1535, 1536, 1577, 128, 1535, 1576, 1577, 1535, 1576, 1577, 128, 1536, 1537, 1578, 1536, 1537, 1578, 128, 1536, 1577, 1578, 1536, 1577, 1578, 128, 1537, 1538, 1579, 1537, 1538, 1579, 128, 1537, 1578, 1579, 1537, 1578, 1579, 128, 1538, 1539, 1580, 1538, 1539, 1580, 128, 1538, 1579, 1580, 1538, 1579, 1580, 128, 1539, 1540, 1581, 1539, 1540, 1581, 128, 1539, 1580, 1581, 1539, 1580, 1581, 128, 1540, 1541, 1582, 1540, 1541, 1582, 128, 1540, 1581, 1582, 1540, 1581, 1582, 128, 1541, 1542, 1583, 1541, 1542, 1583, 128, 1541, 1582, 1583, 1541, 1582, 1583, 128, 1542, 1543, 1584, 1542, 1543, 1584, 128, 1542, 1583, 1584, 1542, 1583, 1584, 128, 1543, 1544, 1585, 1543, 1544, 1585, 128, 1543, 1584, 1585, 1543, 1584, 1585, 128, 1544, 1545, 1586, 1544, 1545, 1586, 128, 1544, 1585, 1586, 1544, 1585, 1586, 128, 1545, 1546, 1587, 1545, 1546, 1587, 128, 1545, 1586, 1587, 1545, 1586, 1587, 128, 1546, 1547, 1588, 1546, 1547, 1588, 128, 1546, 1587, 1588, 1546, 1587, 1588, 128, 1547, 1548, 1589, 1547, 1548, 1589, 128, 1547, 1588, 1589, 1547, 1588, 1589, 128, 1548, 1549, 1590, 1548, 1549, 1590, 128, 1548, 1589, 1590, 1548, 1589, 1590, 128, 1549, 1550, 1591, 1549, 1550, 1591, 128, 1549, 1590, 1591, 1549, 1590, 1591, 128, 1550, 1551, 1592, 1550, 1551, 1592, 128, 1550, 1591, 1592, 1550, 1591, 1592, 128, 1551, 1552, 1593, 1551, 1552, 1593, 128, 1551, 1592, 1593, 1551, 1592, 1593, 128, 1552, 1553, 1594, 1552, 1553, 1594, 128, 1552, 1593, 1594, 1552, 1593, 1594, 128, 1553, 1554, 1595, 1553, 1554, 1595, 128, 1553, 1594, 1595, 1553, 1594, 1595, 128, 1554, 1555, 1596, 1554, 1555, 1596, 128, 1554, 1595, 1596, 1554, 1595, 1596, 128, 1555, 1556, 1597, 1555, 1556, 1597, 128, 1555, 1596, 1597, 1555, 1596, 1597, 128, 1556, 1557, 1598, 1556, 1557, 1598, 128, 1556, 1597, 1598, 1556, 1597, 1598, 128, 1558, 1559, 1600, 1558, 1559, 1600, 128, 1558, 1599, 1600, 1558, 1599, 1600, 128, 1559, 1560, 1601, 1559, 1560, 1601, 128, 1559, 1600, 1601, 1559, 1600, 1601, 128, 1560, 1561, 1602, 1560, 1561, 1602, 128, 1560, 1601, 1602, 1560, 1601, 1602, 128, 1561, 1562, 1603, 1561, 1562, 1603, 128, 1561, 1602, 1603, 1561, 1602, 1603, 128, 1562, 1563, 1604, 1562, 1563, 1604, 128, 1562, 1603, 1604, 1562, 1603, 1604, 128, 1563, 1564, 1605, 1563, 1564, 1605, 128, 1563, 1604, 1605, 1563, 1604, 1605, 128, 1564, 1565, 1606, 1564, 1565, 1606, 128, 1564, 1605, 1606, 1564, 1605, 1606, 128, 1565, 1566, 1607, 1565, 1566, 1607, 128, 1565, 1606, 1607, 1565, 1606, 1607, 128, 1566, 1567, 1608, 1566, 1567, 1608, 128, 1566, 1607, 1608, 1566, 1607, 1608, 128, 1567, 1568, 1609, 1567, 1568, 1609, 128, 1567, 1608, 1609, 1567, 1608, 1609, 128, 1568, 1569, 1610, 1568, 1569, 1610, 128, 1568, 1609, 1610, 1568, 1609, 1610, 128, 1569, 1570, 1611, 1569, 1570, 1611, 128, 1569, 1610, 1611, 1569, 1610, 1611, 128, 1570, 1571, 1612, 1570, 1571, 1612, 128, 1570, 1611, 1612, 1570, 1611, 1612, 128, 1571, 1572, 1613, 1571, 1572, 1613, 128, 1571, 1612, 1613, 1571, 1612, 1613, 128, 1572, 1573, 1614, 1572, 1573, 1614, 128, 1572, 1613, 1614, 1572, 1613, 1614, 128, 1573, 1574, 1615, 1573, 1574, 1615, 128, 1573, 1614, 1615, 1573, 1614, 1615, 128, 1574, 1575, 1616, 1574, 1575, 1616, 128, 1574, 1615, 1616, 1574, 1615, 1616, 128, 1575, 1576, 1617, 1575, 1576, 1617, 128, 1575, 1616, 1617, 1575, 1616, 1617, 128, 1576, 1577, 1618, 1576, 1577, 1618, 128, 1576, 1617, 1618, 1576, 1617, 1618, 128, 1577, 1578, 1619, 1577, 1578, 1619, 128, 1577, 1618, 1619, 1577, 1618, 1619, 128, 1578, 1579, 1620, 1578, 1579, 1620, 128, 1578, 1619, 1620, 1578, 1619, 1620, 128, 1579, 1580, 1621, 1579, 1580, 1621, 128, 1579, 1620, 1621, 1579, 1620, 1621, 128, 1580, 1581, 1622, 1580, 1581, 1622, 128, 1580, 1621, 1622, 1580, 1621, 1622, 128, 1581, 1582, 1623, 1581, 1582, 1623, 128, 1581, 1622, 1623, 1581, 1622, 1623, 128, 1582, 1583, 1624, 1582, 1583, 1624, 128, 1582, 1623, 1624, 1582, 1623, 1624, 128, 1583, 1584, 1625, 1583, 1584, 1625, 128, 1583, 1624, 1625, 1583, 1624, 1625, 128, 1584, 1585, 1626, 1584, 1585, 1626, 128, 1584, 1625, 1626, 1584, 1625, 1626, 128, 1585, 1586, 1627, 1585, 1586, 1627, 128, 1585, 1626, 1627, 1585, 1626, 1627, 128, 1586, 1587, 1628, 1586, 1587, 1628, 128, 1586, 1627, 1628, 1586, 1627, 1628, 128, 1587, 1588, 1629, 1587, 1588, 1629, 128, 1587, 1628, 1629, 1587, 1628, 1629, 128, 1588, 1589, 1630, 1588, 1589, 1630, 128, 1588, 1629, 1630, 1588, 1629, 1630, 128, 1589, 1590, 1631, 1589, 1590, 1631, 128, 1589, 1630, 1631, 1589, 1630, 1631, 128, 1590, 1591, 1632, 1590, 1591, 1632, 128, 1590, 1631, 1632, 1590, 1631, 1632, 128, 1591, 1592, 1633, 1591, 1592, 1633, 128, 1591, 1632, 1633, 1591, 1632, 1633, 128, 1592, 1593, 1634, 1592, 1593, 1634, 128, 1592, 1633, 1634, 1592, 1633, 1634, 128, 1593, 1594, 1635, 1593, 1594, 1635, 128, 1593, 1634, 1635, 1593, 1634, 1635, 128, 1594, 1595, 1636, 1594, 1595, 1636, 128, 1594, 1635, 1636, 1594, 1635, 1636, 128, 1595, 1596, 1637, 1595, 1596, 1637, 128, 1595, 1636, 1637, 1595, 1636, 1637, 128, 1596, 1597, 1638, 1596, 1597, 1638, 128, 1596, 1637, 1638, 1596, 1637, 1638, 128, 1597, 1598, 1639, 1597, 1598, 1639, 128, 1597, 1638, 1639, 1597, 1638, 1639, 128, 1599, 1600, 1641, 1599, 1600, 1641, 128, 1599, 1640, 1641, 1599, 1640, 1641, 128, 1600, 1601, 1642, 1600, 1601, 1642, 128, 1600, 1641, 1642, 1600, 1641, 1642, 128, 1601, 1602, 1643, 1601, 1602, 1643, 128, 1601, 1642, 1643, 1601, 1642, 1643, 128, 1602, 1603, 1644, 1602, 1603, 1644, 128, 1602, 1643, 1644, 1602, 1643, 1644, 128, 1603, 1604, 1645, 1603, 1604, 1645, 128, 1603, 1644, 1645, 1603, 1644, 1645, 128, 1604, 1605, 1646, 1604, 1605, 1646, 128, 1604, 1645, 1646, 1604, 1645, 1646, 128, 1605, 1606, 1647, 1605, 1606, 1647, 128, 1605, 1646, 1647, 1605, 1646, 1647, 128, 1606, 1607, 1648, 1606, 1607, 1648, 128, 1606, 1647, 1648, 1606, 1647, 1648, 128, 1607, 1608, 1649, 1607, 1608, 1649, 128, 1607, 1648, 1649, 1607, 1648, 1649, 128, 1608, 1609, 1650, 1608, 1609, 1650, 128, 1608, 1649, 1650, 1608, 1649, 1650, 128, 1609, 1610, 1651, 1609, 1610, 1651, 128, 1609, 1650, 1651, 1609, 1650, 1651, 128, 1610, 1611, 1652, 1610, 1611, 1652, 128, 1610, 1651, 1652, 1610, 1651, 1652, 128, 1611, 1612, 1653, 1611, 1612, 1653, 128, 1611, 1652, 1653, 1611, 1652, 1653, 128, 1612, 1613, 1654, 1612, 1613, 1654, 128, 1612, 1653, 1654, 1612, 1653, 1654, 128, 1613, 1614, 1655, 1613, 1614, 1655, 128, 1613, 1654, 1655, 1613, 1654, 1655, 128, 1614, 1615, 1656, 1614, 1615, 1656, 128, 1614, 1655, 1656, 1614, 1655, 1656, 128, 1615, 1616, 1657, 1615, 1616, 1657, 128, 1615, 1656, 1657, 1615, 1656, 1657, 128, 1616, 1617, 1658, 1616, 1617, 1658, 128, 1616, 1657, 1658, 1616, 1657, 1658, 128, 1617, 1618, 1659, 1617, 1618, 1659, 128, 1617, 1658, 1659, 1617, 1658, 1659, 128, 1618, 1619, 1660, 1618, 1619, 1660, 128, 1618, 1659, 1660, 1618, 1659, 1660, 128, 1619, 1620, 1661, 1619, 1620, 1661, 128, 1619, 1660, 1661, 1619, 1660, 1661, 128, 1620, 1621, 1662, 1620, 1621, 1662, 128, 1620, 1661, 1662, 1620, 1661, 1662, 128, 1621, 1622, 1663, 1621, 1622, 1663, 128, 1621, 1662, 1663, 1621, 1662, 1663, 128, 1622, 1623, 1664, 1622, 1623, 1664, 128, 1622, 1663, 1664, 1622, 1663, 1664, 128, 1623, 1624, 1665, 1623, 1624, 1665, 128, 1623, 1664, 1665, 1623, 1664, 1665, 128, 1624, 1625, 1666, 1624, 1625, 1666, 128, 1624, 1665, 1666, 1624, 1665, 1666, 128, 1625, 1626, 1667, 1625, 1626, 1667, 128, 1625, 1666, 1667, 1625, 1666, 1667, 128, 1626, 1627, 1668, 1626, 1627, 1668, 128, 1626, 1667, 1668, 1626, 1667, 1668, 128, 1627, 1628, 1669, 1627, 1628, 1669, 128, 1627, 1668, 1669, 1627, 1668, 1669, 128, 1628, 1629, 1670, 1628, 1629, 1670, 128, 1628, 1669, 1670, 1628, 1669, 1670, 128, 1629, 1630, 1671, 1629, 1630, 1671, 128, 1629, 1670, 1671, 1629, 1670, 1671, 128, 1630, 1631, 1672, 1630, 1631, 1672, 128, 1630, 1671, 1672, 1630, 1671, 1672, 128, 1631, 1632, 1673, 1631, 1632, 1673, 128, 1631, 1672, 1673, 1631, 1672, 1673, 128, 1632, 1633, 1674, 1632, 1633, 1674, 128, 1632, 1673, 1674, 1632, 1673, 1674, 128, 1633, 1634, 1675, 1633, 1634, 1675, 128, 1633, 1674, 1675, 1633, 1674, 1675, 128, 1634, 1635, 1676, 1634, 1635, 1676, 128, 1634, 1675, 1676, 1634, 1675, 1676, 128, 1635, 1636, 1677, 1635, 1636, 1677, 128, 1635, 1676, 1677, 1635, 1676, 1677, 128, 1636, 1637, 1678, 1636, 1637, 1678, 128, 1636, 1677, 1678, 1636, 1677, 1678, 128, 1637, 1638, 1679, 1637, 1638, 1679, 128, 1637, 1678, 1679, 1637, 1678, 1679, 128, 1638, 1639, 1680, 1638, 1639, 1680, 128, 1638, 1679, 1680, 1638, 1679, 1680], \"colors\": [12844850, 16768512, 14352156, 16764672, 16738048, 16769536, 16445440, 16760832, 11403081, 7143305, 5046186, 190708, 42239, 22783, 10495, 254, 213, 186, 163, 159, 136, 132, 127, 127, 132, 127, 132, 136, 132, 154, 168, 182, 209, 255, 8447, 21759, 42239, 50431, 5504931, 11206476, 12844850, 12844850, 16754176, 16734464, 16729600, 16720128, 16729600, 16736256, 16761856, 13106991, 9305961, 7602051, 3145671, 51455, 37119, 8447, 5375, 227, 168, 163, 150, 141, 136, 132, 127, 132, 132, 132, 136, 132, 150, 159, 163, 227, 255, 4351, 40191, 54527, 3604416, 6750096, 12058431, 12844850, 15400716, 16735232, 16718336, 14548992, 16717312, 16724992, 16719104, 16739072, 16759040, 12844850, 9699171, 4390835, 57594, 29951, 7423, 245, 232, 191, 191, 150, 154, 141, 132, 127, 127, 136, 136, 136, 150, 145, 172, 172, 227, 8447, 18687, 33023, 42239, 3145671, 4652976, 16706816, 15400716, 16746752, 16726784, 15204352, 13959168, 8323072, 13369344, 16741888, 16722944, 16765696, 15400716, 7602051, 2293716, 48383, 40191, 23807, 255, 254, 200, 195, 145, 145, 136, 136, 132, 127, 132, 132, 141, 159, 150, 172, 195, 245, 6399, 17663, 28927, 47359, 8191865, 6291350, 16769536, 16746752, 16741888, 16731392, 16726784, 14548992, 9502720, 8323072, 11927552, 16720128, 16746752, 16765696, 8847216, 4194231, 58615, 37119, 11519, 250, 213, 222, 186, 150, 141, 141, 136, 136, 132, 136, 136, 145, 150, 172, 159, 213, 213, 10495, 22783, 39167, 57594, 3604416, 9305961, 16755200, 16741888, 16746752, 16736256, 16730624, 14548992, 13697024, 11010048, 16737280, 16726784, 16750592, 16769536, 11403081, 5701536, 57594, 38143, 14591, 7423, 245, 186, 182, 159, 150, 136, 132, 136, 127, 141, 127, 141, 154, 150, 186, 177, 254, 5375, 18687, 46335, 54527, 3801021, 10944335, 16768512, 16746752, 15858693, 16734464, 16721920, 16722944, 16387840, 16732416, 16760832, 16724992, 16744704, 16771328, 7602051, 3145671, 388337, 45311, 23807, 1279, 232, 195, 177, 154, 141, 132, 136, 132, 132, 141, 127, 141, 145, 154, 172, 182, 236, 2303, 15615, 39167, 55551, 4194231, 9502566, 13958946, 15858693, 16445440, 16738048, 16762880, 16738048, 16650752, 16732416, 16743936, 16748544, 16769536, 10157916, 6750096, 1900506, 388337, 28927, 17663, 255, 232, 191, 186, 159, 141, 132, 136, 127, 132, 127, 127, 141, 136, 159, 172, 200, 200, 5375, 13567, 33023, 51455, 6291350, 5046186, 12058431, 16445440, 11403081, 12058431, 16756992, 16759040, 16756224, 16752384, 16054274, 16768512, 15204111, 6553491, 3997626, 56574, 54527, 13567, 7423, 254, 200, 186, 163, 150, 136, 145, 132, 127, 127, 127, 127, 127, 141, 150, 191, 195, 218, 255, 10495, 28927, 47359, 49407, 4652976, 8650611, 11403081, 7995261, 13106991, 12255036, 15858693, 14745365, 15597320, 14548760, 11403081, 12648246, 5242790, 57594, 40191, 27903, 10495, 2303, 245, 182, 168, 159, 150, 141, 136, 127, 127, 127, 132, 132, 127, 136, 150, 154, 172, 222, 254, 255, 15615, 21759, 43263, 58615, 4849581, 7995261, 5046186, 7405446, 4652976, 11599686, 8650611, 10157916, 7798656, 6946701, 6094746, 585965, 56574, 29951, 13567, 11519, 7423, 209, 200, 191, 163, 150, 136, 132, 132, 132, 127, 127, 127, 132, 145, 141, 150, 172, 163, 213, 255, 3327, 22783, 27903, 49407, 1046759, 5046186, 56574, 190708, 6094746, 3997626, 6946701, 6291350, 3801021, 1900506, 849130, 40191, 42239, 23807, 9471, 254, 232, 222, 177, 159, 154, 136, 132, 132, 127, 127, 127, 127, 127, 132, 141, 132, 136, 163, 191, 209, 245, 255, 12543, 22783, 42239, 38143, 56574, 34047, 49407, 49407, 585965, 57594, 56574, 52479, 41215, 30975, 26879, 13567, 15615, 255, 245, 222, 191, 168, 145, 141, 127, 132, 127, 127, 127, 127, 127, 127, 132, 132, 136, 136, 168, 150, 200, 204, 250, 2303, 13567, 26879, 36095, 34047, 29951, 31999, 44287, 24831, 37119, 38143, 20735, 27903, 22783, 16639, 7423, 1279, 241, 213, 191, 182, 154, 141, 141, 141, 132, 132, 127, 127, 127, 127, 127, 127, 127, 136, 141, 145, 150, 177, 186, 236, 245, 1279, 4351, 8447, 29951, 9471, 12543, 10495, 7423, 18687, 25855, 7423, 14591, 1279, 1279, 245, 200, 213, 177, 163, 168, 141, 136, 127, 127, 127, 132, 127, 127, 127, 127, 127, 127, 127, 136, 136, 136, 150, 163, 177, 204, 195, 218, 255, 6399, 9471, 6399, 2303, 245, 7423, 3327, 9471, 255, 255, 255, 250, 209, 200, 186, 168, 159, 141, 141, 136, 136, 132, 132, 127, 127, 127, 127, 127, 127, 127, 127, 132, 127, 136, 127, 145, 159, 177, 182, 200, 232, 241, 6399, 204, 227, 250, 254, 254, 254, 222, 227, 227, 191, 200, 186, 168, 150, 136, 132, 136, 127, 127, 127, 127, 127, 127, 127, 127, 127, 127, 127, 127, 127, 136, 136, 141, 141, 154, 159, 159, 186, 177, 213, 204, 200, 186, 204, 209, 222, 204, 186, 172, 182, 186, 163, 177, 154, 136, 136, 127, 141, 127, 127, 132, 127, 127, 127, 127, 127, 127, 127, 127, 127, 127, 127, 127, 132, 127, 141, 150, 154, 163, 182, 182, 200, 168, 163, 182, 191, 191, 159, 186, 177, 163, 150, 150, 145, 150, 141, 145, 132, 127, 127, 127, 127, 127, 127, 127, 127, 127, 127, 127, 127, 127, 127, 127, 127, 141, 132, 141, 136, 141, 145, 145, 159, 168, 136, 154, 145, 154, 150, 159, 177, 154, 150, 154, 136, 127, 141, 132, 127, 132, 127, 127, 132, 127, 127, 127, 127, 127, 127, 127, 127, 127, 127, 127, 127, 127, 127, 127, 127, 132, 141, 159, 150, 141, 136, 141, 141, 141, 150, 141, 150, 145, 145, 145, 136, 141, 136, 132, 136, 132, 127, 127, 127, 127, 127, 127, 127, 127, 127, 127, 127, 127, 127, 127, 127, 127, 127, 127, 127, 127, 127, 132, 136, 141, 141, 141, 141, 136, 136, 141, 145, 141, 136, 136, 136, 132, 127, 127, 132, 127, 127, 132, 127, 127, 127, 127, 127, 127, 127, 127, 127, 127, 127, 127, 127, 127, 127, 127, 127, 127, 127, 127, 132, 127, 136, 132, 141, 132, 127, 132, 136, 132, 141, 132, 127, 127, 127, 132, 127, 127, 127, 127, 127, 127, 127, 127, 127, 127, 127, 127, 127, 127, 127, 127, 127, 127, 127, 127, 127, 127, 127, 127, 127, 127, 127, 127, 127, 132, 127, 127, 127, 127, 136, 127, 132, 132, 132, 127, 127, 127, 127, 127, 127, 127, 127, 127, 127, 127, 127, 127, 127, 127, 127, 127, 127, 127, 127, 127, 127, 127, 127, 127, 127, 127, 127, 127, 127, 132, 127, 127, 127, 127, 127, 132, 127, 127, 132, 127, 127, 132, 127, 127, 127, 127, 127, 127, 127, 127, 127, 127, 127, 127, 127, 127, 127, 127, 127, 127, 127, 127, 127, 127, 127, 127, 132, 127, 127, 127, 127, 127, 127, 127, 127, 132, 132, 127, 127, 127, 127, 127, 127, 127, 127, 127, 127, 127, 127, 127, 127, 127, 127, 127, 127, 127, 127, 127, 127, 127, 127, 127, 127, 127, 127, 127, 127, 127, 127, 127, 127, 136, 127, 127, 132, 127, 136, 132, 127, 136, 127, 132, 127, 127, 127, 132, 127, 127, 127, 127, 127, 127, 127, 127, 127, 127, 127, 127, 127, 127, 127, 127, 127, 127, 127, 127, 127, 127, 127, 127, 127, 127, 127, 127, 127, 136, 141, 132, 132, 141, 136, 136, 132, 141, 127, 127, 132, 127, 132, 127, 127, 127, 127, 127, 127, 127, 127, 127, 127, 127, 127, 127, 127, 127, 127, 127, 127, 127, 127, 127, 132, 127, 127, 141, 127, 145, 150, 150, 141, 136, 150, 154, 136, 132, 141, 136, 141, 136, 127, 132, 127, 127, 127, 127, 127, 127, 127, 127, 127, 127, 127, 127, 127, 127, 127, 127, 127, 127, 132, 132, 132, 127, 136, 132, 136, 145, 136, 159, 163, 168, 145, 177, 172, 150, 154, 136, 150, 141, 136, 132, 127, 127, 127, 127, 127, 127, 127, 127, 127, 127, 127, 127, 127, 127, 127, 127, 127, 127, 127, 132, 136, 132, 136, 150, 150, 150, 136, 168, 163, 145, 159, 182, 186, 172, 159, 163, 159, 163, 132, 145, 136, 127, 132, 127, 127, 127, 127, 127, 127, 127, 127, 127, 127, 127, 127, 127, 127, 132, 127, 141, 150, 132, 132, 159, 136, 159, 154, 168, 177, 182, 213, 209, 218, 218, 204, 213, 177, 177, 159, 168, 145, 150, 145, 141, 127, 127, 127, 127, 127, 127, 127, 127, 127, 127, 127, 127, 127, 127, 127, 127, 136, 136, 141, 141, 159, 145, 177, 172, 177, 218, 227, 254, 222, 241, 255, 227, 204, 245, 209, 191, 182, 172, 163, 150, 141, 132, 132, 136, 132, 127, 127, 127, 127, 127, 127, 127, 127, 127, 127, 127, 127, 132, 141, 150, 150, 163, 163, 172, 186, 218, 255, 255, 255, 11519, 1279, 10495, 1279, 3327, 250, 232, 227, 209, 177, 168, 163, 150, 141, 136, 136, 127, 127, 127, 127, 127, 127, 127, 127, 127, 127, 127, 136, 132, 141, 145, 154, 163, 186, 186, 250, 255, 255, 14591, 17663, 17663, 11519, 21759, 23807, 16639, 16639, 9471, 13567, 254, 245, 200, 172, 150, 159, 154, 145, 136, 127, 132, 127, 127, 127, 127, 127, 127, 127, 127, 127, 136, 136, 136, 159, 168, 213, 218, 195, 250, 4351, 14591, 22783, 28927, 42239, 36095, 42239, 30975, 35071, 25855, 24831, 21759, 8447, 250, 227, 182, 163, 182, 163, 136, 141, 132, 127, 127, 132, 127, 127, 127, 127, 127, 136, 136, 132, 141, 145, 182, 177, 209, 222, 254, 5375, 20735, 22783, 31999, 53503, 50431, 849130, 53503, 585965, 53503, 33023, 43263, 29951, 19711, 17663, 245, 250, 204, 195, 154, 163, 132, 132, 132, 132, 127, 127, 127, 127, 132, 132, 127, 141, 141, 154, 154, 195, 218, 241, 1279, 15615, 23807, 30975, 31999, 1638365, 849130, 3997626, 3801021, 6094746, 2490320, 3342275, 2097111, 56574, 37119, 23807, 17663, 9471, 1279, 232, 191, 186, 159, 163, 136, 132, 127, 127, 127, 127, 127, 127, 136, 136, 145, 145, 150, 191, 177, 222, 255, 10495, 28927, 22783, 41215, 1638365, 1244388, 9502566, 9305961, 12058431, 7798656, 11599686, 9502566, 7995261, 1441761, 388337, 43263, 25855, 19711, 3327, 232, 204, 200, 159, 145, 127, 141, 127, 132, 127, 127, 127, 132, 141, 132, 141, 145, 182, 182, 222, 245, 10495, 11519, 30975, 44287, 56574, 1244388, 9305961], \"materials\": [{\"colorDiffuse\": [1, 0, 0], \"DbgColor\": 15658734, \"DbgName\": \"dummy\", \"DbgIndex\": 0}], \"vertices\": [-0.5, -0.50000000000000078, 0.0, -0.47499999999999998, -0.50000000000000078, 0.0, -0.45000000000000001, -0.50000000000000078, 0.0, -0.42499999999999999, -0.50000000000000078, 0.0, -0.40000000000000002, -0.50000000000000078, 0.0, -0.375, -0.50000000000000078, 0.0, -0.34999999999999998, -0.50000000000000078, 0.0, -0.32500000000000001, -0.50000000000000078, 0.0, -0.29999999999999999, -0.50000000000000078, 0.0, -0.27500000000000002, -0.50000000000000078, 0.0, -0.25, -0.50000000000000078, 0.0, -0.22499999999999998, -0.50000000000000078, 0.0, -0.20000000000000001, -0.50000000000000078, 0.0, -0.17499999999999999, -0.50000000000000078, 0.0, -0.15000000000000002, -0.50000000000000078, 0.0, -0.125, -0.50000000000000078, 0.0, -0.099999999999999978, -0.50000000000000078, 0.0, -0.075000000000000011, -0.50000000000000078, 0.0, -0.049999999999999989, -0.50000000000000078, 0.0, -0.025000000000000022, -0.50000000000000078, 0.0, 0.0, -0.50000000000000078, 0.0, 0.025000000000000022, -0.50000000000000078, 0.0, 0.050000000000000044, -0.50000000000000078, 0.0, 0.074999999999999956, -0.50000000000000078, 0.0, 0.099999999999999978, -0.50000000000000078, 0.0, 0.125, -0.50000000000000078, 0.0, 0.15000000000000002, -0.50000000000000078, 0.0, 0.17500000000000004, -0.50000000000000078, 0.0, 0.19999999999999996, -0.50000000000000078, 0.0, 0.22499999999999998, -0.50000000000000078, 0.0, 0.25, -0.50000000000000078, 0.0, 0.27500000000000002, -0.50000000000000078, 0.0, 0.30000000000000004, -0.50000000000000078, 0.0, 0.32499999999999996, -0.50000000000000078, 0.0, 0.34999999999999998, -0.50000000000000078, 0.0, 0.375, -0.50000000000000078, 0.0, 0.40000000000000002, -0.50000000000000078, 0.0, 0.42500000000000004, -0.50000000000000078, 0.0, 0.44999999999999996, -0.50000000000000078, 0.0, 0.47499999999999998, -0.50000000000000078, 0.0, 0.5, -0.50000000000000078, 0.0, -0.5, -0.47500000000000075, 0.0, -0.47499999999999998, -0.47500000000000075, 0.0, -0.45000000000000001, -0.47500000000000075, 0.0, -0.42499999999999999, -0.47500000000000075, 0.0, -0.40000000000000002, -0.47500000000000075, 0.0, -0.375, -0.47500000000000075, 0.0, -0.34999999999999998, -0.47500000000000075, 0.0, -0.32500000000000001, -0.47500000000000075, 0.0, -0.29999999999999999, -0.47500000000000075, 0.0, -0.27500000000000002, -0.47500000000000075, 0.0, -0.25, -0.47500000000000075, 0.0, -0.22499999999999998, -0.47500000000000075, 0.0, -0.20000000000000001, -0.47500000000000075, 0.0, -0.17499999999999999, -0.47500000000000075, 0.0, -0.15000000000000002, -0.47500000000000075, 0.0, -0.125, -0.47500000000000075, 0.0, -0.099999999999999978, -0.47500000000000075, 0.0, -0.075000000000000011, -0.47500000000000075, 0.0, -0.049999999999999989, -0.47500000000000075, 0.0, -0.025000000000000022, -0.47500000000000075, 0.0, 0.0, -0.47500000000000075, 0.0, 0.025000000000000022, -0.47500000000000075, 0.0, 0.050000000000000044, -0.47500000000000075, 0.0, 0.074999999999999956, -0.47500000000000075, 0.0, 0.099999999999999978, -0.47500000000000075, 0.0, 0.125, -0.47500000000000075, 0.0, 0.15000000000000002, -0.47500000000000075, 0.0, 0.17500000000000004, -0.47500000000000075, 0.0, 0.19999999999999996, -0.47500000000000075, 0.0, 0.22499999999999998, -0.47500000000000075, 0.0, 0.25, -0.47500000000000075, 0.0, 0.27500000000000002, -0.47500000000000075, 0.0, 0.30000000000000004, -0.47500000000000075, 0.0, 0.32499999999999996, -0.47500000000000075, 0.0, 0.34999999999999998, -0.47500000000000075, 0.0, 0.375, -0.47500000000000075, 0.0, 0.40000000000000002, -0.47500000000000075, 0.0, 0.42500000000000004, -0.47500000000000075, 0.0, 0.44999999999999996, -0.47500000000000075, 0.0, 0.47499999999999998, -0.47500000000000075, 0.0, 0.5, -0.47500000000000075, 0.0, -0.5, -0.45000000000000079, 0.0, -0.47499999999999998, -0.45000000000000079, 0.0, -0.45000000000000001, -0.45000000000000079, 0.0, -0.42499999999999999, -0.45000000000000079, 0.0, -0.40000000000000002, -0.45000000000000079, 0.0, -0.375, -0.45000000000000079, 0.0, -0.34999999999999998, -0.45000000000000079, 0.0, -0.32500000000000001, -0.45000000000000079, 0.0, -0.29999999999999999, -0.45000000000000079, 0.0, -0.27500000000000002, -0.45000000000000079, 0.0, -0.25, -0.45000000000000079, 0.0, -0.22499999999999998, -0.45000000000000079, 0.0, -0.20000000000000001, -0.45000000000000079, 0.0, -0.17499999999999999, -0.45000000000000079, 0.0, -0.15000000000000002, -0.45000000000000079, 0.0, -0.125, -0.45000000000000079, 0.0, -0.099999999999999978, -0.45000000000000079, 0.0, -0.075000000000000011, -0.45000000000000079, 0.0, -0.049999999999999989, -0.45000000000000079, 0.0, -0.025000000000000022, -0.45000000000000079, 0.0, 0.0, -0.45000000000000079, 0.0, 0.025000000000000022, -0.45000000000000079, 0.0, 0.050000000000000044, -0.45000000000000079, 0.0, 0.074999999999999956, -0.45000000000000079, 0.0, 0.099999999999999978, -0.45000000000000079, 0.0, 0.125, -0.45000000000000079, 0.0, 0.15000000000000002, -0.45000000000000079, 0.0, 0.17500000000000004, -0.45000000000000079, 0.0, 0.19999999999999996, -0.45000000000000079, 0.0, 0.22499999999999998, -0.45000000000000079, 0.0, 0.25, -0.45000000000000079, 0.0, 0.27500000000000002, -0.45000000000000079, 0.0, 0.30000000000000004, -0.45000000000000079, 0.0, 0.32499999999999996, -0.45000000000000079, 0.0, 0.34999999999999998, -0.45000000000000079, 0.0, 0.375, -0.45000000000000079, 0.0, 0.40000000000000002, -0.45000000000000079, 0.0, 0.42500000000000004, -0.45000000000000079, 0.0, 0.44999999999999996, -0.45000000000000079, 0.0, 0.47499999999999998, -0.45000000000000079, 0.0, 0.5, -0.45000000000000079, 0.0, -0.5, -0.42500000000000077, 0.0, -0.47499999999999998, -0.42500000000000077, 0.0, -0.45000000000000001, -0.42500000000000077, 0.0, -0.42499999999999999, -0.42500000000000077, 0.0, -0.40000000000000002, -0.42500000000000077, 0.0, -0.375, -0.42500000000000077, 0.0, -0.34999999999999998, -0.42500000000000077, 0.0, -0.32500000000000001, -0.42500000000000077, 0.0, -0.29999999999999999, -0.42500000000000077, 0.0, -0.27500000000000002, -0.42500000000000077, 0.0, -0.25, -0.42500000000000077, 0.0, -0.22499999999999998, -0.42500000000000077, 0.0, -0.20000000000000001, -0.42500000000000077, 0.0, -0.17499999999999999, -0.42500000000000077, 0.0, -0.15000000000000002, -0.42500000000000077, 0.0, -0.125, -0.42500000000000077, 0.0, -0.099999999999999978, -0.42500000000000077, 0.0, -0.075000000000000011, -0.42500000000000077, 0.0, -0.049999999999999989, -0.42500000000000077, 0.0, -0.025000000000000022, -0.42500000000000077, 0.0, 0.0, -0.42500000000000077, 0.0, 0.025000000000000022, -0.42500000000000077, 0.0, 0.050000000000000044, -0.42500000000000077, 0.0, 0.074999999999999956, -0.42500000000000077, 0.0, 0.099999999999999978, -0.42500000000000077, 0.0, 0.125, -0.42500000000000077, 0.0, 0.15000000000000002, -0.42500000000000077, 0.0, 0.17500000000000004, -0.42500000000000077, 0.0, 0.19999999999999996, -0.42500000000000077, 0.0, 0.22499999999999998, -0.42500000000000077, 0.0, 0.25, -0.42500000000000077, 0.0, 0.27500000000000002, -0.42500000000000077, 0.0, 0.30000000000000004, -0.42500000000000077, 0.0, 0.32499999999999996, -0.42500000000000077, 0.0, 0.34999999999999998, -0.42500000000000077, 0.0, 0.375, -0.42500000000000077, 0.0, 0.40000000000000002, -0.42500000000000077, 0.0, 0.42500000000000004, -0.42500000000000077, 0.0, 0.44999999999999996, -0.42500000000000077, 0.0, 0.47499999999999998, -0.42500000000000077, 0.0, 0.5, -0.42500000000000077, 0.0, -0.5, -0.4000000000000008, 0.0, -0.47499999999999998, -0.4000000000000008, 0.0, -0.45000000000000001, -0.4000000000000008, 0.0, -0.42499999999999999, -0.4000000000000008, 0.0, -0.40000000000000002, -0.4000000000000008, 0.0, -0.375, -0.4000000000000008, 0.0, -0.34999999999999998, -0.4000000000000008, 0.0, -0.32500000000000001, -0.4000000000000008, 0.0, -0.29999999999999999, -0.4000000000000008, 0.0, -0.27500000000000002, -0.4000000000000008, 0.0, -0.25, -0.4000000000000008, 0.0, -0.22499999999999998, -0.4000000000000008, 0.0, -0.20000000000000001, -0.4000000000000008, 0.0, -0.17499999999999999, -0.4000000000000008, 0.0, -0.15000000000000002, -0.4000000000000008, 0.0, -0.125, -0.4000000000000008, 0.0, -0.099999999999999978, -0.4000000000000008, 0.0, -0.075000000000000011, -0.4000000000000008, 0.0, -0.049999999999999989, -0.4000000000000008, 0.0, -0.025000000000000022, -0.4000000000000008, 0.0, 0.0, -0.4000000000000008, 0.0, 0.025000000000000022, -0.4000000000000008, 0.0, 0.050000000000000044, -0.4000000000000008, 0.0, 0.074999999999999956, -0.4000000000000008, 0.0, 0.099999999999999978, -0.4000000000000008, 0.0, 0.125, -0.4000000000000008, 0.0, 0.15000000000000002, -0.4000000000000008, 0.0, 0.17500000000000004, -0.4000000000000008, 0.0, 0.19999999999999996, -0.4000000000000008, 0.0, 0.22499999999999998, -0.4000000000000008, 0.0, 0.25, -0.4000000000000008, 0.0, 0.27500000000000002, -0.4000000000000008, 0.0, 0.30000000000000004, -0.4000000000000008, 0.0, 0.32499999999999996, -0.4000000000000008, 0.0, 0.34999999999999998, -0.4000000000000008, 0.0, 0.375, -0.4000000000000008, 0.0, 0.40000000000000002, -0.4000000000000008, 0.0, 0.42500000000000004, -0.4000000000000008, 0.0, 0.44999999999999996, -0.4000000000000008, 0.0, 0.47499999999999998, -0.4000000000000008, 0.0, 0.5, -0.4000000000000008, 0.0, -0.5, -0.37500000000000078, 0.0, -0.47499999999999998, -0.37500000000000078, 0.0, -0.45000000000000001, -0.37500000000000078, 0.0, -0.42499999999999999, -0.37500000000000078, 0.0, -0.40000000000000002, -0.37500000000000078, 0.0, -0.375, -0.37500000000000078, 0.0, -0.34999999999999998, -0.37500000000000078, 0.0, -0.32500000000000001, -0.37500000000000078, 0.0, -0.29999999999999999, -0.37500000000000078, 0.0, -0.27500000000000002, -0.37500000000000078, 0.0, -0.25, -0.37500000000000078, 0.0, -0.22499999999999998, -0.37500000000000078, 0.0, -0.20000000000000001, -0.37500000000000078, 0.0, -0.17499999999999999, -0.37500000000000078, 0.0, -0.15000000000000002, -0.37500000000000078, 0.0, -0.125, -0.37500000000000078, 0.0, -0.099999999999999978, -0.37500000000000078, 0.0, -0.075000000000000011, -0.37500000000000078, 0.0, -0.049999999999999989, -0.37500000000000078, 0.0, -0.025000000000000022, -0.37500000000000078, 0.0, 0.0, -0.37500000000000078, 0.0, 0.025000000000000022, -0.37500000000000078, 0.0, 0.050000000000000044, -0.37500000000000078, 0.0, 0.074999999999999956, -0.37500000000000078, 0.0, 0.099999999999999978, -0.37500000000000078, 0.0, 0.125, -0.37500000000000078, 0.0, 0.15000000000000002, -0.37500000000000078, 0.0, 0.17500000000000004, -0.37500000000000078, 0.0, 0.19999999999999996, -0.37500000000000078, 0.0, 0.22499999999999998, -0.37500000000000078, 0.0, 0.25, -0.37500000000000078, 0.0, 0.27500000000000002, -0.37500000000000078, 0.0, 0.30000000000000004, -0.37500000000000078, 0.0, 0.32499999999999996, -0.37500000000000078, 0.0, 0.34999999999999998, -0.37500000000000078, 0.0, 0.375, -0.37500000000000078, 0.0, 0.40000000000000002, -0.37500000000000078, 0.0, 0.42500000000000004, -0.37500000000000078, 0.0, 0.44999999999999996, -0.37500000000000078, 0.0, 0.47499999999999998, -0.37500000000000078, 0.0, 0.5, -0.37500000000000078, 0.0, -0.5, -0.35000000000000075, 0.0, -0.47499999999999998, -0.35000000000000075, 0.0, -0.45000000000000001, -0.35000000000000075, 0.0, -0.42499999999999999, -0.35000000000000075, 0.0, -0.40000000000000002, -0.35000000000000075, 0.0, -0.375, -0.35000000000000075, 0.0, -0.34999999999999998, -0.35000000000000075, 0.0, -0.32500000000000001, -0.35000000000000075, 0.0, -0.29999999999999999, -0.35000000000000075, 0.0, -0.27500000000000002, -0.35000000000000075, 0.0, -0.25, -0.35000000000000075, 0.0, -0.22499999999999998, -0.35000000000000075, 0.0, -0.20000000000000001, -0.35000000000000075, 0.0, -0.17499999999999999, -0.35000000000000075, 0.0, -0.15000000000000002, -0.35000000000000075, 0.0, -0.125, -0.35000000000000075, 0.0, -0.099999999999999978, -0.35000000000000075, 0.0, -0.075000000000000011, -0.35000000000000075, 0.0, -0.049999999999999989, -0.35000000000000075, 0.0, -0.025000000000000022, -0.35000000000000075, 0.0, 0.0, -0.35000000000000075, 0.0, 0.025000000000000022, -0.35000000000000075, 0.0, 0.050000000000000044, -0.35000000000000075, 0.0, 0.074999999999999956, -0.35000000000000075, 0.0, 0.099999999999999978, -0.35000000000000075, 0.0, 0.125, -0.35000000000000075, 0.0, 0.15000000000000002, -0.35000000000000075, 0.0, 0.17500000000000004, -0.35000000000000075, 0.0, 0.19999999999999996, -0.35000000000000075, 0.0, 0.22499999999999998, -0.35000000000000075, 0.0, 0.25, -0.35000000000000075, 0.0, 0.27500000000000002, -0.35000000000000075, 0.0, 0.30000000000000004, -0.35000000000000075, 0.0, 0.32499999999999996, -0.35000000000000075, 0.0, 0.34999999999999998, -0.35000000000000075, 0.0, 0.375, -0.35000000000000075, 0.0, 0.40000000000000002, -0.35000000000000075, 0.0, 0.42500000000000004, -0.35000000000000075, 0.0, 0.44999999999999996, -0.35000000000000075, 0.0, 0.47499999999999998, -0.35000000000000075, 0.0, 0.5, -0.35000000000000075, 0.0, -0.5, -0.32500000000000079, 0.0, -0.47499999999999998, -0.32500000000000079, 0.0, -0.45000000000000001, -0.32500000000000079, 0.0, -0.42499999999999999, -0.32500000000000079, 0.0, -0.40000000000000002, -0.32500000000000079, 0.0, -0.375, -0.32500000000000079, 0.0, -0.34999999999999998, -0.32500000000000079, 0.0, -0.32500000000000001, -0.32500000000000079, 0.0, -0.29999999999999999, -0.32500000000000079, 0.0, -0.27500000000000002, -0.32500000000000079, 0.0, -0.25, -0.32500000000000079, 0.0, -0.22499999999999998, -0.32500000000000079, 0.0, -0.20000000000000001, -0.32500000000000079, 0.0, -0.17499999999999999, -0.32500000000000079, 0.0, -0.15000000000000002, -0.32500000000000079, 0.0, -0.125, -0.32500000000000079, 0.0, -0.099999999999999978, -0.32500000000000079, 0.0, -0.075000000000000011, -0.32500000000000079, 0.0, -0.049999999999999989, -0.32500000000000079, 0.0, -0.025000000000000022, -0.32500000000000079, 0.0, 0.0, -0.32500000000000079, 0.0, 0.025000000000000022, -0.32500000000000079, 0.0, 0.050000000000000044, -0.32500000000000079, 0.0, 0.074999999999999956, -0.32500000000000079, 0.0, 0.099999999999999978, -0.32500000000000079, 0.0, 0.125, -0.32500000000000079, 0.0, 0.15000000000000002, -0.32500000000000079, 0.0, 0.17500000000000004, -0.32500000000000079, 0.0, 0.19999999999999996, -0.32500000000000079, 0.0, 0.22499999999999998, -0.32500000000000079, 0.0, 0.25, -0.32500000000000079, 0.0, 0.27500000000000002, -0.32500000000000079, 0.0, 0.30000000000000004, -0.32500000000000079, 0.0, 0.32499999999999996, -0.32500000000000079, 0.0, 0.34999999999999998, -0.32500000000000079, 0.0, 0.375, -0.32500000000000079, 0.0, 0.40000000000000002, -0.32500000000000079, 0.0, 0.42500000000000004, -0.32500000000000079, 0.0, 0.44999999999999996, -0.32500000000000079, 0.0, 0.47499999999999998, -0.32500000000000079, 0.0, 0.5, -0.32500000000000079, 0.0, -0.5, -0.30000000000000077, 0.0, -0.47499999999999998, -0.30000000000000077, 0.0, -0.45000000000000001, -0.30000000000000077, 0.0, -0.42499999999999999, -0.30000000000000077, 0.0, -0.40000000000000002, -0.30000000000000077, 0.0, -0.375, -0.30000000000000077, 0.0, -0.34999999999999998, -0.30000000000000077, 0.0, -0.32500000000000001, -0.30000000000000077, 0.0, -0.29999999999999999, -0.30000000000000077, 0.0, -0.27500000000000002, -0.30000000000000077, 0.0, -0.25, -0.30000000000000077, 0.0, -0.22499999999999998, -0.30000000000000077, 0.0, -0.20000000000000001, -0.30000000000000077, 0.0, -0.17499999999999999, -0.30000000000000077, 0.0, -0.15000000000000002, -0.30000000000000077, 0.0, -0.125, -0.30000000000000077, 0.0, -0.099999999999999978, -0.30000000000000077, 0.0, -0.075000000000000011, -0.30000000000000077, 0.0, -0.049999999999999989, -0.30000000000000077, 0.0, -0.025000000000000022, -0.30000000000000077, 0.0, 0.0, -0.30000000000000077, 0.0, 0.025000000000000022, -0.30000000000000077, 0.0, 0.050000000000000044, -0.30000000000000077, 0.0, 0.074999999999999956, -0.30000000000000077, 0.0, 0.099999999999999978, -0.30000000000000077, 0.0, 0.125, -0.30000000000000077, 0.0, 0.15000000000000002, -0.30000000000000077, 0.0, 0.17500000000000004, -0.30000000000000077, 0.0, 0.19999999999999996, -0.30000000000000077, 0.0, 0.22499999999999998, -0.30000000000000077, 0.0, 0.25, -0.30000000000000077, 0.0, 0.27500000000000002, -0.30000000000000077, 0.0, 0.30000000000000004, -0.30000000000000077, 0.0, 0.32499999999999996, -0.30000000000000077, 0.0, 0.34999999999999998, -0.30000000000000077, 0.0, 0.375, -0.30000000000000077, 0.0, 0.40000000000000002, -0.30000000000000077, 0.0, 0.42500000000000004, -0.30000000000000077, 0.0, 0.44999999999999996, -0.30000000000000077, 0.0, 0.47499999999999998, -0.30000000000000077, 0.0, 0.5, -0.30000000000000077, 0.0, -0.5, -0.2750000000000008, 0.0, -0.47499999999999998, -0.2750000000000008, 0.0, -0.45000000000000001, -0.2750000000000008, 0.0, -0.42499999999999999, -0.2750000000000008, 0.0, -0.40000000000000002, -0.2750000000000008, 0.0, -0.375, -0.2750000000000008, 0.0, -0.34999999999999998, -0.2750000000000008, 0.0, -0.32500000000000001, -0.2750000000000008, 0.0, -0.29999999999999999, -0.2750000000000008, 0.0, -0.27500000000000002, -0.2750000000000008, 0.0, -0.25, -0.2750000000000008, 0.0, -0.22499999999999998, -0.2750000000000008, 0.0, -0.20000000000000001, -0.2750000000000008, 0.0, -0.17499999999999999, -0.2750000000000008, 0.0, -0.15000000000000002, -0.2750000000000008, 0.0, -0.125, -0.2750000000000008, 0.0, -0.099999999999999978, -0.2750000000000008, 0.0, -0.075000000000000011, -0.2750000000000008, 0.0, -0.049999999999999989, -0.2750000000000008, 0.0, -0.025000000000000022, -0.2750000000000008, 0.0, 0.0, -0.2750000000000008, 0.0, 0.025000000000000022, -0.2750000000000008, 0.0, 0.050000000000000044, -0.2750000000000008, 0.0, 0.074999999999999956, -0.2750000000000008, 0.0, 0.099999999999999978, -0.2750000000000008, 0.0, 0.125, -0.2750000000000008, 0.0, 0.15000000000000002, -0.2750000000000008, 0.0, 0.17500000000000004, -0.2750000000000008, 0.0, 0.19999999999999996, -0.2750000000000008, 0.0, 0.22499999999999998, -0.2750000000000008, 0.0, 0.25, -0.2750000000000008, 0.0, 0.27500000000000002, -0.2750000000000008, 0.0, 0.30000000000000004, -0.2750000000000008, 0.0, 0.32499999999999996, -0.2750000000000008, 0.0, 0.34999999999999998, -0.2750000000000008, 0.0, 0.375, -0.2750000000000008, 0.0, 0.40000000000000002, -0.2750000000000008, 0.0, 0.42500000000000004, -0.2750000000000008, 0.0, 0.44999999999999996, -0.2750000000000008, 0.0, 0.47499999999999998, -0.2750000000000008, 0.0, 0.5, -0.2750000000000008, 0.0, -0.5, -0.25000000000000078, 0.0, -0.47499999999999998, -0.25000000000000078, 0.0, -0.45000000000000001, -0.25000000000000078, 0.0, -0.42499999999999999, -0.25000000000000078, 0.0, -0.40000000000000002, -0.25000000000000078, 0.0, -0.375, -0.25000000000000078, 0.0, -0.34999999999999998, -0.25000000000000078, 0.0, -0.32500000000000001, -0.25000000000000078, 0.0, -0.29999999999999999, -0.25000000000000078, 0.0, -0.27500000000000002, -0.25000000000000078, 0.0, -0.25, -0.25000000000000078, 0.0, -0.22499999999999998, -0.25000000000000078, 0.0, -0.20000000000000001, -0.25000000000000078, 0.0, -0.17499999999999999, -0.25000000000000078, 0.0, -0.15000000000000002, -0.25000000000000078, 0.0, -0.125, -0.25000000000000078, 0.0, -0.099999999999999978, -0.25000000000000078, 0.0, -0.075000000000000011, -0.25000000000000078, 0.0, -0.049999999999999989, -0.25000000000000078, 0.0, -0.025000000000000022, -0.25000000000000078, 0.0, 0.0, -0.25000000000000078, 0.0, 0.025000000000000022, -0.25000000000000078, 0.0, 0.050000000000000044, -0.25000000000000078, 0.0, 0.074999999999999956, -0.25000000000000078, 0.0, 0.099999999999999978, -0.25000000000000078, 0.0, 0.125, -0.25000000000000078, 0.0, 0.15000000000000002, -0.25000000000000078, 0.0, 0.17500000000000004, -0.25000000000000078, 0.0, 0.19999999999999996, -0.25000000000000078, 0.0, 0.22499999999999998, -0.25000000000000078, 0.0, 0.25, -0.25000000000000078, 0.0, 0.27500000000000002, -0.25000000000000078, 0.0, 0.30000000000000004, -0.25000000000000078, 0.0, 0.32499999999999996, -0.25000000000000078, 0.0, 0.34999999999999998, -0.25000000000000078, 0.0, 0.375, -0.25000000000000078, 0.0, 0.40000000000000002, -0.25000000000000078, 0.0, 0.42500000000000004, -0.25000000000000078, 0.0, 0.44999999999999996, -0.25000000000000078, 0.0, 0.47499999999999998, -0.25000000000000078, 0.0, 0.5, -0.25000000000000078, 0.0, -0.5, -0.22500000000000075, 0.0, -0.47499999999999998, -0.22500000000000075, 0.0, -0.45000000000000001, -0.22500000000000075, 0.0, -0.42499999999999999, -0.22500000000000075, 0.0, -0.40000000000000002, -0.22500000000000075, 0.0, -0.375, -0.22500000000000075, 0.0, -0.34999999999999998, -0.22500000000000075, 0.0, -0.32500000000000001, -0.22500000000000075, 0.0, -0.29999999999999999, -0.22500000000000075, 0.0, -0.27500000000000002, -0.22500000000000075, 0.0, -0.25, -0.22500000000000075, 0.0, -0.22499999999999998, -0.22500000000000075, 0.0, -0.20000000000000001, -0.22500000000000075, 0.0, -0.17499999999999999, -0.22500000000000075, 0.0, -0.15000000000000002, -0.22500000000000075, 0.0, -0.125, -0.22500000000000075, 0.0, -0.099999999999999978, -0.22500000000000075, 0.0, -0.075000000000000011, -0.22500000000000075, 0.0, -0.049999999999999989, -0.22500000000000075, 0.0, -0.025000000000000022, -0.22500000000000075, 0.0, 0.0, -0.22500000000000075, 0.0, 0.025000000000000022, -0.22500000000000075, 0.0, 0.050000000000000044, -0.22500000000000075, 0.0, 0.074999999999999956, -0.22500000000000075, 0.0, 0.099999999999999978, -0.22500000000000075, 0.0, 0.125, -0.22500000000000075, 0.0, 0.15000000000000002, -0.22500000000000075, 0.0, 0.17500000000000004, -0.22500000000000075, 0.0, 0.19999999999999996, -0.22500000000000075, 0.0, 0.22499999999999998, -0.22500000000000075, 0.0, 0.25, -0.22500000000000075, 0.0, 0.27500000000000002, -0.22500000000000075, 0.0, 0.30000000000000004, -0.22500000000000075, 0.0, 0.32499999999999996, -0.22500000000000075, 0.0, 0.34999999999999998, -0.22500000000000075, 0.0, 0.375, -0.22500000000000075, 0.0, 0.40000000000000002, -0.22500000000000075, 0.0, 0.42500000000000004, -0.22500000000000075, 0.0, 0.44999999999999996, -0.22500000000000075, 0.0, 0.47499999999999998, -0.22500000000000075, 0.0, 0.5, -0.22500000000000075, 0.0, -0.5, -0.20000000000000079, 0.0, -0.47499999999999998, -0.20000000000000079, 0.0, -0.45000000000000001, -0.20000000000000079, 0.0, -0.42499999999999999, -0.20000000000000079, 0.0, -0.40000000000000002, -0.20000000000000079, 0.0, -0.375, -0.20000000000000079, 0.0, -0.34999999999999998, -0.20000000000000079, 0.0, -0.32500000000000001, -0.20000000000000079, 0.0, -0.29999999999999999, -0.20000000000000079, 0.0, -0.27500000000000002, -0.20000000000000079, 0.0, -0.25, -0.20000000000000079, 0.0, -0.22499999999999998, -0.20000000000000079, 0.0, -0.20000000000000001, -0.20000000000000079, 0.0, -0.17499999999999999, -0.20000000000000079, 0.0, -0.15000000000000002, -0.20000000000000079, 0.0, -0.125, -0.20000000000000079, 0.0, -0.099999999999999978, -0.20000000000000079, 0.0, -0.075000000000000011, -0.20000000000000079, 0.0, -0.049999999999999989, -0.20000000000000079, 0.0, -0.025000000000000022, -0.20000000000000079, 0.0, 0.0, -0.20000000000000079, 0.0, 0.025000000000000022, -0.20000000000000079, 0.0, 0.050000000000000044, -0.20000000000000079, 0.0, 0.074999999999999956, -0.20000000000000079, 0.0, 0.099999999999999978, -0.20000000000000079, 0.0, 0.125, -0.20000000000000079, 0.0, 0.15000000000000002, -0.20000000000000079, 0.0, 0.17500000000000004, -0.20000000000000079, 0.0, 0.19999999999999996, -0.20000000000000079, 0.0, 0.22499999999999998, -0.20000000000000079, 0.0, 0.25, -0.20000000000000079, 0.0, 0.27500000000000002, -0.20000000000000079, 0.0, 0.30000000000000004, -0.20000000000000079, 0.0, 0.32499999999999996, -0.20000000000000079, 0.0, 0.34999999999999998, -0.20000000000000079, 0.0, 0.375, -0.20000000000000079, 0.0, 0.40000000000000002, -0.20000000000000079, 0.0, 0.42500000000000004, -0.20000000000000079, 0.0, 0.44999999999999996, -0.20000000000000079, 0.0, 0.47499999999999998, -0.20000000000000079, 0.0, 0.5, -0.20000000000000079, 0.0, -0.5, -0.17500000000000077, 0.0, -0.47499999999999998, -0.17500000000000077, 0.0, -0.45000000000000001, -0.17500000000000077, 0.0, -0.42499999999999999, -0.17500000000000077, 0.0, -0.40000000000000002, -0.17500000000000077, 0.0, -0.375, -0.17500000000000077, 0.0, -0.34999999999999998, -0.17500000000000077, 0.0, -0.32500000000000001, -0.17500000000000077, 0.0, -0.29999999999999999, -0.17500000000000077, 0.0, -0.27500000000000002, -0.17500000000000077, 0.0, -0.25, -0.17500000000000077, 0.0, -0.22499999999999998, -0.17500000000000077, 0.0, -0.20000000000000001, -0.17500000000000077, 0.0, -0.17499999999999999, -0.17500000000000077, 0.0, -0.15000000000000002, -0.17500000000000077, 0.0, -0.125, -0.17500000000000077, 0.0, -0.099999999999999978, -0.17500000000000077, 0.0, -0.075000000000000011, -0.17500000000000077, 0.0, -0.049999999999999989, -0.17500000000000077, 0.0, -0.025000000000000022, -0.17500000000000077, 0.0, 0.0, -0.17500000000000077, 0.0, 0.025000000000000022, -0.17500000000000077, 0.0, 0.050000000000000044, -0.17500000000000077, 0.0, 0.074999999999999956, -0.17500000000000077, 0.0, 0.099999999999999978, -0.17500000000000077, 0.0, 0.125, -0.17500000000000077, 0.0, 0.15000000000000002, -0.17500000000000077, 0.0, 0.17500000000000004, -0.17500000000000077, 0.0, 0.19999999999999996, -0.17500000000000077, 0.0, 0.22499999999999998, -0.17500000000000077, 0.0, 0.25, -0.17500000000000077, 0.0, 0.27500000000000002, -0.17500000000000077, 0.0, 0.30000000000000004, -0.17500000000000077, 0.0, 0.32499999999999996, -0.17500000000000077, 0.0, 0.34999999999999998, -0.17500000000000077, 0.0, 0.375, -0.17500000000000077, 0.0, 0.40000000000000002, -0.17500000000000077, 0.0, 0.42500000000000004, -0.17500000000000077, 0.0, 0.44999999999999996, -0.17500000000000077, 0.0, 0.47499999999999998, -0.17500000000000077, 0.0, 0.5, -0.17500000000000077, 0.0, -0.5, -0.1500000000000008, 0.0, -0.47499999999999998, -0.1500000000000008, 0.0, -0.45000000000000001, -0.1500000000000008, 0.0, -0.42499999999999999, -0.1500000000000008, 0.0, -0.40000000000000002, -0.1500000000000008, 0.0, -0.375, -0.1500000000000008, 0.0, -0.34999999999999998, -0.1500000000000008, 0.0, -0.32500000000000001, -0.1500000000000008, 0.0, -0.29999999999999999, -0.1500000000000008, 0.0, -0.27500000000000002, -0.1500000000000008, 0.0, -0.25, -0.1500000000000008, 0.0, -0.22499999999999998, -0.1500000000000008, 0.0, -0.20000000000000001, -0.1500000000000008, 0.0, -0.17499999999999999, -0.1500000000000008, 0.0, -0.15000000000000002, -0.1500000000000008, 0.0, -0.125, -0.1500000000000008, 0.0, -0.099999999999999978, -0.1500000000000008, 0.0, -0.075000000000000011, -0.1500000000000008, 0.0, -0.049999999999999989, -0.1500000000000008, 0.0, -0.025000000000000022, -0.1500000000000008, 0.0, 0.0, -0.1500000000000008, 0.0, 0.025000000000000022, -0.1500000000000008, 0.0, 0.050000000000000044, -0.1500000000000008, 0.0, 0.074999999999999956, -0.1500000000000008, 0.0, 0.099999999999999978, -0.1500000000000008, 0.0, 0.125, -0.1500000000000008, 0.0, 0.15000000000000002, -0.1500000000000008, 0.0, 0.17500000000000004, -0.1500000000000008, 0.0, 0.19999999999999996, -0.1500000000000008, 0.0, 0.22499999999999998, -0.1500000000000008, 0.0, 0.25, -0.1500000000000008, 0.0, 0.27500000000000002, -0.1500000000000008, 0.0, 0.30000000000000004, -0.1500000000000008, 0.0, 0.32499999999999996, -0.1500000000000008, 0.0, 0.34999999999999998, -0.1500000000000008, 0.0, 0.375, -0.1500000000000008, 0.0, 0.40000000000000002, -0.1500000000000008, 0.0, 0.42500000000000004, -0.1500000000000008, 0.0, 0.44999999999999996, -0.1500000000000008, 0.0, 0.47499999999999998, -0.1500000000000008, 0.0, 0.5, -0.1500000000000008, 0.0, -0.5, -0.12500000000000078, 0.0, -0.47499999999999998, -0.12500000000000078, 0.0, -0.45000000000000001, -0.12500000000000078, 0.0, -0.42499999999999999, -0.12500000000000078, 0.0, -0.40000000000000002, -0.12500000000000078, 0.0, -0.375, -0.12500000000000078, 0.0, -0.34999999999999998, -0.12500000000000078, 0.0, -0.32500000000000001, -0.12500000000000078, 0.0, -0.29999999999999999, -0.12500000000000078, 0.0, -0.27500000000000002, -0.12500000000000078, 0.0, -0.25, -0.12500000000000078, 0.0, -0.22499999999999998, -0.12500000000000078, 0.0, -0.20000000000000001, -0.12500000000000078, 0.0, -0.17499999999999999, -0.12500000000000078, 0.0, -0.15000000000000002, -0.12500000000000078, 0.0, -0.125, -0.12500000000000078, 0.0, -0.099999999999999978, -0.12500000000000078, 0.0, -0.075000000000000011, -0.12500000000000078, 0.0, -0.049999999999999989, -0.12500000000000078, 0.0, -0.025000000000000022, -0.12500000000000078, 0.0, 0.0, -0.12500000000000078, 0.0, 0.025000000000000022, -0.12500000000000078, 0.0, 0.050000000000000044, -0.12500000000000078, 0.0, 0.074999999999999956, -0.12500000000000078, 0.0, 0.099999999999999978, -0.12500000000000078, 0.0, 0.125, -0.12500000000000078, 0.0, 0.15000000000000002, -0.12500000000000078, 0.0, 0.17500000000000004, -0.12500000000000078, 0.0, 0.19999999999999996, -0.12500000000000078, 0.0, 0.22499999999999998, -0.12500000000000078, 0.0, 0.25, -0.12500000000000078, 0.0, 0.27500000000000002, -0.12500000000000078, 0.0, 0.30000000000000004, -0.12500000000000078, 0.0, 0.32499999999999996, -0.12500000000000078, 0.0, 0.34999999999999998, -0.12500000000000078, 0.0, 0.375, -0.12500000000000078, 0.0, 0.40000000000000002, -0.12500000000000078, 0.0, 0.42500000000000004, -0.12500000000000078, 0.0, 0.44999999999999996, -0.12500000000000078, 0.0, 0.47499999999999998, -0.12500000000000078, 0.0, 0.5, -0.12500000000000078, 0.0, -0.5, -0.10000000000000075, 0.0, -0.47499999999999998, -0.10000000000000075, 0.0, -0.45000000000000001, -0.10000000000000075, 0.0, -0.42499999999999999, -0.10000000000000075, 0.0, -0.40000000000000002, -0.10000000000000075, 0.0, -0.375, -0.10000000000000075, 0.0, -0.34999999999999998, -0.10000000000000075, 0.0, -0.32500000000000001, -0.10000000000000075, 0.0, -0.29999999999999999, -0.10000000000000075, 0.0, -0.27500000000000002, -0.10000000000000075, 0.0, -0.25, -0.10000000000000075, 0.0, -0.22499999999999998, -0.10000000000000075, 0.0, -0.20000000000000001, -0.10000000000000075, 0.0, -0.17499999999999999, -0.10000000000000075, 0.0, -0.15000000000000002, -0.10000000000000075, 0.0, -0.125, -0.10000000000000075, 0.0, -0.099999999999999978, -0.10000000000000075, 0.0, -0.075000000000000011, -0.10000000000000075, 0.0, -0.049999999999999989, -0.10000000000000075, 0.0, -0.025000000000000022, -0.10000000000000075, 0.0, 0.0, -0.10000000000000075, 0.0, 0.025000000000000022, -0.10000000000000075, 0.0, 0.050000000000000044, -0.10000000000000075, 0.0, 0.074999999999999956, -0.10000000000000075, 0.0, 0.099999999999999978, -0.10000000000000075, 0.0, 0.125, -0.10000000000000075, 0.0, 0.15000000000000002, -0.10000000000000075, 0.0, 0.17500000000000004, -0.10000000000000075, 0.0, 0.19999999999999996, -0.10000000000000075, 0.0, 0.22499999999999998, -0.10000000000000075, 0.0, 0.25, -0.10000000000000075, 0.0, 0.27500000000000002, -0.10000000000000075, 0.0, 0.30000000000000004, -0.10000000000000075, 0.0, 0.32499999999999996, -0.10000000000000075, 0.0, 0.34999999999999998, -0.10000000000000075, 0.0, 0.375, -0.10000000000000075, 0.0, 0.40000000000000002, -0.10000000000000075, 0.0, 0.42500000000000004, -0.10000000000000075, 0.0, 0.44999999999999996, -0.10000000000000075, 0.0, 0.47499999999999998, -0.10000000000000075, 0.0, 0.5, -0.10000000000000075, 0.0, -0.5, -0.075000000000000788, 0.0, -0.47499999999999998, -0.075000000000000788, 0.0, -0.45000000000000001, -0.075000000000000788, 0.0, -0.42499999999999999, -0.075000000000000788, 0.0, -0.40000000000000002, -0.075000000000000788, 0.0, -0.375, -0.075000000000000788, 0.0, -0.34999999999999998, -0.075000000000000788, 0.0, -0.32500000000000001, -0.075000000000000788, 0.0, -0.29999999999999999, -0.075000000000000788, 0.0, -0.27500000000000002, -0.075000000000000788, 0.0, -0.25, -0.075000000000000788, 0.0, -0.22499999999999998, -0.075000000000000788, 0.0, -0.20000000000000001, -0.075000000000000788, 0.0, -0.17499999999999999, -0.075000000000000788, 0.0, -0.15000000000000002, -0.075000000000000788, 0.0, -0.125, -0.075000000000000788, 0.0, -0.099999999999999978, -0.075000000000000788, 0.0, -0.075000000000000011, -0.075000000000000788, 0.0, -0.049999999999999989, -0.075000000000000788, 0.0, -0.025000000000000022, -0.075000000000000788, 0.0, 0.0, -0.075000000000000788, 0.0, 0.025000000000000022, -0.075000000000000788, 0.0, 0.050000000000000044, -0.075000000000000788, 0.0, 0.074999999999999956, -0.075000000000000788, 0.0, 0.099999999999999978, -0.075000000000000788, 0.0, 0.125, -0.075000000000000788, 0.0, 0.15000000000000002, -0.075000000000000788, 0.0, 0.17500000000000004, -0.075000000000000788, 0.0, 0.19999999999999996, -0.075000000000000788, 0.0, 0.22499999999999998, -0.075000000000000788, 0.0, 0.25, -0.075000000000000788, 0.0, 0.27500000000000002, -0.075000000000000788, 0.0, 0.30000000000000004, -0.075000000000000788, 0.0, 0.32499999999999996, -0.075000000000000788, 0.0, 0.34999999999999998, -0.075000000000000788, 0.0, 0.375, -0.075000000000000788, 0.0, 0.40000000000000002, -0.075000000000000788, 0.0, 0.42500000000000004, -0.075000000000000788, 0.0, 0.44999999999999996, -0.075000000000000788, 0.0, 0.47499999999999998, -0.075000000000000788, 0.0, 0.5, -0.075000000000000788, 0.0, -0.5, -0.050000000000000766, 0.0, -0.47499999999999998, -0.050000000000000766, 0.0, -0.45000000000000001, -0.050000000000000766, 0.0, -0.42499999999999999, -0.050000000000000766, 0.0, -0.40000000000000002, -0.050000000000000766, 0.0, -0.375, -0.050000000000000766, 0.0, -0.34999999999999998, -0.050000000000000766, 0.0, -0.32500000000000001, -0.050000000000000766, 0.0, -0.29999999999999999, -0.050000000000000766, 0.0, -0.27500000000000002, -0.050000000000000766, 0.0, -0.25, -0.050000000000000766, 0.0, -0.22499999999999998, -0.050000000000000766, 0.0, -0.20000000000000001, -0.050000000000000766, 0.0, -0.17499999999999999, -0.050000000000000766, 0.0, -0.15000000000000002, -0.050000000000000766, 0.0, -0.125, -0.050000000000000766, 0.0, -0.099999999999999978, -0.050000000000000766, 0.0, -0.075000000000000011, -0.050000000000000766, 0.0, -0.049999999999999989, -0.050000000000000766, 0.0, -0.025000000000000022, -0.050000000000000766, 0.0, 0.0, -0.050000000000000766, 0.0, 0.025000000000000022, -0.050000000000000766, 0.0, 0.050000000000000044, -0.050000000000000766, 0.0, 0.074999999999999956, -0.050000000000000766, 0.0, 0.099999999999999978, -0.050000000000000766, 0.0, 0.125, -0.050000000000000766, 0.0, 0.15000000000000002, -0.050000000000000766, 0.0, 0.17500000000000004, -0.050000000000000766, 0.0, 0.19999999999999996, -0.050000000000000766, 0.0, 0.22499999999999998, -0.050000000000000766, 0.0, 0.25, -0.050000000000000766, 0.0, 0.27500000000000002, -0.050000000000000766, 0.0, 0.30000000000000004, -0.050000000000000766, 0.0, 0.32499999999999996, -0.050000000000000766, 0.0, 0.34999999999999998, -0.050000000000000766, 0.0, 0.375, -0.050000000000000766, 0.0, 0.40000000000000002, -0.050000000000000766, 0.0, 0.42500000000000004, -0.050000000000000766, 0.0, 0.44999999999999996, -0.050000000000000766, 0.0, 0.47499999999999998, -0.050000000000000766, 0.0, 0.5, -0.050000000000000766, 0.0, -0.5, -0.025000000000000799, 0.0, -0.47499999999999998, -0.025000000000000799, 0.0, -0.45000000000000001, -0.025000000000000799, 0.0, -0.42499999999999999, -0.025000000000000799, 0.0, -0.40000000000000002, -0.025000000000000799, 0.0, -0.375, -0.025000000000000799, 0.0, -0.34999999999999998, -0.025000000000000799, 0.0, -0.32500000000000001, -0.025000000000000799, 0.0, -0.29999999999999999, -0.025000000000000799, 0.0, -0.27500000000000002, -0.025000000000000799, 0.0, -0.25, -0.025000000000000799, 0.0, -0.22499999999999998, -0.025000000000000799, 0.0, -0.20000000000000001, -0.025000000000000799, 0.0, -0.17499999999999999, -0.025000000000000799, 0.0, -0.15000000000000002, -0.025000000000000799, 0.0, -0.125, -0.025000000000000799, 0.0, -0.099999999999999978, -0.025000000000000799, 0.0, -0.075000000000000011, -0.025000000000000799, 0.0, -0.049999999999999989, -0.025000000000000799, 0.0, -0.025000000000000022, -0.025000000000000799, 0.0, 0.0, -0.025000000000000799, 0.0, 0.025000000000000022, -0.025000000000000799, 0.0, 0.050000000000000044, -0.025000000000000799, 0.0, 0.074999999999999956, -0.025000000000000799, 0.0, 0.099999999999999978, -0.025000000000000799, 0.0, 0.125, -0.025000000000000799, 0.0, 0.15000000000000002, -0.025000000000000799, 0.0, 0.17500000000000004, -0.025000000000000799, 0.0, 0.19999999999999996, -0.025000000000000799, 0.0, 0.22499999999999998, -0.025000000000000799, 0.0, 0.25, -0.025000000000000799, 0.0, 0.27500000000000002, -0.025000000000000799, 0.0, 0.30000000000000004, -0.025000000000000799, 0.0, 0.32499999999999996, -0.025000000000000799, 0.0, 0.34999999999999998, -0.025000000000000799, 0.0, 0.375, -0.025000000000000799, 0.0, 0.40000000000000002, -0.025000000000000799, 0.0, 0.42500000000000004, -0.025000000000000799, 0.0, 0.44999999999999996, -0.025000000000000799, 0.0, 0.47499999999999998, -0.025000000000000799, 0.0, 0.5, -0.025000000000000799, 0.0, -0.5, -7.7715611723760958e-16, 0.0, -0.47499999999999998, -7.7715611723760958e-16, 0.0, -0.45000000000000001, -7.7715611723760958e-16, 0.0, -0.42499999999999999, -7.7715611723760958e-16, 0.0, -0.40000000000000002, -7.7715611723760958e-16, 0.0, -0.375, -7.7715611723760958e-16, 0.0, -0.34999999999999998, -7.7715611723760958e-16, 0.0, -0.32500000000000001, -7.7715611723760958e-16, 0.0, -0.29999999999999999, -7.7715611723760958e-16, 0.0, -0.27500000000000002, -7.7715611723760958e-16, 0.0, -0.25, -7.7715611723760958e-16, 0.0, -0.22499999999999998, -7.7715611723760958e-16, 0.0, -0.20000000000000001, -7.7715611723760958e-16, 0.0, -0.17499999999999999, -7.7715611723760958e-16, 0.0, -0.15000000000000002, -7.7715611723760958e-16, 0.0, -0.125, -7.7715611723760958e-16, 0.0, -0.099999999999999978, -7.7715611723760958e-16, 0.0, -0.075000000000000011, -7.7715611723760958e-16, 0.0, -0.049999999999999989, -7.7715611723760958e-16, 0.0, -0.025000000000000022, -7.7715611723760958e-16, 0.0, 0.0, -7.7715611723760958e-16, 0.0, 0.025000000000000022, -7.7715611723760958e-16, 0.0, 0.050000000000000044, -7.7715611723760958e-16, 0.0, 0.074999999999999956, -7.7715611723760958e-16, 0.0, 0.099999999999999978, -7.7715611723760958e-16, 0.0, 0.125, -7.7715611723760958e-16, 0.0, 0.15000000000000002, -7.7715611723760958e-16, 0.0, 0.17500000000000004, -7.7715611723760958e-16, 0.0, 0.19999999999999996, -7.7715611723760958e-16, 0.0, 0.22499999999999998, -7.7715611723760958e-16, 0.0, 0.25, -7.7715611723760958e-16, 0.0, 0.27500000000000002, -7.7715611723760958e-16, 0.0, 0.30000000000000004, -7.7715611723760958e-16, 0.0, 0.32499999999999996, -7.7715611723760958e-16, 0.0, 0.34999999999999998, -7.7715611723760958e-16, 0.0, 0.375, -7.7715611723760958e-16, 0.0, 0.40000000000000002, -7.7715611723760958e-16, 0.0, 0.42500000000000004, -7.7715611723760958e-16, 0.0, 0.44999999999999996, -7.7715611723760958e-16, 0.0, 0.47499999999999998, -7.7715611723760958e-16, 0.0, 0.5, -7.7715611723760958e-16, 0.0, -0.5, 0.024999999999999245, 0.0, -0.47499999999999998, 0.024999999999999245, 0.0, -0.45000000000000001, 0.024999999999999245, 0.0, -0.42499999999999999, 0.024999999999999245, 0.0, -0.40000000000000002, 0.024999999999999245, 0.0, -0.375, 0.024999999999999245, 0.0, -0.34999999999999998, 0.024999999999999245, 0.0, -0.32500000000000001, 0.024999999999999245, 0.0, -0.29999999999999999, 0.024999999999999245, 0.0, -0.27500000000000002, 0.024999999999999245, 0.0, -0.25, 0.024999999999999245, 0.0, -0.22499999999999998, 0.024999999999999245, 0.0, -0.20000000000000001, 0.024999999999999245, 0.0, -0.17499999999999999, 0.024999999999999245, 0.0, -0.15000000000000002, 0.024999999999999245, 0.0, -0.125, 0.024999999999999245, 0.0, -0.099999999999999978, 0.024999999999999245, 0.0, -0.075000000000000011, 0.024999999999999245, 0.0, -0.049999999999999989, 0.024999999999999245, 0.0, -0.025000000000000022, 0.024999999999999245, 0.0, 0.0, 0.024999999999999245, 0.0, 0.025000000000000022, 0.024999999999999245, 0.0, 0.050000000000000044, 0.024999999999999245, 0.0, 0.074999999999999956, 0.024999999999999245, 0.0, 0.099999999999999978, 0.024999999999999245, 0.0, 0.125, 0.024999999999999245, 0.0, 0.15000000000000002, 0.024999999999999245, 0.0, 0.17500000000000004, 0.024999999999999245, 0.0, 0.19999999999999996, 0.024999999999999245, 0.0, 0.22499999999999998, 0.024999999999999245, 0.0, 0.25, 0.024999999999999245, 0.0, 0.27500000000000002, 0.024999999999999245, 0.0, 0.30000000000000004, 0.024999999999999245, 0.0, 0.32499999999999996, 0.024999999999999245, 0.0, 0.34999999999999998, 0.024999999999999245, 0.0, 0.375, 0.024999999999999245, 0.0, 0.40000000000000002, 0.024999999999999245, 0.0, 0.42500000000000004, 0.024999999999999245, 0.0, 0.44999999999999996, 0.024999999999999245, 0.0, 0.47499999999999998, 0.024999999999999245, 0.0, 0.5, 0.024999999999999245, 0.0, -0.5, 0.049999999999999267, 0.0, -0.47499999999999998, 0.049999999999999267, 0.0, -0.45000000000000001, 0.049999999999999267, 0.0, -0.42499999999999999, 0.049999999999999267, 0.0, -0.40000000000000002, 0.049999999999999267, 0.0, -0.375, 0.049999999999999267, 0.0, -0.34999999999999998, 0.049999999999999267, 0.0, -0.32500000000000001, 0.049999999999999267, 0.0, -0.29999999999999999, 0.049999999999999267, 0.0, -0.27500000000000002, 0.049999999999999267, 0.0, -0.25, 0.049999999999999267, 0.0, -0.22499999999999998, 0.049999999999999267, 0.0, -0.20000000000000001, 0.049999999999999267, 0.0, -0.17499999999999999, 0.049999999999999267, 0.0, -0.15000000000000002, 0.049999999999999267, 0.0, -0.125, 0.049999999999999267, 0.0, -0.099999999999999978, 0.049999999999999267, 0.0, -0.075000000000000011, 0.049999999999999267, 0.0, -0.049999999999999989, 0.049999999999999267, 0.0, -0.025000000000000022, 0.049999999999999267, 0.0, 0.0, 0.049999999999999267, 0.0, 0.025000000000000022, 0.049999999999999267, 0.0, 0.050000000000000044, 0.049999999999999267, 0.0, 0.074999999999999956, 0.049999999999999267, 0.0, 0.099999999999999978, 0.049999999999999267, 0.0, 0.125, 0.049999999999999267, 0.0, 0.15000000000000002, 0.049999999999999267, 0.0, 0.17500000000000004, 0.049999999999999267, 0.0, 0.19999999999999996, 0.049999999999999267, 0.0, 0.22499999999999998, 0.049999999999999267, 0.0, 0.25, 0.049999999999999267, 0.0, 0.27500000000000002, 0.049999999999999267, 0.0, 0.30000000000000004, 0.049999999999999267, 0.0, 0.32499999999999996, 0.049999999999999267, 0.0, 0.34999999999999998, 0.049999999999999267, 0.0, 0.375, 0.049999999999999267, 0.0, 0.40000000000000002, 0.049999999999999267, 0.0, 0.42500000000000004, 0.049999999999999267, 0.0, 0.44999999999999996, 0.049999999999999267, 0.0, 0.47499999999999998, 0.049999999999999267, 0.0, 0.5, 0.049999999999999267, 0.0, -0.5, 0.074999999999999178, 0.0, -0.47499999999999998, 0.074999999999999178, 0.0, -0.45000000000000001, 0.074999999999999178, 0.0, -0.42499999999999999, 0.074999999999999178, 0.0, -0.40000000000000002, 0.074999999999999178, 0.0, -0.375, 0.074999999999999178, 0.0, -0.34999999999999998, 0.074999999999999178, 0.0, -0.32500000000000001, 0.074999999999999178, 0.0, -0.29999999999999999, 0.074999999999999178, 0.0, -0.27500000000000002, 0.074999999999999178, 0.0, -0.25, 0.074999999999999178, 0.0, -0.22499999999999998, 0.074999999999999178, 0.0, -0.20000000000000001, 0.074999999999999178, 0.0, -0.17499999999999999, 0.074999999999999178, 0.0, -0.15000000000000002, 0.074999999999999178, 0.0, -0.125, 0.074999999999999178, 0.0, -0.099999999999999978, 0.074999999999999178, 0.0, -0.075000000000000011, 0.074999999999999178, 0.0, -0.049999999999999989, 0.074999999999999178, 0.0, -0.025000000000000022, 0.074999999999999178, 0.0, 0.0, 0.074999999999999178, 0.0, 0.025000000000000022, 0.074999999999999178, 0.0, 0.050000000000000044, 0.074999999999999178, 0.0, 0.074999999999999956, 0.074999999999999178, 0.0, 0.099999999999999978, 0.074999999999999178, 0.0, 0.125, 0.074999999999999178, 0.0, 0.15000000000000002, 0.074999999999999178, 0.0, 0.17500000000000004, 0.074999999999999178, 0.0, 0.19999999999999996, 0.074999999999999178, 0.0, 0.22499999999999998, 0.074999999999999178, 0.0, 0.25, 0.074999999999999178, 0.0, 0.27500000000000002, 0.074999999999999178, 0.0, 0.30000000000000004, 0.074999999999999178, 0.0, 0.32499999999999996, 0.074999999999999178, 0.0, 0.34999999999999998, 0.074999999999999178, 0.0, 0.375, 0.074999999999999178, 0.0, 0.40000000000000002, 0.074999999999999178, 0.0, 0.42500000000000004, 0.074999999999999178, 0.0, 0.44999999999999996, 0.074999999999999178, 0.0, 0.47499999999999998, 0.074999999999999178, 0.0, 0.5, 0.074999999999999178, 0.0, -0.5, 0.099999999999999201, 0.0, -0.47499999999999998, 0.099999999999999201, 0.0, -0.45000000000000001, 0.099999999999999201, 0.0, -0.42499999999999999, 0.099999999999999201, 0.0, -0.40000000000000002, 0.099999999999999201, 0.0, -0.375, 0.099999999999999201, 0.0, -0.34999999999999998, 0.099999999999999201, 0.0, -0.32500000000000001, 0.099999999999999201, 0.0, -0.29999999999999999, 0.099999999999999201, 0.0, -0.27500000000000002, 0.099999999999999201, 0.0, -0.25, 0.099999999999999201, 0.0, -0.22499999999999998, 0.099999999999999201, 0.0, -0.20000000000000001, 0.099999999999999201, 0.0, -0.17499999999999999, 0.099999999999999201, 0.0, -0.15000000000000002, 0.099999999999999201, 0.0, -0.125, 0.099999999999999201, 0.0, -0.099999999999999978, 0.099999999999999201, 0.0, -0.075000000000000011, 0.099999999999999201, 0.0, -0.049999999999999989, 0.099999999999999201, 0.0, -0.025000000000000022, 0.099999999999999201, 0.0, 0.0, 0.099999999999999201, 0.0, 0.025000000000000022, 0.099999999999999201, 0.0, 0.050000000000000044, 0.099999999999999201, 0.0, 0.074999999999999956, 0.099999999999999201, 0.0, 0.099999999999999978, 0.099999999999999201, 0.0, 0.125, 0.099999999999999201, 0.0, 0.15000000000000002, 0.099999999999999201, 0.0, 0.17500000000000004, 0.099999999999999201, 0.0, 0.19999999999999996, 0.099999999999999201, 0.0, 0.22499999999999998, 0.099999999999999201, 0.0, 0.25, 0.099999999999999201, 0.0, 0.27500000000000002, 0.099999999999999201, 0.0, 0.30000000000000004, 0.099999999999999201, 0.0, 0.32499999999999996, 0.099999999999999201, 0.0, 0.34999999999999998, 0.099999999999999201, 0.0, 0.375, 0.099999999999999201, 0.0, 0.40000000000000002, 0.099999999999999201, 0.0, 0.42500000000000004, 0.099999999999999201, 0.0, 0.44999999999999996, 0.099999999999999201, 0.0, 0.47499999999999998, 0.099999999999999201, 0.0, 0.5, 0.099999999999999201, 0.0, -0.5, 0.12499999999999922, 0.0, -0.47499999999999998, 0.12499999999999922, 0.0, -0.45000000000000001, 0.12499999999999922, 0.0, -0.42499999999999999, 0.12499999999999922, 0.0, -0.40000000000000002, 0.12499999999999922, 0.0, -0.375, 0.12499999999999922, 0.0, -0.34999999999999998, 0.12499999999999922, 0.0, -0.32500000000000001, 0.12499999999999922, 0.0, -0.29999999999999999, 0.12499999999999922, 0.0, -0.27500000000000002, 0.12499999999999922, 0.0, -0.25, 0.12499999999999922, 0.0, -0.22499999999999998, 0.12499999999999922, 0.0, -0.20000000000000001, 0.12499999999999922, 0.0, -0.17499999999999999, 0.12499999999999922, 0.0, -0.15000000000000002, 0.12499999999999922, 0.0, -0.125, 0.12499999999999922, 0.0, -0.099999999999999978, 0.12499999999999922, 0.0, -0.075000000000000011, 0.12499999999999922, 0.0, -0.049999999999999989, 0.12499999999999922, 0.0, -0.025000000000000022, 0.12499999999999922, 0.0, 0.0, 0.12499999999999922, 0.0, 0.025000000000000022, 0.12499999999999922, 0.0, 0.050000000000000044, 0.12499999999999922, 0.0, 0.074999999999999956, 0.12499999999999922, 0.0, 0.099999999999999978, 0.12499999999999922, 0.0, 0.125, 0.12499999999999922, 0.0, 0.15000000000000002, 0.12499999999999922, 0.0, 0.17500000000000004, 0.12499999999999922, 0.0, 0.19999999999999996, 0.12499999999999922, 0.0, 0.22499999999999998, 0.12499999999999922, 0.0, 0.25, 0.12499999999999922, 0.0, 0.27500000000000002, 0.12499999999999922, 0.0, 0.30000000000000004, 0.12499999999999922, 0.0, 0.32499999999999996, 0.12499999999999922, 0.0, 0.34999999999999998, 0.12499999999999922, 0.0, 0.375, 0.12499999999999922, 0.0, 0.40000000000000002, 0.12499999999999922, 0.0, 0.42500000000000004, 0.12499999999999922, 0.0, 0.44999999999999996, 0.12499999999999922, 0.0, 0.47499999999999998, 0.12499999999999922, 0.0, 0.5, 0.12499999999999922, 0.0, -0.5, 0.14999999999999925, 0.0, -0.47499999999999998, 0.14999999999999925, 0.0, -0.45000000000000001, 0.14999999999999925, 0.0, -0.42499999999999999, 0.14999999999999925, 0.0, -0.40000000000000002, 0.14999999999999925, 0.0, -0.375, 0.14999999999999925, 0.0, -0.34999999999999998, 0.14999999999999925, 0.0, -0.32500000000000001, 0.14999999999999925, 0.0, -0.29999999999999999, 0.14999999999999925, 0.0, -0.27500000000000002, 0.14999999999999925, 0.0, -0.25, 0.14999999999999925, 0.0, -0.22499999999999998, 0.14999999999999925, 0.0, -0.20000000000000001, 0.14999999999999925, 0.0, -0.17499999999999999, 0.14999999999999925, 0.0, -0.15000000000000002, 0.14999999999999925, 0.0, -0.125, 0.14999999999999925, 0.0, -0.099999999999999978, 0.14999999999999925, 0.0, -0.075000000000000011, 0.14999999999999925, 0.0, -0.049999999999999989, 0.14999999999999925, 0.0, -0.025000000000000022, 0.14999999999999925, 0.0, 0.0, 0.14999999999999925, 0.0, 0.025000000000000022, 0.14999999999999925, 0.0, 0.050000000000000044, 0.14999999999999925, 0.0, 0.074999999999999956, 0.14999999999999925, 0.0, 0.099999999999999978, 0.14999999999999925, 0.0, 0.125, 0.14999999999999925, 0.0, 0.15000000000000002, 0.14999999999999925, 0.0, 0.17500000000000004, 0.14999999999999925, 0.0, 0.19999999999999996, 0.14999999999999925, 0.0, 0.22499999999999998, 0.14999999999999925, 0.0, 0.25, 0.14999999999999925, 0.0, 0.27500000000000002, 0.14999999999999925, 0.0, 0.30000000000000004, 0.14999999999999925, 0.0, 0.32499999999999996, 0.14999999999999925, 0.0, 0.34999999999999998, 0.14999999999999925, 0.0, 0.375, 0.14999999999999925, 0.0, 0.40000000000000002, 0.14999999999999925, 0.0, 0.42500000000000004, 0.14999999999999925, 0.0, 0.44999999999999996, 0.14999999999999925, 0.0, 0.47499999999999998, 0.14999999999999925, 0.0, 0.5, 0.14999999999999925, 0.0, -0.5, 0.17499999999999927, 0.0, -0.47499999999999998, 0.17499999999999927, 0.0, -0.45000000000000001, 0.17499999999999927, 0.0, -0.42499999999999999, 0.17499999999999927, 0.0, -0.40000000000000002, 0.17499999999999927, 0.0, -0.375, 0.17499999999999927, 0.0, -0.34999999999999998, 0.17499999999999927, 0.0, -0.32500000000000001, 0.17499999999999927, 0.0, -0.29999999999999999, 0.17499999999999927, 0.0, -0.27500000000000002, 0.17499999999999927, 0.0, -0.25, 0.17499999999999927, 0.0, -0.22499999999999998, 0.17499999999999927, 0.0, -0.20000000000000001, 0.17499999999999927, 0.0, -0.17499999999999999, 0.17499999999999927, 0.0, -0.15000000000000002, 0.17499999999999927, 0.0, -0.125, 0.17499999999999927, 0.0, -0.099999999999999978, 0.17499999999999927, 0.0, -0.075000000000000011, 0.17499999999999927, 0.0, -0.049999999999999989, 0.17499999999999927, 0.0, -0.025000000000000022, 0.17499999999999927, 0.0, 0.0, 0.17499999999999927, 0.0, 0.025000000000000022, 0.17499999999999927, 0.0, 0.050000000000000044, 0.17499999999999927, 0.0, 0.074999999999999956, 0.17499999999999927, 0.0, 0.099999999999999978, 0.17499999999999927, 0.0, 0.125, 0.17499999999999927, 0.0, 0.15000000000000002, 0.17499999999999927, 0.0, 0.17500000000000004, 0.17499999999999927, 0.0, 0.19999999999999996, 0.17499999999999927, 0.0, 0.22499999999999998, 0.17499999999999927, 0.0, 0.25, 0.17499999999999927, 0.0, 0.27500000000000002, 0.17499999999999927, 0.0, 0.30000000000000004, 0.17499999999999927, 0.0, 0.32499999999999996, 0.17499999999999927, 0.0, 0.34999999999999998, 0.17499999999999927, 0.0, 0.375, 0.17499999999999927, 0.0, 0.40000000000000002, 0.17499999999999927, 0.0, 0.42500000000000004, 0.17499999999999927, 0.0, 0.44999999999999996, 0.17499999999999927, 0.0, 0.47499999999999998, 0.17499999999999927, 0.0, 0.5, 0.17499999999999927, 0.0, -0.5, 0.19999999999999918, 0.0, -0.47499999999999998, 0.19999999999999918, 0.0, -0.45000000000000001, 0.19999999999999918, 0.0, -0.42499999999999999, 0.19999999999999918, 0.0, -0.40000000000000002, 0.19999999999999918, 0.0, -0.375, 0.19999999999999918, 0.0, -0.34999999999999998, 0.19999999999999918, 0.0, -0.32500000000000001, 0.19999999999999918, 0.0, -0.29999999999999999, 0.19999999999999918, 0.0, -0.27500000000000002, 0.19999999999999918, 0.0, -0.25, 0.19999999999999918, 0.0, -0.22499999999999998, 0.19999999999999918, 0.0, -0.20000000000000001, 0.19999999999999918, 0.0, -0.17499999999999999, 0.19999999999999918, 0.0, -0.15000000000000002, 0.19999999999999918, 0.0, -0.125, 0.19999999999999918, 0.0, -0.099999999999999978, 0.19999999999999918, 0.0, -0.075000000000000011, 0.19999999999999918, 0.0, -0.049999999999999989, 0.19999999999999918, 0.0, -0.025000000000000022, 0.19999999999999918, 0.0, 0.0, 0.19999999999999918, 0.0, 0.025000000000000022, 0.19999999999999918, 0.0, 0.050000000000000044, 0.19999999999999918, 0.0, 0.074999999999999956, 0.19999999999999918, 0.0, 0.099999999999999978, 0.19999999999999918, 0.0, 0.125, 0.19999999999999918, 0.0, 0.15000000000000002, 0.19999999999999918, 0.0, 0.17500000000000004, 0.19999999999999918, 0.0, 0.19999999999999996, 0.19999999999999918, 0.0, 0.22499999999999998, 0.19999999999999918, 0.0, 0.25, 0.19999999999999918, 0.0, 0.27500000000000002, 0.19999999999999918, 0.0, 0.30000000000000004, 0.19999999999999918, 0.0, 0.32499999999999996, 0.19999999999999918, 0.0, 0.34999999999999998, 0.19999999999999918, 0.0, 0.375, 0.19999999999999918, 0.0, 0.40000000000000002, 0.19999999999999918, 0.0, 0.42500000000000004, 0.19999999999999918, 0.0, 0.44999999999999996, 0.19999999999999918, 0.0, 0.47499999999999998, 0.19999999999999918, 0.0, 0.5, 0.19999999999999918, 0.0, -0.5, 0.2249999999999992, 0.0, -0.47499999999999998, 0.2249999999999992, 0.0, -0.45000000000000001, 0.2249999999999992, 0.0, -0.42499999999999999, 0.2249999999999992, 0.0, -0.40000000000000002, 0.2249999999999992, 0.0, -0.375, 0.2249999999999992, 0.0, -0.34999999999999998, 0.2249999999999992, 0.0, -0.32500000000000001, 0.2249999999999992, 0.0, -0.29999999999999999, 0.2249999999999992, 0.0, -0.27500000000000002, 0.2249999999999992, 0.0, -0.25, 0.2249999999999992, 0.0, -0.22499999999999998, 0.2249999999999992, 0.0, -0.20000000000000001, 0.2249999999999992, 0.0, -0.17499999999999999, 0.2249999999999992, 0.0, -0.15000000000000002, 0.2249999999999992, 0.0, -0.125, 0.2249999999999992, 0.0, -0.099999999999999978, 0.2249999999999992, 0.0, -0.075000000000000011, 0.2249999999999992, 0.0, -0.049999999999999989, 0.2249999999999992, 0.0, -0.025000000000000022, 0.2249999999999992, 0.0, 0.0, 0.2249999999999992, 0.0, 0.025000000000000022, 0.2249999999999992, 0.0, 0.050000000000000044, 0.2249999999999992, 0.0, 0.074999999999999956, 0.2249999999999992, 0.0, 0.099999999999999978, 0.2249999999999992, 0.0, 0.125, 0.2249999999999992, 0.0, 0.15000000000000002, 0.2249999999999992, 0.0, 0.17500000000000004, 0.2249999999999992, 0.0, 0.19999999999999996, 0.2249999999999992, 0.0, 0.22499999999999998, 0.2249999999999992, 0.0, 0.25, 0.2249999999999992, 0.0, 0.27500000000000002, 0.2249999999999992, 0.0, 0.30000000000000004, 0.2249999999999992, 0.0, 0.32499999999999996, 0.2249999999999992, 0.0, 0.34999999999999998, 0.2249999999999992, 0.0, 0.375, 0.2249999999999992, 0.0, 0.40000000000000002, 0.2249999999999992, 0.0, 0.42500000000000004, 0.2249999999999992, 0.0, 0.44999999999999996, 0.2249999999999992, 0.0, 0.47499999999999998, 0.2249999999999992, 0.0, 0.5, 0.2249999999999992, 0.0, -0.5, 0.24999999999999922, 0.0, -0.47499999999999998, 0.24999999999999922, 0.0, -0.45000000000000001, 0.24999999999999922, 0.0, -0.42499999999999999, 0.24999999999999922, 0.0, -0.40000000000000002, 0.24999999999999922, 0.0, -0.375, 0.24999999999999922, 0.0, -0.34999999999999998, 0.24999999999999922, 0.0, -0.32500000000000001, 0.24999999999999922, 0.0, -0.29999999999999999, 0.24999999999999922, 0.0, -0.27500000000000002, 0.24999999999999922, 0.0, -0.25, 0.24999999999999922, 0.0, -0.22499999999999998, 0.24999999999999922, 0.0, -0.20000000000000001, 0.24999999999999922, 0.0, -0.17499999999999999, 0.24999999999999922, 0.0, -0.15000000000000002, 0.24999999999999922, 0.0, -0.125, 0.24999999999999922, 0.0, -0.099999999999999978, 0.24999999999999922, 0.0, -0.075000000000000011, 0.24999999999999922, 0.0, -0.049999999999999989, 0.24999999999999922, 0.0, -0.025000000000000022, 0.24999999999999922, 0.0, 0.0, 0.24999999999999922, 0.0, 0.025000000000000022, 0.24999999999999922, 0.0, 0.050000000000000044, 0.24999999999999922, 0.0, 0.074999999999999956, 0.24999999999999922, 0.0, 0.099999999999999978, 0.24999999999999922, 0.0, 0.125, 0.24999999999999922, 0.0, 0.15000000000000002, 0.24999999999999922, 0.0, 0.17500000000000004, 0.24999999999999922, 0.0, 0.19999999999999996, 0.24999999999999922, 0.0, 0.22499999999999998, 0.24999999999999922, 0.0, 0.25, 0.24999999999999922, 0.0, 0.27500000000000002, 0.24999999999999922, 0.0, 0.30000000000000004, 0.24999999999999922, 0.0, 0.32499999999999996, 0.24999999999999922, 0.0, 0.34999999999999998, 0.24999999999999922, 0.0, 0.375, 0.24999999999999922, 0.0, 0.40000000000000002, 0.24999999999999922, 0.0, 0.42500000000000004, 0.24999999999999922, 0.0, 0.44999999999999996, 0.24999999999999922, 0.0, 0.47499999999999998, 0.24999999999999922, 0.0, 0.5, 0.24999999999999922, 0.0, -0.5, 0.27499999999999925, 0.0, -0.47499999999999998, 0.27499999999999925, 0.0, -0.45000000000000001, 0.27499999999999925, 0.0, -0.42499999999999999, 0.27499999999999925, 0.0, -0.40000000000000002, 0.27499999999999925, 0.0, -0.375, 0.27499999999999925, 0.0, -0.34999999999999998, 0.27499999999999925, 0.0, -0.32500000000000001, 0.27499999999999925, 0.0, -0.29999999999999999, 0.27499999999999925, 0.0, -0.27500000000000002, 0.27499999999999925, 0.0, -0.25, 0.27499999999999925, 0.0, -0.22499999999999998, 0.27499999999999925, 0.0, -0.20000000000000001, 0.27499999999999925, 0.0, -0.17499999999999999, 0.27499999999999925, 0.0, -0.15000000000000002, 0.27499999999999925, 0.0, -0.125, 0.27499999999999925, 0.0, -0.099999999999999978, 0.27499999999999925, 0.0, -0.075000000000000011, 0.27499999999999925, 0.0, -0.049999999999999989, 0.27499999999999925, 0.0, -0.025000000000000022, 0.27499999999999925, 0.0, 0.0, 0.27499999999999925, 0.0, 0.025000000000000022, 0.27499999999999925, 0.0, 0.050000000000000044, 0.27499999999999925, 0.0, 0.074999999999999956, 0.27499999999999925, 0.0, 0.099999999999999978, 0.27499999999999925, 0.0, 0.125, 0.27499999999999925, 0.0, 0.15000000000000002, 0.27499999999999925, 0.0, 0.17500000000000004, 0.27499999999999925, 0.0, 0.19999999999999996, 0.27499999999999925, 0.0, 0.22499999999999998, 0.27499999999999925, 0.0, 0.25, 0.27499999999999925, 0.0, 0.27500000000000002, 0.27499999999999925, 0.0, 0.30000000000000004, 0.27499999999999925, 0.0, 0.32499999999999996, 0.27499999999999925, 0.0, 0.34999999999999998, 0.27499999999999925, 0.0, 0.375, 0.27499999999999925, 0.0, 0.40000000000000002, 0.27499999999999925, 0.0, 0.42500000000000004, 0.27499999999999925, 0.0, 0.44999999999999996, 0.27499999999999925, 0.0, 0.47499999999999998, 0.27499999999999925, 0.0, 0.5, 0.27499999999999925, 0.0, -0.5, 0.29999999999999927, 0.0, -0.47499999999999998, 0.29999999999999927, 0.0, -0.45000000000000001, 0.29999999999999927, 0.0, -0.42499999999999999, 0.29999999999999927, 0.0, -0.40000000000000002, 0.29999999999999927, 0.0, -0.375, 0.29999999999999927, 0.0, -0.34999999999999998, 0.29999999999999927, 0.0, -0.32500000000000001, 0.29999999999999927, 0.0, -0.29999999999999999, 0.29999999999999927, 0.0, -0.27500000000000002, 0.29999999999999927, 0.0, -0.25, 0.29999999999999927, 0.0, -0.22499999999999998, 0.29999999999999927, 0.0, -0.20000000000000001, 0.29999999999999927, 0.0, -0.17499999999999999, 0.29999999999999927, 0.0, -0.15000000000000002, 0.29999999999999927, 0.0, -0.125, 0.29999999999999927, 0.0, -0.099999999999999978, 0.29999999999999927, 0.0, -0.075000000000000011, 0.29999999999999927, 0.0, -0.049999999999999989, 0.29999999999999927, 0.0, -0.025000000000000022, 0.29999999999999927, 0.0, 0.0, 0.29999999999999927, 0.0, 0.025000000000000022, 0.29999999999999927, 0.0, 0.050000000000000044, 0.29999999999999927, 0.0, 0.074999999999999956, 0.29999999999999927, 0.0, 0.099999999999999978, 0.29999999999999927, 0.0, 0.125, 0.29999999999999927, 0.0, 0.15000000000000002, 0.29999999999999927, 0.0, 0.17500000000000004, 0.29999999999999927, 0.0, 0.19999999999999996, 0.29999999999999927, 0.0, 0.22499999999999998, 0.29999999999999927, 0.0, 0.25, 0.29999999999999927, 0.0, 0.27500000000000002, 0.29999999999999927, 0.0, 0.30000000000000004, 0.29999999999999927, 0.0, 0.32499999999999996, 0.29999999999999927, 0.0, 0.34999999999999998, 0.29999999999999927, 0.0, 0.375, 0.29999999999999927, 0.0, 0.40000000000000002, 0.29999999999999927, 0.0, 0.42500000000000004, 0.29999999999999927, 0.0, 0.44999999999999996, 0.29999999999999927, 0.0, 0.47499999999999998, 0.29999999999999927, 0.0, 0.5, 0.29999999999999927, 0.0, -0.5, 0.32499999999999918, 0.0, -0.47499999999999998, 0.32499999999999918, 0.0, -0.45000000000000001, 0.32499999999999918, 0.0, -0.42499999999999999, 0.32499999999999918, 0.0, -0.40000000000000002, 0.32499999999999918, 0.0, -0.375, 0.32499999999999918, 0.0, -0.34999999999999998, 0.32499999999999918, 0.0, -0.32500000000000001, 0.32499999999999918, 0.0, -0.29999999999999999, 0.32499999999999918, 0.0, -0.27500000000000002, 0.32499999999999918, 0.0, -0.25, 0.32499999999999918, 0.0, -0.22499999999999998, 0.32499999999999918, 0.0, -0.20000000000000001, 0.32499999999999918, 0.0, -0.17499999999999999, 0.32499999999999918, 0.0, -0.15000000000000002, 0.32499999999999918, 0.0, -0.125, 0.32499999999999918, 0.0, -0.099999999999999978, 0.32499999999999918, 0.0, -0.075000000000000011, 0.32499999999999918, 0.0, -0.049999999999999989, 0.32499999999999918, 0.0, -0.025000000000000022, 0.32499999999999918, 0.0, 0.0, 0.32499999999999918, 0.0, 0.025000000000000022, 0.32499999999999918, 0.0, 0.050000000000000044, 0.32499999999999918, 0.0, 0.074999999999999956, 0.32499999999999918, 0.0, 0.099999999999999978, 0.32499999999999918, 0.0, 0.125, 0.32499999999999918, 0.0, 0.15000000000000002, 0.32499999999999918, 0.0, 0.17500000000000004, 0.32499999999999918, 0.0, 0.19999999999999996, 0.32499999999999918, 0.0, 0.22499999999999998, 0.32499999999999918, 0.0, 0.25, 0.32499999999999918, 0.0, 0.27500000000000002, 0.32499999999999918, 0.0, 0.30000000000000004, 0.32499999999999918, 0.0, 0.32499999999999996, 0.32499999999999918, 0.0, 0.34999999999999998, 0.32499999999999918, 0.0, 0.375, 0.32499999999999918, 0.0, 0.40000000000000002, 0.32499999999999918, 0.0, 0.42500000000000004, 0.32499999999999918, 0.0, 0.44999999999999996, 0.32499999999999918, 0.0, 0.47499999999999998, 0.32499999999999918, 0.0, 0.5, 0.32499999999999918, 0.0, -0.5, 0.3499999999999992, 0.0, -0.47499999999999998, 0.3499999999999992, 0.0, -0.45000000000000001, 0.3499999999999992, 0.0, -0.42499999999999999, 0.3499999999999992, 0.0, -0.40000000000000002, 0.3499999999999992, 0.0, -0.375, 0.3499999999999992, 0.0, -0.34999999999999998, 0.3499999999999992, 0.0, -0.32500000000000001, 0.3499999999999992, 0.0, -0.29999999999999999, 0.3499999999999992, 0.0, -0.27500000000000002, 0.3499999999999992, 0.0, -0.25, 0.3499999999999992, 0.0, -0.22499999999999998, 0.3499999999999992, 0.0, -0.20000000000000001, 0.3499999999999992, 0.0, -0.17499999999999999, 0.3499999999999992, 0.0, -0.15000000000000002, 0.3499999999999992, 0.0, -0.125, 0.3499999999999992, 0.0, -0.099999999999999978, 0.3499999999999992, 0.0, -0.075000000000000011, 0.3499999999999992, 0.0, -0.049999999999999989, 0.3499999999999992, 0.0, -0.025000000000000022, 0.3499999999999992, 0.0, 0.0, 0.3499999999999992, 0.0, 0.025000000000000022, 0.3499999999999992, 0.0, 0.050000000000000044, 0.3499999999999992, 0.0, 0.074999999999999956, 0.3499999999999992, 0.0, 0.099999999999999978, 0.3499999999999992, 0.0, 0.125, 0.3499999999999992, 0.0, 0.15000000000000002, 0.3499999999999992, 0.0, 0.17500000000000004, 0.3499999999999992, 0.0, 0.19999999999999996, 0.3499999999999992, 0.0, 0.22499999999999998, 0.3499999999999992, 0.0, 0.25, 0.3499999999999992, 0.0, 0.27500000000000002, 0.3499999999999992, 0.0, 0.30000000000000004, 0.3499999999999992, 0.0, 0.32499999999999996, 0.3499999999999992, 0.0, 0.34999999999999998, 0.3499999999999992, 0.0, 0.375, 0.3499999999999992, 0.0, 0.40000000000000002, 0.3499999999999992, 0.0, 0.42500000000000004, 0.3499999999999992, 0.0, 0.44999999999999996, 0.3499999999999992, 0.0, 0.47499999999999998, 0.3499999999999992, 0.0, 0.5, 0.3499999999999992, 0.0, -0.5, 0.37499999999999922, 0.0, -0.47499999999999998, 0.37499999999999922, 0.0, -0.45000000000000001, 0.37499999999999922, 0.0, -0.42499999999999999, 0.37499999999999922, 0.0, -0.40000000000000002, 0.37499999999999922, 0.0, -0.375, 0.37499999999999922, 0.0, -0.34999999999999998, 0.37499999999999922, 0.0, -0.32500000000000001, 0.37499999999999922, 0.0, -0.29999999999999999, 0.37499999999999922, 0.0, -0.27500000000000002, 0.37499999999999922, 0.0, -0.25, 0.37499999999999922, 0.0, -0.22499999999999998, 0.37499999999999922, 0.0, -0.20000000000000001, 0.37499999999999922, 0.0, -0.17499999999999999, 0.37499999999999922, 0.0, -0.15000000000000002, 0.37499999999999922, 0.0, -0.125, 0.37499999999999922, 0.0, -0.099999999999999978, 0.37499999999999922, 0.0, -0.075000000000000011, 0.37499999999999922, 0.0, -0.049999999999999989, 0.37499999999999922, 0.0, -0.025000000000000022, 0.37499999999999922, 0.0, 0.0, 0.37499999999999922, 0.0, 0.025000000000000022, 0.37499999999999922, 0.0, 0.050000000000000044, 0.37499999999999922, 0.0, 0.074999999999999956, 0.37499999999999922, 0.0, 0.099999999999999978, 0.37499999999999922, 0.0, 0.125, 0.37499999999999922, 0.0, 0.15000000000000002, 0.37499999999999922, 0.0, 0.17500000000000004, 0.37499999999999922, 0.0, 0.19999999999999996, 0.37499999999999922, 0.0, 0.22499999999999998, 0.37499999999999922, 0.0, 0.25, 0.37499999999999922, 0.0, 0.27500000000000002, 0.37499999999999922, 0.0, 0.30000000000000004, 0.37499999999999922, 0.0, 0.32499999999999996, 0.37499999999999922, 0.0, 0.34999999999999998, 0.37499999999999922, 0.0, 0.375, 0.37499999999999922, 0.0, 0.40000000000000002, 0.37499999999999922, 0.0, 0.42500000000000004, 0.37499999999999922, 0.0, 0.44999999999999996, 0.37499999999999922, 0.0, 0.47499999999999998, 0.37499999999999922, 0.0, 0.5, 0.37499999999999922, 0.0, -0.5, 0.39999999999999925, 0.0, -0.47499999999999998, 0.39999999999999925, 0.0, -0.45000000000000001, 0.39999999999999925, 0.0, -0.42499999999999999, 0.39999999999999925, 0.0, -0.40000000000000002, 0.39999999999999925, 0.0, -0.375, 0.39999999999999925, 0.0, -0.34999999999999998, 0.39999999999999925, 0.0, -0.32500000000000001, 0.39999999999999925, 0.0, -0.29999999999999999, 0.39999999999999925, 0.0, -0.27500000000000002, 0.39999999999999925, 0.0, -0.25, 0.39999999999999925, 0.0, -0.22499999999999998, 0.39999999999999925, 0.0, -0.20000000000000001, 0.39999999999999925, 0.0, -0.17499999999999999, 0.39999999999999925, 0.0, -0.15000000000000002, 0.39999999999999925, 0.0, -0.125, 0.39999999999999925, 0.0, -0.099999999999999978, 0.39999999999999925, 0.0, -0.075000000000000011, 0.39999999999999925, 0.0, -0.049999999999999989, 0.39999999999999925, 0.0, -0.025000000000000022, 0.39999999999999925, 0.0, 0.0, 0.39999999999999925, 0.0, 0.025000000000000022, 0.39999999999999925, 0.0, 0.050000000000000044, 0.39999999999999925, 0.0, 0.074999999999999956, 0.39999999999999925, 0.0, 0.099999999999999978, 0.39999999999999925, 0.0, 0.125, 0.39999999999999925, 0.0, 0.15000000000000002, 0.39999999999999925, 0.0, 0.17500000000000004, 0.39999999999999925, 0.0, 0.19999999999999996, 0.39999999999999925, 0.0, 0.22499999999999998, 0.39999999999999925, 0.0, 0.25, 0.39999999999999925, 0.0, 0.27500000000000002, 0.39999999999999925, 0.0, 0.30000000000000004, 0.39999999999999925, 0.0, 0.32499999999999996, 0.39999999999999925, 0.0, 0.34999999999999998, 0.39999999999999925, 0.0, 0.375, 0.39999999999999925, 0.0, 0.40000000000000002, 0.39999999999999925, 0.0, 0.42500000000000004, 0.39999999999999925, 0.0, 0.44999999999999996, 0.39999999999999925, 0.0, 0.47499999999999998, 0.39999999999999925, 0.0, 0.5, 0.39999999999999925, 0.0, -0.5, 0.42499999999999927, 0.0, -0.47499999999999998, 0.42499999999999927, 0.0, -0.45000000000000001, 0.42499999999999927, 0.0, -0.42499999999999999, 0.42499999999999927, 0.0, -0.40000000000000002, 0.42499999999999927, 0.0, -0.375, 0.42499999999999927, 0.0, -0.34999999999999998, 0.42499999999999927, 0.0, -0.32500000000000001, 0.42499999999999927, 0.0, -0.29999999999999999, 0.42499999999999927, 0.0, -0.27500000000000002, 0.42499999999999927, 0.0, -0.25, 0.42499999999999927, 0.0, -0.22499999999999998, 0.42499999999999927, 0.0, -0.20000000000000001, 0.42499999999999927, 0.0, -0.17499999999999999, 0.42499999999999927, 0.0, -0.15000000000000002, 0.42499999999999927, 0.0, -0.125, 0.42499999999999927, 0.0, -0.099999999999999978, 0.42499999999999927, 0.0, -0.075000000000000011, 0.42499999999999927, 0.0, -0.049999999999999989, 0.42499999999999927, 0.0, -0.025000000000000022, 0.42499999999999927, 0.0, 0.0, 0.42499999999999927, 0.0, 0.025000000000000022, 0.42499999999999927, 0.0, 0.050000000000000044, 0.42499999999999927, 0.0, 0.074999999999999956, 0.42499999999999927, 0.0, 0.099999999999999978, 0.42499999999999927, 0.0, 0.125, 0.42499999999999927, 0.0, 0.15000000000000002, 0.42499999999999927, 0.0, 0.17500000000000004, 0.42499999999999927, 0.0, 0.19999999999999996, 0.42499999999999927, 0.0, 0.22499999999999998, 0.42499999999999927, 0.0, 0.25, 0.42499999999999927, 0.0, 0.27500000000000002, 0.42499999999999927, 0.0, 0.30000000000000004, 0.42499999999999927, 0.0, 0.32499999999999996, 0.42499999999999927, 0.0, 0.34999999999999998, 0.42499999999999927, 0.0, 0.375, 0.42499999999999927, 0.0, 0.40000000000000002, 0.42499999999999927, 0.0, 0.42500000000000004, 0.42499999999999927, 0.0, 0.44999999999999996, 0.42499999999999927, 0.0, 0.47499999999999998, 0.42499999999999927, 0.0, 0.5, 0.42499999999999927, 0.0, -0.5, 0.44999999999999918, 0.0, -0.47499999999999998, 0.44999999999999918, 0.0, -0.45000000000000001, 0.44999999999999918, 0.0, -0.42499999999999999, 0.44999999999999918, 0.0, -0.40000000000000002, 0.44999999999999918, 0.0, -0.375, 0.44999999999999918, 0.0, -0.34999999999999998, 0.44999999999999918, 0.0, -0.32500000000000001, 0.44999999999999918, 0.0, -0.29999999999999999, 0.44999999999999918, 0.0, -0.27500000000000002, 0.44999999999999918, 0.0, -0.25, 0.44999999999999918, 0.0, -0.22499999999999998, 0.44999999999999918, 0.0, -0.20000000000000001, 0.44999999999999918, 0.0, -0.17499999999999999, 0.44999999999999918, 0.0, -0.15000000000000002, 0.44999999999999918, 0.0, -0.125, 0.44999999999999918, 0.0, -0.099999999999999978, 0.44999999999999918, 0.0, -0.075000000000000011, 0.44999999999999918, 0.0, -0.049999999999999989, 0.44999999999999918, 0.0, -0.025000000000000022, 0.44999999999999918, 0.0, 0.0, 0.44999999999999918, 0.0, 0.025000000000000022, 0.44999999999999918, 0.0, 0.050000000000000044, 0.44999999999999918, 0.0, 0.074999999999999956, 0.44999999999999918, 0.0, 0.099999999999999978, 0.44999999999999918, 0.0, 0.125, 0.44999999999999918, 0.0, 0.15000000000000002, 0.44999999999999918, 0.0, 0.17500000000000004, 0.44999999999999918, 0.0, 0.19999999999999996, 0.44999999999999918, 0.0, 0.22499999999999998, 0.44999999999999918, 0.0, 0.25, 0.44999999999999918, 0.0, 0.27500000000000002, 0.44999999999999918, 0.0, 0.30000000000000004, 0.44999999999999918, 0.0, 0.32499999999999996, 0.44999999999999918, 0.0, 0.34999999999999998, 0.44999999999999918, 0.0, 0.375, 0.44999999999999918, 0.0, 0.40000000000000002, 0.44999999999999918, 0.0, 0.42500000000000004, 0.44999999999999918, 0.0, 0.44999999999999996, 0.44999999999999918, 0.0, 0.47499999999999998, 0.44999999999999918, 0.0, 0.5, 0.44999999999999918, 0.0, -0.5, 0.4749999999999992, 0.0, -0.47499999999999998, 0.4749999999999992, 0.0, -0.45000000000000001, 0.4749999999999992, 0.0, -0.42499999999999999, 0.4749999999999992, 0.0, -0.40000000000000002, 0.4749999999999992, 0.0, -0.375, 0.4749999999999992, 0.0, -0.34999999999999998, 0.4749999999999992, 0.0, -0.32500000000000001, 0.4749999999999992, 0.0, -0.29999999999999999, 0.4749999999999992, 0.0, -0.27500000000000002, 0.4749999999999992, 0.0, -0.25, 0.4749999999999992, 0.0, -0.22499999999999998, 0.4749999999999992, 0.0, -0.20000000000000001, 0.4749999999999992, 0.0, -0.17499999999999999, 0.4749999999999992, 0.0, -0.15000000000000002, 0.4749999999999992, 0.0, -0.125, 0.4749999999999992, 0.0, -0.099999999999999978, 0.4749999999999992, 0.0, -0.075000000000000011, 0.4749999999999992, 0.0, -0.049999999999999989, 0.4749999999999992, 0.0, -0.025000000000000022, 0.4749999999999992, 0.0, 0.0, 0.4749999999999992, 0.0, 0.025000000000000022, 0.4749999999999992, 0.0, 0.050000000000000044, 0.4749999999999992, 0.0, 0.074999999999999956, 0.4749999999999992, 0.0, 0.099999999999999978, 0.4749999999999992, 0.0, 0.125, 0.4749999999999992, 0.0, 0.15000000000000002, 0.4749999999999992, 0.0, 0.17500000000000004, 0.4749999999999992, 0.0, 0.19999999999999996, 0.4749999999999992, 0.0, 0.22499999999999998, 0.4749999999999992, 0.0, 0.25, 0.4749999999999992, 0.0, 0.27500000000000002, 0.4749999999999992, 0.0, 0.30000000000000004, 0.4749999999999992, 0.0, 0.32499999999999996, 0.4749999999999992, 0.0, 0.34999999999999998, 0.4749999999999992, 0.0, 0.375, 0.4749999999999992, 0.0, 0.40000000000000002, 0.4749999999999992, 0.0, 0.42500000000000004, 0.4749999999999992, 0.0, 0.44999999999999996, 0.4749999999999992, 0.0, 0.47499999999999998, 0.4749999999999992, 0.0, 0.5, 0.4749999999999992, 0.0, -0.5, 0.49999999999999922, 0.0, -0.47499999999999998, 0.49999999999999922, 0.0, -0.45000000000000001, 0.49999999999999922, 0.0, -0.42499999999999999, 0.49999999999999922, 0.0, -0.40000000000000002, 0.49999999999999922, 0.0, -0.375, 0.49999999999999922, 0.0, -0.34999999999999998, 0.49999999999999922, 0.0, -0.32500000000000001, 0.49999999999999922, 0.0, -0.29999999999999999, 0.49999999999999922, 0.0, -0.27500000000000002, 0.49999999999999922, 0.0, -0.25, 0.49999999999999922, 0.0, -0.22499999999999998, 0.49999999999999922, 0.0, -0.20000000000000001, 0.49999999999999922, 0.0, -0.17499999999999999, 0.49999999999999922, 0.0, -0.15000000000000002, 0.49999999999999922, 0.0, -0.125, 0.49999999999999922, 0.0, -0.099999999999999978, 0.49999999999999922, 0.0, -0.075000000000000011, 0.49999999999999922, 0.0, -0.049999999999999989, 0.49999999999999922, 0.0, -0.025000000000000022, 0.49999999999999922, 0.0, 0.0, 0.49999999999999922, 0.0, 0.025000000000000022, 0.49999999999999922, 0.0, 0.050000000000000044, 0.49999999999999922, 0.0, 0.074999999999999956, 0.49999999999999922, 0.0, 0.099999999999999978, 0.49999999999999922, 0.0, 0.125, 0.49999999999999922, 0.0, 0.15000000000000002, 0.49999999999999922, 0.0, 0.17500000000000004, 0.49999999999999922, 0.0, 0.19999999999999996, 0.49999999999999922, 0.0, 0.22499999999999998, 0.49999999999999922, 0.0, 0.25, 0.49999999999999922, 0.0, 0.27500000000000002, 0.49999999999999922, 0.0, 0.30000000000000004, 0.49999999999999922, 0.0, 0.32499999999999996, 0.49999999999999922, 0.0, 0.34999999999999998, 0.49999999999999922, 0.0, 0.375, 0.49999999999999922, 0.0, 0.40000000000000002, 0.49999999999999922, 0.0, 0.42500000000000004, 0.49999999999999922, 0.0, 0.44999999999999996, 0.49999999999999922, 0.0, 0.47499999999999998, 0.49999999999999922, 0.0, 0.5, 0.49999999999999922, 0.0], \"metadata\": {\"formatVersion\": 3}}, \"733742ec-fd23-4ab1-8efc-48092d77bdce\", true, 1.0, true, 500);\n</script>\n\n\n\n"}, "metadata": {}}], "metadata": {"collapsed": false, "trusted": true}}, {"execution_count": 8, "cell_type": "code", "source": "model.mesh", "outputs": [{"output_type": "display_data", "data": {"text/plain": "<IPython.core.display.HTML object>", "text/html": "<div style=\"width: 500px; height: 375px;\" id=\"09184b68-4873-4647-afdc-3b7ceccb67f4\" ></div><script>\n renderPyURDMEMesh({\"faces\": [128, 0, 1, 42, 0, 1, 42, 128, 0, 41, 42, 0, 41, 42, 128, 1, 2, 43, 1, 2, 43, 128, 1, 42, 43, 1, 42, 43, 128, 2, 3, 44, 2, 3, 44, 128, 2, 43, 44, 2, 43, 44, 128, 3, 4, 45, 3, 4, 45, 128, 3, 44, 45, 3, 44, 45, 128, 4, 5, 46, 4, 5, 46, 128, 4, 45, 46, 4, 45, 46, 128, 5, 6, 47, 5, 6, 47, 128, 5, 46, 47, 5, 46, 47, 128, 6, 7, 48, 6, 7, 48, 128, 6, 47, 48, 6, 47, 48, 128, 7, 8, 49, 7, 8, 49, 128, 7, 48, 49, 7, 48, 49, 128, 8, 9, 50, 8, 9, 50, 128, 8, 49, 50, 8, 49, 50, 128, 9, 10, 51, 9, 10, 51, 128, 9, 50, 51, 9, 50, 51, 128, 10, 11, 52, 10, 11, 52, 128, 10, 51, 52, 10, 51, 52, 128, 11, 12, 53, 11, 12, 53, 128, 11, 52, 53, 11, 52, 53, 128, 12, 13, 54, 12, 13, 54, 128, 12, 53, 54, 12, 53, 54, 128, 13, 14, 55, 13, 14, 55, 128, 13, 54, 55, 13, 54, 55, 128, 14, 15, 56, 14, 15, 56, 128, 14, 55, 56, 14, 55, 56, 128, 15, 16, 57, 15, 16, 57, 128, 15, 56, 57, 15, 56, 57, 128, 16, 17, 58, 16, 17, 58, 128, 16, 57, 58, 16, 57, 58, 128, 17, 18, 59, 17, 18, 59, 128, 17, 58, 59, 17, 58, 59, 128, 18, 19, 60, 18, 19, 60, 128, 18, 59, 60, 18, 59, 60, 128, 19, 20, 61, 19, 20, 61, 128, 19, 60, 61, 19, 60, 61, 128, 20, 21, 62, 20, 21, 62, 128, 20, 61, 62, 20, 61, 62, 128, 21, 22, 63, 21, 22, 63, 128, 21, 62, 63, 21, 62, 63, 128, 22, 23, 64, 22, 23, 64, 128, 22, 63, 64, 22, 63, 64, 128, 23, 24, 65, 23, 24, 65, 128, 23, 64, 65, 23, 64, 65, 128, 24, 25, 66, 24, 25, 66, 128, 24, 65, 66, 24, 65, 66, 128, 25, 26, 67, 25, 26, 67, 128, 25, 66, 67, 25, 66, 67, 128, 26, 27, 68, 26, 27, 68, 128, 26, 67, 68, 26, 67, 68, 128, 27, 28, 69, 27, 28, 69, 128, 27, 68, 69, 27, 68, 69, 128, 28, 29, 70, 28, 29, 70, 128, 28, 69, 70, 28, 69, 70, 128, 29, 30, 71, 29, 30, 71, 128, 29, 70, 71, 29, 70, 71, 128, 30, 31, 72, 30, 31, 72, 128, 30, 71, 72, 30, 71, 72, 128, 31, 32, 73, 31, 32, 73, 128, 31, 72, 73, 31, 72, 73, 128, 32, 33, 74, 32, 33, 74, 128, 32, 73, 74, 32, 73, 74, 128, 33, 34, 75, 33, 34, 75, 128, 33, 74, 75, 33, 74, 75, 128, 34, 35, 76, 34, 35, 76, 128, 34, 75, 76, 34, 75, 76, 128, 35, 36, 77, 35, 36, 77, 128, 35, 76, 77, 35, 76, 77, 128, 36, 37, 78, 36, 37, 78, 128, 36, 77, 78, 36, 77, 78, 128, 37, 38, 79, 37, 38, 79, 128, 37, 78, 79, 37, 78, 79, 128, 38, 39, 80, 38, 39, 80, 128, 38, 79, 80, 38, 79, 80, 128, 39, 40, 81, 39, 40, 81, 128, 39, 80, 81, 39, 80, 81, 128, 41, 42, 83, 41, 42, 83, 128, 41, 82, 83, 41, 82, 83, 128, 42, 43, 84, 42, 43, 84, 128, 42, 83, 84, 42, 83, 84, 128, 43, 44, 85, 43, 44, 85, 128, 43, 84, 85, 43, 84, 85, 128, 44, 45, 86, 44, 45, 86, 128, 44, 85, 86, 44, 85, 86, 128, 45, 46, 87, 45, 46, 87, 128, 45, 86, 87, 45, 86, 87, 128, 46, 47, 88, 46, 47, 88, 128, 46, 87, 88, 46, 87, 88, 128, 47, 48, 89, 47, 48, 89, 128, 47, 88, 89, 47, 88, 89, 128, 48, 49, 90, 48, 49, 90, 128, 48, 89, 90, 48, 89, 90, 128, 49, 50, 91, 49, 50, 91, 128, 49, 90, 91, 49, 90, 91, 128, 50, 51, 92, 50, 51, 92, 128, 50, 91, 92, 50, 91, 92, 128, 51, 52, 93, 51, 52, 93, 128, 51, 92, 93, 51, 92, 93, 128, 52, 53, 94, 52, 53, 94, 128, 52, 93, 94, 52, 93, 94, 128, 53, 54, 95, 53, 54, 95, 128, 53, 94, 95, 53, 94, 95, 128, 54, 55, 96, 54, 55, 96, 128, 54, 95, 96, 54, 95, 96, 128, 55, 56, 97, 55, 56, 97, 128, 55, 96, 97, 55, 96, 97, 128, 56, 57, 98, 56, 57, 98, 128, 56, 97, 98, 56, 97, 98, 128, 57, 58, 99, 57, 58, 99, 128, 57, 98, 99, 57, 98, 99, 128, 58, 59, 100, 58, 59, 100, 128, 58, 99, 100, 58, 99, 100, 128, 59, 60, 101, 59, 60, 101, 128, 59, 100, 101, 59, 100, 101, 128, 60, 61, 102, 60, 61, 102, 128, 60, 101, 102, 60, 101, 102, 128, 61, 62, 103, 61, 62, 103, 128, 61, 102, 103, 61, 102, 103, 128, 62, 63, 104, 62, 63, 104, 128, 62, 103, 104, 62, 103, 104, 128, 63, 64, 105, 63, 64, 105, 128, 63, 104, 105, 63, 104, 105, 128, 64, 65, 106, 64, 65, 106, 128, 64, 105, 106, 64, 105, 106, 128, 65, 66, 107, 65, 66, 107, 128, 65, 106, 107, 65, 106, 107, 128, 66, 67, 108, 66, 67, 108, 128, 66, 107, 108, 66, 107, 108, 128, 67, 68, 109, 67, 68, 109, 128, 67, 108, 109, 67, 108, 109, 128, 68, 69, 110, 68, 69, 110, 128, 68, 109, 110, 68, 109, 110, 128, 69, 70, 111, 69, 70, 111, 128, 69, 110, 111, 69, 110, 111, 128, 70, 71, 112, 70, 71, 112, 128, 70, 111, 112, 70, 111, 112, 128, 71, 72, 113, 71, 72, 113, 128, 71, 112, 113, 71, 112, 113, 128, 72, 73, 114, 72, 73, 114, 128, 72, 113, 114, 72, 113, 114, 128, 73, 74, 115, 73, 74, 115, 128, 73, 114, 115, 73, 114, 115, 128, 74, 75, 116, 74, 75, 116, 128, 74, 115, 116, 74, 115, 116, 128, 75, 76, 117, 75, 76, 117, 128, 75, 116, 117, 75, 116, 117, 128, 76, 77, 118, 76, 77, 118, 128, 76, 117, 118, 76, 117, 118, 128, 77, 78, 119, 77, 78, 119, 128, 77, 118, 119, 77, 118, 119, 128, 78, 79, 120, 78, 79, 120, 128, 78, 119, 120, 78, 119, 120, 128, 79, 80, 121, 79, 80, 121, 128, 79, 120, 121, 79, 120, 121, 128, 80, 81, 122, 80, 81, 122, 128, 80, 121, 122, 80, 121, 122, 128, 82, 83, 124, 82, 83, 124, 128, 82, 123, 124, 82, 123, 124, 128, 83, 84, 125, 83, 84, 125, 128, 83, 124, 125, 83, 124, 125, 128, 84, 85, 126, 84, 85, 126, 128, 84, 125, 126, 84, 125, 126, 128, 85, 86, 127, 85, 86, 127, 128, 85, 126, 127, 85, 126, 127, 128, 86, 87, 128, 86, 87, 128, 128, 86, 127, 128, 86, 127, 128, 128, 87, 88, 129, 87, 88, 129, 128, 87, 128, 129, 87, 128, 129, 128, 88, 89, 130, 88, 89, 130, 128, 88, 129, 130, 88, 129, 130, 128, 89, 90, 131, 89, 90, 131, 128, 89, 130, 131, 89, 130, 131, 128, 90, 91, 132, 90, 91, 132, 128, 90, 131, 132, 90, 131, 132, 128, 91, 92, 133, 91, 92, 133, 128, 91, 132, 133, 91, 132, 133, 128, 92, 93, 134, 92, 93, 134, 128, 92, 133, 134, 92, 133, 134, 128, 93, 94, 135, 93, 94, 135, 128, 93, 134, 135, 93, 134, 135, 128, 94, 95, 136, 94, 95, 136, 128, 94, 135, 136, 94, 135, 136, 128, 95, 96, 137, 95, 96, 137, 128, 95, 136, 137, 95, 136, 137, 128, 96, 97, 138, 96, 97, 138, 128, 96, 137, 138, 96, 137, 138, 128, 97, 98, 139, 97, 98, 139, 128, 97, 138, 139, 97, 138, 139, 128, 98, 99, 140, 98, 99, 140, 128, 98, 139, 140, 98, 139, 140, 128, 99, 100, 141, 99, 100, 141, 128, 99, 140, 141, 99, 140, 141, 128, 100, 101, 142, 100, 101, 142, 128, 100, 141, 142, 100, 141, 142, 128, 101, 102, 143, 101, 102, 143, 128, 101, 142, 143, 101, 142, 143, 128, 102, 103, 144, 102, 103, 144, 128, 102, 143, 144, 102, 143, 144, 128, 103, 104, 145, 103, 104, 145, 128, 103, 144, 145, 103, 144, 145, 128, 104, 105, 146, 104, 105, 146, 128, 104, 145, 146, 104, 145, 146, 128, 105, 106, 147, 105, 106, 147, 128, 105, 146, 147, 105, 146, 147, 128, 106, 107, 148, 106, 107, 148, 128, 106, 147, 148, 106, 147, 148, 128, 107, 108, 149, 107, 108, 149, 128, 107, 148, 149, 107, 148, 149, 128, 108, 109, 150, 108, 109, 150, 128, 108, 149, 150, 108, 149, 150, 128, 109, 110, 151, 109, 110, 151, 128, 109, 150, 151, 109, 150, 151, 128, 110, 111, 152, 110, 111, 152, 128, 110, 151, 152, 110, 151, 152, 128, 111, 112, 153, 111, 112, 153, 128, 111, 152, 153, 111, 152, 153, 128, 112, 113, 154, 112, 113, 154, 128, 112, 153, 154, 112, 153, 154, 128, 113, 114, 155, 113, 114, 155, 128, 113, 154, 155, 113, 154, 155, 128, 114, 115, 156, 114, 115, 156, 128, 114, 155, 156, 114, 155, 156, 128, 115, 116, 157, 115, 116, 157, 128, 115, 156, 157, 115, 156, 157, 128, 116, 117, 158, 116, 117, 158, 128, 116, 157, 158, 116, 157, 158, 128, 117, 118, 159, 117, 118, 159, 128, 117, 158, 159, 117, 158, 159, 128, 118, 119, 160, 118, 119, 160, 128, 118, 159, 160, 118, 159, 160, 128, 119, 120, 161, 119, 120, 161, 128, 119, 160, 161, 119, 160, 161, 128, 120, 121, 162, 120, 121, 162, 128, 120, 161, 162, 120, 161, 162, 128, 121, 122, 163, 121, 122, 163, 128, 121, 162, 163, 121, 162, 163, 128, 123, 124, 165, 123, 124, 165, 128, 123, 164, 165, 123, 164, 165, 128, 124, 125, 166, 124, 125, 166, 128, 124, 165, 166, 124, 165, 166, 128, 125, 126, 167, 125, 126, 167, 128, 125, 166, 167, 125, 166, 167, 128, 126, 127, 168, 126, 127, 168, 128, 126, 167, 168, 126, 167, 168, 128, 127, 128, 169, 127, 128, 169, 128, 127, 168, 169, 127, 168, 169, 128, 128, 129, 170, 128, 129, 170, 128, 128, 169, 170, 128, 169, 170, 128, 129, 130, 171, 129, 130, 171, 128, 129, 170, 171, 129, 170, 171, 128, 130, 131, 172, 130, 131, 172, 128, 130, 171, 172, 130, 171, 172, 128, 131, 132, 173, 131, 132, 173, 128, 131, 172, 173, 131, 172, 173, 128, 132, 133, 174, 132, 133, 174, 128, 132, 173, 174, 132, 173, 174, 128, 133, 134, 175, 133, 134, 175, 128, 133, 174, 175, 133, 174, 175, 128, 134, 135, 176, 134, 135, 176, 128, 134, 175, 176, 134, 175, 176, 128, 135, 136, 177, 135, 136, 177, 128, 135, 176, 177, 135, 176, 177, 128, 136, 137, 178, 136, 137, 178, 128, 136, 177, 178, 136, 177, 178, 128, 137, 138, 179, 137, 138, 179, 128, 137, 178, 179, 137, 178, 179, 128, 138, 139, 180, 138, 139, 180, 128, 138, 179, 180, 138, 179, 180, 128, 139, 140, 181, 139, 140, 181, 128, 139, 180, 181, 139, 180, 181, 128, 140, 141, 182, 140, 141, 182, 128, 140, 181, 182, 140, 181, 182, 128, 141, 142, 183, 141, 142, 183, 128, 141, 182, 183, 141, 182, 183, 128, 142, 143, 184, 142, 143, 184, 128, 142, 183, 184, 142, 183, 184, 128, 143, 144, 185, 143, 144, 185, 128, 143, 184, 185, 143, 184, 185, 128, 144, 145, 186, 144, 145, 186, 128, 144, 185, 186, 144, 185, 186, 128, 145, 146, 187, 145, 146, 187, 128, 145, 186, 187, 145, 186, 187, 128, 146, 147, 188, 146, 147, 188, 128, 146, 187, 188, 146, 187, 188, 128, 147, 148, 189, 147, 148, 189, 128, 147, 188, 189, 147, 188, 189, 128, 148, 149, 190, 148, 149, 190, 128, 148, 189, 190, 148, 189, 190, 128, 149, 150, 191, 149, 150, 191, 128, 149, 190, 191, 149, 190, 191, 128, 150, 151, 192, 150, 151, 192, 128, 150, 191, 192, 150, 191, 192, 128, 151, 152, 193, 151, 152, 193, 128, 151, 192, 193, 151, 192, 193, 128, 152, 153, 194, 152, 153, 194, 128, 152, 193, 194, 152, 193, 194, 128, 153, 154, 195, 153, 154, 195, 128, 153, 194, 195, 153, 194, 195, 128, 154, 155, 196, 154, 155, 196, 128, 154, 195, 196, 154, 195, 196, 128, 155, 156, 197, 155, 156, 197, 128, 155, 196, 197, 155, 196, 197, 128, 156, 157, 198, 156, 157, 198, 128, 156, 197, 198, 156, 197, 198, 128, 157, 158, 199, 157, 158, 199, 128, 157, 198, 199, 157, 198, 199, 128, 158, 159, 200, 158, 159, 200, 128, 158, 199, 200, 158, 199, 200, 128, 159, 160, 201, 159, 160, 201, 128, 159, 200, 201, 159, 200, 201, 128, 160, 161, 202, 160, 161, 202, 128, 160, 201, 202, 160, 201, 202, 128, 161, 162, 203, 161, 162, 203, 128, 161, 202, 203, 161, 202, 203, 128, 162, 163, 204, 162, 163, 204, 128, 162, 203, 204, 162, 203, 204, 128, 164, 165, 206, 164, 165, 206, 128, 164, 205, 206, 164, 205, 206, 128, 165, 166, 207, 165, 166, 207, 128, 165, 206, 207, 165, 206, 207, 128, 166, 167, 208, 166, 167, 208, 128, 166, 207, 208, 166, 207, 208, 128, 167, 168, 209, 167, 168, 209, 128, 167, 208, 209, 167, 208, 209, 128, 168, 169, 210, 168, 169, 210, 128, 168, 209, 210, 168, 209, 210, 128, 169, 170, 211, 169, 170, 211, 128, 169, 210, 211, 169, 210, 211, 128, 170, 171, 212, 170, 171, 212, 128, 170, 211, 212, 170, 211, 212, 128, 171, 172, 213, 171, 172, 213, 128, 171, 212, 213, 171, 212, 213, 128, 172, 173, 214, 172, 173, 214, 128, 172, 213, 214, 172, 213, 214, 128, 173, 174, 215, 173, 174, 215, 128, 173, 214, 215, 173, 214, 215, 128, 174, 175, 216, 174, 175, 216, 128, 174, 215, 216, 174, 215, 216, 128, 175, 176, 217, 175, 176, 217, 128, 175, 216, 217, 175, 216, 217, 128, 176, 177, 218, 176, 177, 218, 128, 176, 217, 218, 176, 217, 218, 128, 177, 178, 219, 177, 178, 219, 128, 177, 218, 219, 177, 218, 219, 128, 178, 179, 220, 178, 179, 220, 128, 178, 219, 220, 178, 219, 220, 128, 179, 180, 221, 179, 180, 221, 128, 179, 220, 221, 179, 220, 221, 128, 180, 181, 222, 180, 181, 222, 128, 180, 221, 222, 180, 221, 222, 128, 181, 182, 223, 181, 182, 223, 128, 181, 222, 223, 181, 222, 223, 128, 182, 183, 224, 182, 183, 224, 128, 182, 223, 224, 182, 223, 224, 128, 183, 184, 225, 183, 184, 225, 128, 183, 224, 225, 183, 224, 225, 128, 184, 185, 226, 184, 185, 226, 128, 184, 225, 226, 184, 225, 226, 128, 185, 186, 227, 185, 186, 227, 128, 185, 226, 227, 185, 226, 227, 128, 186, 187, 228, 186, 187, 228, 128, 186, 227, 228, 186, 227, 228, 128, 187, 188, 229, 187, 188, 229, 128, 187, 228, 229, 187, 228, 229, 128, 188, 189, 230, 188, 189, 230, 128, 188, 229, 230, 188, 229, 230, 128, 189, 190, 231, 189, 190, 231, 128, 189, 230, 231, 189, 230, 231, 128, 190, 191, 232, 190, 191, 232, 128, 190, 231, 232, 190, 231, 232, 128, 191, 192, 233, 191, 192, 233, 128, 191, 232, 233, 191, 232, 233, 128, 192, 193, 234, 192, 193, 234, 128, 192, 233, 234, 192, 233, 234, 128, 193, 194, 235, 193, 194, 235, 128, 193, 234, 235, 193, 234, 235, 128, 194, 195, 236, 194, 195, 236, 128, 194, 235, 236, 194, 235, 236, 128, 195, 196, 237, 195, 196, 237, 128, 195, 236, 237, 195, 236, 237, 128, 196, 197, 238, 196, 197, 238, 128, 196, 237, 238, 196, 237, 238, 128, 197, 198, 239, 197, 198, 239, 128, 197, 238, 239, 197, 238, 239, 128, 198, 199, 240, 198, 199, 240, 128, 198, 239, 240, 198, 239, 240, 128, 199, 200, 241, 199, 200, 241, 128, 199, 240, 241, 199, 240, 241, 128, 200, 201, 242, 200, 201, 242, 128, 200, 241, 242, 200, 241, 242, 128, 201, 202, 243, 201, 202, 243, 128, 201, 242, 243, 201, 242, 243, 128, 202, 203, 244, 202, 203, 244, 128, 202, 243, 244, 202, 243, 244, 128, 203, 204, 245, 203, 204, 245, 128, 203, 244, 245, 203, 244, 245, 128, 205, 206, 247, 205, 206, 247, 128, 205, 246, 247, 205, 246, 247, 128, 206, 207, 248, 206, 207, 248, 128, 206, 247, 248, 206, 247, 248, 128, 207, 208, 249, 207, 208, 249, 128, 207, 248, 249, 207, 248, 249, 128, 208, 209, 250, 208, 209, 250, 128, 208, 249, 250, 208, 249, 250, 128, 209, 210, 251, 209, 210, 251, 128, 209, 250, 251, 209, 250, 251, 128, 210, 211, 252, 210, 211, 252, 128, 210, 251, 252, 210, 251, 252, 128, 211, 212, 253, 211, 212, 253, 128, 211, 252, 253, 211, 252, 253, 128, 212, 213, 254, 212, 213, 254, 128, 212, 253, 254, 212, 253, 254, 128, 213, 214, 255, 213, 214, 255, 128, 213, 254, 255, 213, 254, 255, 128, 214, 215, 256, 214, 215, 256, 128, 214, 255, 256, 214, 255, 256, 128, 215, 216, 257, 215, 216, 257, 128, 215, 256, 257, 215, 256, 257, 128, 216, 217, 258, 216, 217, 258, 128, 216, 257, 258, 216, 257, 258, 128, 217, 218, 259, 217, 218, 259, 128, 217, 258, 259, 217, 258, 259, 128, 218, 219, 260, 218, 219, 260, 128, 218, 259, 260, 218, 259, 260, 128, 219, 220, 261, 219, 220, 261, 128, 219, 260, 261, 219, 260, 261, 128, 220, 221, 262, 220, 221, 262, 128, 220, 261, 262, 220, 261, 262, 128, 221, 222, 263, 221, 222, 263, 128, 221, 262, 263, 221, 262, 263, 128, 222, 223, 264, 222, 223, 264, 128, 222, 263, 264, 222, 263, 264, 128, 223, 224, 265, 223, 224, 265, 128, 223, 264, 265, 223, 264, 265, 128, 224, 225, 266, 224, 225, 266, 128, 224, 265, 266, 224, 265, 266, 128, 225, 226, 267, 225, 226, 267, 128, 225, 266, 267, 225, 266, 267, 128, 226, 227, 268, 226, 227, 268, 128, 226, 267, 268, 226, 267, 268, 128, 227, 228, 269, 227, 228, 269, 128, 227, 268, 269, 227, 268, 269, 128, 228, 229, 270, 228, 229, 270, 128, 228, 269, 270, 228, 269, 270, 128, 229, 230, 271, 229, 230, 271, 128, 229, 270, 271, 229, 270, 271, 128, 230, 231, 272, 230, 231, 272, 128, 230, 271, 272, 230, 271, 272, 128, 231, 232, 273, 231, 232, 273, 128, 231, 272, 273, 231, 272, 273, 128, 232, 233, 274, 232, 233, 274, 128, 232, 273, 274, 232, 273, 274, 128, 233, 234, 275, 233, 234, 275, 128, 233, 274, 275, 233, 274, 275, 128, 234, 235, 276, 234, 235, 276, 128, 234, 275, 276, 234, 275, 276, 128, 235, 236, 277, 235, 236, 277, 128, 235, 276, 277, 235, 276, 277, 128, 236, 237, 278, 236, 237, 278, 128, 236, 277, 278, 236, 277, 278, 128, 237, 238, 279, 237, 238, 279, 128, 237, 278, 279, 237, 278, 279, 128, 238, 239, 280, 238, 239, 280, 128, 238, 279, 280, 238, 279, 280, 128, 239, 240, 281, 239, 240, 281, 128, 239, 280, 281, 239, 280, 281, 128, 240, 241, 282, 240, 241, 282, 128, 240, 281, 282, 240, 281, 282, 128, 241, 242, 283, 241, 242, 283, 128, 241, 282, 283, 241, 282, 283, 128, 242, 243, 284, 242, 243, 284, 128, 242, 283, 284, 242, 283, 284, 128, 243, 244, 285, 243, 244, 285, 128, 243, 284, 285, 243, 284, 285, 128, 244, 245, 286, 244, 245, 286, 128, 244, 285, 286, 244, 285, 286, 128, 246, 247, 288, 246, 247, 288, 128, 246, 287, 288, 246, 287, 288, 128, 247, 248, 289, 247, 248, 289, 128, 247, 288, 289, 247, 288, 289, 128, 248, 249, 290, 248, 249, 290, 128, 248, 289, 290, 248, 289, 290, 128, 249, 250, 291, 249, 250, 291, 128, 249, 290, 291, 249, 290, 291, 128, 250, 251, 292, 250, 251, 292, 128, 250, 291, 292, 250, 291, 292, 128, 251, 252, 293, 251, 252, 293, 128, 251, 292, 293, 251, 292, 293, 128, 252, 253, 294, 252, 253, 294, 128, 252, 293, 294, 252, 293, 294, 128, 253, 254, 295, 253, 254, 295, 128, 253, 294, 295, 253, 294, 295, 128, 254, 255, 296, 254, 255, 296, 128, 254, 295, 296, 254, 295, 296, 128, 255, 256, 297, 255, 256, 297, 128, 255, 296, 297, 255, 296, 297, 128, 256, 257, 298, 256, 257, 298, 128, 256, 297, 298, 256, 297, 298, 128, 257, 258, 299, 257, 258, 299, 128, 257, 298, 299, 257, 298, 299, 128, 258, 259, 300, 258, 259, 300, 128, 258, 299, 300, 258, 299, 300, 128, 259, 260, 301, 259, 260, 301, 128, 259, 300, 301, 259, 300, 301, 128, 260, 261, 302, 260, 261, 302, 128, 260, 301, 302, 260, 301, 302, 128, 261, 262, 303, 261, 262, 303, 128, 261, 302, 303, 261, 302, 303, 128, 262, 263, 304, 262, 263, 304, 128, 262, 303, 304, 262, 303, 304, 128, 263, 264, 305, 263, 264, 305, 128, 263, 304, 305, 263, 304, 305, 128, 264, 265, 306, 264, 265, 306, 128, 264, 305, 306, 264, 305, 306, 128, 265, 266, 307, 265, 266, 307, 128, 265, 306, 307, 265, 306, 307, 128, 266, 267, 308, 266, 267, 308, 128, 266, 307, 308, 266, 307, 308, 128, 267, 268, 309, 267, 268, 309, 128, 267, 308, 309, 267, 308, 309, 128, 268, 269, 310, 268, 269, 310, 128, 268, 309, 310, 268, 309, 310, 128, 269, 270, 311, 269, 270, 311, 128, 269, 310, 311, 269, 310, 311, 128, 270, 271, 312, 270, 271, 312, 128, 270, 311, 312, 270, 311, 312, 128, 271, 272, 313, 271, 272, 313, 128, 271, 312, 313, 271, 312, 313, 128, 272, 273, 314, 272, 273, 314, 128, 272, 313, 314, 272, 313, 314, 128, 273, 274, 315, 273, 274, 315, 128, 273, 314, 315, 273, 314, 315, 128, 274, 275, 316, 274, 275, 316, 128, 274, 315, 316, 274, 315, 316, 128, 275, 276, 317, 275, 276, 317, 128, 275, 316, 317, 275, 316, 317, 128, 276, 277, 318, 276, 277, 318, 128, 276, 317, 318, 276, 317, 318, 128, 277, 278, 319, 277, 278, 319, 128, 277, 318, 319, 277, 318, 319, 128, 278, 279, 320, 278, 279, 320, 128, 278, 319, 320, 278, 319, 320, 128, 279, 280, 321, 279, 280, 321, 128, 279, 320, 321, 279, 320, 321, 128, 280, 281, 322, 280, 281, 322, 128, 280, 321, 322, 280, 321, 322, 128, 281, 282, 323, 281, 282, 323, 128, 281, 322, 323, 281, 322, 323, 128, 282, 283, 324, 282, 283, 324, 128, 282, 323, 324, 282, 323, 324, 128, 283, 284, 325, 283, 284, 325, 128, 283, 324, 325, 283, 324, 325, 128, 284, 285, 326, 284, 285, 326, 128, 284, 325, 326, 284, 325, 326, 128, 285, 286, 327, 285, 286, 327, 128, 285, 326, 327, 285, 326, 327, 128, 287, 288, 329, 287, 288, 329, 128, 287, 328, 329, 287, 328, 329, 128, 288, 289, 330, 288, 289, 330, 128, 288, 329, 330, 288, 329, 330, 128, 289, 290, 331, 289, 290, 331, 128, 289, 330, 331, 289, 330, 331, 128, 290, 291, 332, 290, 291, 332, 128, 290, 331, 332, 290, 331, 332, 128, 291, 292, 333, 291, 292, 333, 128, 291, 332, 333, 291, 332, 333, 128, 292, 293, 334, 292, 293, 334, 128, 292, 333, 334, 292, 333, 334, 128, 293, 294, 335, 293, 294, 335, 128, 293, 334, 335, 293, 334, 335, 128, 294, 295, 336, 294, 295, 336, 128, 294, 335, 336, 294, 335, 336, 128, 295, 296, 337, 295, 296, 337, 128, 295, 336, 337, 295, 336, 337, 128, 296, 297, 338, 296, 297, 338, 128, 296, 337, 338, 296, 337, 338, 128, 297, 298, 339, 297, 298, 339, 128, 297, 338, 339, 297, 338, 339, 128, 298, 299, 340, 298, 299, 340, 128, 298, 339, 340, 298, 339, 340, 128, 299, 300, 341, 299, 300, 341, 128, 299, 340, 341, 299, 340, 341, 128, 300, 301, 342, 300, 301, 342, 128, 300, 341, 342, 300, 341, 342, 128, 301, 302, 343, 301, 302, 343, 128, 301, 342, 343, 301, 342, 343, 128, 302, 303, 344, 302, 303, 344, 128, 302, 343, 344, 302, 343, 344, 128, 303, 304, 345, 303, 304, 345, 128, 303, 344, 345, 303, 344, 345, 128, 304, 305, 346, 304, 305, 346, 128, 304, 345, 346, 304, 345, 346, 128, 305, 306, 347, 305, 306, 347, 128, 305, 346, 347, 305, 346, 347, 128, 306, 307, 348, 306, 307, 348, 128, 306, 347, 348, 306, 347, 348, 128, 307, 308, 349, 307, 308, 349, 128, 307, 348, 349, 307, 348, 349, 128, 308, 309, 350, 308, 309, 350, 128, 308, 349, 350, 308, 349, 350, 128, 309, 310, 351, 309, 310, 351, 128, 309, 350, 351, 309, 350, 351, 128, 310, 311, 352, 310, 311, 352, 128, 310, 351, 352, 310, 351, 352, 128, 311, 312, 353, 311, 312, 353, 128, 311, 352, 353, 311, 352, 353, 128, 312, 313, 354, 312, 313, 354, 128, 312, 353, 354, 312, 353, 354, 128, 313, 314, 355, 313, 314, 355, 128, 313, 354, 355, 313, 354, 355, 128, 314, 315, 356, 314, 315, 356, 128, 314, 355, 356, 314, 355, 356, 128, 315, 316, 357, 315, 316, 357, 128, 315, 356, 357, 315, 356, 357, 128, 316, 317, 358, 316, 317, 358, 128, 316, 357, 358, 316, 357, 358, 128, 317, 318, 359, 317, 318, 359, 128, 317, 358, 359, 317, 358, 359, 128, 318, 319, 360, 318, 319, 360, 128, 318, 359, 360, 318, 359, 360, 128, 319, 320, 361, 319, 320, 361, 128, 319, 360, 361, 319, 360, 361, 128, 320, 321, 362, 320, 321, 362, 128, 320, 361, 362, 320, 361, 362, 128, 321, 322, 363, 321, 322, 363, 128, 321, 362, 363, 321, 362, 363, 128, 322, 323, 364, 322, 323, 364, 128, 322, 363, 364, 322, 363, 364, 128, 323, 324, 365, 323, 324, 365, 128, 323, 364, 365, 323, 364, 365, 128, 324, 325, 366, 324, 325, 366, 128, 324, 365, 366, 324, 365, 366, 128, 325, 326, 367, 325, 326, 367, 128, 325, 366, 367, 325, 366, 367, 128, 326, 327, 368, 326, 327, 368, 128, 326, 367, 368, 326, 367, 368, 128, 328, 329, 370, 328, 329, 370, 128, 328, 369, 370, 328, 369, 370, 128, 329, 330, 371, 329, 330, 371, 128, 329, 370, 371, 329, 370, 371, 128, 330, 331, 372, 330, 331, 372, 128, 330, 371, 372, 330, 371, 372, 128, 331, 332, 373, 331, 332, 373, 128, 331, 372, 373, 331, 372, 373, 128, 332, 333, 374, 332, 333, 374, 128, 332, 373, 374, 332, 373, 374, 128, 333, 334, 375, 333, 334, 375, 128, 333, 374, 375, 333, 374, 375, 128, 334, 335, 376, 334, 335, 376, 128, 334, 375, 376, 334, 375, 376, 128, 335, 336, 377, 335, 336, 377, 128, 335, 376, 377, 335, 376, 377, 128, 336, 337, 378, 336, 337, 378, 128, 336, 377, 378, 336, 377, 378, 128, 337, 338, 379, 337, 338, 379, 128, 337, 378, 379, 337, 378, 379, 128, 338, 339, 380, 338, 339, 380, 128, 338, 379, 380, 338, 379, 380, 128, 339, 340, 381, 339, 340, 381, 128, 339, 380, 381, 339, 380, 381, 128, 340, 341, 382, 340, 341, 382, 128, 340, 381, 382, 340, 381, 382, 128, 341, 342, 383, 341, 342, 383, 128, 341, 382, 383, 341, 382, 383, 128, 342, 343, 384, 342, 343, 384, 128, 342, 383, 384, 342, 383, 384, 128, 343, 344, 385, 343, 344, 385, 128, 343, 384, 385, 343, 384, 385, 128, 344, 345, 386, 344, 345, 386, 128, 344, 385, 386, 344, 385, 386, 128, 345, 346, 387, 345, 346, 387, 128, 345, 386, 387, 345, 386, 387, 128, 346, 347, 388, 346, 347, 388, 128, 346, 387, 388, 346, 387, 388, 128, 347, 348, 389, 347, 348, 389, 128, 347, 388, 389, 347, 388, 389, 128, 348, 349, 390, 348, 349, 390, 128, 348, 389, 390, 348, 389, 390, 128, 349, 350, 391, 349, 350, 391, 128, 349, 390, 391, 349, 390, 391, 128, 350, 351, 392, 350, 351, 392, 128, 350, 391, 392, 350, 391, 392, 128, 351, 352, 393, 351, 352, 393, 128, 351, 392, 393, 351, 392, 393, 128, 352, 353, 394, 352, 353, 394, 128, 352, 393, 394, 352, 393, 394, 128, 353, 354, 395, 353, 354, 395, 128, 353, 394, 395, 353, 394, 395, 128, 354, 355, 396, 354, 355, 396, 128, 354, 395, 396, 354, 395, 396, 128, 355, 356, 397, 355, 356, 397, 128, 355, 396, 397, 355, 396, 397, 128, 356, 357, 398, 356, 357, 398, 128, 356, 397, 398, 356, 397, 398, 128, 357, 358, 399, 357, 358, 399, 128, 357, 398, 399, 357, 398, 399, 128, 358, 359, 400, 358, 359, 400, 128, 358, 399, 400, 358, 399, 400, 128, 359, 360, 401, 359, 360, 401, 128, 359, 400, 401, 359, 400, 401, 128, 360, 361, 402, 360, 361, 402, 128, 360, 401, 402, 360, 401, 402, 128, 361, 362, 403, 361, 362, 403, 128, 361, 402, 403, 361, 402, 403, 128, 362, 363, 404, 362, 363, 404, 128, 362, 403, 404, 362, 403, 404, 128, 363, 364, 405, 363, 364, 405, 128, 363, 404, 405, 363, 404, 405, 128, 364, 365, 406, 364, 365, 406, 128, 364, 405, 406, 364, 405, 406, 128, 365, 366, 407, 365, 366, 407, 128, 365, 406, 407, 365, 406, 407, 128, 366, 367, 408, 366, 367, 408, 128, 366, 407, 408, 366, 407, 408, 128, 367, 368, 409, 367, 368, 409, 128, 367, 408, 409, 367, 408, 409, 128, 369, 370, 411, 369, 370, 411, 128, 369, 410, 411, 369, 410, 411, 128, 370, 371, 412, 370, 371, 412, 128, 370, 411, 412, 370, 411, 412, 128, 371, 372, 413, 371, 372, 413, 128, 371, 412, 413, 371, 412, 413, 128, 372, 373, 414, 372, 373, 414, 128, 372, 413, 414, 372, 413, 414, 128, 373, 374, 415, 373, 374, 415, 128, 373, 414, 415, 373, 414, 415, 128, 374, 375, 416, 374, 375, 416, 128, 374, 415, 416, 374, 415, 416, 128, 375, 376, 417, 375, 376, 417, 128, 375, 416, 417, 375, 416, 417, 128, 376, 377, 418, 376, 377, 418, 128, 376, 417, 418, 376, 417, 418, 128, 377, 378, 419, 377, 378, 419, 128, 377, 418, 419, 377, 418, 419, 128, 378, 379, 420, 378, 379, 420, 128, 378, 419, 420, 378, 419, 420, 128, 379, 380, 421, 379, 380, 421, 128, 379, 420, 421, 379, 420, 421, 128, 380, 381, 422, 380, 381, 422, 128, 380, 421, 422, 380, 421, 422, 128, 381, 382, 423, 381, 382, 423, 128, 381, 422, 423, 381, 422, 423, 128, 382, 383, 424, 382, 383, 424, 128, 382, 423, 424, 382, 423, 424, 128, 383, 384, 425, 383, 384, 425, 128, 383, 424, 425, 383, 424, 425, 128, 384, 385, 426, 384, 385, 426, 128, 384, 425, 426, 384, 425, 426, 128, 385, 386, 427, 385, 386, 427, 128, 385, 426, 427, 385, 426, 427, 128, 386, 387, 428, 386, 387, 428, 128, 386, 427, 428, 386, 427, 428, 128, 387, 388, 429, 387, 388, 429, 128, 387, 428, 429, 387, 428, 429, 128, 388, 389, 430, 388, 389, 430, 128, 388, 429, 430, 388, 429, 430, 128, 389, 390, 431, 389, 390, 431, 128, 389, 430, 431, 389, 430, 431, 128, 390, 391, 432, 390, 391, 432, 128, 390, 431, 432, 390, 431, 432, 128, 391, 392, 433, 391, 392, 433, 128, 391, 432, 433, 391, 432, 433, 128, 392, 393, 434, 392, 393, 434, 128, 392, 433, 434, 392, 433, 434, 128, 393, 394, 435, 393, 394, 435, 128, 393, 434, 435, 393, 434, 435, 128, 394, 395, 436, 394, 395, 436, 128, 394, 435, 436, 394, 435, 436, 128, 395, 396, 437, 395, 396, 437, 128, 395, 436, 437, 395, 436, 437, 128, 396, 397, 438, 396, 397, 438, 128, 396, 437, 438, 396, 437, 438, 128, 397, 398, 439, 397, 398, 439, 128, 397, 438, 439, 397, 438, 439, 128, 398, 399, 440, 398, 399, 440, 128, 398, 439, 440, 398, 439, 440, 128, 399, 400, 441, 399, 400, 441, 128, 399, 440, 441, 399, 440, 441, 128, 400, 401, 442, 400, 401, 442, 128, 400, 441, 442, 400, 441, 442, 128, 401, 402, 443, 401, 402, 443, 128, 401, 442, 443, 401, 442, 443, 128, 402, 403, 444, 402, 403, 444, 128, 402, 443, 444, 402, 443, 444, 128, 403, 404, 445, 403, 404, 445, 128, 403, 444, 445, 403, 444, 445, 128, 404, 405, 446, 404, 405, 446, 128, 404, 445, 446, 404, 445, 446, 128, 405, 406, 447, 405, 406, 447, 128, 405, 446, 447, 405, 446, 447, 128, 406, 407, 448, 406, 407, 448, 128, 406, 447, 448, 406, 447, 448, 128, 407, 408, 449, 407, 408, 449, 128, 407, 448, 449, 407, 448, 449, 128, 408, 409, 450, 408, 409, 450, 128, 408, 449, 450, 408, 449, 450, 128, 410, 411, 452, 410, 411, 452, 128, 410, 451, 452, 410, 451, 452, 128, 411, 412, 453, 411, 412, 453, 128, 411, 452, 453, 411, 452, 453, 128, 412, 413, 454, 412, 413, 454, 128, 412, 453, 454, 412, 453, 454, 128, 413, 414, 455, 413, 414, 455, 128, 413, 454, 455, 413, 454, 455, 128, 414, 415, 456, 414, 415, 456, 128, 414, 455, 456, 414, 455, 456, 128, 415, 416, 457, 415, 416, 457, 128, 415, 456, 457, 415, 456, 457, 128, 416, 417, 458, 416, 417, 458, 128, 416, 457, 458, 416, 457, 458, 128, 417, 418, 459, 417, 418, 459, 128, 417, 458, 459, 417, 458, 459, 128, 418, 419, 460, 418, 419, 460, 128, 418, 459, 460, 418, 459, 460, 128, 419, 420, 461, 419, 420, 461, 128, 419, 460, 461, 419, 460, 461, 128, 420, 421, 462, 420, 421, 462, 128, 420, 461, 462, 420, 461, 462, 128, 421, 422, 463, 421, 422, 463, 128, 421, 462, 463, 421, 462, 463, 128, 422, 423, 464, 422, 423, 464, 128, 422, 463, 464, 422, 463, 464, 128, 423, 424, 465, 423, 424, 465, 128, 423, 464, 465, 423, 464, 465, 128, 424, 425, 466, 424, 425, 466, 128, 424, 465, 466, 424, 465, 466, 128, 425, 426, 467, 425, 426, 467, 128, 425, 466, 467, 425, 466, 467, 128, 426, 427, 468, 426, 427, 468, 128, 426, 467, 468, 426, 467, 468, 128, 427, 428, 469, 427, 428, 469, 128, 427, 468, 469, 427, 468, 469, 128, 428, 429, 470, 428, 429, 470, 128, 428, 469, 470, 428, 469, 470, 128, 429, 430, 471, 429, 430, 471, 128, 429, 470, 471, 429, 470, 471, 128, 430, 431, 472, 430, 431, 472, 128, 430, 471, 472, 430, 471, 472, 128, 431, 432, 473, 431, 432, 473, 128, 431, 472, 473, 431, 472, 473, 128, 432, 433, 474, 432, 433, 474, 128, 432, 473, 474, 432, 473, 474, 128, 433, 434, 475, 433, 434, 475, 128, 433, 474, 475, 433, 474, 475, 128, 434, 435, 476, 434, 435, 476, 128, 434, 475, 476, 434, 475, 476, 128, 435, 436, 477, 435, 436, 477, 128, 435, 476, 477, 435, 476, 477, 128, 436, 437, 478, 436, 437, 478, 128, 436, 477, 478, 436, 477, 478, 128, 437, 438, 479, 437, 438, 479, 128, 437, 478, 479, 437, 478, 479, 128, 438, 439, 480, 438, 439, 480, 128, 438, 479, 480, 438, 479, 480, 128, 439, 440, 481, 439, 440, 481, 128, 439, 480, 481, 439, 480, 481, 128, 440, 441, 482, 440, 441, 482, 128, 440, 481, 482, 440, 481, 482, 128, 441, 442, 483, 441, 442, 483, 128, 441, 482, 483, 441, 482, 483, 128, 442, 443, 484, 442, 443, 484, 128, 442, 483, 484, 442, 483, 484, 128, 443, 444, 485, 443, 444, 485, 128, 443, 484, 485, 443, 484, 485, 128, 444, 445, 486, 444, 445, 486, 128, 444, 485, 486, 444, 485, 486, 128, 445, 446, 487, 445, 446, 487, 128, 445, 486, 487, 445, 486, 487, 128, 446, 447, 488, 446, 447, 488, 128, 446, 487, 488, 446, 487, 488, 128, 447, 448, 489, 447, 448, 489, 128, 447, 488, 489, 447, 488, 489, 128, 448, 449, 490, 448, 449, 490, 128, 448, 489, 490, 448, 489, 490, 128, 449, 450, 491, 449, 450, 491, 128, 449, 490, 491, 449, 490, 491, 128, 451, 452, 493, 451, 452, 493, 128, 451, 492, 493, 451, 492, 493, 128, 452, 453, 494, 452, 453, 494, 128, 452, 493, 494, 452, 493, 494, 128, 453, 454, 495, 453, 454, 495, 128, 453, 494, 495, 453, 494, 495, 128, 454, 455, 496, 454, 455, 496, 128, 454, 495, 496, 454, 495, 496, 128, 455, 456, 497, 455, 456, 497, 128, 455, 496, 497, 455, 496, 497, 128, 456, 457, 498, 456, 457, 498, 128, 456, 497, 498, 456, 497, 498, 128, 457, 458, 499, 457, 458, 499, 128, 457, 498, 499, 457, 498, 499, 128, 458, 459, 500, 458, 459, 500, 128, 458, 499, 500, 458, 499, 500, 128, 459, 460, 501, 459, 460, 501, 128, 459, 500, 501, 459, 500, 501, 128, 460, 461, 502, 460, 461, 502, 128, 460, 501, 502, 460, 501, 502, 128, 461, 462, 503, 461, 462, 503, 128, 461, 502, 503, 461, 502, 503, 128, 462, 463, 504, 462, 463, 504, 128, 462, 503, 504, 462, 503, 504, 128, 463, 464, 505, 463, 464, 505, 128, 463, 504, 505, 463, 504, 505, 128, 464, 465, 506, 464, 465, 506, 128, 464, 505, 506, 464, 505, 506, 128, 465, 466, 507, 465, 466, 507, 128, 465, 506, 507, 465, 506, 507, 128, 466, 467, 508, 466, 467, 508, 128, 466, 507, 508, 466, 507, 508, 128, 467, 468, 509, 467, 468, 509, 128, 467, 508, 509, 467, 508, 509, 128, 468, 469, 510, 468, 469, 510, 128, 468, 509, 510, 468, 509, 510, 128, 469, 470, 511, 469, 470, 511, 128, 469, 510, 511, 469, 510, 511, 128, 470, 471, 512, 470, 471, 512, 128, 470, 511, 512, 470, 511, 512, 128, 471, 472, 513, 471, 472, 513, 128, 471, 512, 513, 471, 512, 513, 128, 472, 473, 514, 472, 473, 514, 128, 472, 513, 514, 472, 513, 514, 128, 473, 474, 515, 473, 474, 515, 128, 473, 514, 515, 473, 514, 515, 128, 474, 475, 516, 474, 475, 516, 128, 474, 515, 516, 474, 515, 516, 128, 475, 476, 517, 475, 476, 517, 128, 475, 516, 517, 475, 516, 517, 128, 476, 477, 518, 476, 477, 518, 128, 476, 517, 518, 476, 517, 518, 128, 477, 478, 519, 477, 478, 519, 128, 477, 518, 519, 477, 518, 519, 128, 478, 479, 520, 478, 479, 520, 128, 478, 519, 520, 478, 519, 520, 128, 479, 480, 521, 479, 480, 521, 128, 479, 520, 521, 479, 520, 521, 128, 480, 481, 522, 480, 481, 522, 128, 480, 521, 522, 480, 521, 522, 128, 481, 482, 523, 481, 482, 523, 128, 481, 522, 523, 481, 522, 523, 128, 482, 483, 524, 482, 483, 524, 128, 482, 523, 524, 482, 523, 524, 128, 483, 484, 525, 483, 484, 525, 128, 483, 524, 525, 483, 524, 525, 128, 484, 485, 526, 484, 485, 526, 128, 484, 525, 526, 484, 525, 526, 128, 485, 486, 527, 485, 486, 527, 128, 485, 526, 527, 485, 526, 527, 128, 486, 487, 528, 486, 487, 528, 128, 486, 527, 528, 486, 527, 528, 128, 487, 488, 529, 487, 488, 529, 128, 487, 528, 529, 487, 528, 529, 128, 488, 489, 530, 488, 489, 530, 128, 488, 529, 530, 488, 529, 530, 128, 489, 490, 531, 489, 490, 531, 128, 489, 530, 531, 489, 530, 531, 128, 490, 491, 532, 490, 491, 532, 128, 490, 531, 532, 490, 531, 532, 128, 492, 493, 534, 492, 493, 534, 128, 492, 533, 534, 492, 533, 534, 128, 493, 494, 535, 493, 494, 535, 128, 493, 534, 535, 493, 534, 535, 128, 494, 495, 536, 494, 495, 536, 128, 494, 535, 536, 494, 535, 536, 128, 495, 496, 537, 495, 496, 537, 128, 495, 536, 537, 495, 536, 537, 128, 496, 497, 538, 496, 497, 538, 128, 496, 537, 538, 496, 537, 538, 128, 497, 498, 539, 497, 498, 539, 128, 497, 538, 539, 497, 538, 539, 128, 498, 499, 540, 498, 499, 540, 128, 498, 539, 540, 498, 539, 540, 128, 499, 500, 541, 499, 500, 541, 128, 499, 540, 541, 499, 540, 541, 128, 500, 501, 542, 500, 501, 542, 128, 500, 541, 542, 500, 541, 542, 128, 501, 502, 543, 501, 502, 543, 128, 501, 542, 543, 501, 542, 543, 128, 502, 503, 544, 502, 503, 544, 128, 502, 543, 544, 502, 543, 544, 128, 503, 504, 545, 503, 504, 545, 128, 503, 544, 545, 503, 544, 545, 128, 504, 505, 546, 504, 505, 546, 128, 504, 545, 546, 504, 545, 546, 128, 505, 506, 547, 505, 506, 547, 128, 505, 546, 547, 505, 546, 547, 128, 506, 507, 548, 506, 507, 548, 128, 506, 547, 548, 506, 547, 548, 128, 507, 508, 549, 507, 508, 549, 128, 507, 548, 549, 507, 548, 549, 128, 508, 509, 550, 508, 509, 550, 128, 508, 549, 550, 508, 549, 550, 128, 509, 510, 551, 509, 510, 551, 128, 509, 550, 551, 509, 550, 551, 128, 510, 511, 552, 510, 511, 552, 128, 510, 551, 552, 510, 551, 552, 128, 511, 512, 553, 511, 512, 553, 128, 511, 552, 553, 511, 552, 553, 128, 512, 513, 554, 512, 513, 554, 128, 512, 553, 554, 512, 553, 554, 128, 513, 514, 555, 513, 514, 555, 128, 513, 554, 555, 513, 554, 555, 128, 514, 515, 556, 514, 515, 556, 128, 514, 555, 556, 514, 555, 556, 128, 515, 516, 557, 515, 516, 557, 128, 515, 556, 557, 515, 556, 557, 128, 516, 517, 558, 516, 517, 558, 128, 516, 557, 558, 516, 557, 558, 128, 517, 518, 559, 517, 518, 559, 128, 517, 558, 559, 517, 558, 559, 128, 518, 519, 560, 518, 519, 560, 128, 518, 559, 560, 518, 559, 560, 128, 519, 520, 561, 519, 520, 561, 128, 519, 560, 561, 519, 560, 561, 128, 520, 521, 562, 520, 521, 562, 128, 520, 561, 562, 520, 561, 562, 128, 521, 522, 563, 521, 522, 563, 128, 521, 562, 563, 521, 562, 563, 128, 522, 523, 564, 522, 523, 564, 128, 522, 563, 564, 522, 563, 564, 128, 523, 524, 565, 523, 524, 565, 128, 523, 564, 565, 523, 564, 565, 128, 524, 525, 566, 524, 525, 566, 128, 524, 565, 566, 524, 565, 566, 128, 525, 526, 567, 525, 526, 567, 128, 525, 566, 567, 525, 566, 567, 128, 526, 527, 568, 526, 527, 568, 128, 526, 567, 568, 526, 567, 568, 128, 527, 528, 569, 527, 528, 569, 128, 527, 568, 569, 527, 568, 569, 128, 528, 529, 570, 528, 529, 570, 128, 528, 569, 570, 528, 569, 570, 128, 529, 530, 571, 529, 530, 571, 128, 529, 570, 571, 529, 570, 571, 128, 530, 531, 572, 530, 531, 572, 128, 530, 571, 572, 530, 571, 572, 128, 531, 532, 573, 531, 532, 573, 128, 531, 572, 573, 531, 572, 573, 128, 533, 534, 575, 533, 534, 575, 128, 533, 574, 575, 533, 574, 575, 128, 534, 535, 576, 534, 535, 576, 128, 534, 575, 576, 534, 575, 576, 128, 535, 536, 577, 535, 536, 577, 128, 535, 576, 577, 535, 576, 577, 128, 536, 537, 578, 536, 537, 578, 128, 536, 577, 578, 536, 577, 578, 128, 537, 538, 579, 537, 538, 579, 128, 537, 578, 579, 537, 578, 579, 128, 538, 539, 580, 538, 539, 580, 128, 538, 579, 580, 538, 579, 580, 128, 539, 540, 581, 539, 540, 581, 128, 539, 580, 581, 539, 580, 581, 128, 540, 541, 582, 540, 541, 582, 128, 540, 581, 582, 540, 581, 582, 128, 541, 542, 583, 541, 542, 583, 128, 541, 582, 583, 541, 582, 583, 128, 542, 543, 584, 542, 543, 584, 128, 542, 583, 584, 542, 583, 584, 128, 543, 544, 585, 543, 544, 585, 128, 543, 584, 585, 543, 584, 585, 128, 544, 545, 586, 544, 545, 586, 128, 544, 585, 586, 544, 585, 586, 128, 545, 546, 587, 545, 546, 587, 128, 545, 586, 587, 545, 586, 587, 128, 546, 547, 588, 546, 547, 588, 128, 546, 587, 588, 546, 587, 588, 128, 547, 548, 589, 547, 548, 589, 128, 547, 588, 589, 547, 588, 589, 128, 548, 549, 590, 548, 549, 590, 128, 548, 589, 590, 548, 589, 590, 128, 549, 550, 591, 549, 550, 591, 128, 549, 590, 591, 549, 590, 591, 128, 550, 551, 592, 550, 551, 592, 128, 550, 591, 592, 550, 591, 592, 128, 551, 552, 593, 551, 552, 593, 128, 551, 592, 593, 551, 592, 593, 128, 552, 553, 594, 552, 553, 594, 128, 552, 593, 594, 552, 593, 594, 128, 553, 554, 595, 553, 554, 595, 128, 553, 594, 595, 553, 594, 595, 128, 554, 555, 596, 554, 555, 596, 128, 554, 595, 596, 554, 595, 596, 128, 555, 556, 597, 555, 556, 597, 128, 555, 596, 597, 555, 596, 597, 128, 556, 557, 598, 556, 557, 598, 128, 556, 597, 598, 556, 597, 598, 128, 557, 558, 599, 557, 558, 599, 128, 557, 598, 599, 557, 598, 599, 128, 558, 559, 600, 558, 559, 600, 128, 558, 599, 600, 558, 599, 600, 128, 559, 560, 601, 559, 560, 601, 128, 559, 600, 601, 559, 600, 601, 128, 560, 561, 602, 560, 561, 602, 128, 560, 601, 602, 560, 601, 602, 128, 561, 562, 603, 561, 562, 603, 128, 561, 602, 603, 561, 602, 603, 128, 562, 563, 604, 562, 563, 604, 128, 562, 603, 604, 562, 603, 604, 128, 563, 564, 605, 563, 564, 605, 128, 563, 604, 605, 563, 604, 605, 128, 564, 565, 606, 564, 565, 606, 128, 564, 605, 606, 564, 605, 606, 128, 565, 566, 607, 565, 566, 607, 128, 565, 606, 607, 565, 606, 607, 128, 566, 567, 608, 566, 567, 608, 128, 566, 607, 608, 566, 607, 608, 128, 567, 568, 609, 567, 568, 609, 128, 567, 608, 609, 567, 608, 609, 128, 568, 569, 610, 568, 569, 610, 128, 568, 609, 610, 568, 609, 610, 128, 569, 570, 611, 569, 570, 611, 128, 569, 610, 611, 569, 610, 611, 128, 570, 571, 612, 570, 571, 612, 128, 570, 611, 612, 570, 611, 612, 128, 571, 572, 613, 571, 572, 613, 128, 571, 612, 613, 571, 612, 613, 128, 572, 573, 614, 572, 573, 614, 128, 572, 613, 614, 572, 613, 614, 128, 574, 575, 616, 574, 575, 616, 128, 574, 615, 616, 574, 615, 616, 128, 575, 576, 617, 575, 576, 617, 128, 575, 616, 617, 575, 616, 617, 128, 576, 577, 618, 576, 577, 618, 128, 576, 617, 618, 576, 617, 618, 128, 577, 578, 619, 577, 578, 619, 128, 577, 618, 619, 577, 618, 619, 128, 578, 579, 620, 578, 579, 620, 128, 578, 619, 620, 578, 619, 620, 128, 579, 580, 621, 579, 580, 621, 128, 579, 620, 621, 579, 620, 621, 128, 580, 581, 622, 580, 581, 622, 128, 580, 621, 622, 580, 621, 622, 128, 581, 582, 623, 581, 582, 623, 128, 581, 622, 623, 581, 622, 623, 128, 582, 583, 624, 582, 583, 624, 128, 582, 623, 624, 582, 623, 624, 128, 583, 584, 625, 583, 584, 625, 128, 583, 624, 625, 583, 624, 625, 128, 584, 585, 626, 584, 585, 626, 128, 584, 625, 626, 584, 625, 626, 128, 585, 586, 627, 585, 586, 627, 128, 585, 626, 627, 585, 626, 627, 128, 586, 587, 628, 586, 587, 628, 128, 586, 627, 628, 586, 627, 628, 128, 587, 588, 629, 587, 588, 629, 128, 587, 628, 629, 587, 628, 629, 128, 588, 589, 630, 588, 589, 630, 128, 588, 629, 630, 588, 629, 630, 128, 589, 590, 631, 589, 590, 631, 128, 589, 630, 631, 589, 630, 631, 128, 590, 591, 632, 590, 591, 632, 128, 590, 631, 632, 590, 631, 632, 128, 591, 592, 633, 591, 592, 633, 128, 591, 632, 633, 591, 632, 633, 128, 592, 593, 634, 592, 593, 634, 128, 592, 633, 634, 592, 633, 634, 128, 593, 594, 635, 593, 594, 635, 128, 593, 634, 635, 593, 634, 635, 128, 594, 595, 636, 594, 595, 636, 128, 594, 635, 636, 594, 635, 636, 128, 595, 596, 637, 595, 596, 637, 128, 595, 636, 637, 595, 636, 637, 128, 596, 597, 638, 596, 597, 638, 128, 596, 637, 638, 596, 637, 638, 128, 597, 598, 639, 597, 598, 639, 128, 597, 638, 639, 597, 638, 639, 128, 598, 599, 640, 598, 599, 640, 128, 598, 639, 640, 598, 639, 640, 128, 599, 600, 641, 599, 600, 641, 128, 599, 640, 641, 599, 640, 641, 128, 600, 601, 642, 600, 601, 642, 128, 600, 641, 642, 600, 641, 642, 128, 601, 602, 643, 601, 602, 643, 128, 601, 642, 643, 601, 642, 643, 128, 602, 603, 644, 602, 603, 644, 128, 602, 643, 644, 602, 643, 644, 128, 603, 604, 645, 603, 604, 645, 128, 603, 644, 645, 603, 644, 645, 128, 604, 605, 646, 604, 605, 646, 128, 604, 645, 646, 604, 645, 646, 128, 605, 606, 647, 605, 606, 647, 128, 605, 646, 647, 605, 646, 647, 128, 606, 607, 648, 606, 607, 648, 128, 606, 647, 648, 606, 647, 648, 128, 607, 608, 649, 607, 608, 649, 128, 607, 648, 649, 607, 648, 649, 128, 608, 609, 650, 608, 609, 650, 128, 608, 649, 650, 608, 649, 650, 128, 609, 610, 651, 609, 610, 651, 128, 609, 650, 651, 609, 650, 651, 128, 610, 611, 652, 610, 611, 652, 128, 610, 651, 652, 610, 651, 652, 128, 611, 612, 653, 611, 612, 653, 128, 611, 652, 653, 611, 652, 653, 128, 612, 613, 654, 612, 613, 654, 128, 612, 653, 654, 612, 653, 654, 128, 613, 614, 655, 613, 614, 655, 128, 613, 654, 655, 613, 654, 655, 128, 615, 616, 657, 615, 616, 657, 128, 615, 656, 657, 615, 656, 657, 128, 616, 617, 658, 616, 617, 658, 128, 616, 657, 658, 616, 657, 658, 128, 617, 618, 659, 617, 618, 659, 128, 617, 658, 659, 617, 658, 659, 128, 618, 619, 660, 618, 619, 660, 128, 618, 659, 660, 618, 659, 660, 128, 619, 620, 661, 619, 620, 661, 128, 619, 660, 661, 619, 660, 661, 128, 620, 621, 662, 620, 621, 662, 128, 620, 661, 662, 620, 661, 662, 128, 621, 622, 663, 621, 622, 663, 128, 621, 662, 663, 621, 662, 663, 128, 622, 623, 664, 622, 623, 664, 128, 622, 663, 664, 622, 663, 664, 128, 623, 624, 665, 623, 624, 665, 128, 623, 664, 665, 623, 664, 665, 128, 624, 625, 666, 624, 625, 666, 128, 624, 665, 666, 624, 665, 666, 128, 625, 626, 667, 625, 626, 667, 128, 625, 666, 667, 625, 666, 667, 128, 626, 627, 668, 626, 627, 668, 128, 626, 667, 668, 626, 667, 668, 128, 627, 628, 669, 627, 628, 669, 128, 627, 668, 669, 627, 668, 669, 128, 628, 629, 670, 628, 629, 670, 128, 628, 669, 670, 628, 669, 670, 128, 629, 630, 671, 629, 630, 671, 128, 629, 670, 671, 629, 670, 671, 128, 630, 631, 672, 630, 631, 672, 128, 630, 671, 672, 630, 671, 672, 128, 631, 632, 673, 631, 632, 673, 128, 631, 672, 673, 631, 672, 673, 128, 632, 633, 674, 632, 633, 674, 128, 632, 673, 674, 632, 673, 674, 128, 633, 634, 675, 633, 634, 675, 128, 633, 674, 675, 633, 674, 675, 128, 634, 635, 676, 634, 635, 676, 128, 634, 675, 676, 634, 675, 676, 128, 635, 636, 677, 635, 636, 677, 128, 635, 676, 677, 635, 676, 677, 128, 636, 637, 678, 636, 637, 678, 128, 636, 677, 678, 636, 677, 678, 128, 637, 638, 679, 637, 638, 679, 128, 637, 678, 679, 637, 678, 679, 128, 638, 639, 680, 638, 639, 680, 128, 638, 679, 680, 638, 679, 680, 128, 639, 640, 681, 639, 640, 681, 128, 639, 680, 681, 639, 680, 681, 128, 640, 641, 682, 640, 641, 682, 128, 640, 681, 682, 640, 681, 682, 128, 641, 642, 683, 641, 642, 683, 128, 641, 682, 683, 641, 682, 683, 128, 642, 643, 684, 642, 643, 684, 128, 642, 683, 684, 642, 683, 684, 128, 643, 644, 685, 643, 644, 685, 128, 643, 684, 685, 643, 684, 685, 128, 644, 645, 686, 644, 645, 686, 128, 644, 685, 686, 644, 685, 686, 128, 645, 646, 687, 645, 646, 687, 128, 645, 686, 687, 645, 686, 687, 128, 646, 647, 688, 646, 647, 688, 128, 646, 687, 688, 646, 687, 688, 128, 647, 648, 689, 647, 648, 689, 128, 647, 688, 689, 647, 688, 689, 128, 648, 649, 690, 648, 649, 690, 128, 648, 689, 690, 648, 689, 690, 128, 649, 650, 691, 649, 650, 691, 128, 649, 690, 691, 649, 690, 691, 128, 650, 651, 692, 650, 651, 692, 128, 650, 691, 692, 650, 691, 692, 128, 651, 652, 693, 651, 652, 693, 128, 651, 692, 693, 651, 692, 693, 128, 652, 653, 694, 652, 653, 694, 128, 652, 693, 694, 652, 693, 694, 128, 653, 654, 695, 653, 654, 695, 128, 653, 694, 695, 653, 694, 695, 128, 654, 655, 696, 654, 655, 696, 128, 654, 695, 696, 654, 695, 696, 128, 656, 657, 698, 656, 657, 698, 128, 656, 697, 698, 656, 697, 698, 128, 657, 658, 699, 657, 658, 699, 128, 657, 698, 699, 657, 698, 699, 128, 658, 659, 700, 658, 659, 700, 128, 658, 699, 700, 658, 699, 700, 128, 659, 660, 701, 659, 660, 701, 128, 659, 700, 701, 659, 700, 701, 128, 660, 661, 702, 660, 661, 702, 128, 660, 701, 702, 660, 701, 702, 128, 661, 662, 703, 661, 662, 703, 128, 661, 702, 703, 661, 702, 703, 128, 662, 663, 704, 662, 663, 704, 128, 662, 703, 704, 662, 703, 704, 128, 663, 664, 705, 663, 664, 705, 128, 663, 704, 705, 663, 704, 705, 128, 664, 665, 706, 664, 665, 706, 128, 664, 705, 706, 664, 705, 706, 128, 665, 666, 707, 665, 666, 707, 128, 665, 706, 707, 665, 706, 707, 128, 666, 667, 708, 666, 667, 708, 128, 666, 707, 708, 666, 707, 708, 128, 667, 668, 709, 667, 668, 709, 128, 667, 708, 709, 667, 708, 709, 128, 668, 669, 710, 668, 669, 710, 128, 668, 709, 710, 668, 709, 710, 128, 669, 670, 711, 669, 670, 711, 128, 669, 710, 711, 669, 710, 711, 128, 670, 671, 712, 670, 671, 712, 128, 670, 711, 712, 670, 711, 712, 128, 671, 672, 713, 671, 672, 713, 128, 671, 712, 713, 671, 712, 713, 128, 672, 673, 714, 672, 673, 714, 128, 672, 713, 714, 672, 713, 714, 128, 673, 674, 715, 673, 674, 715, 128, 673, 714, 715, 673, 714, 715, 128, 674, 675, 716, 674, 675, 716, 128, 674, 715, 716, 674, 715, 716, 128, 675, 676, 717, 675, 676, 717, 128, 675, 716, 717, 675, 716, 717, 128, 676, 677, 718, 676, 677, 718, 128, 676, 717, 718, 676, 717, 718, 128, 677, 678, 719, 677, 678, 719, 128, 677, 718, 719, 677, 718, 719, 128, 678, 679, 720, 678, 679, 720, 128, 678, 719, 720, 678, 719, 720, 128, 679, 680, 721, 679, 680, 721, 128, 679, 720, 721, 679, 720, 721, 128, 680, 681, 722, 680, 681, 722, 128, 680, 721, 722, 680, 721, 722, 128, 681, 682, 723, 681, 682, 723, 128, 681, 722, 723, 681, 722, 723, 128, 682, 683, 724, 682, 683, 724, 128, 682, 723, 724, 682, 723, 724, 128, 683, 684, 725, 683, 684, 725, 128, 683, 724, 725, 683, 724, 725, 128, 684, 685, 726, 684, 685, 726, 128, 684, 725, 726, 684, 725, 726, 128, 685, 686, 727, 685, 686, 727, 128, 685, 726, 727, 685, 726, 727, 128, 686, 687, 728, 686, 687, 728, 128, 686, 727, 728, 686, 727, 728, 128, 687, 688, 729, 687, 688, 729, 128, 687, 728, 729, 687, 728, 729, 128, 688, 689, 730, 688, 689, 730, 128, 688, 729, 730, 688, 729, 730, 128, 689, 690, 731, 689, 690, 731, 128, 689, 730, 731, 689, 730, 731, 128, 690, 691, 732, 690, 691, 732, 128, 690, 731, 732, 690, 731, 732, 128, 691, 692, 733, 691, 692, 733, 128, 691, 732, 733, 691, 732, 733, 128, 692, 693, 734, 692, 693, 734, 128, 692, 733, 734, 692, 733, 734, 128, 693, 694, 735, 693, 694, 735, 128, 693, 734, 735, 693, 734, 735, 128, 694, 695, 736, 694, 695, 736, 128, 694, 735, 736, 694, 735, 736, 128, 695, 696, 737, 695, 696, 737, 128, 695, 736, 737, 695, 736, 737, 128, 697, 698, 739, 697, 698, 739, 128, 697, 738, 739, 697, 738, 739, 128, 698, 699, 740, 698, 699, 740, 128, 698, 739, 740, 698, 739, 740, 128, 699, 700, 741, 699, 700, 741, 128, 699, 740, 741, 699, 740, 741, 128, 700, 701, 742, 700, 701, 742, 128, 700, 741, 742, 700, 741, 742, 128, 701, 702, 743, 701, 702, 743, 128, 701, 742, 743, 701, 742, 743, 128, 702, 703, 744, 702, 703, 744, 128, 702, 743, 744, 702, 743, 744, 128, 703, 704, 745, 703, 704, 745, 128, 703, 744, 745, 703, 744, 745, 128, 704, 705, 746, 704, 705, 746, 128, 704, 745, 746, 704, 745, 746, 128, 705, 706, 747, 705, 706, 747, 128, 705, 746, 747, 705, 746, 747, 128, 706, 707, 748, 706, 707, 748, 128, 706, 747, 748, 706, 747, 748, 128, 707, 708, 749, 707, 708, 749, 128, 707, 748, 749, 707, 748, 749, 128, 708, 709, 750, 708, 709, 750, 128, 708, 749, 750, 708, 749, 750, 128, 709, 710, 751, 709, 710, 751, 128, 709, 750, 751, 709, 750, 751, 128, 710, 711, 752, 710, 711, 752, 128, 710, 751, 752, 710, 751, 752, 128, 711, 712, 753, 711, 712, 753, 128, 711, 752, 753, 711, 752, 753, 128, 712, 713, 754, 712, 713, 754, 128, 712, 753, 754, 712, 753, 754, 128, 713, 714, 755, 713, 714, 755, 128, 713, 754, 755, 713, 754, 755, 128, 714, 715, 756, 714, 715, 756, 128, 714, 755, 756, 714, 755, 756, 128, 715, 716, 757, 715, 716, 757, 128, 715, 756, 757, 715, 756, 757, 128, 716, 717, 758, 716, 717, 758, 128, 716, 757, 758, 716, 757, 758, 128, 717, 718, 759, 717, 718, 759, 128, 717, 758, 759, 717, 758, 759, 128, 718, 719, 760, 718, 719, 760, 128, 718, 759, 760, 718, 759, 760, 128, 719, 720, 761, 719, 720, 761, 128, 719, 760, 761, 719, 760, 761, 128, 720, 721, 762, 720, 721, 762, 128, 720, 761, 762, 720, 761, 762, 128, 721, 722, 763, 721, 722, 763, 128, 721, 762, 763, 721, 762, 763, 128, 722, 723, 764, 722, 723, 764, 128, 722, 763, 764, 722, 763, 764, 128, 723, 724, 765, 723, 724, 765, 128, 723, 764, 765, 723, 764, 765, 128, 724, 725, 766, 724, 725, 766, 128, 724, 765, 766, 724, 765, 766, 128, 725, 726, 767, 725, 726, 767, 128, 725, 766, 767, 725, 766, 767, 128, 726, 727, 768, 726, 727, 768, 128, 726, 767, 768, 726, 767, 768, 128, 727, 728, 769, 727, 728, 769, 128, 727, 768, 769, 727, 768, 769, 128, 728, 729, 770, 728, 729, 770, 128, 728, 769, 770, 728, 769, 770, 128, 729, 730, 771, 729, 730, 771, 128, 729, 770, 771, 729, 770, 771, 128, 730, 731, 772, 730, 731, 772, 128, 730, 771, 772, 730, 771, 772, 128, 731, 732, 773, 731, 732, 773, 128, 731, 772, 773, 731, 772, 773, 128, 732, 733, 774, 732, 733, 774, 128, 732, 773, 774, 732, 773, 774, 128, 733, 734, 775, 733, 734, 775, 128, 733, 774, 775, 733, 774, 775, 128, 734, 735, 776, 734, 735, 776, 128, 734, 775, 776, 734, 775, 776, 128, 735, 736, 777, 735, 736, 777, 128, 735, 776, 777, 735, 776, 777, 128, 736, 737, 778, 736, 737, 778, 128, 736, 777, 778, 736, 777, 778, 128, 738, 739, 780, 738, 739, 780, 128, 738, 779, 780, 738, 779, 780, 128, 739, 740, 781, 739, 740, 781, 128, 739, 780, 781, 739, 780, 781, 128, 740, 741, 782, 740, 741, 782, 128, 740, 781, 782, 740, 781, 782, 128, 741, 742, 783, 741, 742, 783, 128, 741, 782, 783, 741, 782, 783, 128, 742, 743, 784, 742, 743, 784, 128, 742, 783, 784, 742, 783, 784, 128, 743, 744, 785, 743, 744, 785, 128, 743, 784, 785, 743, 784, 785, 128, 744, 745, 786, 744, 745, 786, 128, 744, 785, 786, 744, 785, 786, 128, 745, 746, 787, 745, 746, 787, 128, 745, 786, 787, 745, 786, 787, 128, 746, 747, 788, 746, 747, 788, 128, 746, 787, 788, 746, 787, 788, 128, 747, 748, 789, 747, 748, 789, 128, 747, 788, 789, 747, 788, 789, 128, 748, 749, 790, 748, 749, 790, 128, 748, 789, 790, 748, 789, 790, 128, 749, 750, 791, 749, 750, 791, 128, 749, 790, 791, 749, 790, 791, 128, 750, 751, 792, 750, 751, 792, 128, 750, 791, 792, 750, 791, 792, 128, 751, 752, 793, 751, 752, 793, 128, 751, 792, 793, 751, 792, 793, 128, 752, 753, 794, 752, 753, 794, 128, 752, 793, 794, 752, 793, 794, 128, 753, 754, 795, 753, 754, 795, 128, 753, 794, 795, 753, 794, 795, 128, 754, 755, 796, 754, 755, 796, 128, 754, 795, 796, 754, 795, 796, 128, 755, 756, 797, 755, 756, 797, 128, 755, 796, 797, 755, 796, 797, 128, 756, 757, 798, 756, 757, 798, 128, 756, 797, 798, 756, 797, 798, 128, 757, 758, 799, 757, 758, 799, 128, 757, 798, 799, 757, 798, 799, 128, 758, 759, 800, 758, 759, 800, 128, 758, 799, 800, 758, 799, 800, 128, 759, 760, 801, 759, 760, 801, 128, 759, 800, 801, 759, 800, 801, 128, 760, 761, 802, 760, 761, 802, 128, 760, 801, 802, 760, 801, 802, 128, 761, 762, 803, 761, 762, 803, 128, 761, 802, 803, 761, 802, 803, 128, 762, 763, 804, 762, 763, 804, 128, 762, 803, 804, 762, 803, 804, 128, 763, 764, 805, 763, 764, 805, 128, 763, 804, 805, 763, 804, 805, 128, 764, 765, 806, 764, 765, 806, 128, 764, 805, 806, 764, 805, 806, 128, 765, 766, 807, 765, 766, 807, 128, 765, 806, 807, 765, 806, 807, 128, 766, 767, 808, 766, 767, 808, 128, 766, 807, 808, 766, 807, 808, 128, 767, 768, 809, 767, 768, 809, 128, 767, 808, 809, 767, 808, 809, 128, 768, 769, 810, 768, 769, 810, 128, 768, 809, 810, 768, 809, 810, 128, 769, 770, 811, 769, 770, 811, 128, 769, 810, 811, 769, 810, 811, 128, 770, 771, 812, 770, 771, 812, 128, 770, 811, 812, 770, 811, 812, 128, 771, 772, 813, 771, 772, 813, 128, 771, 812, 813, 771, 812, 813, 128, 772, 773, 814, 772, 773, 814, 128, 772, 813, 814, 772, 813, 814, 128, 773, 774, 815, 773, 774, 815, 128, 773, 814, 815, 773, 814, 815, 128, 774, 775, 816, 774, 775, 816, 128, 774, 815, 816, 774, 815, 816, 128, 775, 776, 817, 775, 776, 817, 128, 775, 816, 817, 775, 816, 817, 128, 776, 777, 818, 776, 777, 818, 128, 776, 817, 818, 776, 817, 818, 128, 777, 778, 819, 777, 778, 819, 128, 777, 818, 819, 777, 818, 819, 128, 779, 780, 821, 779, 780, 821, 128, 779, 820, 821, 779, 820, 821, 128, 780, 781, 822, 780, 781, 822, 128, 780, 821, 822, 780, 821, 822, 128, 781, 782, 823, 781, 782, 823, 128, 781, 822, 823, 781, 822, 823, 128, 782, 783, 824, 782, 783, 824, 128, 782, 823, 824, 782, 823, 824, 128, 783, 784, 825, 783, 784, 825, 128, 783, 824, 825, 783, 824, 825, 128, 784, 785, 826, 784, 785, 826, 128, 784, 825, 826, 784, 825, 826, 128, 785, 786, 827, 785, 786, 827, 128, 785, 826, 827, 785, 826, 827, 128, 786, 787, 828, 786, 787, 828, 128, 786, 827, 828, 786, 827, 828, 128, 787, 788, 829, 787, 788, 829, 128, 787, 828, 829, 787, 828, 829, 128, 788, 789, 830, 788, 789, 830, 128, 788, 829, 830, 788, 829, 830, 128, 789, 790, 831, 789, 790, 831, 128, 789, 830, 831, 789, 830, 831, 128, 790, 791, 832, 790, 791, 832, 128, 790, 831, 832, 790, 831, 832, 128, 791, 792, 833, 791, 792, 833, 128, 791, 832, 833, 791, 832, 833, 128, 792, 793, 834, 792, 793, 834, 128, 792, 833, 834, 792, 833, 834, 128, 793, 794, 835, 793, 794, 835, 128, 793, 834, 835, 793, 834, 835, 128, 794, 795, 836, 794, 795, 836, 128, 794, 835, 836, 794, 835, 836, 128, 795, 796, 837, 795, 796, 837, 128, 795, 836, 837, 795, 836, 837, 128, 796, 797, 838, 796, 797, 838, 128, 796, 837, 838, 796, 837, 838, 128, 797, 798, 839, 797, 798, 839, 128, 797, 838, 839, 797, 838, 839, 128, 798, 799, 840, 798, 799, 840, 128, 798, 839, 840, 798, 839, 840, 128, 799, 800, 841, 799, 800, 841, 128, 799, 840, 841, 799, 840, 841, 128, 800, 801, 842, 800, 801, 842, 128, 800, 841, 842, 800, 841, 842, 128, 801, 802, 843, 801, 802, 843, 128, 801, 842, 843, 801, 842, 843, 128, 802, 803, 844, 802, 803, 844, 128, 802, 843, 844, 802, 843, 844, 128, 803, 804, 845, 803, 804, 845, 128, 803, 844, 845, 803, 844, 845, 128, 804, 805, 846, 804, 805, 846, 128, 804, 845, 846, 804, 845, 846, 128, 805, 806, 847, 805, 806, 847, 128, 805, 846, 847, 805, 846, 847, 128, 806, 807, 848, 806, 807, 848, 128, 806, 847, 848, 806, 847, 848, 128, 807, 808, 849, 807, 808, 849, 128, 807, 848, 849, 807, 848, 849, 128, 808, 809, 850, 808, 809, 850, 128, 808, 849, 850, 808, 849, 850, 128, 809, 810, 851, 809, 810, 851, 128, 809, 850, 851, 809, 850, 851, 128, 810, 811, 852, 810, 811, 852, 128, 810, 851, 852, 810, 851, 852, 128, 811, 812, 853, 811, 812, 853, 128, 811, 852, 853, 811, 852, 853, 128, 812, 813, 854, 812, 813, 854, 128, 812, 853, 854, 812, 853, 854, 128, 813, 814, 855, 813, 814, 855, 128, 813, 854, 855, 813, 854, 855, 128, 814, 815, 856, 814, 815, 856, 128, 814, 855, 856, 814, 855, 856, 128, 815, 816, 857, 815, 816, 857, 128, 815, 856, 857, 815, 856, 857, 128, 816, 817, 858, 816, 817, 858, 128, 816, 857, 858, 816, 857, 858, 128, 817, 818, 859, 817, 818, 859, 128, 817, 858, 859, 817, 858, 859, 128, 818, 819, 860, 818, 819, 860, 128, 818, 859, 860, 818, 859, 860, 128, 820, 821, 862, 820, 821, 862, 128, 820, 861, 862, 820, 861, 862, 128, 821, 822, 863, 821, 822, 863, 128, 821, 862, 863, 821, 862, 863, 128, 822, 823, 864, 822, 823, 864, 128, 822, 863, 864, 822, 863, 864, 128, 823, 824, 865, 823, 824, 865, 128, 823, 864, 865, 823, 864, 865, 128, 824, 825, 866, 824, 825, 866, 128, 824, 865, 866, 824, 865, 866, 128, 825, 826, 867, 825, 826, 867, 128, 825, 866, 867, 825, 866, 867, 128, 826, 827, 868, 826, 827, 868, 128, 826, 867, 868, 826, 867, 868, 128, 827, 828, 869, 827, 828, 869, 128, 827, 868, 869, 827, 868, 869, 128, 828, 829, 870, 828, 829, 870, 128, 828, 869, 870, 828, 869, 870, 128, 829, 830, 871, 829, 830, 871, 128, 829, 870, 871, 829, 870, 871, 128, 830, 831, 872, 830, 831, 872, 128, 830, 871, 872, 830, 871, 872, 128, 831, 832, 873, 831, 832, 873, 128, 831, 872, 873, 831, 872, 873, 128, 832, 833, 874, 832, 833, 874, 128, 832, 873, 874, 832, 873, 874, 128, 833, 834, 875, 833, 834, 875, 128, 833, 874, 875, 833, 874, 875, 128, 834, 835, 876, 834, 835, 876, 128, 834, 875, 876, 834, 875, 876, 128, 835, 836, 877, 835, 836, 877, 128, 835, 876, 877, 835, 876, 877, 128, 836, 837, 878, 836, 837, 878, 128, 836, 877, 878, 836, 877, 878, 128, 837, 838, 879, 837, 838, 879, 128, 837, 878, 879, 837, 878, 879, 128, 838, 839, 880, 838, 839, 880, 128, 838, 879, 880, 838, 879, 880, 128, 839, 840, 881, 839, 840, 881, 128, 839, 880, 881, 839, 880, 881, 128, 840, 841, 882, 840, 841, 882, 128, 840, 881, 882, 840, 881, 882, 128, 841, 842, 883, 841, 842, 883, 128, 841, 882, 883, 841, 882, 883, 128, 842, 843, 884, 842, 843, 884, 128, 842, 883, 884, 842, 883, 884, 128, 843, 844, 885, 843, 844, 885, 128, 843, 884, 885, 843, 884, 885, 128, 844, 845, 886, 844, 845, 886, 128, 844, 885, 886, 844, 885, 886, 128, 845, 846, 887, 845, 846, 887, 128, 845, 886, 887, 845, 886, 887, 128, 846, 847, 888, 846, 847, 888, 128, 846, 887, 888, 846, 887, 888, 128, 847, 848, 889, 847, 848, 889, 128, 847, 888, 889, 847, 888, 889, 128, 848, 849, 890, 848, 849, 890, 128, 848, 889, 890, 848, 889, 890, 128, 849, 850, 891, 849, 850, 891, 128, 849, 890, 891, 849, 890, 891, 128, 850, 851, 892, 850, 851, 892, 128, 850, 891, 892, 850, 891, 892, 128, 851, 852, 893, 851, 852, 893, 128, 851, 892, 893, 851, 892, 893, 128, 852, 853, 894, 852, 853, 894, 128, 852, 893, 894, 852, 893, 894, 128, 853, 854, 895, 853, 854, 895, 128, 853, 894, 895, 853, 894, 895, 128, 854, 855, 896, 854, 855, 896, 128, 854, 895, 896, 854, 895, 896, 128, 855, 856, 897, 855, 856, 897, 128, 855, 896, 897, 855, 896, 897, 128, 856, 857, 898, 856, 857, 898, 128, 856, 897, 898, 856, 897, 898, 128, 857, 858, 899, 857, 858, 899, 128, 857, 898, 899, 857, 898, 899, 128, 858, 859, 900, 858, 859, 900, 128, 858, 899, 900, 858, 899, 900, 128, 859, 860, 901, 859, 860, 901, 128, 859, 900, 901, 859, 900, 901, 128, 861, 862, 903, 861, 862, 903, 128, 861, 902, 903, 861, 902, 903, 128, 862, 863, 904, 862, 863, 904, 128, 862, 903, 904, 862, 903, 904, 128, 863, 864, 905, 863, 864, 905, 128, 863, 904, 905, 863, 904, 905, 128, 864, 865, 906, 864, 865, 906, 128, 864, 905, 906, 864, 905, 906, 128, 865, 866, 907, 865, 866, 907, 128, 865, 906, 907, 865, 906, 907, 128, 866, 867, 908, 866, 867, 908, 128, 866, 907, 908, 866, 907, 908, 128, 867, 868, 909, 867, 868, 909, 128, 867, 908, 909, 867, 908, 909, 128, 868, 869, 910, 868, 869, 910, 128, 868, 909, 910, 868, 909, 910, 128, 869, 870, 911, 869, 870, 911, 128, 869, 910, 911, 869, 910, 911, 128, 870, 871, 912, 870, 871, 912, 128, 870, 911, 912, 870, 911, 912, 128, 871, 872, 913, 871, 872, 913, 128, 871, 912, 913, 871, 912, 913, 128, 872, 873, 914, 872, 873, 914, 128, 872, 913, 914, 872, 913, 914, 128, 873, 874, 915, 873, 874, 915, 128, 873, 914, 915, 873, 914, 915, 128, 874, 875, 916, 874, 875, 916, 128, 874, 915, 916, 874, 915, 916, 128, 875, 876, 917, 875, 876, 917, 128, 875, 916, 917, 875, 916, 917, 128, 876, 877, 918, 876, 877, 918, 128, 876, 917, 918, 876, 917, 918, 128, 877, 878, 919, 877, 878, 919, 128, 877, 918, 919, 877, 918, 919, 128, 878, 879, 920, 878, 879, 920, 128, 878, 919, 920, 878, 919, 920, 128, 879, 880, 921, 879, 880, 921, 128, 879, 920, 921, 879, 920, 921, 128, 880, 881, 922, 880, 881, 922, 128, 880, 921, 922, 880, 921, 922, 128, 881, 882, 923, 881, 882, 923, 128, 881, 922, 923, 881, 922, 923, 128, 882, 883, 924, 882, 883, 924, 128, 882, 923, 924, 882, 923, 924, 128, 883, 884, 925, 883, 884, 925, 128, 883, 924, 925, 883, 924, 925, 128, 884, 885, 926, 884, 885, 926, 128, 884, 925, 926, 884, 925, 926, 128, 885, 886, 927, 885, 886, 927, 128, 885, 926, 927, 885, 926, 927, 128, 886, 887, 928, 886, 887, 928, 128, 886, 927, 928, 886, 927, 928, 128, 887, 888, 929, 887, 888, 929, 128, 887, 928, 929, 887, 928, 929, 128, 888, 889, 930, 888, 889, 930, 128, 888, 929, 930, 888, 929, 930, 128, 889, 890, 931, 889, 890, 931, 128, 889, 930, 931, 889, 930, 931, 128, 890, 891, 932, 890, 891, 932, 128, 890, 931, 932, 890, 931, 932, 128, 891, 892, 933, 891, 892, 933, 128, 891, 932, 933, 891, 932, 933, 128, 892, 893, 934, 892, 893, 934, 128, 892, 933, 934, 892, 933, 934, 128, 893, 894, 935, 893, 894, 935, 128, 893, 934, 935, 893, 934, 935, 128, 894, 895, 936, 894, 895, 936, 128, 894, 935, 936, 894, 935, 936, 128, 895, 896, 937, 895, 896, 937, 128, 895, 936, 937, 895, 936, 937, 128, 896, 897, 938, 896, 897, 938, 128, 896, 937, 938, 896, 937, 938, 128, 897, 898, 939, 897, 898, 939, 128, 897, 938, 939, 897, 938, 939, 128, 898, 899, 940, 898, 899, 940, 128, 898, 939, 940, 898, 939, 940, 128, 899, 900, 941, 899, 900, 941, 128, 899, 940, 941, 899, 940, 941, 128, 900, 901, 942, 900, 901, 942, 128, 900, 941, 942, 900, 941, 942, 128, 902, 903, 944, 902, 903, 944, 128, 902, 943, 944, 902, 943, 944, 128, 903, 904, 945, 903, 904, 945, 128, 903, 944, 945, 903, 944, 945, 128, 904, 905, 946, 904, 905, 946, 128, 904, 945, 946, 904, 945, 946, 128, 905, 906, 947, 905, 906, 947, 128, 905, 946, 947, 905, 946, 947, 128, 906, 907, 948, 906, 907, 948, 128, 906, 947, 948, 906, 947, 948, 128, 907, 908, 949, 907, 908, 949, 128, 907, 948, 949, 907, 948, 949, 128, 908, 909, 950, 908, 909, 950, 128, 908, 949, 950, 908, 949, 950, 128, 909, 910, 951, 909, 910, 951, 128, 909, 950, 951, 909, 950, 951, 128, 910, 911, 952, 910, 911, 952, 128, 910, 951, 952, 910, 951, 952, 128, 911, 912, 953, 911, 912, 953, 128, 911, 952, 953, 911, 952, 953, 128, 912, 913, 954, 912, 913, 954, 128, 912, 953, 954, 912, 953, 954, 128, 913, 914, 955, 913, 914, 955, 128, 913, 954, 955, 913, 954, 955, 128, 914, 915, 956, 914, 915, 956, 128, 914, 955, 956, 914, 955, 956, 128, 915, 916, 957, 915, 916, 957, 128, 915, 956, 957, 915, 956, 957, 128, 916, 917, 958, 916, 917, 958, 128, 916, 957, 958, 916, 957, 958, 128, 917, 918, 959, 917, 918, 959, 128, 917, 958, 959, 917, 958, 959, 128, 918, 919, 960, 918, 919, 960, 128, 918, 959, 960, 918, 959, 960, 128, 919, 920, 961, 919, 920, 961, 128, 919, 960, 961, 919, 960, 961, 128, 920, 921, 962, 920, 921, 962, 128, 920, 961, 962, 920, 961, 962, 128, 921, 922, 963, 921, 922, 963, 128, 921, 962, 963, 921, 962, 963, 128, 922, 923, 964, 922, 923, 964, 128, 922, 963, 964, 922, 963, 964, 128, 923, 924, 965, 923, 924, 965, 128, 923, 964, 965, 923, 964, 965, 128, 924, 925, 966, 924, 925, 966, 128, 924, 965, 966, 924, 965, 966, 128, 925, 926, 967, 925, 926, 967, 128, 925, 966, 967, 925, 966, 967, 128, 926, 927, 968, 926, 927, 968, 128, 926, 967, 968, 926, 967, 968, 128, 927, 928, 969, 927, 928, 969, 128, 927, 968, 969, 927, 968, 969, 128, 928, 929, 970, 928, 929, 970, 128, 928, 969, 970, 928, 969, 970, 128, 929, 930, 971, 929, 930, 971, 128, 929, 970, 971, 929, 970, 971, 128, 930, 931, 972, 930, 931, 972, 128, 930, 971, 972, 930, 971, 972, 128, 931, 932, 973, 931, 932, 973, 128, 931, 972, 973, 931, 972, 973, 128, 932, 933, 974, 932, 933, 974, 128, 932, 973, 974, 932, 973, 974, 128, 933, 934, 975, 933, 934, 975, 128, 933, 974, 975, 933, 974, 975, 128, 934, 935, 976, 934, 935, 976, 128, 934, 975, 976, 934, 975, 976, 128, 935, 936, 977, 935, 936, 977, 128, 935, 976, 977, 935, 976, 977, 128, 936, 937, 978, 936, 937, 978, 128, 936, 977, 978, 936, 977, 978, 128, 937, 938, 979, 937, 938, 979, 128, 937, 978, 979, 937, 978, 979, 128, 938, 939, 980, 938, 939, 980, 128, 938, 979, 980, 938, 979, 980, 128, 939, 940, 981, 939, 940, 981, 128, 939, 980, 981, 939, 980, 981, 128, 940, 941, 982, 940, 941, 982, 128, 940, 981, 982, 940, 981, 982, 128, 941, 942, 983, 941, 942, 983, 128, 941, 982, 983, 941, 982, 983, 128, 943, 944, 985, 943, 944, 985, 128, 943, 984, 985, 943, 984, 985, 128, 944, 945, 986, 944, 945, 986, 128, 944, 985, 986, 944, 985, 986, 128, 945, 946, 987, 945, 946, 987, 128, 945, 986, 987, 945, 986, 987, 128, 946, 947, 988, 946, 947, 988, 128, 946, 987, 988, 946, 987, 988, 128, 947, 948, 989, 947, 948, 989, 128, 947, 988, 989, 947, 988, 989, 128, 948, 949, 990, 948, 949, 990, 128, 948, 989, 990, 948, 989, 990, 128, 949, 950, 991, 949, 950, 991, 128, 949, 990, 991, 949, 990, 991, 128, 950, 951, 992, 950, 951, 992, 128, 950, 991, 992, 950, 991, 992, 128, 951, 952, 993, 951, 952, 993, 128, 951, 992, 993, 951, 992, 993, 128, 952, 953, 994, 952, 953, 994, 128, 952, 993, 994, 952, 993, 994, 128, 953, 954, 995, 953, 954, 995, 128, 953, 994, 995, 953, 994, 995, 128, 954, 955, 996, 954, 955, 996, 128, 954, 995, 996, 954, 995, 996, 128, 955, 956, 997, 955, 956, 997, 128, 955, 996, 997, 955, 996, 997, 128, 956, 957, 998, 956, 957, 998, 128, 956, 997, 998, 956, 997, 998, 128, 957, 958, 999, 957, 958, 999, 128, 957, 998, 999, 957, 998, 999, 128, 958, 959, 1000, 958, 959, 1000, 128, 958, 999, 1000, 958, 999, 1000, 128, 959, 960, 1001, 959, 960, 1001, 128, 959, 1000, 1001, 959, 1000, 1001, 128, 960, 961, 1002, 960, 961, 1002, 128, 960, 1001, 1002, 960, 1001, 1002, 128, 961, 962, 1003, 961, 962, 1003, 128, 961, 1002, 1003, 961, 1002, 1003, 128, 962, 963, 1004, 962, 963, 1004, 128, 962, 1003, 1004, 962, 1003, 1004, 128, 963, 964, 1005, 963, 964, 1005, 128, 963, 1004, 1005, 963, 1004, 1005, 128, 964, 965, 1006, 964, 965, 1006, 128, 964, 1005, 1006, 964, 1005, 1006, 128, 965, 966, 1007, 965, 966, 1007, 128, 965, 1006, 1007, 965, 1006, 1007, 128, 966, 967, 1008, 966, 967, 1008, 128, 966, 1007, 1008, 966, 1007, 1008, 128, 967, 968, 1009, 967, 968, 1009, 128, 967, 1008, 1009, 967, 1008, 1009, 128, 968, 969, 1010, 968, 969, 1010, 128, 968, 1009, 1010, 968, 1009, 1010, 128, 969, 970, 1011, 969, 970, 1011, 128, 969, 1010, 1011, 969, 1010, 1011, 128, 970, 971, 1012, 970, 971, 1012, 128, 970, 1011, 1012, 970, 1011, 1012, 128, 971, 972, 1013, 971, 972, 1013, 128, 971, 1012, 1013, 971, 1012, 1013, 128, 972, 973, 1014, 972, 973, 1014, 128, 972, 1013, 1014, 972, 1013, 1014, 128, 973, 974, 1015, 973, 974, 1015, 128, 973, 1014, 1015, 973, 1014, 1015, 128, 974, 975, 1016, 974, 975, 1016, 128, 974, 1015, 1016, 974, 1015, 1016, 128, 975, 976, 1017, 975, 976, 1017, 128, 975, 1016, 1017, 975, 1016, 1017, 128, 976, 977, 1018, 976, 977, 1018, 128, 976, 1017, 1018, 976, 1017, 1018, 128, 977, 978, 1019, 977, 978, 1019, 128, 977, 1018, 1019, 977, 1018, 1019, 128, 978, 979, 1020, 978, 979, 1020, 128, 978, 1019, 1020, 978, 1019, 1020, 128, 979, 980, 1021, 979, 980, 1021, 128, 979, 1020, 1021, 979, 1020, 1021, 128, 980, 981, 1022, 980, 981, 1022, 128, 980, 1021, 1022, 980, 1021, 1022, 128, 981, 982, 1023, 981, 982, 1023, 128, 981, 1022, 1023, 981, 1022, 1023, 128, 982, 983, 1024, 982, 983, 1024, 128, 982, 1023, 1024, 982, 1023, 1024, 128, 984, 985, 1026, 984, 985, 1026, 128, 984, 1025, 1026, 984, 1025, 1026, 128, 985, 986, 1027, 985, 986, 1027, 128, 985, 1026, 1027, 985, 1026, 1027, 128, 986, 987, 1028, 986, 987, 1028, 128, 986, 1027, 1028, 986, 1027, 1028, 128, 987, 988, 1029, 987, 988, 1029, 128, 987, 1028, 1029, 987, 1028, 1029, 128, 988, 989, 1030, 988, 989, 1030, 128, 988, 1029, 1030, 988, 1029, 1030, 128, 989, 990, 1031, 989, 990, 1031, 128, 989, 1030, 1031, 989, 1030, 1031, 128, 990, 991, 1032, 990, 991, 1032, 128, 990, 1031, 1032, 990, 1031, 1032, 128, 991, 992, 1033, 991, 992, 1033, 128, 991, 1032, 1033, 991, 1032, 1033, 128, 992, 993, 1034, 992, 993, 1034, 128, 992, 1033, 1034, 992, 1033, 1034, 128, 993, 994, 1035, 993, 994, 1035, 128, 993, 1034, 1035, 993, 1034, 1035, 128, 994, 995, 1036, 994, 995, 1036, 128, 994, 1035, 1036, 994, 1035, 1036, 128, 995, 996, 1037, 995, 996, 1037, 128, 995, 1036, 1037, 995, 1036, 1037, 128, 996, 997, 1038, 996, 997, 1038, 128, 996, 1037, 1038, 996, 1037, 1038, 128, 997, 998, 1039, 997, 998, 1039, 128, 997, 1038, 1039, 997, 1038, 1039, 128, 998, 999, 1040, 998, 999, 1040, 128, 998, 1039, 1040, 998, 1039, 1040, 128, 999, 1000, 1041, 999, 1000, 1041, 128, 999, 1040, 1041, 999, 1040, 1041, 128, 1000, 1001, 1042, 1000, 1001, 1042, 128, 1000, 1041, 1042, 1000, 1041, 1042, 128, 1001, 1002, 1043, 1001, 1002, 1043, 128, 1001, 1042, 1043, 1001, 1042, 1043, 128, 1002, 1003, 1044, 1002, 1003, 1044, 128, 1002, 1043, 1044, 1002, 1043, 1044, 128, 1003, 1004, 1045, 1003, 1004, 1045, 128, 1003, 1044, 1045, 1003, 1044, 1045, 128, 1004, 1005, 1046, 1004, 1005, 1046, 128, 1004, 1045, 1046, 1004, 1045, 1046, 128, 1005, 1006, 1047, 1005, 1006, 1047, 128, 1005, 1046, 1047, 1005, 1046, 1047, 128, 1006, 1007, 1048, 1006, 1007, 1048, 128, 1006, 1047, 1048, 1006, 1047, 1048, 128, 1007, 1008, 1049, 1007, 1008, 1049, 128, 1007, 1048, 1049, 1007, 1048, 1049, 128, 1008, 1009, 1050, 1008, 1009, 1050, 128, 1008, 1049, 1050, 1008, 1049, 1050, 128, 1009, 1010, 1051, 1009, 1010, 1051, 128, 1009, 1050, 1051, 1009, 1050, 1051, 128, 1010, 1011, 1052, 1010, 1011, 1052, 128, 1010, 1051, 1052, 1010, 1051, 1052, 128, 1011, 1012, 1053, 1011, 1012, 1053, 128, 1011, 1052, 1053, 1011, 1052, 1053, 128, 1012, 1013, 1054, 1012, 1013, 1054, 128, 1012, 1053, 1054, 1012, 1053, 1054, 128, 1013, 1014, 1055, 1013, 1014, 1055, 128, 1013, 1054, 1055, 1013, 1054, 1055, 128, 1014, 1015, 1056, 1014, 1015, 1056, 128, 1014, 1055, 1056, 1014, 1055, 1056, 128, 1015, 1016, 1057, 1015, 1016, 1057, 128, 1015, 1056, 1057, 1015, 1056, 1057, 128, 1016, 1017, 1058, 1016, 1017, 1058, 128, 1016, 1057, 1058, 1016, 1057, 1058, 128, 1017, 1018, 1059, 1017, 1018, 1059, 128, 1017, 1058, 1059, 1017, 1058, 1059, 128, 1018, 1019, 1060, 1018, 1019, 1060, 128, 1018, 1059, 1060, 1018, 1059, 1060, 128, 1019, 1020, 1061, 1019, 1020, 1061, 128, 1019, 1060, 1061, 1019, 1060, 1061, 128, 1020, 1021, 1062, 1020, 1021, 1062, 128, 1020, 1061, 1062, 1020, 1061, 1062, 128, 1021, 1022, 1063, 1021, 1022, 1063, 128, 1021, 1062, 1063, 1021, 1062, 1063, 128, 1022, 1023, 1064, 1022, 1023, 1064, 128, 1022, 1063, 1064, 1022, 1063, 1064, 128, 1023, 1024, 1065, 1023, 1024, 1065, 128, 1023, 1064, 1065, 1023, 1064, 1065, 128, 1025, 1026, 1067, 1025, 1026, 1067, 128, 1025, 1066, 1067, 1025, 1066, 1067, 128, 1026, 1027, 1068, 1026, 1027, 1068, 128, 1026, 1067, 1068, 1026, 1067, 1068, 128, 1027, 1028, 1069, 1027, 1028, 1069, 128, 1027, 1068, 1069, 1027, 1068, 1069, 128, 1028, 1029, 1070, 1028, 1029, 1070, 128, 1028, 1069, 1070, 1028, 1069, 1070, 128, 1029, 1030, 1071, 1029, 1030, 1071, 128, 1029, 1070, 1071, 1029, 1070, 1071, 128, 1030, 1031, 1072, 1030, 1031, 1072, 128, 1030, 1071, 1072, 1030, 1071, 1072, 128, 1031, 1032, 1073, 1031, 1032, 1073, 128, 1031, 1072, 1073, 1031, 1072, 1073, 128, 1032, 1033, 1074, 1032, 1033, 1074, 128, 1032, 1073, 1074, 1032, 1073, 1074, 128, 1033, 1034, 1075, 1033, 1034, 1075, 128, 1033, 1074, 1075, 1033, 1074, 1075, 128, 1034, 1035, 1076, 1034, 1035, 1076, 128, 1034, 1075, 1076, 1034, 1075, 1076, 128, 1035, 1036, 1077, 1035, 1036, 1077, 128, 1035, 1076, 1077, 1035, 1076, 1077, 128, 1036, 1037, 1078, 1036, 1037, 1078, 128, 1036, 1077, 1078, 1036, 1077, 1078, 128, 1037, 1038, 1079, 1037, 1038, 1079, 128, 1037, 1078, 1079, 1037, 1078, 1079, 128, 1038, 1039, 1080, 1038, 1039, 1080, 128, 1038, 1079, 1080, 1038, 1079, 1080, 128, 1039, 1040, 1081, 1039, 1040, 1081, 128, 1039, 1080, 1081, 1039, 1080, 1081, 128, 1040, 1041, 1082, 1040, 1041, 1082, 128, 1040, 1081, 1082, 1040, 1081, 1082, 128, 1041, 1042, 1083, 1041, 1042, 1083, 128, 1041, 1082, 1083, 1041, 1082, 1083, 128, 1042, 1043, 1084, 1042, 1043, 1084, 128, 1042, 1083, 1084, 1042, 1083, 1084, 128, 1043, 1044, 1085, 1043, 1044, 1085, 128, 1043, 1084, 1085, 1043, 1084, 1085, 128, 1044, 1045, 1086, 1044, 1045, 1086, 128, 1044, 1085, 1086, 1044, 1085, 1086, 128, 1045, 1046, 1087, 1045, 1046, 1087, 128, 1045, 1086, 1087, 1045, 1086, 1087, 128, 1046, 1047, 1088, 1046, 1047, 1088, 128, 1046, 1087, 1088, 1046, 1087, 1088, 128, 1047, 1048, 1089, 1047, 1048, 1089, 128, 1047, 1088, 1089, 1047, 1088, 1089, 128, 1048, 1049, 1090, 1048, 1049, 1090, 128, 1048, 1089, 1090, 1048, 1089, 1090, 128, 1049, 1050, 1091, 1049, 1050, 1091, 128, 1049, 1090, 1091, 1049, 1090, 1091, 128, 1050, 1051, 1092, 1050, 1051, 1092, 128, 1050, 1091, 1092, 1050, 1091, 1092, 128, 1051, 1052, 1093, 1051, 1052, 1093, 128, 1051, 1092, 1093, 1051, 1092, 1093, 128, 1052, 1053, 1094, 1052, 1053, 1094, 128, 1052, 1093, 1094, 1052, 1093, 1094, 128, 1053, 1054, 1095, 1053, 1054, 1095, 128, 1053, 1094, 1095, 1053, 1094, 1095, 128, 1054, 1055, 1096, 1054, 1055, 1096, 128, 1054, 1095, 1096, 1054, 1095, 1096, 128, 1055, 1056, 1097, 1055, 1056, 1097, 128, 1055, 1096, 1097, 1055, 1096, 1097, 128, 1056, 1057, 1098, 1056, 1057, 1098, 128, 1056, 1097, 1098, 1056, 1097, 1098, 128, 1057, 1058, 1099, 1057, 1058, 1099, 128, 1057, 1098, 1099, 1057, 1098, 1099, 128, 1058, 1059, 1100, 1058, 1059, 1100, 128, 1058, 1099, 1100, 1058, 1099, 1100, 128, 1059, 1060, 1101, 1059, 1060, 1101, 128, 1059, 1100, 1101, 1059, 1100, 1101, 128, 1060, 1061, 1102, 1060, 1061, 1102, 128, 1060, 1101, 1102, 1060, 1101, 1102, 128, 1061, 1062, 1103, 1061, 1062, 1103, 128, 1061, 1102, 1103, 1061, 1102, 1103, 128, 1062, 1063, 1104, 1062, 1063, 1104, 128, 1062, 1103, 1104, 1062, 1103, 1104, 128, 1063, 1064, 1105, 1063, 1064, 1105, 128, 1063, 1104, 1105, 1063, 1104, 1105, 128, 1064, 1065, 1106, 1064, 1065, 1106, 128, 1064, 1105, 1106, 1064, 1105, 1106, 128, 1066, 1067, 1108, 1066, 1067, 1108, 128, 1066, 1107, 1108, 1066, 1107, 1108, 128, 1067, 1068, 1109, 1067, 1068, 1109, 128, 1067, 1108, 1109, 1067, 1108, 1109, 128, 1068, 1069, 1110, 1068, 1069, 1110, 128, 1068, 1109, 1110, 1068, 1109, 1110, 128, 1069, 1070, 1111, 1069, 1070, 1111, 128, 1069, 1110, 1111, 1069, 1110, 1111, 128, 1070, 1071, 1112, 1070, 1071, 1112, 128, 1070, 1111, 1112, 1070, 1111, 1112, 128, 1071, 1072, 1113, 1071, 1072, 1113, 128, 1071, 1112, 1113, 1071, 1112, 1113, 128, 1072, 1073, 1114, 1072, 1073, 1114, 128, 1072, 1113, 1114, 1072, 1113, 1114, 128, 1073, 1074, 1115, 1073, 1074, 1115, 128, 1073, 1114, 1115, 1073, 1114, 1115, 128, 1074, 1075, 1116, 1074, 1075, 1116, 128, 1074, 1115, 1116, 1074, 1115, 1116, 128, 1075, 1076, 1117, 1075, 1076, 1117, 128, 1075, 1116, 1117, 1075, 1116, 1117, 128, 1076, 1077, 1118, 1076, 1077, 1118, 128, 1076, 1117, 1118, 1076, 1117, 1118, 128, 1077, 1078, 1119, 1077, 1078, 1119, 128, 1077, 1118, 1119, 1077, 1118, 1119, 128, 1078, 1079, 1120, 1078, 1079, 1120, 128, 1078, 1119, 1120, 1078, 1119, 1120, 128, 1079, 1080, 1121, 1079, 1080, 1121, 128, 1079, 1120, 1121, 1079, 1120, 1121, 128, 1080, 1081, 1122, 1080, 1081, 1122, 128, 1080, 1121, 1122, 1080, 1121, 1122, 128, 1081, 1082, 1123, 1081, 1082, 1123, 128, 1081, 1122, 1123, 1081, 1122, 1123, 128, 1082, 1083, 1124, 1082, 1083, 1124, 128, 1082, 1123, 1124, 1082, 1123, 1124, 128, 1083, 1084, 1125, 1083, 1084, 1125, 128, 1083, 1124, 1125, 1083, 1124, 1125, 128, 1084, 1085, 1126, 1084, 1085, 1126, 128, 1084, 1125, 1126, 1084, 1125, 1126, 128, 1085, 1086, 1127, 1085, 1086, 1127, 128, 1085, 1126, 1127, 1085, 1126, 1127, 128, 1086, 1087, 1128, 1086, 1087, 1128, 128, 1086, 1127, 1128, 1086, 1127, 1128, 128, 1087, 1088, 1129, 1087, 1088, 1129, 128, 1087, 1128, 1129, 1087, 1128, 1129, 128, 1088, 1089, 1130, 1088, 1089, 1130, 128, 1088, 1129, 1130, 1088, 1129, 1130, 128, 1089, 1090, 1131, 1089, 1090, 1131, 128, 1089, 1130, 1131, 1089, 1130, 1131, 128, 1090, 1091, 1132, 1090, 1091, 1132, 128, 1090, 1131, 1132, 1090, 1131, 1132, 128, 1091, 1092, 1133, 1091, 1092, 1133, 128, 1091, 1132, 1133, 1091, 1132, 1133, 128, 1092, 1093, 1134, 1092, 1093, 1134, 128, 1092, 1133, 1134, 1092, 1133, 1134, 128, 1093, 1094, 1135, 1093, 1094, 1135, 128, 1093, 1134, 1135, 1093, 1134, 1135, 128, 1094, 1095, 1136, 1094, 1095, 1136, 128, 1094, 1135, 1136, 1094, 1135, 1136, 128, 1095, 1096, 1137, 1095, 1096, 1137, 128, 1095, 1136, 1137, 1095, 1136, 1137, 128, 1096, 1097, 1138, 1096, 1097, 1138, 128, 1096, 1137, 1138, 1096, 1137, 1138, 128, 1097, 1098, 1139, 1097, 1098, 1139, 128, 1097, 1138, 1139, 1097, 1138, 1139, 128, 1098, 1099, 1140, 1098, 1099, 1140, 128, 1098, 1139, 1140, 1098, 1139, 1140, 128, 1099, 1100, 1141, 1099, 1100, 1141, 128, 1099, 1140, 1141, 1099, 1140, 1141, 128, 1100, 1101, 1142, 1100, 1101, 1142, 128, 1100, 1141, 1142, 1100, 1141, 1142, 128, 1101, 1102, 1143, 1101, 1102, 1143, 128, 1101, 1142, 1143, 1101, 1142, 1143, 128, 1102, 1103, 1144, 1102, 1103, 1144, 128, 1102, 1143, 1144, 1102, 1143, 1144, 128, 1103, 1104, 1145, 1103, 1104, 1145, 128, 1103, 1144, 1145, 1103, 1144, 1145, 128, 1104, 1105, 1146, 1104, 1105, 1146, 128, 1104, 1145, 1146, 1104, 1145, 1146, 128, 1105, 1106, 1147, 1105, 1106, 1147, 128, 1105, 1146, 1147, 1105, 1146, 1147, 128, 1107, 1108, 1149, 1107, 1108, 1149, 128, 1107, 1148, 1149, 1107, 1148, 1149, 128, 1108, 1109, 1150, 1108, 1109, 1150, 128, 1108, 1149, 1150, 1108, 1149, 1150, 128, 1109, 1110, 1151, 1109, 1110, 1151, 128, 1109, 1150, 1151, 1109, 1150, 1151, 128, 1110, 1111, 1152, 1110, 1111, 1152, 128, 1110, 1151, 1152, 1110, 1151, 1152, 128, 1111, 1112, 1153, 1111, 1112, 1153, 128, 1111, 1152, 1153, 1111, 1152, 1153, 128, 1112, 1113, 1154, 1112, 1113, 1154, 128, 1112, 1153, 1154, 1112, 1153, 1154, 128, 1113, 1114, 1155, 1113, 1114, 1155, 128, 1113, 1154, 1155, 1113, 1154, 1155, 128, 1114, 1115, 1156, 1114, 1115, 1156, 128, 1114, 1155, 1156, 1114, 1155, 1156, 128, 1115, 1116, 1157, 1115, 1116, 1157, 128, 1115, 1156, 1157, 1115, 1156, 1157, 128, 1116, 1117, 1158, 1116, 1117, 1158, 128, 1116, 1157, 1158, 1116, 1157, 1158, 128, 1117, 1118, 1159, 1117, 1118, 1159, 128, 1117, 1158, 1159, 1117, 1158, 1159, 128, 1118, 1119, 1160, 1118, 1119, 1160, 128, 1118, 1159, 1160, 1118, 1159, 1160, 128, 1119, 1120, 1161, 1119, 1120, 1161, 128, 1119, 1160, 1161, 1119, 1160, 1161, 128, 1120, 1121, 1162, 1120, 1121, 1162, 128, 1120, 1161, 1162, 1120, 1161, 1162, 128, 1121, 1122, 1163, 1121, 1122, 1163, 128, 1121, 1162, 1163, 1121, 1162, 1163, 128, 1122, 1123, 1164, 1122, 1123, 1164, 128, 1122, 1163, 1164, 1122, 1163, 1164, 128, 1123, 1124, 1165, 1123, 1124, 1165, 128, 1123, 1164, 1165, 1123, 1164, 1165, 128, 1124, 1125, 1166, 1124, 1125, 1166, 128, 1124, 1165, 1166, 1124, 1165, 1166, 128, 1125, 1126, 1167, 1125, 1126, 1167, 128, 1125, 1166, 1167, 1125, 1166, 1167, 128, 1126, 1127, 1168, 1126, 1127, 1168, 128, 1126, 1167, 1168, 1126, 1167, 1168, 128, 1127, 1128, 1169, 1127, 1128, 1169, 128, 1127, 1168, 1169, 1127, 1168, 1169, 128, 1128, 1129, 1170, 1128, 1129, 1170, 128, 1128, 1169, 1170, 1128, 1169, 1170, 128, 1129, 1130, 1171, 1129, 1130, 1171, 128, 1129, 1170, 1171, 1129, 1170, 1171, 128, 1130, 1131, 1172, 1130, 1131, 1172, 128, 1130, 1171, 1172, 1130, 1171, 1172, 128, 1131, 1132, 1173, 1131, 1132, 1173, 128, 1131, 1172, 1173, 1131, 1172, 1173, 128, 1132, 1133, 1174, 1132, 1133, 1174, 128, 1132, 1173, 1174, 1132, 1173, 1174, 128, 1133, 1134, 1175, 1133, 1134, 1175, 128, 1133, 1174, 1175, 1133, 1174, 1175, 128, 1134, 1135, 1176, 1134, 1135, 1176, 128, 1134, 1175, 1176, 1134, 1175, 1176, 128, 1135, 1136, 1177, 1135, 1136, 1177, 128, 1135, 1176, 1177, 1135, 1176, 1177, 128, 1136, 1137, 1178, 1136, 1137, 1178, 128, 1136, 1177, 1178, 1136, 1177, 1178, 128, 1137, 1138, 1179, 1137, 1138, 1179, 128, 1137, 1178, 1179, 1137, 1178, 1179, 128, 1138, 1139, 1180, 1138, 1139, 1180, 128, 1138, 1179, 1180, 1138, 1179, 1180, 128, 1139, 1140, 1181, 1139, 1140, 1181, 128, 1139, 1180, 1181, 1139, 1180, 1181, 128, 1140, 1141, 1182, 1140, 1141, 1182, 128, 1140, 1181, 1182, 1140, 1181, 1182, 128, 1141, 1142, 1183, 1141, 1142, 1183, 128, 1141, 1182, 1183, 1141, 1182, 1183, 128, 1142, 1143, 1184, 1142, 1143, 1184, 128, 1142, 1183, 1184, 1142, 1183, 1184, 128, 1143, 1144, 1185, 1143, 1144, 1185, 128, 1143, 1184, 1185, 1143, 1184, 1185, 128, 1144, 1145, 1186, 1144, 1145, 1186, 128, 1144, 1185, 1186, 1144, 1185, 1186, 128, 1145, 1146, 1187, 1145, 1146, 1187, 128, 1145, 1186, 1187, 1145, 1186, 1187, 128, 1146, 1147, 1188, 1146, 1147, 1188, 128, 1146, 1187, 1188, 1146, 1187, 1188, 128, 1148, 1149, 1190, 1148, 1149, 1190, 128, 1148, 1189, 1190, 1148, 1189, 1190, 128, 1149, 1150, 1191, 1149, 1150, 1191, 128, 1149, 1190, 1191, 1149, 1190, 1191, 128, 1150, 1151, 1192, 1150, 1151, 1192, 128, 1150, 1191, 1192, 1150, 1191, 1192, 128, 1151, 1152, 1193, 1151, 1152, 1193, 128, 1151, 1192, 1193, 1151, 1192, 1193, 128, 1152, 1153, 1194, 1152, 1153, 1194, 128, 1152, 1193, 1194, 1152, 1193, 1194, 128, 1153, 1154, 1195, 1153, 1154, 1195, 128, 1153, 1194, 1195, 1153, 1194, 1195, 128, 1154, 1155, 1196, 1154, 1155, 1196, 128, 1154, 1195, 1196, 1154, 1195, 1196, 128, 1155, 1156, 1197, 1155, 1156, 1197, 128, 1155, 1196, 1197, 1155, 1196, 1197, 128, 1156, 1157, 1198, 1156, 1157, 1198, 128, 1156, 1197, 1198, 1156, 1197, 1198, 128, 1157, 1158, 1199, 1157, 1158, 1199, 128, 1157, 1198, 1199, 1157, 1198, 1199, 128, 1158, 1159, 1200, 1158, 1159, 1200, 128, 1158, 1199, 1200, 1158, 1199, 1200, 128, 1159, 1160, 1201, 1159, 1160, 1201, 128, 1159, 1200, 1201, 1159, 1200, 1201, 128, 1160, 1161, 1202, 1160, 1161, 1202, 128, 1160, 1201, 1202, 1160, 1201, 1202, 128, 1161, 1162, 1203, 1161, 1162, 1203, 128, 1161, 1202, 1203, 1161, 1202, 1203, 128, 1162, 1163, 1204, 1162, 1163, 1204, 128, 1162, 1203, 1204, 1162, 1203, 1204, 128, 1163, 1164, 1205, 1163, 1164, 1205, 128, 1163, 1204, 1205, 1163, 1204, 1205, 128, 1164, 1165, 1206, 1164, 1165, 1206, 128, 1164, 1205, 1206, 1164, 1205, 1206, 128, 1165, 1166, 1207, 1165, 1166, 1207, 128, 1165, 1206, 1207, 1165, 1206, 1207, 128, 1166, 1167, 1208, 1166, 1167, 1208, 128, 1166, 1207, 1208, 1166, 1207, 1208, 128, 1167, 1168, 1209, 1167, 1168, 1209, 128, 1167, 1208, 1209, 1167, 1208, 1209, 128, 1168, 1169, 1210, 1168, 1169, 1210, 128, 1168, 1209, 1210, 1168, 1209, 1210, 128, 1169, 1170, 1211, 1169, 1170, 1211, 128, 1169, 1210, 1211, 1169, 1210, 1211, 128, 1170, 1171, 1212, 1170, 1171, 1212, 128, 1170, 1211, 1212, 1170, 1211, 1212, 128, 1171, 1172, 1213, 1171, 1172, 1213, 128, 1171, 1212, 1213, 1171, 1212, 1213, 128, 1172, 1173, 1214, 1172, 1173, 1214, 128, 1172, 1213, 1214, 1172, 1213, 1214, 128, 1173, 1174, 1215, 1173, 1174, 1215, 128, 1173, 1214, 1215, 1173, 1214, 1215, 128, 1174, 1175, 1216, 1174, 1175, 1216, 128, 1174, 1215, 1216, 1174, 1215, 1216, 128, 1175, 1176, 1217, 1175, 1176, 1217, 128, 1175, 1216, 1217, 1175, 1216, 1217, 128, 1176, 1177, 1218, 1176, 1177, 1218, 128, 1176, 1217, 1218, 1176, 1217, 1218, 128, 1177, 1178, 1219, 1177, 1178, 1219, 128, 1177, 1218, 1219, 1177, 1218, 1219, 128, 1178, 1179, 1220, 1178, 1179, 1220, 128, 1178, 1219, 1220, 1178, 1219, 1220, 128, 1179, 1180, 1221, 1179, 1180, 1221, 128, 1179, 1220, 1221, 1179, 1220, 1221, 128, 1180, 1181, 1222, 1180, 1181, 1222, 128, 1180, 1221, 1222, 1180, 1221, 1222, 128, 1181, 1182, 1223, 1181, 1182, 1223, 128, 1181, 1222, 1223, 1181, 1222, 1223, 128, 1182, 1183, 1224, 1182, 1183, 1224, 128, 1182, 1223, 1224, 1182, 1223, 1224, 128, 1183, 1184, 1225, 1183, 1184, 1225, 128, 1183, 1224, 1225, 1183, 1224, 1225, 128, 1184, 1185, 1226, 1184, 1185, 1226, 128, 1184, 1225, 1226, 1184, 1225, 1226, 128, 1185, 1186, 1227, 1185, 1186, 1227, 128, 1185, 1226, 1227, 1185, 1226, 1227, 128, 1186, 1187, 1228, 1186, 1187, 1228, 128, 1186, 1227, 1228, 1186, 1227, 1228, 128, 1187, 1188, 1229, 1187, 1188, 1229, 128, 1187, 1228, 1229, 1187, 1228, 1229, 128, 1189, 1190, 1231, 1189, 1190, 1231, 128, 1189, 1230, 1231, 1189, 1230, 1231, 128, 1190, 1191, 1232, 1190, 1191, 1232, 128, 1190, 1231, 1232, 1190, 1231, 1232, 128, 1191, 1192, 1233, 1191, 1192, 1233, 128, 1191, 1232, 1233, 1191, 1232, 1233, 128, 1192, 1193, 1234, 1192, 1193, 1234, 128, 1192, 1233, 1234, 1192, 1233, 1234, 128, 1193, 1194, 1235, 1193, 1194, 1235, 128, 1193, 1234, 1235, 1193, 1234, 1235, 128, 1194, 1195, 1236, 1194, 1195, 1236, 128, 1194, 1235, 1236, 1194, 1235, 1236, 128, 1195, 1196, 1237, 1195, 1196, 1237, 128, 1195, 1236, 1237, 1195, 1236, 1237, 128, 1196, 1197, 1238, 1196, 1197, 1238, 128, 1196, 1237, 1238, 1196, 1237, 1238, 128, 1197, 1198, 1239, 1197, 1198, 1239, 128, 1197, 1238, 1239, 1197, 1238, 1239, 128, 1198, 1199, 1240, 1198, 1199, 1240, 128, 1198, 1239, 1240, 1198, 1239, 1240, 128, 1199, 1200, 1241, 1199, 1200, 1241, 128, 1199, 1240, 1241, 1199, 1240, 1241, 128, 1200, 1201, 1242, 1200, 1201, 1242, 128, 1200, 1241, 1242, 1200, 1241, 1242, 128, 1201, 1202, 1243, 1201, 1202, 1243, 128, 1201, 1242, 1243, 1201, 1242, 1243, 128, 1202, 1203, 1244, 1202, 1203, 1244, 128, 1202, 1243, 1244, 1202, 1243, 1244, 128, 1203, 1204, 1245, 1203, 1204, 1245, 128, 1203, 1244, 1245, 1203, 1244, 1245, 128, 1204, 1205, 1246, 1204, 1205, 1246, 128, 1204, 1245, 1246, 1204, 1245, 1246, 128, 1205, 1206, 1247, 1205, 1206, 1247, 128, 1205, 1246, 1247, 1205, 1246, 1247, 128, 1206, 1207, 1248, 1206, 1207, 1248, 128, 1206, 1247, 1248, 1206, 1247, 1248, 128, 1207, 1208, 1249, 1207, 1208, 1249, 128, 1207, 1248, 1249, 1207, 1248, 1249, 128, 1208, 1209, 1250, 1208, 1209, 1250, 128, 1208, 1249, 1250, 1208, 1249, 1250, 128, 1209, 1210, 1251, 1209, 1210, 1251, 128, 1209, 1250, 1251, 1209, 1250, 1251, 128, 1210, 1211, 1252, 1210, 1211, 1252, 128, 1210, 1251, 1252, 1210, 1251, 1252, 128, 1211, 1212, 1253, 1211, 1212, 1253, 128, 1211, 1252, 1253, 1211, 1252, 1253, 128, 1212, 1213, 1254, 1212, 1213, 1254, 128, 1212, 1253, 1254, 1212, 1253, 1254, 128, 1213, 1214, 1255, 1213, 1214, 1255, 128, 1213, 1254, 1255, 1213, 1254, 1255, 128, 1214, 1215, 1256, 1214, 1215, 1256, 128, 1214, 1255, 1256, 1214, 1255, 1256, 128, 1215, 1216, 1257, 1215, 1216, 1257, 128, 1215, 1256, 1257, 1215, 1256, 1257, 128, 1216, 1217, 1258, 1216, 1217, 1258, 128, 1216, 1257, 1258, 1216, 1257, 1258, 128, 1217, 1218, 1259, 1217, 1218, 1259, 128, 1217, 1258, 1259, 1217, 1258, 1259, 128, 1218, 1219, 1260, 1218, 1219, 1260, 128, 1218, 1259, 1260, 1218, 1259, 1260, 128, 1219, 1220, 1261, 1219, 1220, 1261, 128, 1219, 1260, 1261, 1219, 1260, 1261, 128, 1220, 1221, 1262, 1220, 1221, 1262, 128, 1220, 1261, 1262, 1220, 1261, 1262, 128, 1221, 1222, 1263, 1221, 1222, 1263, 128, 1221, 1262, 1263, 1221, 1262, 1263, 128, 1222, 1223, 1264, 1222, 1223, 1264, 128, 1222, 1263, 1264, 1222, 1263, 1264, 128, 1223, 1224, 1265, 1223, 1224, 1265, 128, 1223, 1264, 1265, 1223, 1264, 1265, 128, 1224, 1225, 1266, 1224, 1225, 1266, 128, 1224, 1265, 1266, 1224, 1265, 1266, 128, 1225, 1226, 1267, 1225, 1226, 1267, 128, 1225, 1266, 1267, 1225, 1266, 1267, 128, 1226, 1227, 1268, 1226, 1227, 1268, 128, 1226, 1267, 1268, 1226, 1267, 1268, 128, 1227, 1228, 1269, 1227, 1228, 1269, 128, 1227, 1268, 1269, 1227, 1268, 1269, 128, 1228, 1229, 1270, 1228, 1229, 1270, 128, 1228, 1269, 1270, 1228, 1269, 1270, 128, 1230, 1231, 1272, 1230, 1231, 1272, 128, 1230, 1271, 1272, 1230, 1271, 1272, 128, 1231, 1232, 1273, 1231, 1232, 1273, 128, 1231, 1272, 1273, 1231, 1272, 1273, 128, 1232, 1233, 1274, 1232, 1233, 1274, 128, 1232, 1273, 1274, 1232, 1273, 1274, 128, 1233, 1234, 1275, 1233, 1234, 1275, 128, 1233, 1274, 1275, 1233, 1274, 1275, 128, 1234, 1235, 1276, 1234, 1235, 1276, 128, 1234, 1275, 1276, 1234, 1275, 1276, 128, 1235, 1236, 1277, 1235, 1236, 1277, 128, 1235, 1276, 1277, 1235, 1276, 1277, 128, 1236, 1237, 1278, 1236, 1237, 1278, 128, 1236, 1277, 1278, 1236, 1277, 1278, 128, 1237, 1238, 1279, 1237, 1238, 1279, 128, 1237, 1278, 1279, 1237, 1278, 1279, 128, 1238, 1239, 1280, 1238, 1239, 1280, 128, 1238, 1279, 1280, 1238, 1279, 1280, 128, 1239, 1240, 1281, 1239, 1240, 1281, 128, 1239, 1280, 1281, 1239, 1280, 1281, 128, 1240, 1241, 1282, 1240, 1241, 1282, 128, 1240, 1281, 1282, 1240, 1281, 1282, 128, 1241, 1242, 1283, 1241, 1242, 1283, 128, 1241, 1282, 1283, 1241, 1282, 1283, 128, 1242, 1243, 1284, 1242, 1243, 1284, 128, 1242, 1283, 1284, 1242, 1283, 1284, 128, 1243, 1244, 1285, 1243, 1244, 1285, 128, 1243, 1284, 1285, 1243, 1284, 1285, 128, 1244, 1245, 1286, 1244, 1245, 1286, 128, 1244, 1285, 1286, 1244, 1285, 1286, 128, 1245, 1246, 1287, 1245, 1246, 1287, 128, 1245, 1286, 1287, 1245, 1286, 1287, 128, 1246, 1247, 1288, 1246, 1247, 1288, 128, 1246, 1287, 1288, 1246, 1287, 1288, 128, 1247, 1248, 1289, 1247, 1248, 1289, 128, 1247, 1288, 1289, 1247, 1288, 1289, 128, 1248, 1249, 1290, 1248, 1249, 1290, 128, 1248, 1289, 1290, 1248, 1289, 1290, 128, 1249, 1250, 1291, 1249, 1250, 1291, 128, 1249, 1290, 1291, 1249, 1290, 1291, 128, 1250, 1251, 1292, 1250, 1251, 1292, 128, 1250, 1291, 1292, 1250, 1291, 1292, 128, 1251, 1252, 1293, 1251, 1252, 1293, 128, 1251, 1292, 1293, 1251, 1292, 1293, 128, 1252, 1253, 1294, 1252, 1253, 1294, 128, 1252, 1293, 1294, 1252, 1293, 1294, 128, 1253, 1254, 1295, 1253, 1254, 1295, 128, 1253, 1294, 1295, 1253, 1294, 1295, 128, 1254, 1255, 1296, 1254, 1255, 1296, 128, 1254, 1295, 1296, 1254, 1295, 1296, 128, 1255, 1256, 1297, 1255, 1256, 1297, 128, 1255, 1296, 1297, 1255, 1296, 1297, 128, 1256, 1257, 1298, 1256, 1257, 1298, 128, 1256, 1297, 1298, 1256, 1297, 1298, 128, 1257, 1258, 1299, 1257, 1258, 1299, 128, 1257, 1298, 1299, 1257, 1298, 1299, 128, 1258, 1259, 1300, 1258, 1259, 1300, 128, 1258, 1299, 1300, 1258, 1299, 1300, 128, 1259, 1260, 1301, 1259, 1260, 1301, 128, 1259, 1300, 1301, 1259, 1300, 1301, 128, 1260, 1261, 1302, 1260, 1261, 1302, 128, 1260, 1301, 1302, 1260, 1301, 1302, 128, 1261, 1262, 1303, 1261, 1262, 1303, 128, 1261, 1302, 1303, 1261, 1302, 1303, 128, 1262, 1263, 1304, 1262, 1263, 1304, 128, 1262, 1303, 1304, 1262, 1303, 1304, 128, 1263, 1264, 1305, 1263, 1264, 1305, 128, 1263, 1304, 1305, 1263, 1304, 1305, 128, 1264, 1265, 1306, 1264, 1265, 1306, 128, 1264, 1305, 1306, 1264, 1305, 1306, 128, 1265, 1266, 1307, 1265, 1266, 1307, 128, 1265, 1306, 1307, 1265, 1306, 1307, 128, 1266, 1267, 1308, 1266, 1267, 1308, 128, 1266, 1307, 1308, 1266, 1307, 1308, 128, 1267, 1268, 1309, 1267, 1268, 1309, 128, 1267, 1308, 1309, 1267, 1308, 1309, 128, 1268, 1269, 1310, 1268, 1269, 1310, 128, 1268, 1309, 1310, 1268, 1309, 1310, 128, 1269, 1270, 1311, 1269, 1270, 1311, 128, 1269, 1310, 1311, 1269, 1310, 1311, 128, 1271, 1272, 1313, 1271, 1272, 1313, 128, 1271, 1312, 1313, 1271, 1312, 1313, 128, 1272, 1273, 1314, 1272, 1273, 1314, 128, 1272, 1313, 1314, 1272, 1313, 1314, 128, 1273, 1274, 1315, 1273, 1274, 1315, 128, 1273, 1314, 1315, 1273, 1314, 1315, 128, 1274, 1275, 1316, 1274, 1275, 1316, 128, 1274, 1315, 1316, 1274, 1315, 1316, 128, 1275, 1276, 1317, 1275, 1276, 1317, 128, 1275, 1316, 1317, 1275, 1316, 1317, 128, 1276, 1277, 1318, 1276, 1277, 1318, 128, 1276, 1317, 1318, 1276, 1317, 1318, 128, 1277, 1278, 1319, 1277, 1278, 1319, 128, 1277, 1318, 1319, 1277, 1318, 1319, 128, 1278, 1279, 1320, 1278, 1279, 1320, 128, 1278, 1319, 1320, 1278, 1319, 1320, 128, 1279, 1280, 1321, 1279, 1280, 1321, 128, 1279, 1320, 1321, 1279, 1320, 1321, 128, 1280, 1281, 1322, 1280, 1281, 1322, 128, 1280, 1321, 1322, 1280, 1321, 1322, 128, 1281, 1282, 1323, 1281, 1282, 1323, 128, 1281, 1322, 1323, 1281, 1322, 1323, 128, 1282, 1283, 1324, 1282, 1283, 1324, 128, 1282, 1323, 1324, 1282, 1323, 1324, 128, 1283, 1284, 1325, 1283, 1284, 1325, 128, 1283, 1324, 1325, 1283, 1324, 1325, 128, 1284, 1285, 1326, 1284, 1285, 1326, 128, 1284, 1325, 1326, 1284, 1325, 1326, 128, 1285, 1286, 1327, 1285, 1286, 1327, 128, 1285, 1326, 1327, 1285, 1326, 1327, 128, 1286, 1287, 1328, 1286, 1287, 1328, 128, 1286, 1327, 1328, 1286, 1327, 1328, 128, 1287, 1288, 1329, 1287, 1288, 1329, 128, 1287, 1328, 1329, 1287, 1328, 1329, 128, 1288, 1289, 1330, 1288, 1289, 1330, 128, 1288, 1329, 1330, 1288, 1329, 1330, 128, 1289, 1290, 1331, 1289, 1290, 1331, 128, 1289, 1330, 1331, 1289, 1330, 1331, 128, 1290, 1291, 1332, 1290, 1291, 1332, 128, 1290, 1331, 1332, 1290, 1331, 1332, 128, 1291, 1292, 1333, 1291, 1292, 1333, 128, 1291, 1332, 1333, 1291, 1332, 1333, 128, 1292, 1293, 1334, 1292, 1293, 1334, 128, 1292, 1333, 1334, 1292, 1333, 1334, 128, 1293, 1294, 1335, 1293, 1294, 1335, 128, 1293, 1334, 1335, 1293, 1334, 1335, 128, 1294, 1295, 1336, 1294, 1295, 1336, 128, 1294, 1335, 1336, 1294, 1335, 1336, 128, 1295, 1296, 1337, 1295, 1296, 1337, 128, 1295, 1336, 1337, 1295, 1336, 1337, 128, 1296, 1297, 1338, 1296, 1297, 1338, 128, 1296, 1337, 1338, 1296, 1337, 1338, 128, 1297, 1298, 1339, 1297, 1298, 1339, 128, 1297, 1338, 1339, 1297, 1338, 1339, 128, 1298, 1299, 1340, 1298, 1299, 1340, 128, 1298, 1339, 1340, 1298, 1339, 1340, 128, 1299, 1300, 1341, 1299, 1300, 1341, 128, 1299, 1340, 1341, 1299, 1340, 1341, 128, 1300, 1301, 1342, 1300, 1301, 1342, 128, 1300, 1341, 1342, 1300, 1341, 1342, 128, 1301, 1302, 1343, 1301, 1302, 1343, 128, 1301, 1342, 1343, 1301, 1342, 1343, 128, 1302, 1303, 1344, 1302, 1303, 1344, 128, 1302, 1343, 1344, 1302, 1343, 1344, 128, 1303, 1304, 1345, 1303, 1304, 1345, 128, 1303, 1344, 1345, 1303, 1344, 1345, 128, 1304, 1305, 1346, 1304, 1305, 1346, 128, 1304, 1345, 1346, 1304, 1345, 1346, 128, 1305, 1306, 1347, 1305, 1306, 1347, 128, 1305, 1346, 1347, 1305, 1346, 1347, 128, 1306, 1307, 1348, 1306, 1307, 1348, 128, 1306, 1347, 1348, 1306, 1347, 1348, 128, 1307, 1308, 1349, 1307, 1308, 1349, 128, 1307, 1348, 1349, 1307, 1348, 1349, 128, 1308, 1309, 1350, 1308, 1309, 1350, 128, 1308, 1349, 1350, 1308, 1349, 1350, 128, 1309, 1310, 1351, 1309, 1310, 1351, 128, 1309, 1350, 1351, 1309, 1350, 1351, 128, 1310, 1311, 1352, 1310, 1311, 1352, 128, 1310, 1351, 1352, 1310, 1351, 1352, 128, 1312, 1313, 1354, 1312, 1313, 1354, 128, 1312, 1353, 1354, 1312, 1353, 1354, 128, 1313, 1314, 1355, 1313, 1314, 1355, 128, 1313, 1354, 1355, 1313, 1354, 1355, 128, 1314, 1315, 1356, 1314, 1315, 1356, 128, 1314, 1355, 1356, 1314, 1355, 1356, 128, 1315, 1316, 1357, 1315, 1316, 1357, 128, 1315, 1356, 1357, 1315, 1356, 1357, 128, 1316, 1317, 1358, 1316, 1317, 1358, 128, 1316, 1357, 1358, 1316, 1357, 1358, 128, 1317, 1318, 1359, 1317, 1318, 1359, 128, 1317, 1358, 1359, 1317, 1358, 1359, 128, 1318, 1319, 1360, 1318, 1319, 1360, 128, 1318, 1359, 1360, 1318, 1359, 1360, 128, 1319, 1320, 1361, 1319, 1320, 1361, 128, 1319, 1360, 1361, 1319, 1360, 1361, 128, 1320, 1321, 1362, 1320, 1321, 1362, 128, 1320, 1361, 1362, 1320, 1361, 1362, 128, 1321, 1322, 1363, 1321, 1322, 1363, 128, 1321, 1362, 1363, 1321, 1362, 1363, 128, 1322, 1323, 1364, 1322, 1323, 1364, 128, 1322, 1363, 1364, 1322, 1363, 1364, 128, 1323, 1324, 1365, 1323, 1324, 1365, 128, 1323, 1364, 1365, 1323, 1364, 1365, 128, 1324, 1325, 1366, 1324, 1325, 1366, 128, 1324, 1365, 1366, 1324, 1365, 1366, 128, 1325, 1326, 1367, 1325, 1326, 1367, 128, 1325, 1366, 1367, 1325, 1366, 1367, 128, 1326, 1327, 1368, 1326, 1327, 1368, 128, 1326, 1367, 1368, 1326, 1367, 1368, 128, 1327, 1328, 1369, 1327, 1328, 1369, 128, 1327, 1368, 1369, 1327, 1368, 1369, 128, 1328, 1329, 1370, 1328, 1329, 1370, 128, 1328, 1369, 1370, 1328, 1369, 1370, 128, 1329, 1330, 1371, 1329, 1330, 1371, 128, 1329, 1370, 1371, 1329, 1370, 1371, 128, 1330, 1331, 1372, 1330, 1331, 1372, 128, 1330, 1371, 1372, 1330, 1371, 1372, 128, 1331, 1332, 1373, 1331, 1332, 1373, 128, 1331, 1372, 1373, 1331, 1372, 1373, 128, 1332, 1333, 1374, 1332, 1333, 1374, 128, 1332, 1373, 1374, 1332, 1373, 1374, 128, 1333, 1334, 1375, 1333, 1334, 1375, 128, 1333, 1374, 1375, 1333, 1374, 1375, 128, 1334, 1335, 1376, 1334, 1335, 1376, 128, 1334, 1375, 1376, 1334, 1375, 1376, 128, 1335, 1336, 1377, 1335, 1336, 1377, 128, 1335, 1376, 1377, 1335, 1376, 1377, 128, 1336, 1337, 1378, 1336, 1337, 1378, 128, 1336, 1377, 1378, 1336, 1377, 1378, 128, 1337, 1338, 1379, 1337, 1338, 1379, 128, 1337, 1378, 1379, 1337, 1378, 1379, 128, 1338, 1339, 1380, 1338, 1339, 1380, 128, 1338, 1379, 1380, 1338, 1379, 1380, 128, 1339, 1340, 1381, 1339, 1340, 1381, 128, 1339, 1380, 1381, 1339, 1380, 1381, 128, 1340, 1341, 1382, 1340, 1341, 1382, 128, 1340, 1381, 1382, 1340, 1381, 1382, 128, 1341, 1342, 1383, 1341, 1342, 1383, 128, 1341, 1382, 1383, 1341, 1382, 1383, 128, 1342, 1343, 1384, 1342, 1343, 1384, 128, 1342, 1383, 1384, 1342, 1383, 1384, 128, 1343, 1344, 1385, 1343, 1344, 1385, 128, 1343, 1384, 1385, 1343, 1384, 1385, 128, 1344, 1345, 1386, 1344, 1345, 1386, 128, 1344, 1385, 1386, 1344, 1385, 1386, 128, 1345, 1346, 1387, 1345, 1346, 1387, 128, 1345, 1386, 1387, 1345, 1386, 1387, 128, 1346, 1347, 1388, 1346, 1347, 1388, 128, 1346, 1387, 1388, 1346, 1387, 1388, 128, 1347, 1348, 1389, 1347, 1348, 1389, 128, 1347, 1388, 1389, 1347, 1388, 1389, 128, 1348, 1349, 1390, 1348, 1349, 1390, 128, 1348, 1389, 1390, 1348, 1389, 1390, 128, 1349, 1350, 1391, 1349, 1350, 1391, 128, 1349, 1390, 1391, 1349, 1390, 1391, 128, 1350, 1351, 1392, 1350, 1351, 1392, 128, 1350, 1391, 1392, 1350, 1391, 1392, 128, 1351, 1352, 1393, 1351, 1352, 1393, 128, 1351, 1392, 1393, 1351, 1392, 1393, 128, 1353, 1354, 1395, 1353, 1354, 1395, 128, 1353, 1394, 1395, 1353, 1394, 1395, 128, 1354, 1355, 1396, 1354, 1355, 1396, 128, 1354, 1395, 1396, 1354, 1395, 1396, 128, 1355, 1356, 1397, 1355, 1356, 1397, 128, 1355, 1396, 1397, 1355, 1396, 1397, 128, 1356, 1357, 1398, 1356, 1357, 1398, 128, 1356, 1397, 1398, 1356, 1397, 1398, 128, 1357, 1358, 1399, 1357, 1358, 1399, 128, 1357, 1398, 1399, 1357, 1398, 1399, 128, 1358, 1359, 1400, 1358, 1359, 1400, 128, 1358, 1399, 1400, 1358, 1399, 1400, 128, 1359, 1360, 1401, 1359, 1360, 1401, 128, 1359, 1400, 1401, 1359, 1400, 1401, 128, 1360, 1361, 1402, 1360, 1361, 1402, 128, 1360, 1401, 1402, 1360, 1401, 1402, 128, 1361, 1362, 1403, 1361, 1362, 1403, 128, 1361, 1402, 1403, 1361, 1402, 1403, 128, 1362, 1363, 1404, 1362, 1363, 1404, 128, 1362, 1403, 1404, 1362, 1403, 1404, 128, 1363, 1364, 1405, 1363, 1364, 1405, 128, 1363, 1404, 1405, 1363, 1404, 1405, 128, 1364, 1365, 1406, 1364, 1365, 1406, 128, 1364, 1405, 1406, 1364, 1405, 1406, 128, 1365, 1366, 1407, 1365, 1366, 1407, 128, 1365, 1406, 1407, 1365, 1406, 1407, 128, 1366, 1367, 1408, 1366, 1367, 1408, 128, 1366, 1407, 1408, 1366, 1407, 1408, 128, 1367, 1368, 1409, 1367, 1368, 1409, 128, 1367, 1408, 1409, 1367, 1408, 1409, 128, 1368, 1369, 1410, 1368, 1369, 1410, 128, 1368, 1409, 1410, 1368, 1409, 1410, 128, 1369, 1370, 1411, 1369, 1370, 1411, 128, 1369, 1410, 1411, 1369, 1410, 1411, 128, 1370, 1371, 1412, 1370, 1371, 1412, 128, 1370, 1411, 1412, 1370, 1411, 1412, 128, 1371, 1372, 1413, 1371, 1372, 1413, 128, 1371, 1412, 1413, 1371, 1412, 1413, 128, 1372, 1373, 1414, 1372, 1373, 1414, 128, 1372, 1413, 1414, 1372, 1413, 1414, 128, 1373, 1374, 1415, 1373, 1374, 1415, 128, 1373, 1414, 1415, 1373, 1414, 1415, 128, 1374, 1375, 1416, 1374, 1375, 1416, 128, 1374, 1415, 1416, 1374, 1415, 1416, 128, 1375, 1376, 1417, 1375, 1376, 1417, 128, 1375, 1416, 1417, 1375, 1416, 1417, 128, 1376, 1377, 1418, 1376, 1377, 1418, 128, 1376, 1417, 1418, 1376, 1417, 1418, 128, 1377, 1378, 1419, 1377, 1378, 1419, 128, 1377, 1418, 1419, 1377, 1418, 1419, 128, 1378, 1379, 1420, 1378, 1379, 1420, 128, 1378, 1419, 1420, 1378, 1419, 1420, 128, 1379, 1380, 1421, 1379, 1380, 1421, 128, 1379, 1420, 1421, 1379, 1420, 1421, 128, 1380, 1381, 1422, 1380, 1381, 1422, 128, 1380, 1421, 1422, 1380, 1421, 1422, 128, 1381, 1382, 1423, 1381, 1382, 1423, 128, 1381, 1422, 1423, 1381, 1422, 1423, 128, 1382, 1383, 1424, 1382, 1383, 1424, 128, 1382, 1423, 1424, 1382, 1423, 1424, 128, 1383, 1384, 1425, 1383, 1384, 1425, 128, 1383, 1424, 1425, 1383, 1424, 1425, 128, 1384, 1385, 1426, 1384, 1385, 1426, 128, 1384, 1425, 1426, 1384, 1425, 1426, 128, 1385, 1386, 1427, 1385, 1386, 1427, 128, 1385, 1426, 1427, 1385, 1426, 1427, 128, 1386, 1387, 1428, 1386, 1387, 1428, 128, 1386, 1427, 1428, 1386, 1427, 1428, 128, 1387, 1388, 1429, 1387, 1388, 1429, 128, 1387, 1428, 1429, 1387, 1428, 1429, 128, 1388, 1389, 1430, 1388, 1389, 1430, 128, 1388, 1429, 1430, 1388, 1429, 1430, 128, 1389, 1390, 1431, 1389, 1390, 1431, 128, 1389, 1430, 1431, 1389, 1430, 1431, 128, 1390, 1391, 1432, 1390, 1391, 1432, 128, 1390, 1431, 1432, 1390, 1431, 1432, 128, 1391, 1392, 1433, 1391, 1392, 1433, 128, 1391, 1432, 1433, 1391, 1432, 1433, 128, 1392, 1393, 1434, 1392, 1393, 1434, 128, 1392, 1433, 1434, 1392, 1433, 1434, 128, 1394, 1395, 1436, 1394, 1395, 1436, 128, 1394, 1435, 1436, 1394, 1435, 1436, 128, 1395, 1396, 1437, 1395, 1396, 1437, 128, 1395, 1436, 1437, 1395, 1436, 1437, 128, 1396, 1397, 1438, 1396, 1397, 1438, 128, 1396, 1437, 1438, 1396, 1437, 1438, 128, 1397, 1398, 1439, 1397, 1398, 1439, 128, 1397, 1438, 1439, 1397, 1438, 1439, 128, 1398, 1399, 1440, 1398, 1399, 1440, 128, 1398, 1439, 1440, 1398, 1439, 1440, 128, 1399, 1400, 1441, 1399, 1400, 1441, 128, 1399, 1440, 1441, 1399, 1440, 1441, 128, 1400, 1401, 1442, 1400, 1401, 1442, 128, 1400, 1441, 1442, 1400, 1441, 1442, 128, 1401, 1402, 1443, 1401, 1402, 1443, 128, 1401, 1442, 1443, 1401, 1442, 1443, 128, 1402, 1403, 1444, 1402, 1403, 1444, 128, 1402, 1443, 1444, 1402, 1443, 1444, 128, 1403, 1404, 1445, 1403, 1404, 1445, 128, 1403, 1444, 1445, 1403, 1444, 1445, 128, 1404, 1405, 1446, 1404, 1405, 1446, 128, 1404, 1445, 1446, 1404, 1445, 1446, 128, 1405, 1406, 1447, 1405, 1406, 1447, 128, 1405, 1446, 1447, 1405, 1446, 1447, 128, 1406, 1407, 1448, 1406, 1407, 1448, 128, 1406, 1447, 1448, 1406, 1447, 1448, 128, 1407, 1408, 1449, 1407, 1408, 1449, 128, 1407, 1448, 1449, 1407, 1448, 1449, 128, 1408, 1409, 1450, 1408, 1409, 1450, 128, 1408, 1449, 1450, 1408, 1449, 1450, 128, 1409, 1410, 1451, 1409, 1410, 1451, 128, 1409, 1450, 1451, 1409, 1450, 1451, 128, 1410, 1411, 1452, 1410, 1411, 1452, 128, 1410, 1451, 1452, 1410, 1451, 1452, 128, 1411, 1412, 1453, 1411, 1412, 1453, 128, 1411, 1452, 1453, 1411, 1452, 1453, 128, 1412, 1413, 1454, 1412, 1413, 1454, 128, 1412, 1453, 1454, 1412, 1453, 1454, 128, 1413, 1414, 1455, 1413, 1414, 1455, 128, 1413, 1454, 1455, 1413, 1454, 1455, 128, 1414, 1415, 1456, 1414, 1415, 1456, 128, 1414, 1455, 1456, 1414, 1455, 1456, 128, 1415, 1416, 1457, 1415, 1416, 1457, 128, 1415, 1456, 1457, 1415, 1456, 1457, 128, 1416, 1417, 1458, 1416, 1417, 1458, 128, 1416, 1457, 1458, 1416, 1457, 1458, 128, 1417, 1418, 1459, 1417, 1418, 1459, 128, 1417, 1458, 1459, 1417, 1458, 1459, 128, 1418, 1419, 1460, 1418, 1419, 1460, 128, 1418, 1459, 1460, 1418, 1459, 1460, 128, 1419, 1420, 1461, 1419, 1420, 1461, 128, 1419, 1460, 1461, 1419, 1460, 1461, 128, 1420, 1421, 1462, 1420, 1421, 1462, 128, 1420, 1461, 1462, 1420, 1461, 1462, 128, 1421, 1422, 1463, 1421, 1422, 1463, 128, 1421, 1462, 1463, 1421, 1462, 1463, 128, 1422, 1423, 1464, 1422, 1423, 1464, 128, 1422, 1463, 1464, 1422, 1463, 1464, 128, 1423, 1424, 1465, 1423, 1424, 1465, 128, 1423, 1464, 1465, 1423, 1464, 1465, 128, 1424, 1425, 1466, 1424, 1425, 1466, 128, 1424, 1465, 1466, 1424, 1465, 1466, 128, 1425, 1426, 1467, 1425, 1426, 1467, 128, 1425, 1466, 1467, 1425, 1466, 1467, 128, 1426, 1427, 1468, 1426, 1427, 1468, 128, 1426, 1467, 1468, 1426, 1467, 1468, 128, 1427, 1428, 1469, 1427, 1428, 1469, 128, 1427, 1468, 1469, 1427, 1468, 1469, 128, 1428, 1429, 1470, 1428, 1429, 1470, 128, 1428, 1469, 1470, 1428, 1469, 1470, 128, 1429, 1430, 1471, 1429, 1430, 1471, 128, 1429, 1470, 1471, 1429, 1470, 1471, 128, 1430, 1431, 1472, 1430, 1431, 1472, 128, 1430, 1471, 1472, 1430, 1471, 1472, 128, 1431, 1432, 1473, 1431, 1432, 1473, 128, 1431, 1472, 1473, 1431, 1472, 1473, 128, 1432, 1433, 1474, 1432, 1433, 1474, 128, 1432, 1473, 1474, 1432, 1473, 1474, 128, 1433, 1434, 1475, 1433, 1434, 1475, 128, 1433, 1474, 1475, 1433, 1474, 1475, 128, 1435, 1436, 1477, 1435, 1436, 1477, 128, 1435, 1476, 1477, 1435, 1476, 1477, 128, 1436, 1437, 1478, 1436, 1437, 1478, 128, 1436, 1477, 1478, 1436, 1477, 1478, 128, 1437, 1438, 1479, 1437, 1438, 1479, 128, 1437, 1478, 1479, 1437, 1478, 1479, 128, 1438, 1439, 1480, 1438, 1439, 1480, 128, 1438, 1479, 1480, 1438, 1479, 1480, 128, 1439, 1440, 1481, 1439, 1440, 1481, 128, 1439, 1480, 1481, 1439, 1480, 1481, 128, 1440, 1441, 1482, 1440, 1441, 1482, 128, 1440, 1481, 1482, 1440, 1481, 1482, 128, 1441, 1442, 1483, 1441, 1442, 1483, 128, 1441, 1482, 1483, 1441, 1482, 1483, 128, 1442, 1443, 1484, 1442, 1443, 1484, 128, 1442, 1483, 1484, 1442, 1483, 1484, 128, 1443, 1444, 1485, 1443, 1444, 1485, 128, 1443, 1484, 1485, 1443, 1484, 1485, 128, 1444, 1445, 1486, 1444, 1445, 1486, 128, 1444, 1485, 1486, 1444, 1485, 1486, 128, 1445, 1446, 1487, 1445, 1446, 1487, 128, 1445, 1486, 1487, 1445, 1486, 1487, 128, 1446, 1447, 1488, 1446, 1447, 1488, 128, 1446, 1487, 1488, 1446, 1487, 1488, 128, 1447, 1448, 1489, 1447, 1448, 1489, 128, 1447, 1488, 1489, 1447, 1488, 1489, 128, 1448, 1449, 1490, 1448, 1449, 1490, 128, 1448, 1489, 1490, 1448, 1489, 1490, 128, 1449, 1450, 1491, 1449, 1450, 1491, 128, 1449, 1490, 1491, 1449, 1490, 1491, 128, 1450, 1451, 1492, 1450, 1451, 1492, 128, 1450, 1491, 1492, 1450, 1491, 1492, 128, 1451, 1452, 1493, 1451, 1452, 1493, 128, 1451, 1492, 1493, 1451, 1492, 1493, 128, 1452, 1453, 1494, 1452, 1453, 1494, 128, 1452, 1493, 1494, 1452, 1493, 1494, 128, 1453, 1454, 1495, 1453, 1454, 1495, 128, 1453, 1494, 1495, 1453, 1494, 1495, 128, 1454, 1455, 1496, 1454, 1455, 1496, 128, 1454, 1495, 1496, 1454, 1495, 1496, 128, 1455, 1456, 1497, 1455, 1456, 1497, 128, 1455, 1496, 1497, 1455, 1496, 1497, 128, 1456, 1457, 1498, 1456, 1457, 1498, 128, 1456, 1497, 1498, 1456, 1497, 1498, 128, 1457, 1458, 1499, 1457, 1458, 1499, 128, 1457, 1498, 1499, 1457, 1498, 1499, 128, 1458, 1459, 1500, 1458, 1459, 1500, 128, 1458, 1499, 1500, 1458, 1499, 1500, 128, 1459, 1460, 1501, 1459, 1460, 1501, 128, 1459, 1500, 1501, 1459, 1500, 1501, 128, 1460, 1461, 1502, 1460, 1461, 1502, 128, 1460, 1501, 1502, 1460, 1501, 1502, 128, 1461, 1462, 1503, 1461, 1462, 1503, 128, 1461, 1502, 1503, 1461, 1502, 1503, 128, 1462, 1463, 1504, 1462, 1463, 1504, 128, 1462, 1503, 1504, 1462, 1503, 1504, 128, 1463, 1464, 1505, 1463, 1464, 1505, 128, 1463, 1504, 1505, 1463, 1504, 1505, 128, 1464, 1465, 1506, 1464, 1465, 1506, 128, 1464, 1505, 1506, 1464, 1505, 1506, 128, 1465, 1466, 1507, 1465, 1466, 1507, 128, 1465, 1506, 1507, 1465, 1506, 1507, 128, 1466, 1467, 1508, 1466, 1467, 1508, 128, 1466, 1507, 1508, 1466, 1507, 1508, 128, 1467, 1468, 1509, 1467, 1468, 1509, 128, 1467, 1508, 1509, 1467, 1508, 1509, 128, 1468, 1469, 1510, 1468, 1469, 1510, 128, 1468, 1509, 1510, 1468, 1509, 1510, 128, 1469, 1470, 1511, 1469, 1470, 1511, 128, 1469, 1510, 1511, 1469, 1510, 1511, 128, 1470, 1471, 1512, 1470, 1471, 1512, 128, 1470, 1511, 1512, 1470, 1511, 1512, 128, 1471, 1472, 1513, 1471, 1472, 1513, 128, 1471, 1512, 1513, 1471, 1512, 1513, 128, 1472, 1473, 1514, 1472, 1473, 1514, 128, 1472, 1513, 1514, 1472, 1513, 1514, 128, 1473, 1474, 1515, 1473, 1474, 1515, 128, 1473, 1514, 1515, 1473, 1514, 1515, 128, 1474, 1475, 1516, 1474, 1475, 1516, 128, 1474, 1515, 1516, 1474, 1515, 1516, 128, 1476, 1477, 1518, 1476, 1477, 1518, 128, 1476, 1517, 1518, 1476, 1517, 1518, 128, 1477, 1478, 1519, 1477, 1478, 1519, 128, 1477, 1518, 1519, 1477, 1518, 1519, 128, 1478, 1479, 1520, 1478, 1479, 1520, 128, 1478, 1519, 1520, 1478, 1519, 1520, 128, 1479, 1480, 1521, 1479, 1480, 1521, 128, 1479, 1520, 1521, 1479, 1520, 1521, 128, 1480, 1481, 1522, 1480, 1481, 1522, 128, 1480, 1521, 1522, 1480, 1521, 1522, 128, 1481, 1482, 1523, 1481, 1482, 1523, 128, 1481, 1522, 1523, 1481, 1522, 1523, 128, 1482, 1483, 1524, 1482, 1483, 1524, 128, 1482, 1523, 1524, 1482, 1523, 1524, 128, 1483, 1484, 1525, 1483, 1484, 1525, 128, 1483, 1524, 1525, 1483, 1524, 1525, 128, 1484, 1485, 1526, 1484, 1485, 1526, 128, 1484, 1525, 1526, 1484, 1525, 1526, 128, 1485, 1486, 1527, 1485, 1486, 1527, 128, 1485, 1526, 1527, 1485, 1526, 1527, 128, 1486, 1487, 1528, 1486, 1487, 1528, 128, 1486, 1527, 1528, 1486, 1527, 1528, 128, 1487, 1488, 1529, 1487, 1488, 1529, 128, 1487, 1528, 1529, 1487, 1528, 1529, 128, 1488, 1489, 1530, 1488, 1489, 1530, 128, 1488, 1529, 1530, 1488, 1529, 1530, 128, 1489, 1490, 1531, 1489, 1490, 1531, 128, 1489, 1530, 1531, 1489, 1530, 1531, 128, 1490, 1491, 1532, 1490, 1491, 1532, 128, 1490, 1531, 1532, 1490, 1531, 1532, 128, 1491, 1492, 1533, 1491, 1492, 1533, 128, 1491, 1532, 1533, 1491, 1532, 1533, 128, 1492, 1493, 1534, 1492, 1493, 1534, 128, 1492, 1533, 1534, 1492, 1533, 1534, 128, 1493, 1494, 1535, 1493, 1494, 1535, 128, 1493, 1534, 1535, 1493, 1534, 1535, 128, 1494, 1495, 1536, 1494, 1495, 1536, 128, 1494, 1535, 1536, 1494, 1535, 1536, 128, 1495, 1496, 1537, 1495, 1496, 1537, 128, 1495, 1536, 1537, 1495, 1536, 1537, 128, 1496, 1497, 1538, 1496, 1497, 1538, 128, 1496, 1537, 1538, 1496, 1537, 1538, 128, 1497, 1498, 1539, 1497, 1498, 1539, 128, 1497, 1538, 1539, 1497, 1538, 1539, 128, 1498, 1499, 1540, 1498, 1499, 1540, 128, 1498, 1539, 1540, 1498, 1539, 1540, 128, 1499, 1500, 1541, 1499, 1500, 1541, 128, 1499, 1540, 1541, 1499, 1540, 1541, 128, 1500, 1501, 1542, 1500, 1501, 1542, 128, 1500, 1541, 1542, 1500, 1541, 1542, 128, 1501, 1502, 1543, 1501, 1502, 1543, 128, 1501, 1542, 1543, 1501, 1542, 1543, 128, 1502, 1503, 1544, 1502, 1503, 1544, 128, 1502, 1543, 1544, 1502, 1543, 1544, 128, 1503, 1504, 1545, 1503, 1504, 1545, 128, 1503, 1544, 1545, 1503, 1544, 1545, 128, 1504, 1505, 1546, 1504, 1505, 1546, 128, 1504, 1545, 1546, 1504, 1545, 1546, 128, 1505, 1506, 1547, 1505, 1506, 1547, 128, 1505, 1546, 1547, 1505, 1546, 1547, 128, 1506, 1507, 1548, 1506, 1507, 1548, 128, 1506, 1547, 1548, 1506, 1547, 1548, 128, 1507, 1508, 1549, 1507, 1508, 1549, 128, 1507, 1548, 1549, 1507, 1548, 1549, 128, 1508, 1509, 1550, 1508, 1509, 1550, 128, 1508, 1549, 1550, 1508, 1549, 1550, 128, 1509, 1510, 1551, 1509, 1510, 1551, 128, 1509, 1550, 1551, 1509, 1550, 1551, 128, 1510, 1511, 1552, 1510, 1511, 1552, 128, 1510, 1551, 1552, 1510, 1551, 1552, 128, 1511, 1512, 1553, 1511, 1512, 1553, 128, 1511, 1552, 1553, 1511, 1552, 1553, 128, 1512, 1513, 1554, 1512, 1513, 1554, 128, 1512, 1553, 1554, 1512, 1553, 1554, 128, 1513, 1514, 1555, 1513, 1514, 1555, 128, 1513, 1554, 1555, 1513, 1554, 1555, 128, 1514, 1515, 1556, 1514, 1515, 1556, 128, 1514, 1555, 1556, 1514, 1555, 1556, 128, 1515, 1516, 1557, 1515, 1516, 1557, 128, 1515, 1556, 1557, 1515, 1556, 1557, 128, 1517, 1518, 1559, 1517, 1518, 1559, 128, 1517, 1558, 1559, 1517, 1558, 1559, 128, 1518, 1519, 1560, 1518, 1519, 1560, 128, 1518, 1559, 1560, 1518, 1559, 1560, 128, 1519, 1520, 1561, 1519, 1520, 1561, 128, 1519, 1560, 1561, 1519, 1560, 1561, 128, 1520, 1521, 1562, 1520, 1521, 1562, 128, 1520, 1561, 1562, 1520, 1561, 1562, 128, 1521, 1522, 1563, 1521, 1522, 1563, 128, 1521, 1562, 1563, 1521, 1562, 1563, 128, 1522, 1523, 1564, 1522, 1523, 1564, 128, 1522, 1563, 1564, 1522, 1563, 1564, 128, 1523, 1524, 1565, 1523, 1524, 1565, 128, 1523, 1564, 1565, 1523, 1564, 1565, 128, 1524, 1525, 1566, 1524, 1525, 1566, 128, 1524, 1565, 1566, 1524, 1565, 1566, 128, 1525, 1526, 1567, 1525, 1526, 1567, 128, 1525, 1566, 1567, 1525, 1566, 1567, 128, 1526, 1527, 1568, 1526, 1527, 1568, 128, 1526, 1567, 1568, 1526, 1567, 1568, 128, 1527, 1528, 1569, 1527, 1528, 1569, 128, 1527, 1568, 1569, 1527, 1568, 1569, 128, 1528, 1529, 1570, 1528, 1529, 1570, 128, 1528, 1569, 1570, 1528, 1569, 1570, 128, 1529, 1530, 1571, 1529, 1530, 1571, 128, 1529, 1570, 1571, 1529, 1570, 1571, 128, 1530, 1531, 1572, 1530, 1531, 1572, 128, 1530, 1571, 1572, 1530, 1571, 1572, 128, 1531, 1532, 1573, 1531, 1532, 1573, 128, 1531, 1572, 1573, 1531, 1572, 1573, 128, 1532, 1533, 1574, 1532, 1533, 1574, 128, 1532, 1573, 1574, 1532, 1573, 1574, 128, 1533, 1534, 1575, 1533, 1534, 1575, 128, 1533, 1574, 1575, 1533, 1574, 1575, 128, 1534, 1535, 1576, 1534, 1535, 1576, 128, 1534, 1575, 1576, 1534, 1575, 1576, 128, 1535, 1536, 1577, 1535, 1536, 1577, 128, 1535, 1576, 1577, 1535, 1576, 1577, 128, 1536, 1537, 1578, 1536, 1537, 1578, 128, 1536, 1577, 1578, 1536, 1577, 1578, 128, 1537, 1538, 1579, 1537, 1538, 1579, 128, 1537, 1578, 1579, 1537, 1578, 1579, 128, 1538, 1539, 1580, 1538, 1539, 1580, 128, 1538, 1579, 1580, 1538, 1579, 1580, 128, 1539, 1540, 1581, 1539, 1540, 1581, 128, 1539, 1580, 1581, 1539, 1580, 1581, 128, 1540, 1541, 1582, 1540, 1541, 1582, 128, 1540, 1581, 1582, 1540, 1581, 1582, 128, 1541, 1542, 1583, 1541, 1542, 1583, 128, 1541, 1582, 1583, 1541, 1582, 1583, 128, 1542, 1543, 1584, 1542, 1543, 1584, 128, 1542, 1583, 1584, 1542, 1583, 1584, 128, 1543, 1544, 1585, 1543, 1544, 1585, 128, 1543, 1584, 1585, 1543, 1584, 1585, 128, 1544, 1545, 1586, 1544, 1545, 1586, 128, 1544, 1585, 1586, 1544, 1585, 1586, 128, 1545, 1546, 1587, 1545, 1546, 1587, 128, 1545, 1586, 1587, 1545, 1586, 1587, 128, 1546, 1547, 1588, 1546, 1547, 1588, 128, 1546, 1587, 1588, 1546, 1587, 1588, 128, 1547, 1548, 1589, 1547, 1548, 1589, 128, 1547, 1588, 1589, 1547, 1588, 1589, 128, 1548, 1549, 1590, 1548, 1549, 1590, 128, 1548, 1589, 1590, 1548, 1589, 1590, 128, 1549, 1550, 1591, 1549, 1550, 1591, 128, 1549, 1590, 1591, 1549, 1590, 1591, 128, 1550, 1551, 1592, 1550, 1551, 1592, 128, 1550, 1591, 1592, 1550, 1591, 1592, 128, 1551, 1552, 1593, 1551, 1552, 1593, 128, 1551, 1592, 1593, 1551, 1592, 1593, 128, 1552, 1553, 1594, 1552, 1553, 1594, 128, 1552, 1593, 1594, 1552, 1593, 1594, 128, 1553, 1554, 1595, 1553, 1554, 1595, 128, 1553, 1594, 1595, 1553, 1594, 1595, 128, 1554, 1555, 1596, 1554, 1555, 1596, 128, 1554, 1595, 1596, 1554, 1595, 1596, 128, 1555, 1556, 1597, 1555, 1556, 1597, 128, 1555, 1596, 1597, 1555, 1596, 1597, 128, 1556, 1557, 1598, 1556, 1557, 1598, 128, 1556, 1597, 1598, 1556, 1597, 1598, 128, 1558, 1559, 1600, 1558, 1559, 1600, 128, 1558, 1599, 1600, 1558, 1599, 1600, 128, 1559, 1560, 1601, 1559, 1560, 1601, 128, 1559, 1600, 1601, 1559, 1600, 1601, 128, 1560, 1561, 1602, 1560, 1561, 1602, 128, 1560, 1601, 1602, 1560, 1601, 1602, 128, 1561, 1562, 1603, 1561, 1562, 1603, 128, 1561, 1602, 1603, 1561, 1602, 1603, 128, 1562, 1563, 1604, 1562, 1563, 1604, 128, 1562, 1603, 1604, 1562, 1603, 1604, 128, 1563, 1564, 1605, 1563, 1564, 1605, 128, 1563, 1604, 1605, 1563, 1604, 1605, 128, 1564, 1565, 1606, 1564, 1565, 1606, 128, 1564, 1605, 1606, 1564, 1605, 1606, 128, 1565, 1566, 1607, 1565, 1566, 1607, 128, 1565, 1606, 1607, 1565, 1606, 1607, 128, 1566, 1567, 1608, 1566, 1567, 1608, 128, 1566, 1607, 1608, 1566, 1607, 1608, 128, 1567, 1568, 1609, 1567, 1568, 1609, 128, 1567, 1608, 1609, 1567, 1608, 1609, 128, 1568, 1569, 1610, 1568, 1569, 1610, 128, 1568, 1609, 1610, 1568, 1609, 1610, 128, 1569, 1570, 1611, 1569, 1570, 1611, 128, 1569, 1610, 1611, 1569, 1610, 1611, 128, 1570, 1571, 1612, 1570, 1571, 1612, 128, 1570, 1611, 1612, 1570, 1611, 1612, 128, 1571, 1572, 1613, 1571, 1572, 1613, 128, 1571, 1612, 1613, 1571, 1612, 1613, 128, 1572, 1573, 1614, 1572, 1573, 1614, 128, 1572, 1613, 1614, 1572, 1613, 1614, 128, 1573, 1574, 1615, 1573, 1574, 1615, 128, 1573, 1614, 1615, 1573, 1614, 1615, 128, 1574, 1575, 1616, 1574, 1575, 1616, 128, 1574, 1615, 1616, 1574, 1615, 1616, 128, 1575, 1576, 1617, 1575, 1576, 1617, 128, 1575, 1616, 1617, 1575, 1616, 1617, 128, 1576, 1577, 1618, 1576, 1577, 1618, 128, 1576, 1617, 1618, 1576, 1617, 1618, 128, 1577, 1578, 1619, 1577, 1578, 1619, 128, 1577, 1618, 1619, 1577, 1618, 1619, 128, 1578, 1579, 1620, 1578, 1579, 1620, 128, 1578, 1619, 1620, 1578, 1619, 1620, 128, 1579, 1580, 1621, 1579, 1580, 1621, 128, 1579, 1620, 1621, 1579, 1620, 1621, 128, 1580, 1581, 1622, 1580, 1581, 1622, 128, 1580, 1621, 1622, 1580, 1621, 1622, 128, 1581, 1582, 1623, 1581, 1582, 1623, 128, 1581, 1622, 1623, 1581, 1622, 1623, 128, 1582, 1583, 1624, 1582, 1583, 1624, 128, 1582, 1623, 1624, 1582, 1623, 1624, 128, 1583, 1584, 1625, 1583, 1584, 1625, 128, 1583, 1624, 1625, 1583, 1624, 1625, 128, 1584, 1585, 1626, 1584, 1585, 1626, 128, 1584, 1625, 1626, 1584, 1625, 1626, 128, 1585, 1586, 1627, 1585, 1586, 1627, 128, 1585, 1626, 1627, 1585, 1626, 1627, 128, 1586, 1587, 1628, 1586, 1587, 1628, 128, 1586, 1627, 1628, 1586, 1627, 1628, 128, 1587, 1588, 1629, 1587, 1588, 1629, 128, 1587, 1628, 1629, 1587, 1628, 1629, 128, 1588, 1589, 1630, 1588, 1589, 1630, 128, 1588, 1629, 1630, 1588, 1629, 1630, 128, 1589, 1590, 1631, 1589, 1590, 1631, 128, 1589, 1630, 1631, 1589, 1630, 1631, 128, 1590, 1591, 1632, 1590, 1591, 1632, 128, 1590, 1631, 1632, 1590, 1631, 1632, 128, 1591, 1592, 1633, 1591, 1592, 1633, 128, 1591, 1632, 1633, 1591, 1632, 1633, 128, 1592, 1593, 1634, 1592, 1593, 1634, 128, 1592, 1633, 1634, 1592, 1633, 1634, 128, 1593, 1594, 1635, 1593, 1594, 1635, 128, 1593, 1634, 1635, 1593, 1634, 1635, 128, 1594, 1595, 1636, 1594, 1595, 1636, 128, 1594, 1635, 1636, 1594, 1635, 1636, 128, 1595, 1596, 1637, 1595, 1596, 1637, 128, 1595, 1636, 1637, 1595, 1636, 1637, 128, 1596, 1597, 1638, 1596, 1597, 1638, 128, 1596, 1637, 1638, 1596, 1637, 1638, 128, 1597, 1598, 1639, 1597, 1598, 1639, 128, 1597, 1638, 1639, 1597, 1638, 1639, 128, 1599, 1600, 1641, 1599, 1600, 1641, 128, 1599, 1640, 1641, 1599, 1640, 1641, 128, 1600, 1601, 1642, 1600, 1601, 1642, 128, 1600, 1641, 1642, 1600, 1641, 1642, 128, 1601, 1602, 1643, 1601, 1602, 1643, 128, 1601, 1642, 1643, 1601, 1642, 1643, 128, 1602, 1603, 1644, 1602, 1603, 1644, 128, 1602, 1643, 1644, 1602, 1643, 1644, 128, 1603, 1604, 1645, 1603, 1604, 1645, 128, 1603, 1644, 1645, 1603, 1644, 1645, 128, 1604, 1605, 1646, 1604, 1605, 1646, 128, 1604, 1645, 1646, 1604, 1645, 1646, 128, 1605, 1606, 1647, 1605, 1606, 1647, 128, 1605, 1646, 1647, 1605, 1646, 1647, 128, 1606, 1607, 1648, 1606, 1607, 1648, 128, 1606, 1647, 1648, 1606, 1647, 1648, 128, 1607, 1608, 1649, 1607, 1608, 1649, 128, 1607, 1648, 1649, 1607, 1648, 1649, 128, 1608, 1609, 1650, 1608, 1609, 1650, 128, 1608, 1649, 1650, 1608, 1649, 1650, 128, 1609, 1610, 1651, 1609, 1610, 1651, 128, 1609, 1650, 1651, 1609, 1650, 1651, 128, 1610, 1611, 1652, 1610, 1611, 1652, 128, 1610, 1651, 1652, 1610, 1651, 1652, 128, 1611, 1612, 1653, 1611, 1612, 1653, 128, 1611, 1652, 1653, 1611, 1652, 1653, 128, 1612, 1613, 1654, 1612, 1613, 1654, 128, 1612, 1653, 1654, 1612, 1653, 1654, 128, 1613, 1614, 1655, 1613, 1614, 1655, 128, 1613, 1654, 1655, 1613, 1654, 1655, 128, 1614, 1615, 1656, 1614, 1615, 1656, 128, 1614, 1655, 1656, 1614, 1655, 1656, 128, 1615, 1616, 1657, 1615, 1616, 1657, 128, 1615, 1656, 1657, 1615, 1656, 1657, 128, 1616, 1617, 1658, 1616, 1617, 1658, 128, 1616, 1657, 1658, 1616, 1657, 1658, 128, 1617, 1618, 1659, 1617, 1618, 1659, 128, 1617, 1658, 1659, 1617, 1658, 1659, 128, 1618, 1619, 1660, 1618, 1619, 1660, 128, 1618, 1659, 1660, 1618, 1659, 1660, 128, 1619, 1620, 1661, 1619, 1620, 1661, 128, 1619, 1660, 1661, 1619, 1660, 1661, 128, 1620, 1621, 1662, 1620, 1621, 1662, 128, 1620, 1661, 1662, 1620, 1661, 1662, 128, 1621, 1622, 1663, 1621, 1622, 1663, 128, 1621, 1662, 1663, 1621, 1662, 1663, 128, 1622, 1623, 1664, 1622, 1623, 1664, 128, 1622, 1663, 1664, 1622, 1663, 1664, 128, 1623, 1624, 1665, 1623, 1624, 1665, 128, 1623, 1664, 1665, 1623, 1664, 1665, 128, 1624, 1625, 1666, 1624, 1625, 1666, 128, 1624, 1665, 1666, 1624, 1665, 1666, 128, 1625, 1626, 1667, 1625, 1626, 1667, 128, 1625, 1666, 1667, 1625, 1666, 1667, 128, 1626, 1627, 1668, 1626, 1627, 1668, 128, 1626, 1667, 1668, 1626, 1667, 1668, 128, 1627, 1628, 1669, 1627, 1628, 1669, 128, 1627, 1668, 1669, 1627, 1668, 1669, 128, 1628, 1629, 1670, 1628, 1629, 1670, 128, 1628, 1669, 1670, 1628, 1669, 1670, 128, 1629, 1630, 1671, 1629, 1630, 1671, 128, 1629, 1670, 1671, 1629, 1670, 1671, 128, 1630, 1631, 1672, 1630, 1631, 1672, 128, 1630, 1671, 1672, 1630, 1671, 1672, 128, 1631, 1632, 1673, 1631, 1632, 1673, 128, 1631, 1672, 1673, 1631, 1672, 1673, 128, 1632, 1633, 1674, 1632, 1633, 1674, 128, 1632, 1673, 1674, 1632, 1673, 1674, 128, 1633, 1634, 1675, 1633, 1634, 1675, 128, 1633, 1674, 1675, 1633, 1674, 1675, 128, 1634, 1635, 1676, 1634, 1635, 1676, 128, 1634, 1675, 1676, 1634, 1675, 1676, 128, 1635, 1636, 1677, 1635, 1636, 1677, 128, 1635, 1676, 1677, 1635, 1676, 1677, 128, 1636, 1637, 1678, 1636, 1637, 1678, 128, 1636, 1677, 1678, 1636, 1677, 1678, 128, 1637, 1638, 1679, 1637, 1638, 1679, 128, 1637, 1678, 1679, 1637, 1678, 1679, 128, 1638, 1639, 1680, 1638, 1639, 1680, 128, 1638, 1679, 1680, 1638, 1679, 1680], \"colors\": [255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255], \"materials\": [{\"colorDiffuse\": [1, 0, 0], \"DbgColor\": 15658734, \"DbgName\": \"dummy\", \"DbgIndex\": 0}], \"vertices\": [-0.5, -0.50000000000000078, 0.0, -0.47499999999999998, -0.50000000000000078, 0.0, -0.45000000000000001, -0.50000000000000078, 0.0, -0.42499999999999999, -0.50000000000000078, 0.0, -0.40000000000000002, -0.50000000000000078, 0.0, -0.375, -0.50000000000000078, 0.0, -0.34999999999999998, -0.50000000000000078, 0.0, -0.32500000000000001, -0.50000000000000078, 0.0, -0.29999999999999999, -0.50000000000000078, 0.0, -0.27500000000000002, -0.50000000000000078, 0.0, -0.25, -0.50000000000000078, 0.0, -0.22499999999999998, -0.50000000000000078, 0.0, -0.20000000000000001, -0.50000000000000078, 0.0, -0.17499999999999999, -0.50000000000000078, 0.0, -0.15000000000000002, -0.50000000000000078, 0.0, -0.125, -0.50000000000000078, 0.0, -0.099999999999999978, -0.50000000000000078, 0.0, -0.075000000000000011, -0.50000000000000078, 0.0, -0.049999999999999989, -0.50000000000000078, 0.0, -0.025000000000000022, -0.50000000000000078, 0.0, 0.0, -0.50000000000000078, 0.0, 0.025000000000000022, -0.50000000000000078, 0.0, 0.050000000000000044, -0.50000000000000078, 0.0, 0.074999999999999956, -0.50000000000000078, 0.0, 0.099999999999999978, -0.50000000000000078, 0.0, 0.125, -0.50000000000000078, 0.0, 0.15000000000000002, -0.50000000000000078, 0.0, 0.17500000000000004, -0.50000000000000078, 0.0, 0.19999999999999996, -0.50000000000000078, 0.0, 0.22499999999999998, -0.50000000000000078, 0.0, 0.25, -0.50000000000000078, 0.0, 0.27500000000000002, -0.50000000000000078, 0.0, 0.30000000000000004, -0.50000000000000078, 0.0, 0.32499999999999996, -0.50000000000000078, 0.0, 0.34999999999999998, -0.50000000000000078, 0.0, 0.375, -0.50000000000000078, 0.0, 0.40000000000000002, -0.50000000000000078, 0.0, 0.42500000000000004, -0.50000000000000078, 0.0, 0.44999999999999996, -0.50000000000000078, 0.0, 0.47499999999999998, -0.50000000000000078, 0.0, 0.5, -0.50000000000000078, 0.0, -0.5, -0.47500000000000075, 0.0, -0.47499999999999998, -0.47500000000000075, 0.0, -0.45000000000000001, -0.47500000000000075, 0.0, -0.42499999999999999, -0.47500000000000075, 0.0, -0.40000000000000002, -0.47500000000000075, 0.0, -0.375, -0.47500000000000075, 0.0, -0.34999999999999998, -0.47500000000000075, 0.0, -0.32500000000000001, -0.47500000000000075, 0.0, -0.29999999999999999, -0.47500000000000075, 0.0, -0.27500000000000002, -0.47500000000000075, 0.0, -0.25, -0.47500000000000075, 0.0, -0.22499999999999998, -0.47500000000000075, 0.0, -0.20000000000000001, -0.47500000000000075, 0.0, -0.17499999999999999, -0.47500000000000075, 0.0, -0.15000000000000002, -0.47500000000000075, 0.0, -0.125, -0.47500000000000075, 0.0, -0.099999999999999978, -0.47500000000000075, 0.0, -0.075000000000000011, -0.47500000000000075, 0.0, -0.049999999999999989, -0.47500000000000075, 0.0, -0.025000000000000022, -0.47500000000000075, 0.0, 0.0, -0.47500000000000075, 0.0, 0.025000000000000022, -0.47500000000000075, 0.0, 0.050000000000000044, -0.47500000000000075, 0.0, 0.074999999999999956, -0.47500000000000075, 0.0, 0.099999999999999978, -0.47500000000000075, 0.0, 0.125, -0.47500000000000075, 0.0, 0.15000000000000002, -0.47500000000000075, 0.0, 0.17500000000000004, -0.47500000000000075, 0.0, 0.19999999999999996, -0.47500000000000075, 0.0, 0.22499999999999998, -0.47500000000000075, 0.0, 0.25, -0.47500000000000075, 0.0, 0.27500000000000002, -0.47500000000000075, 0.0, 0.30000000000000004, -0.47500000000000075, 0.0, 0.32499999999999996, -0.47500000000000075, 0.0, 0.34999999999999998, -0.47500000000000075, 0.0, 0.375, -0.47500000000000075, 0.0, 0.40000000000000002, -0.47500000000000075, 0.0, 0.42500000000000004, -0.47500000000000075, 0.0, 0.44999999999999996, -0.47500000000000075, 0.0, 0.47499999999999998, -0.47500000000000075, 0.0, 0.5, -0.47500000000000075, 0.0, -0.5, -0.45000000000000079, 0.0, -0.47499999999999998, -0.45000000000000079, 0.0, -0.45000000000000001, -0.45000000000000079, 0.0, -0.42499999999999999, -0.45000000000000079, 0.0, -0.40000000000000002, -0.45000000000000079, 0.0, -0.375, -0.45000000000000079, 0.0, -0.34999999999999998, -0.45000000000000079, 0.0, -0.32500000000000001, -0.45000000000000079, 0.0, -0.29999999999999999, -0.45000000000000079, 0.0, -0.27500000000000002, -0.45000000000000079, 0.0, -0.25, -0.45000000000000079, 0.0, -0.22499999999999998, -0.45000000000000079, 0.0, -0.20000000000000001, -0.45000000000000079, 0.0, -0.17499999999999999, -0.45000000000000079, 0.0, -0.15000000000000002, -0.45000000000000079, 0.0, -0.125, -0.45000000000000079, 0.0, -0.099999999999999978, -0.45000000000000079, 0.0, -0.075000000000000011, -0.45000000000000079, 0.0, -0.049999999999999989, -0.45000000000000079, 0.0, -0.025000000000000022, -0.45000000000000079, 0.0, 0.0, -0.45000000000000079, 0.0, 0.025000000000000022, -0.45000000000000079, 0.0, 0.050000000000000044, -0.45000000000000079, 0.0, 0.074999999999999956, -0.45000000000000079, 0.0, 0.099999999999999978, -0.45000000000000079, 0.0, 0.125, -0.45000000000000079, 0.0, 0.15000000000000002, -0.45000000000000079, 0.0, 0.17500000000000004, -0.45000000000000079, 0.0, 0.19999999999999996, -0.45000000000000079, 0.0, 0.22499999999999998, -0.45000000000000079, 0.0, 0.25, -0.45000000000000079, 0.0, 0.27500000000000002, -0.45000000000000079, 0.0, 0.30000000000000004, -0.45000000000000079, 0.0, 0.32499999999999996, -0.45000000000000079, 0.0, 0.34999999999999998, -0.45000000000000079, 0.0, 0.375, -0.45000000000000079, 0.0, 0.40000000000000002, -0.45000000000000079, 0.0, 0.42500000000000004, -0.45000000000000079, 0.0, 0.44999999999999996, -0.45000000000000079, 0.0, 0.47499999999999998, -0.45000000000000079, 0.0, 0.5, -0.45000000000000079, 0.0, -0.5, -0.42500000000000077, 0.0, -0.47499999999999998, -0.42500000000000077, 0.0, -0.45000000000000001, -0.42500000000000077, 0.0, -0.42499999999999999, -0.42500000000000077, 0.0, -0.40000000000000002, -0.42500000000000077, 0.0, -0.375, -0.42500000000000077, 0.0, -0.34999999999999998, -0.42500000000000077, 0.0, -0.32500000000000001, -0.42500000000000077, 0.0, -0.29999999999999999, -0.42500000000000077, 0.0, -0.27500000000000002, -0.42500000000000077, 0.0, -0.25, -0.42500000000000077, 0.0, -0.22499999999999998, -0.42500000000000077, 0.0, -0.20000000000000001, -0.42500000000000077, 0.0, -0.17499999999999999, -0.42500000000000077, 0.0, -0.15000000000000002, -0.42500000000000077, 0.0, -0.125, -0.42500000000000077, 0.0, -0.099999999999999978, -0.42500000000000077, 0.0, -0.075000000000000011, -0.42500000000000077, 0.0, -0.049999999999999989, -0.42500000000000077, 0.0, -0.025000000000000022, -0.42500000000000077, 0.0, 0.0, -0.42500000000000077, 0.0, 0.025000000000000022, -0.42500000000000077, 0.0, 0.050000000000000044, -0.42500000000000077, 0.0, 0.074999999999999956, -0.42500000000000077, 0.0, 0.099999999999999978, -0.42500000000000077, 0.0, 0.125, -0.42500000000000077, 0.0, 0.15000000000000002, -0.42500000000000077, 0.0, 0.17500000000000004, -0.42500000000000077, 0.0, 0.19999999999999996, -0.42500000000000077, 0.0, 0.22499999999999998, -0.42500000000000077, 0.0, 0.25, -0.42500000000000077, 0.0, 0.27500000000000002, -0.42500000000000077, 0.0, 0.30000000000000004, -0.42500000000000077, 0.0, 0.32499999999999996, -0.42500000000000077, 0.0, 0.34999999999999998, -0.42500000000000077, 0.0, 0.375, -0.42500000000000077, 0.0, 0.40000000000000002, -0.42500000000000077, 0.0, 0.42500000000000004, -0.42500000000000077, 0.0, 0.44999999999999996, -0.42500000000000077, 0.0, 0.47499999999999998, -0.42500000000000077, 0.0, 0.5, -0.42500000000000077, 0.0, -0.5, -0.4000000000000008, 0.0, -0.47499999999999998, -0.4000000000000008, 0.0, -0.45000000000000001, -0.4000000000000008, 0.0, -0.42499999999999999, -0.4000000000000008, 0.0, -0.40000000000000002, -0.4000000000000008, 0.0, -0.375, -0.4000000000000008, 0.0, -0.34999999999999998, -0.4000000000000008, 0.0, -0.32500000000000001, -0.4000000000000008, 0.0, -0.29999999999999999, -0.4000000000000008, 0.0, -0.27500000000000002, -0.4000000000000008, 0.0, -0.25, -0.4000000000000008, 0.0, -0.22499999999999998, -0.4000000000000008, 0.0, -0.20000000000000001, -0.4000000000000008, 0.0, -0.17499999999999999, -0.4000000000000008, 0.0, -0.15000000000000002, -0.4000000000000008, 0.0, -0.125, -0.4000000000000008, 0.0, -0.099999999999999978, -0.4000000000000008, 0.0, -0.075000000000000011, -0.4000000000000008, 0.0, -0.049999999999999989, -0.4000000000000008, 0.0, -0.025000000000000022, -0.4000000000000008, 0.0, 0.0, -0.4000000000000008, 0.0, 0.025000000000000022, -0.4000000000000008, 0.0, 0.050000000000000044, -0.4000000000000008, 0.0, 0.074999999999999956, -0.4000000000000008, 0.0, 0.099999999999999978, -0.4000000000000008, 0.0, 0.125, -0.4000000000000008, 0.0, 0.15000000000000002, -0.4000000000000008, 0.0, 0.17500000000000004, -0.4000000000000008, 0.0, 0.19999999999999996, -0.4000000000000008, 0.0, 0.22499999999999998, -0.4000000000000008, 0.0, 0.25, -0.4000000000000008, 0.0, 0.27500000000000002, -0.4000000000000008, 0.0, 0.30000000000000004, -0.4000000000000008, 0.0, 0.32499999999999996, -0.4000000000000008, 0.0, 0.34999999999999998, -0.4000000000000008, 0.0, 0.375, -0.4000000000000008, 0.0, 0.40000000000000002, -0.4000000000000008, 0.0, 0.42500000000000004, -0.4000000000000008, 0.0, 0.44999999999999996, -0.4000000000000008, 0.0, 0.47499999999999998, -0.4000000000000008, 0.0, 0.5, -0.4000000000000008, 0.0, -0.5, -0.37500000000000078, 0.0, -0.47499999999999998, -0.37500000000000078, 0.0, -0.45000000000000001, -0.37500000000000078, 0.0, -0.42499999999999999, -0.37500000000000078, 0.0, -0.40000000000000002, -0.37500000000000078, 0.0, -0.375, -0.37500000000000078, 0.0, -0.34999999999999998, -0.37500000000000078, 0.0, -0.32500000000000001, -0.37500000000000078, 0.0, -0.29999999999999999, -0.37500000000000078, 0.0, -0.27500000000000002, -0.37500000000000078, 0.0, -0.25, -0.37500000000000078, 0.0, -0.22499999999999998, -0.37500000000000078, 0.0, -0.20000000000000001, -0.37500000000000078, 0.0, -0.17499999999999999, -0.37500000000000078, 0.0, -0.15000000000000002, -0.37500000000000078, 0.0, -0.125, -0.37500000000000078, 0.0, -0.099999999999999978, -0.37500000000000078, 0.0, -0.075000000000000011, -0.37500000000000078, 0.0, -0.049999999999999989, -0.37500000000000078, 0.0, -0.025000000000000022, -0.37500000000000078, 0.0, 0.0, -0.37500000000000078, 0.0, 0.025000000000000022, -0.37500000000000078, 0.0, 0.050000000000000044, -0.37500000000000078, 0.0, 0.074999999999999956, -0.37500000000000078, 0.0, 0.099999999999999978, -0.37500000000000078, 0.0, 0.125, -0.37500000000000078, 0.0, 0.15000000000000002, -0.37500000000000078, 0.0, 0.17500000000000004, -0.37500000000000078, 0.0, 0.19999999999999996, -0.37500000000000078, 0.0, 0.22499999999999998, -0.37500000000000078, 0.0, 0.25, -0.37500000000000078, 0.0, 0.27500000000000002, -0.37500000000000078, 0.0, 0.30000000000000004, -0.37500000000000078, 0.0, 0.32499999999999996, -0.37500000000000078, 0.0, 0.34999999999999998, -0.37500000000000078, 0.0, 0.375, -0.37500000000000078, 0.0, 0.40000000000000002, -0.37500000000000078, 0.0, 0.42500000000000004, -0.37500000000000078, 0.0, 0.44999999999999996, -0.37500000000000078, 0.0, 0.47499999999999998, -0.37500000000000078, 0.0, 0.5, -0.37500000000000078, 0.0, -0.5, -0.35000000000000075, 0.0, -0.47499999999999998, -0.35000000000000075, 0.0, -0.45000000000000001, -0.35000000000000075, 0.0, -0.42499999999999999, -0.35000000000000075, 0.0, -0.40000000000000002, -0.35000000000000075, 0.0, -0.375, -0.35000000000000075, 0.0, -0.34999999999999998, -0.35000000000000075, 0.0, -0.32500000000000001, -0.35000000000000075, 0.0, -0.29999999999999999, -0.35000000000000075, 0.0, -0.27500000000000002, -0.35000000000000075, 0.0, -0.25, -0.35000000000000075, 0.0, -0.22499999999999998, -0.35000000000000075, 0.0, -0.20000000000000001, -0.35000000000000075, 0.0, -0.17499999999999999, -0.35000000000000075, 0.0, -0.15000000000000002, -0.35000000000000075, 0.0, -0.125, -0.35000000000000075, 0.0, -0.099999999999999978, -0.35000000000000075, 0.0, -0.075000000000000011, -0.35000000000000075, 0.0, -0.049999999999999989, -0.35000000000000075, 0.0, -0.025000000000000022, -0.35000000000000075, 0.0, 0.0, -0.35000000000000075, 0.0, 0.025000000000000022, -0.35000000000000075, 0.0, 0.050000000000000044, -0.35000000000000075, 0.0, 0.074999999999999956, -0.35000000000000075, 0.0, 0.099999999999999978, -0.35000000000000075, 0.0, 0.125, -0.35000000000000075, 0.0, 0.15000000000000002, -0.35000000000000075, 0.0, 0.17500000000000004, -0.35000000000000075, 0.0, 0.19999999999999996, -0.35000000000000075, 0.0, 0.22499999999999998, -0.35000000000000075, 0.0, 0.25, -0.35000000000000075, 0.0, 0.27500000000000002, -0.35000000000000075, 0.0, 0.30000000000000004, -0.35000000000000075, 0.0, 0.32499999999999996, -0.35000000000000075, 0.0, 0.34999999999999998, -0.35000000000000075, 0.0, 0.375, -0.35000000000000075, 0.0, 0.40000000000000002, -0.35000000000000075, 0.0, 0.42500000000000004, -0.35000000000000075, 0.0, 0.44999999999999996, -0.35000000000000075, 0.0, 0.47499999999999998, -0.35000000000000075, 0.0, 0.5, -0.35000000000000075, 0.0, -0.5, -0.32500000000000079, 0.0, -0.47499999999999998, -0.32500000000000079, 0.0, -0.45000000000000001, -0.32500000000000079, 0.0, -0.42499999999999999, -0.32500000000000079, 0.0, -0.40000000000000002, -0.32500000000000079, 0.0, -0.375, -0.32500000000000079, 0.0, -0.34999999999999998, -0.32500000000000079, 0.0, -0.32500000000000001, -0.32500000000000079, 0.0, -0.29999999999999999, -0.32500000000000079, 0.0, -0.27500000000000002, -0.32500000000000079, 0.0, -0.25, -0.32500000000000079, 0.0, -0.22499999999999998, -0.32500000000000079, 0.0, -0.20000000000000001, -0.32500000000000079, 0.0, -0.17499999999999999, -0.32500000000000079, 0.0, -0.15000000000000002, -0.32500000000000079, 0.0, -0.125, -0.32500000000000079, 0.0, -0.099999999999999978, -0.32500000000000079, 0.0, -0.075000000000000011, -0.32500000000000079, 0.0, -0.049999999999999989, -0.32500000000000079, 0.0, -0.025000000000000022, -0.32500000000000079, 0.0, 0.0, -0.32500000000000079, 0.0, 0.025000000000000022, -0.32500000000000079, 0.0, 0.050000000000000044, -0.32500000000000079, 0.0, 0.074999999999999956, -0.32500000000000079, 0.0, 0.099999999999999978, -0.32500000000000079, 0.0, 0.125, -0.32500000000000079, 0.0, 0.15000000000000002, -0.32500000000000079, 0.0, 0.17500000000000004, -0.32500000000000079, 0.0, 0.19999999999999996, -0.32500000000000079, 0.0, 0.22499999999999998, -0.32500000000000079, 0.0, 0.25, -0.32500000000000079, 0.0, 0.27500000000000002, -0.32500000000000079, 0.0, 0.30000000000000004, -0.32500000000000079, 0.0, 0.32499999999999996, -0.32500000000000079, 0.0, 0.34999999999999998, -0.32500000000000079, 0.0, 0.375, -0.32500000000000079, 0.0, 0.40000000000000002, -0.32500000000000079, 0.0, 0.42500000000000004, -0.32500000000000079, 0.0, 0.44999999999999996, -0.32500000000000079, 0.0, 0.47499999999999998, -0.32500000000000079, 0.0, 0.5, -0.32500000000000079, 0.0, -0.5, -0.30000000000000077, 0.0, -0.47499999999999998, -0.30000000000000077, 0.0, -0.45000000000000001, -0.30000000000000077, 0.0, -0.42499999999999999, -0.30000000000000077, 0.0, -0.40000000000000002, -0.30000000000000077, 0.0, -0.375, -0.30000000000000077, 0.0, -0.34999999999999998, -0.30000000000000077, 0.0, -0.32500000000000001, -0.30000000000000077, 0.0, -0.29999999999999999, -0.30000000000000077, 0.0, -0.27500000000000002, -0.30000000000000077, 0.0, -0.25, -0.30000000000000077, 0.0, -0.22499999999999998, -0.30000000000000077, 0.0, -0.20000000000000001, -0.30000000000000077, 0.0, -0.17499999999999999, -0.30000000000000077, 0.0, -0.15000000000000002, -0.30000000000000077, 0.0, -0.125, -0.30000000000000077, 0.0, -0.099999999999999978, -0.30000000000000077, 0.0, -0.075000000000000011, -0.30000000000000077, 0.0, -0.049999999999999989, -0.30000000000000077, 0.0, -0.025000000000000022, -0.30000000000000077, 0.0, 0.0, -0.30000000000000077, 0.0, 0.025000000000000022, -0.30000000000000077, 0.0, 0.050000000000000044, -0.30000000000000077, 0.0, 0.074999999999999956, -0.30000000000000077, 0.0, 0.099999999999999978, -0.30000000000000077, 0.0, 0.125, -0.30000000000000077, 0.0, 0.15000000000000002, -0.30000000000000077, 0.0, 0.17500000000000004, -0.30000000000000077, 0.0, 0.19999999999999996, -0.30000000000000077, 0.0, 0.22499999999999998, -0.30000000000000077, 0.0, 0.25, -0.30000000000000077, 0.0, 0.27500000000000002, -0.30000000000000077, 0.0, 0.30000000000000004, -0.30000000000000077, 0.0, 0.32499999999999996, -0.30000000000000077, 0.0, 0.34999999999999998, -0.30000000000000077, 0.0, 0.375, -0.30000000000000077, 0.0, 0.40000000000000002, -0.30000000000000077, 0.0, 0.42500000000000004, -0.30000000000000077, 0.0, 0.44999999999999996, -0.30000000000000077, 0.0, 0.47499999999999998, -0.30000000000000077, 0.0, 0.5, -0.30000000000000077, 0.0, -0.5, -0.2750000000000008, 0.0, -0.47499999999999998, -0.2750000000000008, 0.0, -0.45000000000000001, -0.2750000000000008, 0.0, -0.42499999999999999, -0.2750000000000008, 0.0, -0.40000000000000002, -0.2750000000000008, 0.0, -0.375, -0.2750000000000008, 0.0, -0.34999999999999998, -0.2750000000000008, 0.0, -0.32500000000000001, -0.2750000000000008, 0.0, -0.29999999999999999, -0.2750000000000008, 0.0, -0.27500000000000002, -0.2750000000000008, 0.0, -0.25, -0.2750000000000008, 0.0, -0.22499999999999998, -0.2750000000000008, 0.0, -0.20000000000000001, -0.2750000000000008, 0.0, -0.17499999999999999, -0.2750000000000008, 0.0, -0.15000000000000002, -0.2750000000000008, 0.0, -0.125, -0.2750000000000008, 0.0, -0.099999999999999978, -0.2750000000000008, 0.0, -0.075000000000000011, -0.2750000000000008, 0.0, -0.049999999999999989, -0.2750000000000008, 0.0, -0.025000000000000022, -0.2750000000000008, 0.0, 0.0, -0.2750000000000008, 0.0, 0.025000000000000022, -0.2750000000000008, 0.0, 0.050000000000000044, -0.2750000000000008, 0.0, 0.074999999999999956, -0.2750000000000008, 0.0, 0.099999999999999978, -0.2750000000000008, 0.0, 0.125, -0.2750000000000008, 0.0, 0.15000000000000002, -0.2750000000000008, 0.0, 0.17500000000000004, -0.2750000000000008, 0.0, 0.19999999999999996, -0.2750000000000008, 0.0, 0.22499999999999998, -0.2750000000000008, 0.0, 0.25, -0.2750000000000008, 0.0, 0.27500000000000002, -0.2750000000000008, 0.0, 0.30000000000000004, -0.2750000000000008, 0.0, 0.32499999999999996, -0.2750000000000008, 0.0, 0.34999999999999998, -0.2750000000000008, 0.0, 0.375, -0.2750000000000008, 0.0, 0.40000000000000002, -0.2750000000000008, 0.0, 0.42500000000000004, -0.2750000000000008, 0.0, 0.44999999999999996, -0.2750000000000008, 0.0, 0.47499999999999998, -0.2750000000000008, 0.0, 0.5, -0.2750000000000008, 0.0, -0.5, -0.25000000000000078, 0.0, -0.47499999999999998, -0.25000000000000078, 0.0, -0.45000000000000001, -0.25000000000000078, 0.0, -0.42499999999999999, -0.25000000000000078, 0.0, -0.40000000000000002, -0.25000000000000078, 0.0, -0.375, -0.25000000000000078, 0.0, -0.34999999999999998, -0.25000000000000078, 0.0, -0.32500000000000001, -0.25000000000000078, 0.0, -0.29999999999999999, -0.25000000000000078, 0.0, -0.27500000000000002, -0.25000000000000078, 0.0, -0.25, -0.25000000000000078, 0.0, -0.22499999999999998, -0.25000000000000078, 0.0, -0.20000000000000001, -0.25000000000000078, 0.0, -0.17499999999999999, -0.25000000000000078, 0.0, -0.15000000000000002, -0.25000000000000078, 0.0, -0.125, -0.25000000000000078, 0.0, -0.099999999999999978, -0.25000000000000078, 0.0, -0.075000000000000011, -0.25000000000000078, 0.0, -0.049999999999999989, -0.25000000000000078, 0.0, -0.025000000000000022, -0.25000000000000078, 0.0, 0.0, -0.25000000000000078, 0.0, 0.025000000000000022, -0.25000000000000078, 0.0, 0.050000000000000044, -0.25000000000000078, 0.0, 0.074999999999999956, -0.25000000000000078, 0.0, 0.099999999999999978, -0.25000000000000078, 0.0, 0.125, -0.25000000000000078, 0.0, 0.15000000000000002, -0.25000000000000078, 0.0, 0.17500000000000004, -0.25000000000000078, 0.0, 0.19999999999999996, -0.25000000000000078, 0.0, 0.22499999999999998, -0.25000000000000078, 0.0, 0.25, -0.25000000000000078, 0.0, 0.27500000000000002, -0.25000000000000078, 0.0, 0.30000000000000004, -0.25000000000000078, 0.0, 0.32499999999999996, -0.25000000000000078, 0.0, 0.34999999999999998, -0.25000000000000078, 0.0, 0.375, -0.25000000000000078, 0.0, 0.40000000000000002, -0.25000000000000078, 0.0, 0.42500000000000004, -0.25000000000000078, 0.0, 0.44999999999999996, -0.25000000000000078, 0.0, 0.47499999999999998, -0.25000000000000078, 0.0, 0.5, -0.25000000000000078, 0.0, -0.5, -0.22500000000000075, 0.0, -0.47499999999999998, -0.22500000000000075, 0.0, -0.45000000000000001, -0.22500000000000075, 0.0, -0.42499999999999999, -0.22500000000000075, 0.0, -0.40000000000000002, -0.22500000000000075, 0.0, -0.375, -0.22500000000000075, 0.0, -0.34999999999999998, -0.22500000000000075, 0.0, -0.32500000000000001, -0.22500000000000075, 0.0, -0.29999999999999999, -0.22500000000000075, 0.0, -0.27500000000000002, -0.22500000000000075, 0.0, -0.25, -0.22500000000000075, 0.0, -0.22499999999999998, -0.22500000000000075, 0.0, -0.20000000000000001, -0.22500000000000075, 0.0, -0.17499999999999999, -0.22500000000000075, 0.0, -0.15000000000000002, -0.22500000000000075, 0.0, -0.125, -0.22500000000000075, 0.0, -0.099999999999999978, -0.22500000000000075, 0.0, -0.075000000000000011, -0.22500000000000075, 0.0, -0.049999999999999989, -0.22500000000000075, 0.0, -0.025000000000000022, -0.22500000000000075, 0.0, 0.0, -0.22500000000000075, 0.0, 0.025000000000000022, -0.22500000000000075, 0.0, 0.050000000000000044, -0.22500000000000075, 0.0, 0.074999999999999956, -0.22500000000000075, 0.0, 0.099999999999999978, -0.22500000000000075, 0.0, 0.125, -0.22500000000000075, 0.0, 0.15000000000000002, -0.22500000000000075, 0.0, 0.17500000000000004, -0.22500000000000075, 0.0, 0.19999999999999996, -0.22500000000000075, 0.0, 0.22499999999999998, -0.22500000000000075, 0.0, 0.25, -0.22500000000000075, 0.0, 0.27500000000000002, -0.22500000000000075, 0.0, 0.30000000000000004, -0.22500000000000075, 0.0, 0.32499999999999996, -0.22500000000000075, 0.0, 0.34999999999999998, -0.22500000000000075, 0.0, 0.375, -0.22500000000000075, 0.0, 0.40000000000000002, -0.22500000000000075, 0.0, 0.42500000000000004, -0.22500000000000075, 0.0, 0.44999999999999996, -0.22500000000000075, 0.0, 0.47499999999999998, -0.22500000000000075, 0.0, 0.5, -0.22500000000000075, 0.0, -0.5, -0.20000000000000079, 0.0, -0.47499999999999998, -0.20000000000000079, 0.0, -0.45000000000000001, -0.20000000000000079, 0.0, -0.42499999999999999, -0.20000000000000079, 0.0, -0.40000000000000002, -0.20000000000000079, 0.0, -0.375, -0.20000000000000079, 0.0, -0.34999999999999998, -0.20000000000000079, 0.0, -0.32500000000000001, -0.20000000000000079, 0.0, -0.29999999999999999, -0.20000000000000079, 0.0, -0.27500000000000002, -0.20000000000000079, 0.0, -0.25, -0.20000000000000079, 0.0, -0.22499999999999998, -0.20000000000000079, 0.0, -0.20000000000000001, -0.20000000000000079, 0.0, -0.17499999999999999, -0.20000000000000079, 0.0, -0.15000000000000002, -0.20000000000000079, 0.0, -0.125, -0.20000000000000079, 0.0, -0.099999999999999978, -0.20000000000000079, 0.0, -0.075000000000000011, -0.20000000000000079, 0.0, -0.049999999999999989, -0.20000000000000079, 0.0, -0.025000000000000022, -0.20000000000000079, 0.0, 0.0, -0.20000000000000079, 0.0, 0.025000000000000022, -0.20000000000000079, 0.0, 0.050000000000000044, -0.20000000000000079, 0.0, 0.074999999999999956, -0.20000000000000079, 0.0, 0.099999999999999978, -0.20000000000000079, 0.0, 0.125, -0.20000000000000079, 0.0, 0.15000000000000002, -0.20000000000000079, 0.0, 0.17500000000000004, -0.20000000000000079, 0.0, 0.19999999999999996, -0.20000000000000079, 0.0, 0.22499999999999998, -0.20000000000000079, 0.0, 0.25, -0.20000000000000079, 0.0, 0.27500000000000002, -0.20000000000000079, 0.0, 0.30000000000000004, -0.20000000000000079, 0.0, 0.32499999999999996, -0.20000000000000079, 0.0, 0.34999999999999998, -0.20000000000000079, 0.0, 0.375, -0.20000000000000079, 0.0, 0.40000000000000002, -0.20000000000000079, 0.0, 0.42500000000000004, -0.20000000000000079, 0.0, 0.44999999999999996, -0.20000000000000079, 0.0, 0.47499999999999998, -0.20000000000000079, 0.0, 0.5, -0.20000000000000079, 0.0, -0.5, -0.17500000000000077, 0.0, -0.47499999999999998, -0.17500000000000077, 0.0, -0.45000000000000001, -0.17500000000000077, 0.0, -0.42499999999999999, -0.17500000000000077, 0.0, -0.40000000000000002, -0.17500000000000077, 0.0, -0.375, -0.17500000000000077, 0.0, -0.34999999999999998, -0.17500000000000077, 0.0, -0.32500000000000001, -0.17500000000000077, 0.0, -0.29999999999999999, -0.17500000000000077, 0.0, -0.27500000000000002, -0.17500000000000077, 0.0, -0.25, -0.17500000000000077, 0.0, -0.22499999999999998, -0.17500000000000077, 0.0, -0.20000000000000001, -0.17500000000000077, 0.0, -0.17499999999999999, -0.17500000000000077, 0.0, -0.15000000000000002, -0.17500000000000077, 0.0, -0.125, -0.17500000000000077, 0.0, -0.099999999999999978, -0.17500000000000077, 0.0, -0.075000000000000011, -0.17500000000000077, 0.0, -0.049999999999999989, -0.17500000000000077, 0.0, -0.025000000000000022, -0.17500000000000077, 0.0, 0.0, -0.17500000000000077, 0.0, 0.025000000000000022, -0.17500000000000077, 0.0, 0.050000000000000044, -0.17500000000000077, 0.0, 0.074999999999999956, -0.17500000000000077, 0.0, 0.099999999999999978, -0.17500000000000077, 0.0, 0.125, -0.17500000000000077, 0.0, 0.15000000000000002, -0.17500000000000077, 0.0, 0.17500000000000004, -0.17500000000000077, 0.0, 0.19999999999999996, -0.17500000000000077, 0.0, 0.22499999999999998, -0.17500000000000077, 0.0, 0.25, -0.17500000000000077, 0.0, 0.27500000000000002, -0.17500000000000077, 0.0, 0.30000000000000004, -0.17500000000000077, 0.0, 0.32499999999999996, -0.17500000000000077, 0.0, 0.34999999999999998, -0.17500000000000077, 0.0, 0.375, -0.17500000000000077, 0.0, 0.40000000000000002, -0.17500000000000077, 0.0, 0.42500000000000004, -0.17500000000000077, 0.0, 0.44999999999999996, -0.17500000000000077, 0.0, 0.47499999999999998, -0.17500000000000077, 0.0, 0.5, -0.17500000000000077, 0.0, -0.5, -0.1500000000000008, 0.0, -0.47499999999999998, -0.1500000000000008, 0.0, -0.45000000000000001, -0.1500000000000008, 0.0, -0.42499999999999999, -0.1500000000000008, 0.0, -0.40000000000000002, -0.1500000000000008, 0.0, -0.375, -0.1500000000000008, 0.0, -0.34999999999999998, -0.1500000000000008, 0.0, -0.32500000000000001, -0.1500000000000008, 0.0, -0.29999999999999999, -0.1500000000000008, 0.0, -0.27500000000000002, -0.1500000000000008, 0.0, -0.25, -0.1500000000000008, 0.0, -0.22499999999999998, -0.1500000000000008, 0.0, -0.20000000000000001, -0.1500000000000008, 0.0, -0.17499999999999999, -0.1500000000000008, 0.0, -0.15000000000000002, -0.1500000000000008, 0.0, -0.125, -0.1500000000000008, 0.0, -0.099999999999999978, -0.1500000000000008, 0.0, -0.075000000000000011, -0.1500000000000008, 0.0, -0.049999999999999989, -0.1500000000000008, 0.0, -0.025000000000000022, -0.1500000000000008, 0.0, 0.0, -0.1500000000000008, 0.0, 0.025000000000000022, -0.1500000000000008, 0.0, 0.050000000000000044, -0.1500000000000008, 0.0, 0.074999999999999956, -0.1500000000000008, 0.0, 0.099999999999999978, -0.1500000000000008, 0.0, 0.125, -0.1500000000000008, 0.0, 0.15000000000000002, -0.1500000000000008, 0.0, 0.17500000000000004, -0.1500000000000008, 0.0, 0.19999999999999996, -0.1500000000000008, 0.0, 0.22499999999999998, -0.1500000000000008, 0.0, 0.25, -0.1500000000000008, 0.0, 0.27500000000000002, -0.1500000000000008, 0.0, 0.30000000000000004, -0.1500000000000008, 0.0, 0.32499999999999996, -0.1500000000000008, 0.0, 0.34999999999999998, -0.1500000000000008, 0.0, 0.375, -0.1500000000000008, 0.0, 0.40000000000000002, -0.1500000000000008, 0.0, 0.42500000000000004, -0.1500000000000008, 0.0, 0.44999999999999996, -0.1500000000000008, 0.0, 0.47499999999999998, -0.1500000000000008, 0.0, 0.5, -0.1500000000000008, 0.0, -0.5, -0.12500000000000078, 0.0, -0.47499999999999998, -0.12500000000000078, 0.0, -0.45000000000000001, -0.12500000000000078, 0.0, -0.42499999999999999, -0.12500000000000078, 0.0, -0.40000000000000002, -0.12500000000000078, 0.0, -0.375, -0.12500000000000078, 0.0, -0.34999999999999998, -0.12500000000000078, 0.0, -0.32500000000000001, -0.12500000000000078, 0.0, -0.29999999999999999, -0.12500000000000078, 0.0, -0.27500000000000002, -0.12500000000000078, 0.0, -0.25, -0.12500000000000078, 0.0, -0.22499999999999998, -0.12500000000000078, 0.0, -0.20000000000000001, -0.12500000000000078, 0.0, -0.17499999999999999, -0.12500000000000078, 0.0, -0.15000000000000002, -0.12500000000000078, 0.0, -0.125, -0.12500000000000078, 0.0, -0.099999999999999978, -0.12500000000000078, 0.0, -0.075000000000000011, -0.12500000000000078, 0.0, -0.049999999999999989, -0.12500000000000078, 0.0, -0.025000000000000022, -0.12500000000000078, 0.0, 0.0, -0.12500000000000078, 0.0, 0.025000000000000022, -0.12500000000000078, 0.0, 0.050000000000000044, -0.12500000000000078, 0.0, 0.074999999999999956, -0.12500000000000078, 0.0, 0.099999999999999978, -0.12500000000000078, 0.0, 0.125, -0.12500000000000078, 0.0, 0.15000000000000002, -0.12500000000000078, 0.0, 0.17500000000000004, -0.12500000000000078, 0.0, 0.19999999999999996, -0.12500000000000078, 0.0, 0.22499999999999998, -0.12500000000000078, 0.0, 0.25, -0.12500000000000078, 0.0, 0.27500000000000002, -0.12500000000000078, 0.0, 0.30000000000000004, -0.12500000000000078, 0.0, 0.32499999999999996, -0.12500000000000078, 0.0, 0.34999999999999998, -0.12500000000000078, 0.0, 0.375, -0.12500000000000078, 0.0, 0.40000000000000002, -0.12500000000000078, 0.0, 0.42500000000000004, -0.12500000000000078, 0.0, 0.44999999999999996, -0.12500000000000078, 0.0, 0.47499999999999998, -0.12500000000000078, 0.0, 0.5, -0.12500000000000078, 0.0, -0.5, -0.10000000000000075, 0.0, -0.47499999999999998, -0.10000000000000075, 0.0, -0.45000000000000001, -0.10000000000000075, 0.0, -0.42499999999999999, -0.10000000000000075, 0.0, -0.40000000000000002, -0.10000000000000075, 0.0, -0.375, -0.10000000000000075, 0.0, -0.34999999999999998, -0.10000000000000075, 0.0, -0.32500000000000001, -0.10000000000000075, 0.0, -0.29999999999999999, -0.10000000000000075, 0.0, -0.27500000000000002, -0.10000000000000075, 0.0, -0.25, -0.10000000000000075, 0.0, -0.22499999999999998, -0.10000000000000075, 0.0, -0.20000000000000001, -0.10000000000000075, 0.0, -0.17499999999999999, -0.10000000000000075, 0.0, -0.15000000000000002, -0.10000000000000075, 0.0, -0.125, -0.10000000000000075, 0.0, -0.099999999999999978, -0.10000000000000075, 0.0, -0.075000000000000011, -0.10000000000000075, 0.0, -0.049999999999999989, -0.10000000000000075, 0.0, -0.025000000000000022, -0.10000000000000075, 0.0, 0.0, -0.10000000000000075, 0.0, 0.025000000000000022, -0.10000000000000075, 0.0, 0.050000000000000044, -0.10000000000000075, 0.0, 0.074999999999999956, -0.10000000000000075, 0.0, 0.099999999999999978, -0.10000000000000075, 0.0, 0.125, -0.10000000000000075, 0.0, 0.15000000000000002, -0.10000000000000075, 0.0, 0.17500000000000004, -0.10000000000000075, 0.0, 0.19999999999999996, -0.10000000000000075, 0.0, 0.22499999999999998, -0.10000000000000075, 0.0, 0.25, -0.10000000000000075, 0.0, 0.27500000000000002, -0.10000000000000075, 0.0, 0.30000000000000004, -0.10000000000000075, 0.0, 0.32499999999999996, -0.10000000000000075, 0.0, 0.34999999999999998, -0.10000000000000075, 0.0, 0.375, -0.10000000000000075, 0.0, 0.40000000000000002, -0.10000000000000075, 0.0, 0.42500000000000004, -0.10000000000000075, 0.0, 0.44999999999999996, -0.10000000000000075, 0.0, 0.47499999999999998, -0.10000000000000075, 0.0, 0.5, -0.10000000000000075, 0.0, -0.5, -0.075000000000000788, 0.0, -0.47499999999999998, -0.075000000000000788, 0.0, -0.45000000000000001, -0.075000000000000788, 0.0, -0.42499999999999999, -0.075000000000000788, 0.0, -0.40000000000000002, -0.075000000000000788, 0.0, -0.375, -0.075000000000000788, 0.0, -0.34999999999999998, -0.075000000000000788, 0.0, -0.32500000000000001, -0.075000000000000788, 0.0, -0.29999999999999999, -0.075000000000000788, 0.0, -0.27500000000000002, -0.075000000000000788, 0.0, -0.25, -0.075000000000000788, 0.0, -0.22499999999999998, -0.075000000000000788, 0.0, -0.20000000000000001, -0.075000000000000788, 0.0, -0.17499999999999999, -0.075000000000000788, 0.0, -0.15000000000000002, -0.075000000000000788, 0.0, -0.125, -0.075000000000000788, 0.0, -0.099999999999999978, -0.075000000000000788, 0.0, -0.075000000000000011, -0.075000000000000788, 0.0, -0.049999999999999989, -0.075000000000000788, 0.0, -0.025000000000000022, -0.075000000000000788, 0.0, 0.0, -0.075000000000000788, 0.0, 0.025000000000000022, -0.075000000000000788, 0.0, 0.050000000000000044, -0.075000000000000788, 0.0, 0.074999999999999956, -0.075000000000000788, 0.0, 0.099999999999999978, -0.075000000000000788, 0.0, 0.125, -0.075000000000000788, 0.0, 0.15000000000000002, -0.075000000000000788, 0.0, 0.17500000000000004, -0.075000000000000788, 0.0, 0.19999999999999996, -0.075000000000000788, 0.0, 0.22499999999999998, -0.075000000000000788, 0.0, 0.25, -0.075000000000000788, 0.0, 0.27500000000000002, -0.075000000000000788, 0.0, 0.30000000000000004, -0.075000000000000788, 0.0, 0.32499999999999996, -0.075000000000000788, 0.0, 0.34999999999999998, -0.075000000000000788, 0.0, 0.375, -0.075000000000000788, 0.0, 0.40000000000000002, -0.075000000000000788, 0.0, 0.42500000000000004, -0.075000000000000788, 0.0, 0.44999999999999996, -0.075000000000000788, 0.0, 0.47499999999999998, -0.075000000000000788, 0.0, 0.5, -0.075000000000000788, 0.0, -0.5, -0.050000000000000766, 0.0, -0.47499999999999998, -0.050000000000000766, 0.0, -0.45000000000000001, -0.050000000000000766, 0.0, -0.42499999999999999, -0.050000000000000766, 0.0, -0.40000000000000002, -0.050000000000000766, 0.0, -0.375, -0.050000000000000766, 0.0, -0.34999999999999998, -0.050000000000000766, 0.0, -0.32500000000000001, -0.050000000000000766, 0.0, -0.29999999999999999, -0.050000000000000766, 0.0, -0.27500000000000002, -0.050000000000000766, 0.0, -0.25, -0.050000000000000766, 0.0, -0.22499999999999998, -0.050000000000000766, 0.0, -0.20000000000000001, -0.050000000000000766, 0.0, -0.17499999999999999, -0.050000000000000766, 0.0, -0.15000000000000002, -0.050000000000000766, 0.0, -0.125, -0.050000000000000766, 0.0, -0.099999999999999978, -0.050000000000000766, 0.0, -0.075000000000000011, -0.050000000000000766, 0.0, -0.049999999999999989, -0.050000000000000766, 0.0, -0.025000000000000022, -0.050000000000000766, 0.0, 0.0, -0.050000000000000766, 0.0, 0.025000000000000022, -0.050000000000000766, 0.0, 0.050000000000000044, -0.050000000000000766, 0.0, 0.074999999999999956, -0.050000000000000766, 0.0, 0.099999999999999978, -0.050000000000000766, 0.0, 0.125, -0.050000000000000766, 0.0, 0.15000000000000002, -0.050000000000000766, 0.0, 0.17500000000000004, -0.050000000000000766, 0.0, 0.19999999999999996, -0.050000000000000766, 0.0, 0.22499999999999998, -0.050000000000000766, 0.0, 0.25, -0.050000000000000766, 0.0, 0.27500000000000002, -0.050000000000000766, 0.0, 0.30000000000000004, -0.050000000000000766, 0.0, 0.32499999999999996, -0.050000000000000766, 0.0, 0.34999999999999998, -0.050000000000000766, 0.0, 0.375, -0.050000000000000766, 0.0, 0.40000000000000002, -0.050000000000000766, 0.0, 0.42500000000000004, -0.050000000000000766, 0.0, 0.44999999999999996, -0.050000000000000766, 0.0, 0.47499999999999998, -0.050000000000000766, 0.0, 0.5, -0.050000000000000766, 0.0, -0.5, -0.025000000000000799, 0.0, -0.47499999999999998, -0.025000000000000799, 0.0, -0.45000000000000001, -0.025000000000000799, 0.0, -0.42499999999999999, -0.025000000000000799, 0.0, -0.40000000000000002, -0.025000000000000799, 0.0, -0.375, -0.025000000000000799, 0.0, -0.34999999999999998, -0.025000000000000799, 0.0, -0.32500000000000001, -0.025000000000000799, 0.0, -0.29999999999999999, -0.025000000000000799, 0.0, -0.27500000000000002, -0.025000000000000799, 0.0, -0.25, -0.025000000000000799, 0.0, -0.22499999999999998, -0.025000000000000799, 0.0, -0.20000000000000001, -0.025000000000000799, 0.0, -0.17499999999999999, -0.025000000000000799, 0.0, -0.15000000000000002, -0.025000000000000799, 0.0, -0.125, -0.025000000000000799, 0.0, -0.099999999999999978, -0.025000000000000799, 0.0, -0.075000000000000011, -0.025000000000000799, 0.0, -0.049999999999999989, -0.025000000000000799, 0.0, -0.025000000000000022, -0.025000000000000799, 0.0, 0.0, -0.025000000000000799, 0.0, 0.025000000000000022, -0.025000000000000799, 0.0, 0.050000000000000044, -0.025000000000000799, 0.0, 0.074999999999999956, -0.025000000000000799, 0.0, 0.099999999999999978, -0.025000000000000799, 0.0, 0.125, -0.025000000000000799, 0.0, 0.15000000000000002, -0.025000000000000799, 0.0, 0.17500000000000004, -0.025000000000000799, 0.0, 0.19999999999999996, -0.025000000000000799, 0.0, 0.22499999999999998, -0.025000000000000799, 0.0, 0.25, -0.025000000000000799, 0.0, 0.27500000000000002, -0.025000000000000799, 0.0, 0.30000000000000004, -0.025000000000000799, 0.0, 0.32499999999999996, -0.025000000000000799, 0.0, 0.34999999999999998, -0.025000000000000799, 0.0, 0.375, -0.025000000000000799, 0.0, 0.40000000000000002, -0.025000000000000799, 0.0, 0.42500000000000004, -0.025000000000000799, 0.0, 0.44999999999999996, -0.025000000000000799, 0.0, 0.47499999999999998, -0.025000000000000799, 0.0, 0.5, -0.025000000000000799, 0.0, -0.5, -7.7715611723760958e-16, 0.0, -0.47499999999999998, -7.7715611723760958e-16, 0.0, -0.45000000000000001, -7.7715611723760958e-16, 0.0, -0.42499999999999999, -7.7715611723760958e-16, 0.0, -0.40000000000000002, -7.7715611723760958e-16, 0.0, -0.375, -7.7715611723760958e-16, 0.0, -0.34999999999999998, -7.7715611723760958e-16, 0.0, -0.32500000000000001, -7.7715611723760958e-16, 0.0, -0.29999999999999999, -7.7715611723760958e-16, 0.0, -0.27500000000000002, -7.7715611723760958e-16, 0.0, -0.25, -7.7715611723760958e-16, 0.0, -0.22499999999999998, -7.7715611723760958e-16, 0.0, -0.20000000000000001, -7.7715611723760958e-16, 0.0, -0.17499999999999999, -7.7715611723760958e-16, 0.0, -0.15000000000000002, -7.7715611723760958e-16, 0.0, -0.125, -7.7715611723760958e-16, 0.0, -0.099999999999999978, -7.7715611723760958e-16, 0.0, -0.075000000000000011, -7.7715611723760958e-16, 0.0, -0.049999999999999989, -7.7715611723760958e-16, 0.0, -0.025000000000000022, -7.7715611723760958e-16, 0.0, 0.0, -7.7715611723760958e-16, 0.0, 0.025000000000000022, -7.7715611723760958e-16, 0.0, 0.050000000000000044, -7.7715611723760958e-16, 0.0, 0.074999999999999956, -7.7715611723760958e-16, 0.0, 0.099999999999999978, -7.7715611723760958e-16, 0.0, 0.125, -7.7715611723760958e-16, 0.0, 0.15000000000000002, -7.7715611723760958e-16, 0.0, 0.17500000000000004, -7.7715611723760958e-16, 0.0, 0.19999999999999996, -7.7715611723760958e-16, 0.0, 0.22499999999999998, -7.7715611723760958e-16, 0.0, 0.25, -7.7715611723760958e-16, 0.0, 0.27500000000000002, -7.7715611723760958e-16, 0.0, 0.30000000000000004, -7.7715611723760958e-16, 0.0, 0.32499999999999996, -7.7715611723760958e-16, 0.0, 0.34999999999999998, -7.7715611723760958e-16, 0.0, 0.375, -7.7715611723760958e-16, 0.0, 0.40000000000000002, -7.7715611723760958e-16, 0.0, 0.42500000000000004, -7.7715611723760958e-16, 0.0, 0.44999999999999996, -7.7715611723760958e-16, 0.0, 0.47499999999999998, -7.7715611723760958e-16, 0.0, 0.5, -7.7715611723760958e-16, 0.0, -0.5, 0.024999999999999245, 0.0, -0.47499999999999998, 0.024999999999999245, 0.0, -0.45000000000000001, 0.024999999999999245, 0.0, -0.42499999999999999, 0.024999999999999245, 0.0, -0.40000000000000002, 0.024999999999999245, 0.0, -0.375, 0.024999999999999245, 0.0, -0.34999999999999998, 0.024999999999999245, 0.0, -0.32500000000000001, 0.024999999999999245, 0.0, -0.29999999999999999, 0.024999999999999245, 0.0, -0.27500000000000002, 0.024999999999999245, 0.0, -0.25, 0.024999999999999245, 0.0, -0.22499999999999998, 0.024999999999999245, 0.0, -0.20000000000000001, 0.024999999999999245, 0.0, -0.17499999999999999, 0.024999999999999245, 0.0, -0.15000000000000002, 0.024999999999999245, 0.0, -0.125, 0.024999999999999245, 0.0, -0.099999999999999978, 0.024999999999999245, 0.0, -0.075000000000000011, 0.024999999999999245, 0.0, -0.049999999999999989, 0.024999999999999245, 0.0, -0.025000000000000022, 0.024999999999999245, 0.0, 0.0, 0.024999999999999245, 0.0, 0.025000000000000022, 0.024999999999999245, 0.0, 0.050000000000000044, 0.024999999999999245, 0.0, 0.074999999999999956, 0.024999999999999245, 0.0, 0.099999999999999978, 0.024999999999999245, 0.0, 0.125, 0.024999999999999245, 0.0, 0.15000000000000002, 0.024999999999999245, 0.0, 0.17500000000000004, 0.024999999999999245, 0.0, 0.19999999999999996, 0.024999999999999245, 0.0, 0.22499999999999998, 0.024999999999999245, 0.0, 0.25, 0.024999999999999245, 0.0, 0.27500000000000002, 0.024999999999999245, 0.0, 0.30000000000000004, 0.024999999999999245, 0.0, 0.32499999999999996, 0.024999999999999245, 0.0, 0.34999999999999998, 0.024999999999999245, 0.0, 0.375, 0.024999999999999245, 0.0, 0.40000000000000002, 0.024999999999999245, 0.0, 0.42500000000000004, 0.024999999999999245, 0.0, 0.44999999999999996, 0.024999999999999245, 0.0, 0.47499999999999998, 0.024999999999999245, 0.0, 0.5, 0.024999999999999245, 0.0, -0.5, 0.049999999999999267, 0.0, -0.47499999999999998, 0.049999999999999267, 0.0, -0.45000000000000001, 0.049999999999999267, 0.0, -0.42499999999999999, 0.049999999999999267, 0.0, -0.40000000000000002, 0.049999999999999267, 0.0, -0.375, 0.049999999999999267, 0.0, -0.34999999999999998, 0.049999999999999267, 0.0, -0.32500000000000001, 0.049999999999999267, 0.0, -0.29999999999999999, 0.049999999999999267, 0.0, -0.27500000000000002, 0.049999999999999267, 0.0, -0.25, 0.049999999999999267, 0.0, -0.22499999999999998, 0.049999999999999267, 0.0, -0.20000000000000001, 0.049999999999999267, 0.0, -0.17499999999999999, 0.049999999999999267, 0.0, -0.15000000000000002, 0.049999999999999267, 0.0, -0.125, 0.049999999999999267, 0.0, -0.099999999999999978, 0.049999999999999267, 0.0, -0.075000000000000011, 0.049999999999999267, 0.0, -0.049999999999999989, 0.049999999999999267, 0.0, -0.025000000000000022, 0.049999999999999267, 0.0, 0.0, 0.049999999999999267, 0.0, 0.025000000000000022, 0.049999999999999267, 0.0, 0.050000000000000044, 0.049999999999999267, 0.0, 0.074999999999999956, 0.049999999999999267, 0.0, 0.099999999999999978, 0.049999999999999267, 0.0, 0.125, 0.049999999999999267, 0.0, 0.15000000000000002, 0.049999999999999267, 0.0, 0.17500000000000004, 0.049999999999999267, 0.0, 0.19999999999999996, 0.049999999999999267, 0.0, 0.22499999999999998, 0.049999999999999267, 0.0, 0.25, 0.049999999999999267, 0.0, 0.27500000000000002, 0.049999999999999267, 0.0, 0.30000000000000004, 0.049999999999999267, 0.0, 0.32499999999999996, 0.049999999999999267, 0.0, 0.34999999999999998, 0.049999999999999267, 0.0, 0.375, 0.049999999999999267, 0.0, 0.40000000000000002, 0.049999999999999267, 0.0, 0.42500000000000004, 0.049999999999999267, 0.0, 0.44999999999999996, 0.049999999999999267, 0.0, 0.47499999999999998, 0.049999999999999267, 0.0, 0.5, 0.049999999999999267, 0.0, -0.5, 0.074999999999999178, 0.0, -0.47499999999999998, 0.074999999999999178, 0.0, -0.45000000000000001, 0.074999999999999178, 0.0, -0.42499999999999999, 0.074999999999999178, 0.0, -0.40000000000000002, 0.074999999999999178, 0.0, -0.375, 0.074999999999999178, 0.0, -0.34999999999999998, 0.074999999999999178, 0.0, -0.32500000000000001, 0.074999999999999178, 0.0, -0.29999999999999999, 0.074999999999999178, 0.0, -0.27500000000000002, 0.074999999999999178, 0.0, -0.25, 0.074999999999999178, 0.0, -0.22499999999999998, 0.074999999999999178, 0.0, -0.20000000000000001, 0.074999999999999178, 0.0, -0.17499999999999999, 0.074999999999999178, 0.0, -0.15000000000000002, 0.074999999999999178, 0.0, -0.125, 0.074999999999999178, 0.0, -0.099999999999999978, 0.074999999999999178, 0.0, -0.075000000000000011, 0.074999999999999178, 0.0, -0.049999999999999989, 0.074999999999999178, 0.0, -0.025000000000000022, 0.074999999999999178, 0.0, 0.0, 0.074999999999999178, 0.0, 0.025000000000000022, 0.074999999999999178, 0.0, 0.050000000000000044, 0.074999999999999178, 0.0, 0.074999999999999956, 0.074999999999999178, 0.0, 0.099999999999999978, 0.074999999999999178, 0.0, 0.125, 0.074999999999999178, 0.0, 0.15000000000000002, 0.074999999999999178, 0.0, 0.17500000000000004, 0.074999999999999178, 0.0, 0.19999999999999996, 0.074999999999999178, 0.0, 0.22499999999999998, 0.074999999999999178, 0.0, 0.25, 0.074999999999999178, 0.0, 0.27500000000000002, 0.074999999999999178, 0.0, 0.30000000000000004, 0.074999999999999178, 0.0, 0.32499999999999996, 0.074999999999999178, 0.0, 0.34999999999999998, 0.074999999999999178, 0.0, 0.375, 0.074999999999999178, 0.0, 0.40000000000000002, 0.074999999999999178, 0.0, 0.42500000000000004, 0.074999999999999178, 0.0, 0.44999999999999996, 0.074999999999999178, 0.0, 0.47499999999999998, 0.074999999999999178, 0.0, 0.5, 0.074999999999999178, 0.0, -0.5, 0.099999999999999201, 0.0, -0.47499999999999998, 0.099999999999999201, 0.0, -0.45000000000000001, 0.099999999999999201, 0.0, -0.42499999999999999, 0.099999999999999201, 0.0, -0.40000000000000002, 0.099999999999999201, 0.0, -0.375, 0.099999999999999201, 0.0, -0.34999999999999998, 0.099999999999999201, 0.0, -0.32500000000000001, 0.099999999999999201, 0.0, -0.29999999999999999, 0.099999999999999201, 0.0, -0.27500000000000002, 0.099999999999999201, 0.0, -0.25, 0.099999999999999201, 0.0, -0.22499999999999998, 0.099999999999999201, 0.0, -0.20000000000000001, 0.099999999999999201, 0.0, -0.17499999999999999, 0.099999999999999201, 0.0, -0.15000000000000002, 0.099999999999999201, 0.0, -0.125, 0.099999999999999201, 0.0, -0.099999999999999978, 0.099999999999999201, 0.0, -0.075000000000000011, 0.099999999999999201, 0.0, -0.049999999999999989, 0.099999999999999201, 0.0, -0.025000000000000022, 0.099999999999999201, 0.0, 0.0, 0.099999999999999201, 0.0, 0.025000000000000022, 0.099999999999999201, 0.0, 0.050000000000000044, 0.099999999999999201, 0.0, 0.074999999999999956, 0.099999999999999201, 0.0, 0.099999999999999978, 0.099999999999999201, 0.0, 0.125, 0.099999999999999201, 0.0, 0.15000000000000002, 0.099999999999999201, 0.0, 0.17500000000000004, 0.099999999999999201, 0.0, 0.19999999999999996, 0.099999999999999201, 0.0, 0.22499999999999998, 0.099999999999999201, 0.0, 0.25, 0.099999999999999201, 0.0, 0.27500000000000002, 0.099999999999999201, 0.0, 0.30000000000000004, 0.099999999999999201, 0.0, 0.32499999999999996, 0.099999999999999201, 0.0, 0.34999999999999998, 0.099999999999999201, 0.0, 0.375, 0.099999999999999201, 0.0, 0.40000000000000002, 0.099999999999999201, 0.0, 0.42500000000000004, 0.099999999999999201, 0.0, 0.44999999999999996, 0.099999999999999201, 0.0, 0.47499999999999998, 0.099999999999999201, 0.0, 0.5, 0.099999999999999201, 0.0, -0.5, 0.12499999999999922, 0.0, -0.47499999999999998, 0.12499999999999922, 0.0, -0.45000000000000001, 0.12499999999999922, 0.0, -0.42499999999999999, 0.12499999999999922, 0.0, -0.40000000000000002, 0.12499999999999922, 0.0, -0.375, 0.12499999999999922, 0.0, -0.34999999999999998, 0.12499999999999922, 0.0, -0.32500000000000001, 0.12499999999999922, 0.0, -0.29999999999999999, 0.12499999999999922, 0.0, -0.27500000000000002, 0.12499999999999922, 0.0, -0.25, 0.12499999999999922, 0.0, -0.22499999999999998, 0.12499999999999922, 0.0, -0.20000000000000001, 0.12499999999999922, 0.0, -0.17499999999999999, 0.12499999999999922, 0.0, -0.15000000000000002, 0.12499999999999922, 0.0, -0.125, 0.12499999999999922, 0.0, -0.099999999999999978, 0.12499999999999922, 0.0, -0.075000000000000011, 0.12499999999999922, 0.0, -0.049999999999999989, 0.12499999999999922, 0.0, -0.025000000000000022, 0.12499999999999922, 0.0, 0.0, 0.12499999999999922, 0.0, 0.025000000000000022, 0.12499999999999922, 0.0, 0.050000000000000044, 0.12499999999999922, 0.0, 0.074999999999999956, 0.12499999999999922, 0.0, 0.099999999999999978, 0.12499999999999922, 0.0, 0.125, 0.12499999999999922, 0.0, 0.15000000000000002, 0.12499999999999922, 0.0, 0.17500000000000004, 0.12499999999999922, 0.0, 0.19999999999999996, 0.12499999999999922, 0.0, 0.22499999999999998, 0.12499999999999922, 0.0, 0.25, 0.12499999999999922, 0.0, 0.27500000000000002, 0.12499999999999922, 0.0, 0.30000000000000004, 0.12499999999999922, 0.0, 0.32499999999999996, 0.12499999999999922, 0.0, 0.34999999999999998, 0.12499999999999922, 0.0, 0.375, 0.12499999999999922, 0.0, 0.40000000000000002, 0.12499999999999922, 0.0, 0.42500000000000004, 0.12499999999999922, 0.0, 0.44999999999999996, 0.12499999999999922, 0.0, 0.47499999999999998, 0.12499999999999922, 0.0, 0.5, 0.12499999999999922, 0.0, -0.5, 0.14999999999999925, 0.0, -0.47499999999999998, 0.14999999999999925, 0.0, -0.45000000000000001, 0.14999999999999925, 0.0, -0.42499999999999999, 0.14999999999999925, 0.0, -0.40000000000000002, 0.14999999999999925, 0.0, -0.375, 0.14999999999999925, 0.0, -0.34999999999999998, 0.14999999999999925, 0.0, -0.32500000000000001, 0.14999999999999925, 0.0, -0.29999999999999999, 0.14999999999999925, 0.0, -0.27500000000000002, 0.14999999999999925, 0.0, -0.25, 0.14999999999999925, 0.0, -0.22499999999999998, 0.14999999999999925, 0.0, -0.20000000000000001, 0.14999999999999925, 0.0, -0.17499999999999999, 0.14999999999999925, 0.0, -0.15000000000000002, 0.14999999999999925, 0.0, -0.125, 0.14999999999999925, 0.0, -0.099999999999999978, 0.14999999999999925, 0.0, -0.075000000000000011, 0.14999999999999925, 0.0, -0.049999999999999989, 0.14999999999999925, 0.0, -0.025000000000000022, 0.14999999999999925, 0.0, 0.0, 0.14999999999999925, 0.0, 0.025000000000000022, 0.14999999999999925, 0.0, 0.050000000000000044, 0.14999999999999925, 0.0, 0.074999999999999956, 0.14999999999999925, 0.0, 0.099999999999999978, 0.14999999999999925, 0.0, 0.125, 0.14999999999999925, 0.0, 0.15000000000000002, 0.14999999999999925, 0.0, 0.17500000000000004, 0.14999999999999925, 0.0, 0.19999999999999996, 0.14999999999999925, 0.0, 0.22499999999999998, 0.14999999999999925, 0.0, 0.25, 0.14999999999999925, 0.0, 0.27500000000000002, 0.14999999999999925, 0.0, 0.30000000000000004, 0.14999999999999925, 0.0, 0.32499999999999996, 0.14999999999999925, 0.0, 0.34999999999999998, 0.14999999999999925, 0.0, 0.375, 0.14999999999999925, 0.0, 0.40000000000000002, 0.14999999999999925, 0.0, 0.42500000000000004, 0.14999999999999925, 0.0, 0.44999999999999996, 0.14999999999999925, 0.0, 0.47499999999999998, 0.14999999999999925, 0.0, 0.5, 0.14999999999999925, 0.0, -0.5, 0.17499999999999927, 0.0, -0.47499999999999998, 0.17499999999999927, 0.0, -0.45000000000000001, 0.17499999999999927, 0.0, -0.42499999999999999, 0.17499999999999927, 0.0, -0.40000000000000002, 0.17499999999999927, 0.0, -0.375, 0.17499999999999927, 0.0, -0.34999999999999998, 0.17499999999999927, 0.0, -0.32500000000000001, 0.17499999999999927, 0.0, -0.29999999999999999, 0.17499999999999927, 0.0, -0.27500000000000002, 0.17499999999999927, 0.0, -0.25, 0.17499999999999927, 0.0, -0.22499999999999998, 0.17499999999999927, 0.0, -0.20000000000000001, 0.17499999999999927, 0.0, -0.17499999999999999, 0.17499999999999927, 0.0, -0.15000000000000002, 0.17499999999999927, 0.0, -0.125, 0.17499999999999927, 0.0, -0.099999999999999978, 0.17499999999999927, 0.0, -0.075000000000000011, 0.17499999999999927, 0.0, -0.049999999999999989, 0.17499999999999927, 0.0, -0.025000000000000022, 0.17499999999999927, 0.0, 0.0, 0.17499999999999927, 0.0, 0.025000000000000022, 0.17499999999999927, 0.0, 0.050000000000000044, 0.17499999999999927, 0.0, 0.074999999999999956, 0.17499999999999927, 0.0, 0.099999999999999978, 0.17499999999999927, 0.0, 0.125, 0.17499999999999927, 0.0, 0.15000000000000002, 0.17499999999999927, 0.0, 0.17500000000000004, 0.17499999999999927, 0.0, 0.19999999999999996, 0.17499999999999927, 0.0, 0.22499999999999998, 0.17499999999999927, 0.0, 0.25, 0.17499999999999927, 0.0, 0.27500000000000002, 0.17499999999999927, 0.0, 0.30000000000000004, 0.17499999999999927, 0.0, 0.32499999999999996, 0.17499999999999927, 0.0, 0.34999999999999998, 0.17499999999999927, 0.0, 0.375, 0.17499999999999927, 0.0, 0.40000000000000002, 0.17499999999999927, 0.0, 0.42500000000000004, 0.17499999999999927, 0.0, 0.44999999999999996, 0.17499999999999927, 0.0, 0.47499999999999998, 0.17499999999999927, 0.0, 0.5, 0.17499999999999927, 0.0, -0.5, 0.19999999999999918, 0.0, -0.47499999999999998, 0.19999999999999918, 0.0, -0.45000000000000001, 0.19999999999999918, 0.0, -0.42499999999999999, 0.19999999999999918, 0.0, -0.40000000000000002, 0.19999999999999918, 0.0, -0.375, 0.19999999999999918, 0.0, -0.34999999999999998, 0.19999999999999918, 0.0, -0.32500000000000001, 0.19999999999999918, 0.0, -0.29999999999999999, 0.19999999999999918, 0.0, -0.27500000000000002, 0.19999999999999918, 0.0, -0.25, 0.19999999999999918, 0.0, -0.22499999999999998, 0.19999999999999918, 0.0, -0.20000000000000001, 0.19999999999999918, 0.0, -0.17499999999999999, 0.19999999999999918, 0.0, -0.15000000000000002, 0.19999999999999918, 0.0, -0.125, 0.19999999999999918, 0.0, -0.099999999999999978, 0.19999999999999918, 0.0, -0.075000000000000011, 0.19999999999999918, 0.0, -0.049999999999999989, 0.19999999999999918, 0.0, -0.025000000000000022, 0.19999999999999918, 0.0, 0.0, 0.19999999999999918, 0.0, 0.025000000000000022, 0.19999999999999918, 0.0, 0.050000000000000044, 0.19999999999999918, 0.0, 0.074999999999999956, 0.19999999999999918, 0.0, 0.099999999999999978, 0.19999999999999918, 0.0, 0.125, 0.19999999999999918, 0.0, 0.15000000000000002, 0.19999999999999918, 0.0, 0.17500000000000004, 0.19999999999999918, 0.0, 0.19999999999999996, 0.19999999999999918, 0.0, 0.22499999999999998, 0.19999999999999918, 0.0, 0.25, 0.19999999999999918, 0.0, 0.27500000000000002, 0.19999999999999918, 0.0, 0.30000000000000004, 0.19999999999999918, 0.0, 0.32499999999999996, 0.19999999999999918, 0.0, 0.34999999999999998, 0.19999999999999918, 0.0, 0.375, 0.19999999999999918, 0.0, 0.40000000000000002, 0.19999999999999918, 0.0, 0.42500000000000004, 0.19999999999999918, 0.0, 0.44999999999999996, 0.19999999999999918, 0.0, 0.47499999999999998, 0.19999999999999918, 0.0, 0.5, 0.19999999999999918, 0.0, -0.5, 0.2249999999999992, 0.0, -0.47499999999999998, 0.2249999999999992, 0.0, -0.45000000000000001, 0.2249999999999992, 0.0, -0.42499999999999999, 0.2249999999999992, 0.0, -0.40000000000000002, 0.2249999999999992, 0.0, -0.375, 0.2249999999999992, 0.0, -0.34999999999999998, 0.2249999999999992, 0.0, -0.32500000000000001, 0.2249999999999992, 0.0, -0.29999999999999999, 0.2249999999999992, 0.0, -0.27500000000000002, 0.2249999999999992, 0.0, -0.25, 0.2249999999999992, 0.0, -0.22499999999999998, 0.2249999999999992, 0.0, -0.20000000000000001, 0.2249999999999992, 0.0, -0.17499999999999999, 0.2249999999999992, 0.0, -0.15000000000000002, 0.2249999999999992, 0.0, -0.125, 0.2249999999999992, 0.0, -0.099999999999999978, 0.2249999999999992, 0.0, -0.075000000000000011, 0.2249999999999992, 0.0, -0.049999999999999989, 0.2249999999999992, 0.0, -0.025000000000000022, 0.2249999999999992, 0.0, 0.0, 0.2249999999999992, 0.0, 0.025000000000000022, 0.2249999999999992, 0.0, 0.050000000000000044, 0.2249999999999992, 0.0, 0.074999999999999956, 0.2249999999999992, 0.0, 0.099999999999999978, 0.2249999999999992, 0.0, 0.125, 0.2249999999999992, 0.0, 0.15000000000000002, 0.2249999999999992, 0.0, 0.17500000000000004, 0.2249999999999992, 0.0, 0.19999999999999996, 0.2249999999999992, 0.0, 0.22499999999999998, 0.2249999999999992, 0.0, 0.25, 0.2249999999999992, 0.0, 0.27500000000000002, 0.2249999999999992, 0.0, 0.30000000000000004, 0.2249999999999992, 0.0, 0.32499999999999996, 0.2249999999999992, 0.0, 0.34999999999999998, 0.2249999999999992, 0.0, 0.375, 0.2249999999999992, 0.0, 0.40000000000000002, 0.2249999999999992, 0.0, 0.42500000000000004, 0.2249999999999992, 0.0, 0.44999999999999996, 0.2249999999999992, 0.0, 0.47499999999999998, 0.2249999999999992, 0.0, 0.5, 0.2249999999999992, 0.0, -0.5, 0.24999999999999922, 0.0, -0.47499999999999998, 0.24999999999999922, 0.0, -0.45000000000000001, 0.24999999999999922, 0.0, -0.42499999999999999, 0.24999999999999922, 0.0, -0.40000000000000002, 0.24999999999999922, 0.0, -0.375, 0.24999999999999922, 0.0, -0.34999999999999998, 0.24999999999999922, 0.0, -0.32500000000000001, 0.24999999999999922, 0.0, -0.29999999999999999, 0.24999999999999922, 0.0, -0.27500000000000002, 0.24999999999999922, 0.0, -0.25, 0.24999999999999922, 0.0, -0.22499999999999998, 0.24999999999999922, 0.0, -0.20000000000000001, 0.24999999999999922, 0.0, -0.17499999999999999, 0.24999999999999922, 0.0, -0.15000000000000002, 0.24999999999999922, 0.0, -0.125, 0.24999999999999922, 0.0, -0.099999999999999978, 0.24999999999999922, 0.0, -0.075000000000000011, 0.24999999999999922, 0.0, -0.049999999999999989, 0.24999999999999922, 0.0, -0.025000000000000022, 0.24999999999999922, 0.0, 0.0, 0.24999999999999922, 0.0, 0.025000000000000022, 0.24999999999999922, 0.0, 0.050000000000000044, 0.24999999999999922, 0.0, 0.074999999999999956, 0.24999999999999922, 0.0, 0.099999999999999978, 0.24999999999999922, 0.0, 0.125, 0.24999999999999922, 0.0, 0.15000000000000002, 0.24999999999999922, 0.0, 0.17500000000000004, 0.24999999999999922, 0.0, 0.19999999999999996, 0.24999999999999922, 0.0, 0.22499999999999998, 0.24999999999999922, 0.0, 0.25, 0.24999999999999922, 0.0, 0.27500000000000002, 0.24999999999999922, 0.0, 0.30000000000000004, 0.24999999999999922, 0.0, 0.32499999999999996, 0.24999999999999922, 0.0, 0.34999999999999998, 0.24999999999999922, 0.0, 0.375, 0.24999999999999922, 0.0, 0.40000000000000002, 0.24999999999999922, 0.0, 0.42500000000000004, 0.24999999999999922, 0.0, 0.44999999999999996, 0.24999999999999922, 0.0, 0.47499999999999998, 0.24999999999999922, 0.0, 0.5, 0.24999999999999922, 0.0, -0.5, 0.27499999999999925, 0.0, -0.47499999999999998, 0.27499999999999925, 0.0, -0.45000000000000001, 0.27499999999999925, 0.0, -0.42499999999999999, 0.27499999999999925, 0.0, -0.40000000000000002, 0.27499999999999925, 0.0, -0.375, 0.27499999999999925, 0.0, -0.34999999999999998, 0.27499999999999925, 0.0, -0.32500000000000001, 0.27499999999999925, 0.0, -0.29999999999999999, 0.27499999999999925, 0.0, -0.27500000000000002, 0.27499999999999925, 0.0, -0.25, 0.27499999999999925, 0.0, -0.22499999999999998, 0.27499999999999925, 0.0, -0.20000000000000001, 0.27499999999999925, 0.0, -0.17499999999999999, 0.27499999999999925, 0.0, -0.15000000000000002, 0.27499999999999925, 0.0, -0.125, 0.27499999999999925, 0.0, -0.099999999999999978, 0.27499999999999925, 0.0, -0.075000000000000011, 0.27499999999999925, 0.0, -0.049999999999999989, 0.27499999999999925, 0.0, -0.025000000000000022, 0.27499999999999925, 0.0, 0.0, 0.27499999999999925, 0.0, 0.025000000000000022, 0.27499999999999925, 0.0, 0.050000000000000044, 0.27499999999999925, 0.0, 0.074999999999999956, 0.27499999999999925, 0.0, 0.099999999999999978, 0.27499999999999925, 0.0, 0.125, 0.27499999999999925, 0.0, 0.15000000000000002, 0.27499999999999925, 0.0, 0.17500000000000004, 0.27499999999999925, 0.0, 0.19999999999999996, 0.27499999999999925, 0.0, 0.22499999999999998, 0.27499999999999925, 0.0, 0.25, 0.27499999999999925, 0.0, 0.27500000000000002, 0.27499999999999925, 0.0, 0.30000000000000004, 0.27499999999999925, 0.0, 0.32499999999999996, 0.27499999999999925, 0.0, 0.34999999999999998, 0.27499999999999925, 0.0, 0.375, 0.27499999999999925, 0.0, 0.40000000000000002, 0.27499999999999925, 0.0, 0.42500000000000004, 0.27499999999999925, 0.0, 0.44999999999999996, 0.27499999999999925, 0.0, 0.47499999999999998, 0.27499999999999925, 0.0, 0.5, 0.27499999999999925, 0.0, -0.5, 0.29999999999999927, 0.0, -0.47499999999999998, 0.29999999999999927, 0.0, -0.45000000000000001, 0.29999999999999927, 0.0, -0.42499999999999999, 0.29999999999999927, 0.0, -0.40000000000000002, 0.29999999999999927, 0.0, -0.375, 0.29999999999999927, 0.0, -0.34999999999999998, 0.29999999999999927, 0.0, -0.32500000000000001, 0.29999999999999927, 0.0, -0.29999999999999999, 0.29999999999999927, 0.0, -0.27500000000000002, 0.29999999999999927, 0.0, -0.25, 0.29999999999999927, 0.0, -0.22499999999999998, 0.29999999999999927, 0.0, -0.20000000000000001, 0.29999999999999927, 0.0, -0.17499999999999999, 0.29999999999999927, 0.0, -0.15000000000000002, 0.29999999999999927, 0.0, -0.125, 0.29999999999999927, 0.0, -0.099999999999999978, 0.29999999999999927, 0.0, -0.075000000000000011, 0.29999999999999927, 0.0, -0.049999999999999989, 0.29999999999999927, 0.0, -0.025000000000000022, 0.29999999999999927, 0.0, 0.0, 0.29999999999999927, 0.0, 0.025000000000000022, 0.29999999999999927, 0.0, 0.050000000000000044, 0.29999999999999927, 0.0, 0.074999999999999956, 0.29999999999999927, 0.0, 0.099999999999999978, 0.29999999999999927, 0.0, 0.125, 0.29999999999999927, 0.0, 0.15000000000000002, 0.29999999999999927, 0.0, 0.17500000000000004, 0.29999999999999927, 0.0, 0.19999999999999996, 0.29999999999999927, 0.0, 0.22499999999999998, 0.29999999999999927, 0.0, 0.25, 0.29999999999999927, 0.0, 0.27500000000000002, 0.29999999999999927, 0.0, 0.30000000000000004, 0.29999999999999927, 0.0, 0.32499999999999996, 0.29999999999999927, 0.0, 0.34999999999999998, 0.29999999999999927, 0.0, 0.375, 0.29999999999999927, 0.0, 0.40000000000000002, 0.29999999999999927, 0.0, 0.42500000000000004, 0.29999999999999927, 0.0, 0.44999999999999996, 0.29999999999999927, 0.0, 0.47499999999999998, 0.29999999999999927, 0.0, 0.5, 0.29999999999999927, 0.0, -0.5, 0.32499999999999918, 0.0, -0.47499999999999998, 0.32499999999999918, 0.0, -0.45000000000000001, 0.32499999999999918, 0.0, -0.42499999999999999, 0.32499999999999918, 0.0, -0.40000000000000002, 0.32499999999999918, 0.0, -0.375, 0.32499999999999918, 0.0, -0.34999999999999998, 0.32499999999999918, 0.0, -0.32500000000000001, 0.32499999999999918, 0.0, -0.29999999999999999, 0.32499999999999918, 0.0, -0.27500000000000002, 0.32499999999999918, 0.0, -0.25, 0.32499999999999918, 0.0, -0.22499999999999998, 0.32499999999999918, 0.0, -0.20000000000000001, 0.32499999999999918, 0.0, -0.17499999999999999, 0.32499999999999918, 0.0, -0.15000000000000002, 0.32499999999999918, 0.0, -0.125, 0.32499999999999918, 0.0, -0.099999999999999978, 0.32499999999999918, 0.0, -0.075000000000000011, 0.32499999999999918, 0.0, -0.049999999999999989, 0.32499999999999918, 0.0, -0.025000000000000022, 0.32499999999999918, 0.0, 0.0, 0.32499999999999918, 0.0, 0.025000000000000022, 0.32499999999999918, 0.0, 0.050000000000000044, 0.32499999999999918, 0.0, 0.074999999999999956, 0.32499999999999918, 0.0, 0.099999999999999978, 0.32499999999999918, 0.0, 0.125, 0.32499999999999918, 0.0, 0.15000000000000002, 0.32499999999999918, 0.0, 0.17500000000000004, 0.32499999999999918, 0.0, 0.19999999999999996, 0.32499999999999918, 0.0, 0.22499999999999998, 0.32499999999999918, 0.0, 0.25, 0.32499999999999918, 0.0, 0.27500000000000002, 0.32499999999999918, 0.0, 0.30000000000000004, 0.32499999999999918, 0.0, 0.32499999999999996, 0.32499999999999918, 0.0, 0.34999999999999998, 0.32499999999999918, 0.0, 0.375, 0.32499999999999918, 0.0, 0.40000000000000002, 0.32499999999999918, 0.0, 0.42500000000000004, 0.32499999999999918, 0.0, 0.44999999999999996, 0.32499999999999918, 0.0, 0.47499999999999998, 0.32499999999999918, 0.0, 0.5, 0.32499999999999918, 0.0, -0.5, 0.3499999999999992, 0.0, -0.47499999999999998, 0.3499999999999992, 0.0, -0.45000000000000001, 0.3499999999999992, 0.0, -0.42499999999999999, 0.3499999999999992, 0.0, -0.40000000000000002, 0.3499999999999992, 0.0, -0.375, 0.3499999999999992, 0.0, -0.34999999999999998, 0.3499999999999992, 0.0, -0.32500000000000001, 0.3499999999999992, 0.0, -0.29999999999999999, 0.3499999999999992, 0.0, -0.27500000000000002, 0.3499999999999992, 0.0, -0.25, 0.3499999999999992, 0.0, -0.22499999999999998, 0.3499999999999992, 0.0, -0.20000000000000001, 0.3499999999999992, 0.0, -0.17499999999999999, 0.3499999999999992, 0.0, -0.15000000000000002, 0.3499999999999992, 0.0, -0.125, 0.3499999999999992, 0.0, -0.099999999999999978, 0.3499999999999992, 0.0, -0.075000000000000011, 0.3499999999999992, 0.0, -0.049999999999999989, 0.3499999999999992, 0.0, -0.025000000000000022, 0.3499999999999992, 0.0, 0.0, 0.3499999999999992, 0.0, 0.025000000000000022, 0.3499999999999992, 0.0, 0.050000000000000044, 0.3499999999999992, 0.0, 0.074999999999999956, 0.3499999999999992, 0.0, 0.099999999999999978, 0.3499999999999992, 0.0, 0.125, 0.3499999999999992, 0.0, 0.15000000000000002, 0.3499999999999992, 0.0, 0.17500000000000004, 0.3499999999999992, 0.0, 0.19999999999999996, 0.3499999999999992, 0.0, 0.22499999999999998, 0.3499999999999992, 0.0, 0.25, 0.3499999999999992, 0.0, 0.27500000000000002, 0.3499999999999992, 0.0, 0.30000000000000004, 0.3499999999999992, 0.0, 0.32499999999999996, 0.3499999999999992, 0.0, 0.34999999999999998, 0.3499999999999992, 0.0, 0.375, 0.3499999999999992, 0.0, 0.40000000000000002, 0.3499999999999992, 0.0, 0.42500000000000004, 0.3499999999999992, 0.0, 0.44999999999999996, 0.3499999999999992, 0.0, 0.47499999999999998, 0.3499999999999992, 0.0, 0.5, 0.3499999999999992, 0.0, -0.5, 0.37499999999999922, 0.0, -0.47499999999999998, 0.37499999999999922, 0.0, -0.45000000000000001, 0.37499999999999922, 0.0, -0.42499999999999999, 0.37499999999999922, 0.0, -0.40000000000000002, 0.37499999999999922, 0.0, -0.375, 0.37499999999999922, 0.0, -0.34999999999999998, 0.37499999999999922, 0.0, -0.32500000000000001, 0.37499999999999922, 0.0, -0.29999999999999999, 0.37499999999999922, 0.0, -0.27500000000000002, 0.37499999999999922, 0.0, -0.25, 0.37499999999999922, 0.0, -0.22499999999999998, 0.37499999999999922, 0.0, -0.20000000000000001, 0.37499999999999922, 0.0, -0.17499999999999999, 0.37499999999999922, 0.0, -0.15000000000000002, 0.37499999999999922, 0.0, -0.125, 0.37499999999999922, 0.0, -0.099999999999999978, 0.37499999999999922, 0.0, -0.075000000000000011, 0.37499999999999922, 0.0, -0.049999999999999989, 0.37499999999999922, 0.0, -0.025000000000000022, 0.37499999999999922, 0.0, 0.0, 0.37499999999999922, 0.0, 0.025000000000000022, 0.37499999999999922, 0.0, 0.050000000000000044, 0.37499999999999922, 0.0, 0.074999999999999956, 0.37499999999999922, 0.0, 0.099999999999999978, 0.37499999999999922, 0.0, 0.125, 0.37499999999999922, 0.0, 0.15000000000000002, 0.37499999999999922, 0.0, 0.17500000000000004, 0.37499999999999922, 0.0, 0.19999999999999996, 0.37499999999999922, 0.0, 0.22499999999999998, 0.37499999999999922, 0.0, 0.25, 0.37499999999999922, 0.0, 0.27500000000000002, 0.37499999999999922, 0.0, 0.30000000000000004, 0.37499999999999922, 0.0, 0.32499999999999996, 0.37499999999999922, 0.0, 0.34999999999999998, 0.37499999999999922, 0.0, 0.375, 0.37499999999999922, 0.0, 0.40000000000000002, 0.37499999999999922, 0.0, 0.42500000000000004, 0.37499999999999922, 0.0, 0.44999999999999996, 0.37499999999999922, 0.0, 0.47499999999999998, 0.37499999999999922, 0.0, 0.5, 0.37499999999999922, 0.0, -0.5, 0.39999999999999925, 0.0, -0.47499999999999998, 0.39999999999999925, 0.0, -0.45000000000000001, 0.39999999999999925, 0.0, -0.42499999999999999, 0.39999999999999925, 0.0, -0.40000000000000002, 0.39999999999999925, 0.0, -0.375, 0.39999999999999925, 0.0, -0.34999999999999998, 0.39999999999999925, 0.0, -0.32500000000000001, 0.39999999999999925, 0.0, -0.29999999999999999, 0.39999999999999925, 0.0, -0.27500000000000002, 0.39999999999999925, 0.0, -0.25, 0.39999999999999925, 0.0, -0.22499999999999998, 0.39999999999999925, 0.0, -0.20000000000000001, 0.39999999999999925, 0.0, -0.17499999999999999, 0.39999999999999925, 0.0, -0.15000000000000002, 0.39999999999999925, 0.0, -0.125, 0.39999999999999925, 0.0, -0.099999999999999978, 0.39999999999999925, 0.0, -0.075000000000000011, 0.39999999999999925, 0.0, -0.049999999999999989, 0.39999999999999925, 0.0, -0.025000000000000022, 0.39999999999999925, 0.0, 0.0, 0.39999999999999925, 0.0, 0.025000000000000022, 0.39999999999999925, 0.0, 0.050000000000000044, 0.39999999999999925, 0.0, 0.074999999999999956, 0.39999999999999925, 0.0, 0.099999999999999978, 0.39999999999999925, 0.0, 0.125, 0.39999999999999925, 0.0, 0.15000000000000002, 0.39999999999999925, 0.0, 0.17500000000000004, 0.39999999999999925, 0.0, 0.19999999999999996, 0.39999999999999925, 0.0, 0.22499999999999998, 0.39999999999999925, 0.0, 0.25, 0.39999999999999925, 0.0, 0.27500000000000002, 0.39999999999999925, 0.0, 0.30000000000000004, 0.39999999999999925, 0.0, 0.32499999999999996, 0.39999999999999925, 0.0, 0.34999999999999998, 0.39999999999999925, 0.0, 0.375, 0.39999999999999925, 0.0, 0.40000000000000002, 0.39999999999999925, 0.0, 0.42500000000000004, 0.39999999999999925, 0.0, 0.44999999999999996, 0.39999999999999925, 0.0, 0.47499999999999998, 0.39999999999999925, 0.0, 0.5, 0.39999999999999925, 0.0, -0.5, 0.42499999999999927, 0.0, -0.47499999999999998, 0.42499999999999927, 0.0, -0.45000000000000001, 0.42499999999999927, 0.0, -0.42499999999999999, 0.42499999999999927, 0.0, -0.40000000000000002, 0.42499999999999927, 0.0, -0.375, 0.42499999999999927, 0.0, -0.34999999999999998, 0.42499999999999927, 0.0, -0.32500000000000001, 0.42499999999999927, 0.0, -0.29999999999999999, 0.42499999999999927, 0.0, -0.27500000000000002, 0.42499999999999927, 0.0, -0.25, 0.42499999999999927, 0.0, -0.22499999999999998, 0.42499999999999927, 0.0, -0.20000000000000001, 0.42499999999999927, 0.0, -0.17499999999999999, 0.42499999999999927, 0.0, -0.15000000000000002, 0.42499999999999927, 0.0, -0.125, 0.42499999999999927, 0.0, -0.099999999999999978, 0.42499999999999927, 0.0, -0.075000000000000011, 0.42499999999999927, 0.0, -0.049999999999999989, 0.42499999999999927, 0.0, -0.025000000000000022, 0.42499999999999927, 0.0, 0.0, 0.42499999999999927, 0.0, 0.025000000000000022, 0.42499999999999927, 0.0, 0.050000000000000044, 0.42499999999999927, 0.0, 0.074999999999999956, 0.42499999999999927, 0.0, 0.099999999999999978, 0.42499999999999927, 0.0, 0.125, 0.42499999999999927, 0.0, 0.15000000000000002, 0.42499999999999927, 0.0, 0.17500000000000004, 0.42499999999999927, 0.0, 0.19999999999999996, 0.42499999999999927, 0.0, 0.22499999999999998, 0.42499999999999927, 0.0, 0.25, 0.42499999999999927, 0.0, 0.27500000000000002, 0.42499999999999927, 0.0, 0.30000000000000004, 0.42499999999999927, 0.0, 0.32499999999999996, 0.42499999999999927, 0.0, 0.34999999999999998, 0.42499999999999927, 0.0, 0.375, 0.42499999999999927, 0.0, 0.40000000000000002, 0.42499999999999927, 0.0, 0.42500000000000004, 0.42499999999999927, 0.0, 0.44999999999999996, 0.42499999999999927, 0.0, 0.47499999999999998, 0.42499999999999927, 0.0, 0.5, 0.42499999999999927, 0.0, -0.5, 0.44999999999999918, 0.0, -0.47499999999999998, 0.44999999999999918, 0.0, -0.45000000000000001, 0.44999999999999918, 0.0, -0.42499999999999999, 0.44999999999999918, 0.0, -0.40000000000000002, 0.44999999999999918, 0.0, -0.375, 0.44999999999999918, 0.0, -0.34999999999999998, 0.44999999999999918, 0.0, -0.32500000000000001, 0.44999999999999918, 0.0, -0.29999999999999999, 0.44999999999999918, 0.0, -0.27500000000000002, 0.44999999999999918, 0.0, -0.25, 0.44999999999999918, 0.0, -0.22499999999999998, 0.44999999999999918, 0.0, -0.20000000000000001, 0.44999999999999918, 0.0, -0.17499999999999999, 0.44999999999999918, 0.0, -0.15000000000000002, 0.44999999999999918, 0.0, -0.125, 0.44999999999999918, 0.0, -0.099999999999999978, 0.44999999999999918, 0.0, -0.075000000000000011, 0.44999999999999918, 0.0, -0.049999999999999989, 0.44999999999999918, 0.0, -0.025000000000000022, 0.44999999999999918, 0.0, 0.0, 0.44999999999999918, 0.0, 0.025000000000000022, 0.44999999999999918, 0.0, 0.050000000000000044, 0.44999999999999918, 0.0, 0.074999999999999956, 0.44999999999999918, 0.0, 0.099999999999999978, 0.44999999999999918, 0.0, 0.125, 0.44999999999999918, 0.0, 0.15000000000000002, 0.44999999999999918, 0.0, 0.17500000000000004, 0.44999999999999918, 0.0, 0.19999999999999996, 0.44999999999999918, 0.0, 0.22499999999999998, 0.44999999999999918, 0.0, 0.25, 0.44999999999999918, 0.0, 0.27500000000000002, 0.44999999999999918, 0.0, 0.30000000000000004, 0.44999999999999918, 0.0, 0.32499999999999996, 0.44999999999999918, 0.0, 0.34999999999999998, 0.44999999999999918, 0.0, 0.375, 0.44999999999999918, 0.0, 0.40000000000000002, 0.44999999999999918, 0.0, 0.42500000000000004, 0.44999999999999918, 0.0, 0.44999999999999996, 0.44999999999999918, 0.0, 0.47499999999999998, 0.44999999999999918, 0.0, 0.5, 0.44999999999999918, 0.0, -0.5, 0.4749999999999992, 0.0, -0.47499999999999998, 0.4749999999999992, 0.0, -0.45000000000000001, 0.4749999999999992, 0.0, -0.42499999999999999, 0.4749999999999992, 0.0, -0.40000000000000002, 0.4749999999999992, 0.0, -0.375, 0.4749999999999992, 0.0, -0.34999999999999998, 0.4749999999999992, 0.0, -0.32500000000000001, 0.4749999999999992, 0.0, -0.29999999999999999, 0.4749999999999992, 0.0, -0.27500000000000002, 0.4749999999999992, 0.0, -0.25, 0.4749999999999992, 0.0, -0.22499999999999998, 0.4749999999999992, 0.0, -0.20000000000000001, 0.4749999999999992, 0.0, -0.17499999999999999, 0.4749999999999992, 0.0, -0.15000000000000002, 0.4749999999999992, 0.0, -0.125, 0.4749999999999992, 0.0, -0.099999999999999978, 0.4749999999999992, 0.0, -0.075000000000000011, 0.4749999999999992, 0.0, -0.049999999999999989, 0.4749999999999992, 0.0, -0.025000000000000022, 0.4749999999999992, 0.0, 0.0, 0.4749999999999992, 0.0, 0.025000000000000022, 0.4749999999999992, 0.0, 0.050000000000000044, 0.4749999999999992, 0.0, 0.074999999999999956, 0.4749999999999992, 0.0, 0.099999999999999978, 0.4749999999999992, 0.0, 0.125, 0.4749999999999992, 0.0, 0.15000000000000002, 0.4749999999999992, 0.0, 0.17500000000000004, 0.4749999999999992, 0.0, 0.19999999999999996, 0.4749999999999992, 0.0, 0.22499999999999998, 0.4749999999999992, 0.0, 0.25, 0.4749999999999992, 0.0, 0.27500000000000002, 0.4749999999999992, 0.0, 0.30000000000000004, 0.4749999999999992, 0.0, 0.32499999999999996, 0.4749999999999992, 0.0, 0.34999999999999998, 0.4749999999999992, 0.0, 0.375, 0.4749999999999992, 0.0, 0.40000000000000002, 0.4749999999999992, 0.0, 0.42500000000000004, 0.4749999999999992, 0.0, 0.44999999999999996, 0.4749999999999992, 0.0, 0.47499999999999998, 0.4749999999999992, 0.0, 0.5, 0.4749999999999992, 0.0, -0.5, 0.49999999999999922, 0.0, -0.47499999999999998, 0.49999999999999922, 0.0, -0.45000000000000001, 0.49999999999999922, 0.0, -0.42499999999999999, 0.49999999999999922, 0.0, -0.40000000000000002, 0.49999999999999922, 0.0, -0.375, 0.49999999999999922, 0.0, -0.34999999999999998, 0.49999999999999922, 0.0, -0.32500000000000001, 0.49999999999999922, 0.0, -0.29999999999999999, 0.49999999999999922, 0.0, -0.27500000000000002, 0.49999999999999922, 0.0, -0.25, 0.49999999999999922, 0.0, -0.22499999999999998, 0.49999999999999922, 0.0, -0.20000000000000001, 0.49999999999999922, 0.0, -0.17499999999999999, 0.49999999999999922, 0.0, -0.15000000000000002, 0.49999999999999922, 0.0, -0.125, 0.49999999999999922, 0.0, -0.099999999999999978, 0.49999999999999922, 0.0, -0.075000000000000011, 0.49999999999999922, 0.0, -0.049999999999999989, 0.49999999999999922, 0.0, -0.025000000000000022, 0.49999999999999922, 0.0, 0.0, 0.49999999999999922, 0.0, 0.025000000000000022, 0.49999999999999922, 0.0, 0.050000000000000044, 0.49999999999999922, 0.0, 0.074999999999999956, 0.49999999999999922, 0.0, 0.099999999999999978, 0.49999999999999922, 0.0, 0.125, 0.49999999999999922, 0.0, 0.15000000000000002, 0.49999999999999922, 0.0, 0.17500000000000004, 0.49999999999999922, 0.0, 0.19999999999999996, 0.49999999999999922, 0.0, 0.22499999999999998, 0.49999999999999922, 0.0, 0.25, 0.49999999999999922, 0.0, 0.27500000000000002, 0.49999999999999922, 0.0, 0.30000000000000004, 0.49999999999999922, 0.0, 0.32499999999999996, 0.49999999999999922, 0.0, 0.34999999999999998, 0.49999999999999922, 0.0, 0.375, 0.49999999999999922, 0.0, 0.40000000000000002, 0.49999999999999922, 0.0, 0.42500000000000004, 0.49999999999999922, 0.0, 0.44999999999999996, 0.49999999999999922, 0.0, 0.47499999999999998, 0.49999999999999922, 0.0, 0.5, 0.49999999999999922, 0.0], \"metadata\": {\"formatVersion\": 3}}, \"09184b68-4873-4647-afdc-3b7ceccb67f4\", false, 1.0, true, 500);\n</script>\n\n\n\n\n"}, "metadata": {}}], "metadata": {"collapsed": false, "trusted": true}}, {"execution_count": null, "cell_type": "code", "source": "", "outputs": [], "metadata": {"collapsed": true, "trusted": true}}], "nbformat": 4, "metadata": {"kernelspec": {"display_name": "Python 2", "name": "python2", "language": "python"}, "language_info": {"mimetype": "text/x-python", "nbconvert_exporter": "python", "version": "2.7.3", "name": "python", "file_extension": ".py", "pygments_lexer": "ipython2", "codemirror_mode": {"version": 2, "name": "ipython"}}}} | gpl-3.0 |
michrawson/SVM_Implicit_Surface_Vector_Fields | Fourier.ipynb | 2 | 4824 | {
"cells": [
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false,
"scrolled": false
},
"outputs": [],
"source": [
"% matplotlib inline\n",
"\n",
"import numpy as np\n",
"from numpy import linalg, random, ones, zeros, matrix, eye, dot\n",
"from numpy.linalg import norm, cholesky, inv\n",
"from sklearn.cross_validation import train_test_split\n",
"import mosek\n",
"import math\n",
"from mpl_toolkits.mplot3d import Axes3D\n",
"import matplotlib.pyplot as plt\n",
"import sys\n",
"import time\n",
"import scipy\n",
"from collections import namedtuple\n",
"\n",
"v = .00001\n",
"delta = 0.01\n",
"sigma = 1\n",
"initial_rho = 1\n",
"max_iter = 100\n",
"initial_step_size = .1\n",
"timer_thresh = .1\n",
"ep = .0001\n",
"points_count = 1000\n",
"points_std_from_surface = 0.0001\n",
"# D = 10000\n",
"\n",
"def kernel(x1, x2):\n",
" return math.exp(-1 * math.pow(norm(x1 - x2), 2\n",
" ) / (2 * math.pow(sigma, 2)))\n",
"\n",
"# def kernel_vect(x_list, x2):\n",
"# return np.exp(-1 * np.power(norm(x_list - x2, axis=1), 2) / (2 * math.pow(sigma, 2)))\n",
"\n",
"def z(x1,w,b):\n",
"# w = random.normal(0, 1.0/sigma, size=(D,len(x1)))\n",
"# b = random.uniform(0,2*np.pi,size=D)\n",
" return math.sqrt(2.0/D) * np.cos(np.dot(w, x1) + b)\n",
"\n",
"def get_K():\n",
" start = time.time()\n",
"\n",
" w = random.normal(0, 1.0/sigma, size=(D*len(g_x)*len(g_x),len(g_x[0])))\n",
" b = random.uniform(0,2*np.pi,size=D*len(g_x)*len(g_x))\n",
"\n",
" K = np.zeros((len(g_x), len(g_x)))\n",
" temp_error = 0.0\n",
" counter=0\n",
" for i in range(len(g_x)):\n",
"# K[i, :] = kernel_vect(g_x, g_x[i])\n",
" for j in range(len(g_x)):\n",
"# temp_error += abs(kernel(g_x[j], g_x[i]) - np.dot(z(g_x[j],w[counter,:],b[counter]),\n",
"# z(g_x[i],w[counter,:],b[counter])))\n",
" temp_error += abs(kernel(g_x[j], g_x[i]) - np.dot(\n",
" math.sqrt(2.0/D) * np.cos(np.dot(w[counter,:], g_x[i]) + b[counter]),\n",
" math.sqrt(2.0/D) * np.cos(np.dot(w[counter,:], g_x[j]) + b[counter]))\n",
" )\n",
" counter += 1\n",
"# K[i, j] = kernel(g_x[j], g_x[i])\n",
" \n",
" fourier_error.append(temp_error/(len(g_x)**2))\n",
" \n",
" end = time.time()\n",
" if end - start > timer_thresh:\n",
" print 'get_K:', end - start, 'sec'\n",
" return K\n",
"\n",
"def get_data_points():\n",
" start = time.time()\n",
" points = random.random((points_count, 2)) * 2 * np.pi\n",
"\n",
" x = np.zeros((points_count, 3))\n",
" for p in range(points_count):\n",
" if points_std_from_surface > 0:\n",
" r = random.normal(loc=1, scale=points_std_from_surface)\n",
" else:\n",
" r = 1\n",
" z_cord = r * np.sin(points[p][1])\n",
"\n",
" r_temp = r * np.cos(points[p][1])\n",
" y_cord = r_temp * np.sin(points[p][0])\n",
" x_cord = r_temp * np.cos(points[p][0])\n",
"\n",
" x[p] = np.asarray([x_cord, y_cord, z_cord])\n",
"\n",
" end = time.time()\n",
" if end - start > timer_thresh:\n",
" print 'get_data_points:', end - start, 'sec'\n",
" return x\n",
"\n",
"\n",
"g_x = get_data_points()\n",
"\n",
"fig = plt.figure(figsize=(10, 12))\n",
"ax = fig.add_subplot(111, projection='3d')\n",
"ax.scatter(g_x[:, 0], g_x[:, 1], g_x[:, 2])\n",
"plt.show()\n",
"\n",
"D=1.\n",
"fourier_error=[]\n",
"for i in range(14):\n",
" print 'D',D\n",
" g_K = get_K()\n",
" print 'fourier_error',fourier_error[-1]\n",
" D *= 2\n",
"print fourier_error\n",
"\n"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": []
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 2",
"language": "python",
"name": "python2"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 2
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython2",
"version": "2.7.10"
}
},
"nbformat": 4,
"nbformat_minor": 0
}
| mit |
ng-nicolas/ml-small-projects | notebooks/Sentiment-Analysis-Kaggle-Rotten-Tomatoes.ipynb | 1 | 11687 | {
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Import train and test data."
]
},
{
"cell_type": "code",
"execution_count": 1,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"(3554, 2)"
]
},
"execution_count": 1,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"import urllib\n",
"import pandas as pd\n",
"#train_data_url = \"http://www.inf.utfsm.cl/~jnancu/stanford-subset/polarity.train\"\n",
"#test_data_url = \"http://www.inf.utfsm.cl/~jnancu/stanford-subset/polarity.dev\"\n",
"#train_data_f = urllib.urlretrieve(train_data_url, \"train_data.csv\")\n",
"#test_data_f = urllib.urlretrieve(test_data_url, \"test_data.csv\")\n",
"ftr = open(\"train_data.csv\", \"r\")\n",
"fts = open(\"test_data.csv\", \"r\")\n",
"rows = [line.split(\" \",1) for line in ftr.readlines()]\n",
"train_df = pd.DataFrame(rows, columns=['Sentiment','Text'])\n",
"train_df['Sentiment'] = pd.to_numeric(train_df['Sentiment'])\n",
"rows = [line.split(\" \",1) for line in fts.readlines()]\n",
"test_df = pd.DataFrame(rows, columns=['Sentiment','Text'])\n",
"test_df['Sentiment'] = pd.to_numeric(test_df['Sentiment'])\n",
"train_df.shape\n",
"test_df.shape"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Index([u'Sentiment', u'Text'], dtype='object')\n"
]
},
{
"data": {
"text/html": [
"<div>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>Sentiment</th>\n",
" <th>Text</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>-1</td>\n",
" <td>everything's serious , poetic , earnest and --...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>-1</td>\n",
" <td>narratively , trouble every day is a plodding ...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>1</td>\n",
" <td>a truly wonderful tale combined with stunning ...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>1</td>\n",
" <td>jason patric and ray liotta make for one splen...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>-1</td>\n",
" <td>haneke keeps us at arm's length . guided more ...</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" Sentiment Text\n",
"0 -1 everything's serious , poetic , earnest and --...\n",
"1 -1 narratively , trouble every day is a plodding ...\n",
"2 1 a truly wonderful tale combined with stunning ...\n",
"3 1 jason patric and ray liotta make for one splen...\n",
"4 -1 haneke keeps us at arm's length . guided more ..."
]
},
"execution_count": 2,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# Dataset info.\n",
"print train_df.columns\n",
"train_df[0:5]\n"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"# Text processing.\n",
"# Lematization function and stopwords filter.\n",
"import re, time\n",
"from nltk.corpus import stopwords\n",
"from nltk import WordNetLemmatizer, word_tokenize\n",
"from nltk.stem.porter import PorterStemmer\n",
"\n",
"def word_extractor(text):\n",
" wordlemmatizer = WordNetLemmatizer()\n",
" commonwords = stopwords.words('english')\n",
" text = re.sub(r'([a-z])\\1+', r'\\1\\1',text)#substitute multiple letter by two\n",
" words = \"\"\n",
" wordtokens = [ wordlemmatizer.lemmatize(word.lower()) \\\n",
" for word in word_tokenize(text.decode('utf-8','ignore')) ]\n",
" for word in wordtokens:\n",
" if word not in commonwords:\n",
" words+=\" \"+word\n",
" return words"
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
" love eat cake\n",
" love eating cake\n",
" loved eating cake\n",
" love eating cake\n",
" n't love eating cake\n"
]
}
],
"source": [
"print word_extractor(\"I love to eat cake\")\n",
"print word_extractor(\"I love eating cake\")\n",
"print word_extractor(\"I loved eating the cake\")\n",
"print word_extractor(\"I do not love eating cake\")\n",
"print word_extractor(\"I don't love eating cake\")"
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"(3554, 9663)\n",
"6 10\n",
"4 100\n",
"2 101\n"
]
}
],
"source": [
"import numpy as np\n",
"from sklearn.feature_extraction.text import CountVectorizer\n",
"\n",
"# Text cleaning.\n",
"texts_train = [word_extractor(text) for text in train_df.Text]\n",
"texts_test = [word_extractor(text) for text in test_df.Text]\n",
"\n",
"# Text vectorization.\n",
"vectorizer = CountVectorizer(ngram_range=(1, 1), binary='False')\n",
"vectorizer.fit(np.asarray(texts_train))\n",
"\n",
"# Convert texts to vector representation.\n",
"features_train = vectorizer.transform(texts_train)\n",
"features_test = vectorizer.transform(texts_test)\n",
"\n",
"# Change labels to 0 or 1 from -1 and 1.\n",
"labels_train = np.asarray((train_df.Sentiment.astype(float)+1)/2.0)\n",
"labels_test = np.asarray((test_df.Sentiment.astype(float)+1)/2.0)\n",
"\n",
"print features_train.shape\n",
"\n",
"vocab = vectorizer.get_feature_names()\n",
"dist = list(np.array(features_train.sum(axis=0)).reshape(-1,))\n",
"\n",
"for tag, count in zip(vocab, dist)[0:3]:\n",
" print count, tag"
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"from sklearn.metrics import classification_report\n",
"def score_the_model(model,x,y,xt,yt,text):\n",
" acc_tr = model.score(x,y)\n",
" acc_test = model.score(xt[:-1],yt[:-1])\n",
" print \"Training Accuracy %s: %f\"%(text,acc_tr)\n",
" print \"Test Accuracy %s: %f\"%(text,acc_test)\n",
" print \"Detailed Analysis Testing Results ...\"\n",
" print(classification_report(yt, model.predict(xt), target_names=['+','-']))"
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Usando C= 0.100000\n",
"Training Accuracy LOGISTIC: 0.892234\n",
"Test Accuracy LOGISTIC: 0.719111\n",
"Detailed Analysis Testing Results ...\n",
" precision recall f1-score support\n",
"\n",
" + 0.72 0.72 0.72 1803\n",
" - 0.72 0.71 0.71 1751\n",
"\n",
"avg / total 0.72 0.72 0.72 3554\n",
"\n"
]
}
],
"source": [
"from sklearn.linear_model import LogisticRegression\n",
"def do_LOGIT(x,y,xt,yt):\n",
" start_t = time.time()\n",
" #Cs = [0.01,0.1,10,100,1000]\n",
" Cs = [0.1]\n",
" for C in Cs:\n",
" print \"Usando C= %f\"%C\n",
" model = LogisticRegression(penalty='l2',C=C)\n",
" model = model.fit(x, y)\n",
" score_the_model(model,x,y,xt,yt,\"LOGISTIC\")\n",
"do_LOGIT(features_train,labels_train,features_test,labels_test)"
]
},
{
"cell_type": "code",
"execution_count": 8,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Training Accuracy BernoulliNB: 0.958638\n",
"Test Accuracy BernoulliNB: 0.738531\n",
"Detailed Analysis Testing Results ...\n",
" precision recall f1-score support\n",
"\n",
" + 0.75 0.73 0.74 1803\n",
" - 0.73 0.75 0.74 1751\n",
"\n",
"avg / total 0.74 0.74 0.74 3554\n",
"\n",
"[ 0.72352592 0.27647408] with its dogged hollywood naturalism and the inexorable passage of its characters toward sainthood , windtalkers is nothing but a sticky-sweet soap .\n",
"\n",
"[ 0.07012159 0.92987841] although it's a bit smug and repetitive , this documentary engages your brain in a way few current films do .\n",
"\n",
"[ 0.91373353 0.08626647] it's the kind of movie you can't quite recommend because it is all windup and not much of a pitch , yet you can't bring yourself to dislike it .\n",
"\n",
"[ 0.9946274 0.0053726] the one not-so-small problem with expecting is that the entire exercise has no real point .\n",
"\n",
"[ 0.97900537 0.02099463] apparently writer-director attal thought he need only cast himself and his movie-star wife sitting around in their drawers to justify a film .\n",
"\n"
]
}
],
"source": [
"from sklearn.naive_bayes import BernoulliNB\n",
"from sklearn.cross_validation import train_test_split\n",
"import random\n",
"\n",
"def do_NAIVE_BAYES(x,y,xt,yt):\n",
" model = BernoulliNB()\n",
" model = model.fit(x, y)\n",
" score_the_model(model,x,y,xt,yt,\"BernoulliNB\")\n",
" return model\n",
"\n",
"model = do_NAIVE_BAYES(features_train, labels_train, features_test, labels_test)\n",
"test_pred = model.predict_proba(features_test)\n",
"spl = random.sample(xrange(len(test_pred)), 5)\n",
"for text, sentiment in zip(test_df.Text[spl], test_pred[spl]):\n",
" print sentiment, text"
]
},
{
"cell_type": "code",
"execution_count": 9,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"[ 1.] [[ 0.21690469 0.78309531]]\n",
"[ 0.] [[ 0.87588935 0.12411065]]\n"
]
}
],
"source": [
"# Some examples\n",
"test_text = 'A great movie. A masterpiece without doubt.'\n",
"custom_test = vectorizer.transform([word_extractor(test_text)])\n",
"print model.predict(custom_test), model.predict_proba(custom_test)\n",
"\n",
"test_text = 'an awful movie'\n",
"custom_test = vectorizer.transform([word_extractor(test_text)])\n",
"print model.predict(custom_test), model.predict_proba(custom_test)"
]
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 2",
"language": "python",
"name": "python2"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 2
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython2",
"version": "2.7.12"
}
},
"nbformat": 4,
"nbformat_minor": 2
}
| mit |
Pytoddler/Kaggle-competition | Keras_WIKI(Kaggles)_MLP 困難.ipynb | 1 | 455739 | {
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# 數據預處理"
]
},
{
"cell_type": "code",
"execution_count": 1,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"import numpy as np\n",
"import pandas as pd\n",
"import os\n",
"\n",
"filepath = '/Users/mac/Desktop/Kaggle_datasets/Wiki_traffic/'\n",
"filename01 = 'train_1.csv'\n",
"filename02 = 'key_1.csv'\n",
"filename03 = 'sample_submission_1.csv'\n",
"\n",
"df_train = pd.read_csv(os.path.join(filepath, filename01))\n",
"df_test = pd.read_csv(os.path.join(filepath, filename02))\n",
"df_ans = pd.read_csv(os.path.join(filepath, filename03))"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"<class 'pandas.core.frame.DataFrame'>\n",
"RangeIndex: 145063 entries, 0 to 145062\n",
"Columns: 551 entries, Page to 2016-12-31\n",
"dtypes: float64(550), object(1)\n",
"memory usage: 609.8+ MB\n"
]
}
],
"source": [
"df_train.info()"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>Page</th>\n",
" <th>2015-07-01</th>\n",
" <th>2015-07-02</th>\n",
" <th>2015-07-03</th>\n",
" <th>2015-07-04</th>\n",
" <th>2015-07-05</th>\n",
" <th>2015-07-06</th>\n",
" <th>2015-07-07</th>\n",
" <th>2015-07-08</th>\n",
" <th>2015-07-09</th>\n",
" <th>...</th>\n",
" <th>2016-12-22</th>\n",
" <th>2016-12-23</th>\n",
" <th>2016-12-24</th>\n",
" <th>2016-12-25</th>\n",
" <th>2016-12-26</th>\n",
" <th>2016-12-27</th>\n",
" <th>2016-12-28</th>\n",
" <th>2016-12-29</th>\n",
" <th>2016-12-30</th>\n",
" <th>2016-12-31</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>2NE1_zh.wikipedia.org_all-access_spider</td>\n",
" <td>18.0</td>\n",
" <td>11.0</td>\n",
" <td>5.0</td>\n",
" <td>13.0</td>\n",
" <td>14.0</td>\n",
" <td>9.0</td>\n",
" <td>9.0</td>\n",
" <td>22.0</td>\n",
" <td>26.0</td>\n",
" <td>...</td>\n",
" <td>32.0</td>\n",
" <td>63.0</td>\n",
" <td>15.0</td>\n",
" <td>26.0</td>\n",
" <td>14.0</td>\n",
" <td>20.0</td>\n",
" <td>22.0</td>\n",
" <td>19.0</td>\n",
" <td>18.0</td>\n",
" <td>20.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>2PM_zh.wikipedia.org_all-access_spider</td>\n",
" <td>11.0</td>\n",
" <td>14.0</td>\n",
" <td>15.0</td>\n",
" <td>18.0</td>\n",
" <td>11.0</td>\n",
" <td>13.0</td>\n",
" <td>22.0</td>\n",
" <td>11.0</td>\n",
" <td>10.0</td>\n",
" <td>...</td>\n",
" <td>17.0</td>\n",
" <td>42.0</td>\n",
" <td>28.0</td>\n",
" <td>15.0</td>\n",
" <td>9.0</td>\n",
" <td>30.0</td>\n",
" <td>52.0</td>\n",
" <td>45.0</td>\n",
" <td>26.0</td>\n",
" <td>20.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>3C_zh.wikipedia.org_all-access_spider</td>\n",
" <td>1.0</td>\n",
" <td>0.0</td>\n",
" <td>1.0</td>\n",
" <td>1.0</td>\n",
" <td>0.0</td>\n",
" <td>4.0</td>\n",
" <td>0.0</td>\n",
" <td>3.0</td>\n",
" <td>4.0</td>\n",
" <td>...</td>\n",
" <td>3.0</td>\n",
" <td>1.0</td>\n",
" <td>1.0</td>\n",
" <td>7.0</td>\n",
" <td>4.0</td>\n",
" <td>4.0</td>\n",
" <td>6.0</td>\n",
" <td>3.0</td>\n",
" <td>4.0</td>\n",
" <td>17.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>4minute_zh.wikipedia.org_all-access_spider</td>\n",
" <td>35.0</td>\n",
" <td>13.0</td>\n",
" <td>10.0</td>\n",
" <td>94.0</td>\n",
" <td>4.0</td>\n",
" <td>26.0</td>\n",
" <td>14.0</td>\n",
" <td>9.0</td>\n",
" <td>11.0</td>\n",
" <td>...</td>\n",
" <td>32.0</td>\n",
" <td>10.0</td>\n",
" <td>26.0</td>\n",
" <td>27.0</td>\n",
" <td>16.0</td>\n",
" <td>11.0</td>\n",
" <td>17.0</td>\n",
" <td>19.0</td>\n",
" <td>10.0</td>\n",
" <td>11.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>52_Hz_I_Love_You_zh.wikipedia.org_all-access_s...</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>...</td>\n",
" <td>48.0</td>\n",
" <td>9.0</td>\n",
" <td>25.0</td>\n",
" <td>13.0</td>\n",
" <td>3.0</td>\n",
" <td>11.0</td>\n",
" <td>27.0</td>\n",
" <td>13.0</td>\n",
" <td>36.0</td>\n",
" <td>10.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>5</th>\n",
" <td>5566_zh.wikipedia.org_all-access_spider</td>\n",
" <td>12.0</td>\n",
" <td>7.0</td>\n",
" <td>4.0</td>\n",
" <td>5.0</td>\n",
" <td>20.0</td>\n",
" <td>8.0</td>\n",
" <td>5.0</td>\n",
" <td>17.0</td>\n",
" <td>24.0</td>\n",
" <td>...</td>\n",
" <td>16.0</td>\n",
" <td>27.0</td>\n",
" <td>8.0</td>\n",
" <td>17.0</td>\n",
" <td>32.0</td>\n",
" <td>19.0</td>\n",
" <td>23.0</td>\n",
" <td>17.0</td>\n",
" <td>17.0</td>\n",
" <td>50.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>6</th>\n",
" <td>91Days_zh.wikipedia.org_all-access_spider</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>...</td>\n",
" <td>2.0</td>\n",
" <td>7.0</td>\n",
" <td>33.0</td>\n",
" <td>8.0</td>\n",
" <td>11.0</td>\n",
" <td>4.0</td>\n",
" <td>15.0</td>\n",
" <td>6.0</td>\n",
" <td>8.0</td>\n",
" <td>6.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>7</th>\n",
" <td>A'N'D_zh.wikipedia.org_all-access_spider</td>\n",
" <td>118.0</td>\n",
" <td>26.0</td>\n",
" <td>30.0</td>\n",
" <td>24.0</td>\n",
" <td>29.0</td>\n",
" <td>127.0</td>\n",
" <td>53.0</td>\n",
" <td>37.0</td>\n",
" <td>20.0</td>\n",
" <td>...</td>\n",
" <td>64.0</td>\n",
" <td>35.0</td>\n",
" <td>35.0</td>\n",
" <td>28.0</td>\n",
" <td>20.0</td>\n",
" <td>23.0</td>\n",
" <td>32.0</td>\n",
" <td>39.0</td>\n",
" <td>32.0</td>\n",
" <td>17.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>8</th>\n",
" <td>AKB48_zh.wikipedia.org_all-access_spider</td>\n",
" <td>5.0</td>\n",
" <td>23.0</td>\n",
" <td>14.0</td>\n",
" <td>12.0</td>\n",
" <td>9.0</td>\n",
" <td>9.0</td>\n",
" <td>35.0</td>\n",
" <td>15.0</td>\n",
" <td>14.0</td>\n",
" <td>...</td>\n",
" <td>34.0</td>\n",
" <td>105.0</td>\n",
" <td>72.0</td>\n",
" <td>36.0</td>\n",
" <td>33.0</td>\n",
" <td>30.0</td>\n",
" <td>36.0</td>\n",
" <td>38.0</td>\n",
" <td>31.0</td>\n",
" <td>97.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>9</th>\n",
" <td>ASCII_zh.wikipedia.org_all-access_spider</td>\n",
" <td>6.0</td>\n",
" <td>3.0</td>\n",
" <td>5.0</td>\n",
" <td>12.0</td>\n",
" <td>6.0</td>\n",
" <td>5.0</td>\n",
" <td>4.0</td>\n",
" <td>13.0</td>\n",
" <td>9.0</td>\n",
" <td>...</td>\n",
" <td>25.0</td>\n",
" <td>17.0</td>\n",
" <td>22.0</td>\n",
" <td>29.0</td>\n",
" <td>30.0</td>\n",
" <td>29.0</td>\n",
" <td>35.0</td>\n",
" <td>44.0</td>\n",
" <td>26.0</td>\n",
" <td>41.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>10</th>\n",
" <td>ASTRO_zh.wikipedia.org_all-access_spider</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>1.0</td>\n",
" <td>1.0</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>...</td>\n",
" <td>11.0</td>\n",
" <td>38.0</td>\n",
" <td>85.0</td>\n",
" <td>79.0</td>\n",
" <td>30.0</td>\n",
" <td>14.0</td>\n",
" <td>10.0</td>\n",
" <td>38.0</td>\n",
" <td>12.0</td>\n",
" <td>51.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>11</th>\n",
" <td>Ahq_e-Sports_Club_zh.wikipedia.org_all-access_...</td>\n",
" <td>2.0</td>\n",
" <td>1.0</td>\n",
" <td>4.0</td>\n",
" <td>4.0</td>\n",
" <td>2.0</td>\n",
" <td>6.0</td>\n",
" <td>3.0</td>\n",
" <td>6.0</td>\n",
" <td>9.0</td>\n",
" <td>...</td>\n",
" <td>8.0</td>\n",
" <td>17.0</td>\n",
" <td>18.0</td>\n",
" <td>48.0</td>\n",
" <td>19.0</td>\n",
" <td>14.0</td>\n",
" <td>9.0</td>\n",
" <td>23.0</td>\n",
" <td>11.0</td>\n",
" <td>7.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>12</th>\n",
" <td>All_your_base_are_belong_to_us_zh.wikipedia.or...</td>\n",
" <td>2.0</td>\n",
" <td>5.0</td>\n",
" <td>5.0</td>\n",
" <td>1.0</td>\n",
" <td>3.0</td>\n",
" <td>3.0</td>\n",
" <td>5.0</td>\n",
" <td>3.0</td>\n",
" <td>17.0</td>\n",
" <td>...</td>\n",
" <td>5.0</td>\n",
" <td>4.0</td>\n",
" <td>4.0</td>\n",
" <td>5.0</td>\n",
" <td>2.0</td>\n",
" <td>9.0</td>\n",
" <td>7.0</td>\n",
" <td>4.0</td>\n",
" <td>5.0</td>\n",
" <td>0.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>13</th>\n",
" <td>AlphaGo_zh.wikipedia.org_all-access_spider</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>...</td>\n",
" <td>14.0</td>\n",
" <td>13.0</td>\n",
" <td>14.0</td>\n",
" <td>17.0</td>\n",
" <td>19.0</td>\n",
" <td>56.0</td>\n",
" <td>21.0</td>\n",
" <td>13.0</td>\n",
" <td>21.0</td>\n",
" <td>11.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>14</th>\n",
" <td>Android_zh.wikipedia.org_all-access_spider</td>\n",
" <td>8.0</td>\n",
" <td>27.0</td>\n",
" <td>9.0</td>\n",
" <td>25.0</td>\n",
" <td>25.0</td>\n",
" <td>10.0</td>\n",
" <td>34.0</td>\n",
" <td>22.0</td>\n",
" <td>17.0</td>\n",
" <td>...</td>\n",
" <td>36.0</td>\n",
" <td>36.0</td>\n",
" <td>46.0</td>\n",
" <td>42.0</td>\n",
" <td>40.0</td>\n",
" <td>40.0</td>\n",
" <td>66.0</td>\n",
" <td>43.0</td>\n",
" <td>38.0</td>\n",
" <td>74.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>15</th>\n",
" <td>Angelababy_zh.wikipedia.org_all-access_spider</td>\n",
" <td>40.0</td>\n",
" <td>17.0</td>\n",
" <td>25.0</td>\n",
" <td>42.0</td>\n",
" <td>41.0</td>\n",
" <td>7.0</td>\n",
" <td>18.0</td>\n",
" <td>21.0</td>\n",
" <td>33.0</td>\n",
" <td>...</td>\n",
" <td>27.0</td>\n",
" <td>40.0</td>\n",
" <td>26.0</td>\n",
" <td>30.0</td>\n",
" <td>68.0</td>\n",
" <td>31.0</td>\n",
" <td>77.0</td>\n",
" <td>42.0</td>\n",
" <td>111.0</td>\n",
" <td>37.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>16</th>\n",
" <td>Apink_zh.wikipedia.org_all-access_spider</td>\n",
" <td>61.0</td>\n",
" <td>33.0</td>\n",
" <td>21.0</td>\n",
" <td>10.0</td>\n",
" <td>26.0</td>\n",
" <td>11.0</td>\n",
" <td>39.0</td>\n",
" <td>195.0</td>\n",
" <td>62.0</td>\n",
" <td>...</td>\n",
" <td>14.0</td>\n",
" <td>24.0</td>\n",
" <td>35.0</td>\n",
" <td>34.0</td>\n",
" <td>24.0</td>\n",
" <td>34.0</td>\n",
" <td>28.0</td>\n",
" <td>44.0</td>\n",
" <td>12.0</td>\n",
" <td>31.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>17</th>\n",
" <td>Apple_II_zh.wikipedia.org_all-access_spider</td>\n",
" <td>4.0</td>\n",
" <td>8.0</td>\n",
" <td>4.0</td>\n",
" <td>9.0</td>\n",
" <td>7.0</td>\n",
" <td>4.0</td>\n",
" <td>15.0</td>\n",
" <td>9.0</td>\n",
" <td>17.0</td>\n",
" <td>...</td>\n",
" <td>5.0</td>\n",
" <td>14.0</td>\n",
" <td>8.0</td>\n",
" <td>11.0</td>\n",
" <td>8.0</td>\n",
" <td>24.0</td>\n",
" <td>10.0</td>\n",
" <td>15.0</td>\n",
" <td>12.0</td>\n",
" <td>11.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>18</th>\n",
" <td>As_One_zh.wikipedia.org_all-access_spider</td>\n",
" <td>13.0</td>\n",
" <td>7.0</td>\n",
" <td>14.0</td>\n",
" <td>11.0</td>\n",
" <td>20.0</td>\n",
" <td>5.0</td>\n",
" <td>32.0</td>\n",
" <td>11.0</td>\n",
" <td>6.0</td>\n",
" <td>...</td>\n",
" <td>37.0</td>\n",
" <td>12.0</td>\n",
" <td>7.0</td>\n",
" <td>11.0</td>\n",
" <td>13.0</td>\n",
" <td>17.0</td>\n",
" <td>13.0</td>\n",
" <td>12.0</td>\n",
" <td>9.0</td>\n",
" <td>8.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>19</th>\n",
" <td>B-PROJECT_zh.wikipedia.org_all-access_spider</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>...</td>\n",
" <td>4.0</td>\n",
" <td>26.0</td>\n",
" <td>10.0</td>\n",
" <td>5.0</td>\n",
" <td>5.0</td>\n",
" <td>11.0</td>\n",
" <td>10.0</td>\n",
" <td>4.0</td>\n",
" <td>8.0</td>\n",
" <td>6.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>20</th>\n",
" <td>B1A4_zh.wikipedia.org_all-access_spider</td>\n",
" <td>22.0</td>\n",
" <td>11.0</td>\n",
" <td>23.0</td>\n",
" <td>10.0</td>\n",
" <td>6.0</td>\n",
" <td>12.0</td>\n",
" <td>74.0</td>\n",
" <td>17.0</td>\n",
" <td>38.0</td>\n",
" <td>...</td>\n",
" <td>43.0</td>\n",
" <td>23.0</td>\n",
" <td>52.0</td>\n",
" <td>60.0</td>\n",
" <td>14.0</td>\n",
" <td>19.0</td>\n",
" <td>38.0</td>\n",
" <td>30.0</td>\n",
" <td>21.0</td>\n",
" <td>24.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>21</th>\n",
" <td>BDSM_zh.wikipedia.org_all-access_spider</td>\n",
" <td>25.0</td>\n",
" <td>3.0</td>\n",
" <td>3.0</td>\n",
" <td>4.0</td>\n",
" <td>12.0</td>\n",
" <td>14.0</td>\n",
" <td>16.0</td>\n",
" <td>15.0</td>\n",
" <td>22.0</td>\n",
" <td>...</td>\n",
" <td>12.0</td>\n",
" <td>18.0</td>\n",
" <td>23.0</td>\n",
" <td>17.0</td>\n",
" <td>20.0</td>\n",
" <td>19.0</td>\n",
" <td>20.0</td>\n",
" <td>38.0</td>\n",
" <td>21.0</td>\n",
" <td>16.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>22</th>\n",
" <td>BEAST_zh.wikipedia.org_all-access_spider</td>\n",
" <td>19.0</td>\n",
" <td>6.0</td>\n",
" <td>12.0</td>\n",
" <td>14.0</td>\n",
" <td>13.0</td>\n",
" <td>7.0</td>\n",
" <td>12.0</td>\n",
" <td>64.0</td>\n",
" <td>9.0</td>\n",
" <td>...</td>\n",
" <td>11.0</td>\n",
" <td>13.0</td>\n",
" <td>20.0</td>\n",
" <td>30.0</td>\n",
" <td>16.0</td>\n",
" <td>24.0</td>\n",
" <td>47.0</td>\n",
" <td>26.0</td>\n",
" <td>13.0</td>\n",
" <td>13.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>23</th>\n",
" <td>BIGBANG_zh.wikipedia.org_all-access_spider</td>\n",
" <td>23.0</td>\n",
" <td>24.0</td>\n",
" <td>31.0</td>\n",
" <td>9.0</td>\n",
" <td>21.0</td>\n",
" <td>27.0</td>\n",
" <td>15.0</td>\n",
" <td>8.0</td>\n",
" <td>50.0</td>\n",
" <td>...</td>\n",
" <td>85.0</td>\n",
" <td>63.0</td>\n",
" <td>80.0</td>\n",
" <td>29.0</td>\n",
" <td>37.0</td>\n",
" <td>40.0</td>\n",
" <td>104.0</td>\n",
" <td>39.0</td>\n",
" <td>32.0</td>\n",
" <td>34.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>24</th>\n",
" <td>BLACK_PINK_zh.wikipedia.org_all-access_spider</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>...</td>\n",
" <td>32.0</td>\n",
" <td>56.0</td>\n",
" <td>39.0</td>\n",
" <td>65.0</td>\n",
" <td>78.0</td>\n",
" <td>143.0</td>\n",
" <td>96.0</td>\n",
" <td>63.0</td>\n",
" <td>28.0</td>\n",
" <td>75.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>25</th>\n",
" <td>BLEACH_zh.wikipedia.org_all-access_spider</td>\n",
" <td>11.0</td>\n",
" <td>5.0</td>\n",
" <td>13.0</td>\n",
" <td>8.0</td>\n",
" <td>6.0</td>\n",
" <td>5.0</td>\n",
" <td>8.0</td>\n",
" <td>5.0</td>\n",
" <td>12.0</td>\n",
" <td>...</td>\n",
" <td>16.0</td>\n",
" <td>13.0</td>\n",
" <td>14.0</td>\n",
" <td>15.0</td>\n",
" <td>14.0</td>\n",
" <td>21.0</td>\n",
" <td>15.0</td>\n",
" <td>16.0</td>\n",
" <td>15.0</td>\n",
" <td>28.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>26</th>\n",
" <td>BTOB_zh.wikipedia.org_all-access_spider</td>\n",
" <td>22.0</td>\n",
" <td>67.0</td>\n",
" <td>26.0</td>\n",
" <td>34.0</td>\n",
" <td>38.0</td>\n",
" <td>13.0</td>\n",
" <td>17.0</td>\n",
" <td>33.0</td>\n",
" <td>43.0</td>\n",
" <td>...</td>\n",
" <td>19.0</td>\n",
" <td>17.0</td>\n",
" <td>14.0</td>\n",
" <td>17.0</td>\n",
" <td>28.0</td>\n",
" <td>23.0</td>\n",
" <td>32.0</td>\n",
" <td>37.0</td>\n",
" <td>42.0</td>\n",
" <td>60.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>27</th>\n",
" <td>Beautiful_Mind_zh.wikipedia.org_all-access_spider</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>...</td>\n",
" <td>11.0</td>\n",
" <td>8.0</td>\n",
" <td>6.0</td>\n",
" <td>7.0</td>\n",
" <td>2.0</td>\n",
" <td>11.0</td>\n",
" <td>11.0</td>\n",
" <td>29.0</td>\n",
" <td>12.0</td>\n",
" <td>14.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>28</th>\n",
" <td>Beyond_zh.wikipedia.org_all-access_spider</td>\n",
" <td>291.0</td>\n",
" <td>64.0</td>\n",
" <td>26.0</td>\n",
" <td>20.0</td>\n",
" <td>28.0</td>\n",
" <td>6.0</td>\n",
" <td>20.0</td>\n",
" <td>10.0</td>\n",
" <td>48.0</td>\n",
" <td>...</td>\n",
" <td>23.0</td>\n",
" <td>35.0</td>\n",
" <td>53.0</td>\n",
" <td>35.0</td>\n",
" <td>20.0</td>\n",
" <td>29.0</td>\n",
" <td>40.0</td>\n",
" <td>28.0</td>\n",
" <td>39.0</td>\n",
" <td>75.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>29</th>\n",
" <td>Big_zh.wikipedia.org_all-access_spider</td>\n",
" <td>3.0</td>\n",
" <td>53.0</td>\n",
" <td>11.0</td>\n",
" <td>3.0</td>\n",
" <td>4.0</td>\n",
" <td>3.0</td>\n",
" <td>11.0</td>\n",
" <td>9.0</td>\n",
" <td>5.0</td>\n",
" <td>...</td>\n",
" <td>11.0</td>\n",
" <td>20.0</td>\n",
" <td>9.0</td>\n",
" <td>13.0</td>\n",
" <td>7.0</td>\n",
" <td>17.0</td>\n",
" <td>13.0</td>\n",
" <td>20.0</td>\n",
" <td>19.0</td>\n",
" <td>13.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>...</th>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>145033</th>\n",
" <td>Sin_senos_sí_hay_paraíso_es.wikipedia.org_all-...</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>...</td>\n",
" <td>19.0</td>\n",
" <td>40.0</td>\n",
" <td>22.0</td>\n",
" <td>28.0</td>\n",
" <td>13.0</td>\n",
" <td>53.0</td>\n",
" <td>12.0</td>\n",
" <td>32.0</td>\n",
" <td>11.0</td>\n",
" <td>62.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>145034</th>\n",
" <td>Anexo:Medallero_de_los_Juegos_Olímpicos_de_Río...</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>...</td>\n",
" <td>3.0</td>\n",
" <td>9.0</td>\n",
" <td>1.0</td>\n",
" <td>9.0</td>\n",
" <td>5.0</td>\n",
" <td>5.0</td>\n",
" <td>5.0</td>\n",
" <td>8.0</td>\n",
" <td>5.0</td>\n",
" <td>2.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>145035</th>\n",
" <td>Arrival_(película)_es.wikipedia.org_all-access...</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>...</td>\n",
" <td>10.0</td>\n",
" <td>5.0</td>\n",
" <td>14.0</td>\n",
" <td>8.0</td>\n",
" <td>54.0</td>\n",
" <td>18.0</td>\n",
" <td>46.0</td>\n",
" <td>70.0</td>\n",
" <td>15.0</td>\n",
" <td>6.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>145036</th>\n",
" <td>Anexo:Baloncesto_en_los_Juegos_Olímpicos_de_Rí...</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>...</td>\n",
" <td>1.0</td>\n",
" <td>1.0</td>\n",
" <td>2.0</td>\n",
" <td>5.0</td>\n",
" <td>5.0</td>\n",
" <td>1.0</td>\n",
" <td>0.0</td>\n",
" <td>1.0</td>\n",
" <td>1.0</td>\n",
" <td>1.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>145037</th>\n",
" <td>Hasta_que_te_conocí_(serie_de_televisión)_es.w...</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>...</td>\n",
" <td>18.0</td>\n",
" <td>8.0</td>\n",
" <td>3.0</td>\n",
" <td>4.0</td>\n",
" <td>31.0</td>\n",
" <td>5.0</td>\n",
" <td>1.0</td>\n",
" <td>2.0</td>\n",
" <td>0.0</td>\n",
" <td>2.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>145038</th>\n",
" <td>Westworld_(serie_de_televisión)_es.wikipedia.o...</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>...</td>\n",
" <td>7.0</td>\n",
" <td>23.0</td>\n",
" <td>28.0</td>\n",
" <td>31.0</td>\n",
" <td>26.0</td>\n",
" <td>12.0</td>\n",
" <td>13.0</td>\n",
" <td>12.0</td>\n",
" <td>9.0</td>\n",
" <td>10.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>145039</th>\n",
" <td>Milénico_es.wikipedia.org_all-access_spider</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>...</td>\n",
" <td>14.0</td>\n",
" <td>46.0</td>\n",
" <td>51.0</td>\n",
" <td>11.0</td>\n",
" <td>11.0</td>\n",
" <td>14.0</td>\n",
" <td>26.0</td>\n",
" <td>13.0</td>\n",
" <td>12.0</td>\n",
" <td>7.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>145040</th>\n",
" <td>Moonlight_(película)_es.wikipedia.org_all-acce...</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>...</td>\n",
" <td>3.0</td>\n",
" <td>4.0</td>\n",
" <td>13.0</td>\n",
" <td>10.0</td>\n",
" <td>2.0</td>\n",
" <td>4.0</td>\n",
" <td>4.0</td>\n",
" <td>3.0</td>\n",
" <td>1.0</td>\n",
" <td>2.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>145041</th>\n",
" <td>Sully_(película)_es.wikipedia.org_all-access_s...</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>...</td>\n",
" <td>10.0</td>\n",
" <td>9.0</td>\n",
" <td>8.0</td>\n",
" <td>37.0</td>\n",
" <td>7.0</td>\n",
" <td>5.0</td>\n",
" <td>9.0</td>\n",
" <td>7.0</td>\n",
" <td>10.0</td>\n",
" <td>4.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>145042</th>\n",
" <td>Pulsaciones_(serie_de_televisión)_es.wikipedia...</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>...</td>\n",
" <td>1.0</td>\n",
" <td>2.0</td>\n",
" <td>23.0</td>\n",
" <td>11.0</td>\n",
" <td>4.0</td>\n",
" <td>25.0</td>\n",
" <td>2.0</td>\n",
" <td>14.0</td>\n",
" <td>2.0</td>\n",
" <td>14.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>145043</th>\n",
" <td>2091_(serie_de_televisión)_es.wikipedia.org_al...</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>...</td>\n",
" <td>4.0</td>\n",
" <td>7.0</td>\n",
" <td>3.0</td>\n",
" <td>4.0</td>\n",
" <td>4.0</td>\n",
" <td>2.0</td>\n",
" <td>4.0</td>\n",
" <td>5.0</td>\n",
" <td>2.0</td>\n",
" <td>2.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>145044</th>\n",
" <td>Campeonato_Sudamericano_de_Fútbol_Sub-20_de_20...</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>...</td>\n",
" <td>2.0</td>\n",
" <td>7.0</td>\n",
" <td>8.0</td>\n",
" <td>20.0</td>\n",
" <td>27.0</td>\n",
" <td>11.0</td>\n",
" <td>7.0</td>\n",
" <td>17.0</td>\n",
" <td>13.0</td>\n",
" <td>40.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>145045</th>\n",
" <td>Split_(película)_es.wikipedia.org_all-access_s...</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>...</td>\n",
" <td>2.0</td>\n",
" <td>1.0</td>\n",
" <td>2.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>1.0</td>\n",
" <td>1.0</td>\n",
" <td>1.0</td>\n",
" <td>0.0</td>\n",
" <td>1.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>145046</th>\n",
" <td>Huracán_Matthew_es.wikipedia.org_all-access_sp...</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>...</td>\n",
" <td>6.0</td>\n",
" <td>4.0</td>\n",
" <td>4.0</td>\n",
" <td>6.0</td>\n",
" <td>5.0</td>\n",
" <td>5.0</td>\n",
" <td>13.0</td>\n",
" <td>7.0</td>\n",
" <td>11.0</td>\n",
" <td>7.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>145047</th>\n",
" <td>Fences_(película)_es.wikipedia.org_all-access_...</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>...</td>\n",
" <td>5.0</td>\n",
" <td>3.0</td>\n",
" <td>10.0</td>\n",
" <td>1.0</td>\n",
" <td>6.0</td>\n",
" <td>22.0</td>\n",
" <td>34.0</td>\n",
" <td>1.0</td>\n",
" <td>3.0</td>\n",
" <td>29.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>145048</th>\n",
" <td>Logan_(película)_es.wikipedia.org_all-access_s...</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>...</td>\n",
" <td>26.0</td>\n",
" <td>25.0</td>\n",
" <td>7.0</td>\n",
" <td>5.0</td>\n",
" <td>8.0</td>\n",
" <td>25.0</td>\n",
" <td>2.0</td>\n",
" <td>8.0</td>\n",
" <td>3.0</td>\n",
" <td>1.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>145049</th>\n",
" <td>La_doña_(telenovela_de_2016)_es.wikipedia.org_...</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>...</td>\n",
" <td>6.0</td>\n",
" <td>35.0</td>\n",
" <td>10.0</td>\n",
" <td>3.0</td>\n",
" <td>4.0</td>\n",
" <td>1.0</td>\n",
" <td>31.0</td>\n",
" <td>27.0</td>\n",
" <td>9.0</td>\n",
" <td>135.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>145050</th>\n",
" <td>RTS_(canal_de_televisión)_es.wikipedia.org_all...</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>...</td>\n",
" <td>1.0</td>\n",
" <td>2.0</td>\n",
" <td>7.0</td>\n",
" <td>2.0</td>\n",
" <td>3.0</td>\n",
" <td>2.0</td>\n",
" <td>18.0</td>\n",
" <td>40.0</td>\n",
" <td>1.0</td>\n",
" <td>42.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>145051</th>\n",
" <td>La_ley_del_corazón_es.wikipedia.org_all-access...</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>...</td>\n",
" <td>22.0</td>\n",
" <td>74.0</td>\n",
" <td>222.0</td>\n",
" <td>2.0</td>\n",
" <td>16.0</td>\n",
" <td>21.0</td>\n",
" <td>7.0</td>\n",
" <td>34.0</td>\n",
" <td>37.0</td>\n",
" <td>42.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>145052</th>\n",
" <td>The_Crown_(serie_de_televisión)_es.wikipedia.o...</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>...</td>\n",
" <td>5.0</td>\n",
" <td>83.0</td>\n",
" <td>44.0</td>\n",
" <td>36.0</td>\n",
" <td>9.0</td>\n",
" <td>4.0</td>\n",
" <td>17.0</td>\n",
" <td>6.0</td>\n",
" <td>11.0</td>\n",
" <td>5.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>145053</th>\n",
" <td>Drake_(músico)_es.wikipedia.org_all-access_spider</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>...</td>\n",
" <td>13.0</td>\n",
" <td>7.0</td>\n",
" <td>6.0</td>\n",
" <td>3.0</td>\n",
" <td>3.0</td>\n",
" <td>8.0</td>\n",
" <td>21.0</td>\n",
" <td>14.0</td>\n",
" <td>24.0</td>\n",
" <td>37.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>145054</th>\n",
" <td>Skam_(serie_de_televisión)_es.wikipedia.org_al...</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>...</td>\n",
" <td>8.0</td>\n",
" <td>9.0</td>\n",
" <td>9.0</td>\n",
" <td>19.0</td>\n",
" <td>17.0</td>\n",
" <td>7.0</td>\n",
" <td>13.0</td>\n",
" <td>12.0</td>\n",
" <td>31.0</td>\n",
" <td>11.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>145055</th>\n",
" <td>Legión_(serie_de_televisión)_es.wikipedia.org_...</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>...</td>\n",
" <td>1.0</td>\n",
" <td>2.0</td>\n",
" <td>1.0</td>\n",
" <td>1.0</td>\n",
" <td>3.0</td>\n",
" <td>4.0</td>\n",
" <td>2.0</td>\n",
" <td>4.0</td>\n",
" <td>4.0</td>\n",
" <td>3.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>145056</th>\n",
" <td>Doble_tentación_es.wikipedia.org_all-access_sp...</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>...</td>\n",
" <td>21.0</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>51.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>145057</th>\n",
" <td>Mi_adorable_maldición_es.wikipedia.org_all-acc...</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>...</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>145058</th>\n",
" <td>Underworld_(serie_de_películas)_es.wikipedia.o...</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>...</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>13.0</td>\n",
" <td>12.0</td>\n",
" <td>13.0</td>\n",
" <td>3.0</td>\n",
" <td>5.0</td>\n",
" <td>10.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>145059</th>\n",
" <td>Resident_Evil:_Capítulo_Final_es.wikipedia.org...</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>...</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>145060</th>\n",
" <td>Enamorándome_de_Ramón_es.wikipedia.org_all-acc...</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>...</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>145061</th>\n",
" <td>Hasta_el_último_hombre_es.wikipedia.org_all-ac...</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>...</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>145062</th>\n",
" <td>Francisco_el_matemático_(serie_de_televisión_d...</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>...</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"<p>145063 rows × 551 columns</p>\n",
"</div>"
],
"text/plain": [
" Page 2015-07-01 \\\n",
"0 2NE1_zh.wikipedia.org_all-access_spider 18.0 \n",
"1 2PM_zh.wikipedia.org_all-access_spider 11.0 \n",
"2 3C_zh.wikipedia.org_all-access_spider 1.0 \n",
"3 4minute_zh.wikipedia.org_all-access_spider 35.0 \n",
"4 52_Hz_I_Love_You_zh.wikipedia.org_all-access_s... NaN \n",
"5 5566_zh.wikipedia.org_all-access_spider 12.0 \n",
"6 91Days_zh.wikipedia.org_all-access_spider NaN \n",
"7 A'N'D_zh.wikipedia.org_all-access_spider 118.0 \n",
"8 AKB48_zh.wikipedia.org_all-access_spider 5.0 \n",
"9 ASCII_zh.wikipedia.org_all-access_spider 6.0 \n",
"10 ASTRO_zh.wikipedia.org_all-access_spider NaN \n",
"11 Ahq_e-Sports_Club_zh.wikipedia.org_all-access_... 2.0 \n",
"12 All_your_base_are_belong_to_us_zh.wikipedia.or... 2.0 \n",
"13 AlphaGo_zh.wikipedia.org_all-access_spider NaN \n",
"14 Android_zh.wikipedia.org_all-access_spider 8.0 \n",
"15 Angelababy_zh.wikipedia.org_all-access_spider 40.0 \n",
"16 Apink_zh.wikipedia.org_all-access_spider 61.0 \n",
"17 Apple_II_zh.wikipedia.org_all-access_spider 4.0 \n",
"18 As_One_zh.wikipedia.org_all-access_spider 13.0 \n",
"19 B-PROJECT_zh.wikipedia.org_all-access_spider NaN \n",
"20 B1A4_zh.wikipedia.org_all-access_spider 22.0 \n",
"21 BDSM_zh.wikipedia.org_all-access_spider 25.0 \n",
"22 BEAST_zh.wikipedia.org_all-access_spider 19.0 \n",
"23 BIGBANG_zh.wikipedia.org_all-access_spider 23.0 \n",
"24 BLACK_PINK_zh.wikipedia.org_all-access_spider NaN \n",
"25 BLEACH_zh.wikipedia.org_all-access_spider 11.0 \n",
"26 BTOB_zh.wikipedia.org_all-access_spider 22.0 \n",
"27 Beautiful_Mind_zh.wikipedia.org_all-access_spider NaN \n",
"28 Beyond_zh.wikipedia.org_all-access_spider 291.0 \n",
"29 Big_zh.wikipedia.org_all-access_spider 3.0 \n",
"... ... ... \n",
"145033 Sin_senos_sí_hay_paraíso_es.wikipedia.org_all-... NaN \n",
"145034 Anexo:Medallero_de_los_Juegos_Olímpicos_de_Río... NaN \n",
"145035 Arrival_(película)_es.wikipedia.org_all-access... NaN \n",
"145036 Anexo:Baloncesto_en_los_Juegos_Olímpicos_de_Rí... NaN \n",
"145037 Hasta_que_te_conocí_(serie_de_televisión)_es.w... NaN \n",
"145038 Westworld_(serie_de_televisión)_es.wikipedia.o... NaN \n",
"145039 Milénico_es.wikipedia.org_all-access_spider NaN \n",
"145040 Moonlight_(película)_es.wikipedia.org_all-acce... NaN \n",
"145041 Sully_(película)_es.wikipedia.org_all-access_s... NaN \n",
"145042 Pulsaciones_(serie_de_televisión)_es.wikipedia... NaN \n",
"145043 2091_(serie_de_televisión)_es.wikipedia.org_al... NaN \n",
"145044 Campeonato_Sudamericano_de_Fútbol_Sub-20_de_20... NaN \n",
"145045 Split_(película)_es.wikipedia.org_all-access_s... NaN \n",
"145046 Huracán_Matthew_es.wikipedia.org_all-access_sp... NaN \n",
"145047 Fences_(película)_es.wikipedia.org_all-access_... NaN \n",
"145048 Logan_(película)_es.wikipedia.org_all-access_s... NaN \n",
"145049 La_doña_(telenovela_de_2016)_es.wikipedia.org_... NaN \n",
"145050 RTS_(canal_de_televisión)_es.wikipedia.org_all... NaN \n",
"145051 La_ley_del_corazón_es.wikipedia.org_all-access... NaN \n",
"145052 The_Crown_(serie_de_televisión)_es.wikipedia.o... NaN \n",
"145053 Drake_(músico)_es.wikipedia.org_all-access_spider NaN \n",
"145054 Skam_(serie_de_televisión)_es.wikipedia.org_al... NaN \n",
"145055 Legión_(serie_de_televisión)_es.wikipedia.org_... NaN \n",
"145056 Doble_tentación_es.wikipedia.org_all-access_sp... NaN \n",
"145057 Mi_adorable_maldición_es.wikipedia.org_all-acc... NaN \n",
"145058 Underworld_(serie_de_películas)_es.wikipedia.o... NaN \n",
"145059 Resident_Evil:_Capítulo_Final_es.wikipedia.org... NaN \n",
"145060 Enamorándome_de_Ramón_es.wikipedia.org_all-acc... NaN \n",
"145061 Hasta_el_último_hombre_es.wikipedia.org_all-ac... NaN \n",
"145062 Francisco_el_matemático_(serie_de_televisión_d... NaN \n",
"\n",
" 2015-07-02 2015-07-03 2015-07-04 2015-07-05 2015-07-06 \\\n",
"0 11.0 5.0 13.0 14.0 9.0 \n",
"1 14.0 15.0 18.0 11.0 13.0 \n",
"2 0.0 1.0 1.0 0.0 4.0 \n",
"3 13.0 10.0 94.0 4.0 26.0 \n",
"4 NaN NaN NaN NaN NaN \n",
"5 7.0 4.0 5.0 20.0 8.0 \n",
"6 NaN NaN NaN NaN NaN \n",
"7 26.0 30.0 24.0 29.0 127.0 \n",
"8 23.0 14.0 12.0 9.0 9.0 \n",
"9 3.0 5.0 12.0 6.0 5.0 \n",
"10 NaN NaN NaN NaN 1.0 \n",
"11 1.0 4.0 4.0 2.0 6.0 \n",
"12 5.0 5.0 1.0 3.0 3.0 \n",
"13 NaN NaN NaN NaN NaN \n",
"14 27.0 9.0 25.0 25.0 10.0 \n",
"15 17.0 25.0 42.0 41.0 7.0 \n",
"16 33.0 21.0 10.0 26.0 11.0 \n",
"17 8.0 4.0 9.0 7.0 4.0 \n",
"18 7.0 14.0 11.0 20.0 5.0 \n",
"19 NaN NaN NaN NaN NaN \n",
"20 11.0 23.0 10.0 6.0 12.0 \n",
"21 3.0 3.0 4.0 12.0 14.0 \n",
"22 6.0 12.0 14.0 13.0 7.0 \n",
"23 24.0 31.0 9.0 21.0 27.0 \n",
"24 NaN NaN NaN NaN NaN \n",
"25 5.0 13.0 8.0 6.0 5.0 \n",
"26 67.0 26.0 34.0 38.0 13.0 \n",
"27 NaN NaN NaN NaN NaN \n",
"28 64.0 26.0 20.0 28.0 6.0 \n",
"29 53.0 11.0 3.0 4.0 3.0 \n",
"... ... ... ... ... ... \n",
"145033 NaN NaN NaN NaN NaN \n",
"145034 NaN NaN NaN NaN NaN \n",
"145035 NaN NaN NaN NaN NaN \n",
"145036 NaN NaN NaN NaN NaN \n",
"145037 NaN NaN NaN NaN NaN \n",
"145038 NaN NaN NaN NaN NaN \n",
"145039 NaN NaN NaN NaN NaN \n",
"145040 NaN NaN NaN NaN NaN \n",
"145041 NaN NaN NaN NaN NaN \n",
"145042 NaN NaN NaN NaN NaN \n",
"145043 NaN NaN NaN NaN NaN \n",
"145044 NaN NaN NaN NaN NaN \n",
"145045 NaN NaN NaN NaN NaN \n",
"145046 NaN NaN NaN NaN NaN \n",
"145047 NaN NaN NaN NaN NaN \n",
"145048 NaN NaN NaN NaN NaN \n",
"145049 NaN NaN NaN NaN NaN \n",
"145050 NaN NaN NaN NaN NaN \n",
"145051 NaN NaN NaN NaN NaN \n",
"145052 NaN NaN NaN NaN NaN \n",
"145053 NaN NaN NaN NaN NaN \n",
"145054 NaN NaN NaN NaN NaN \n",
"145055 NaN NaN NaN NaN NaN \n",
"145056 NaN NaN NaN NaN NaN \n",
"145057 NaN NaN NaN NaN NaN \n",
"145058 NaN NaN NaN NaN NaN \n",
"145059 NaN NaN NaN NaN NaN \n",
"145060 NaN NaN NaN NaN NaN \n",
"145061 NaN NaN NaN NaN NaN \n",
"145062 NaN NaN NaN NaN NaN \n",
"\n",
" 2015-07-07 2015-07-08 2015-07-09 ... 2016-12-22 \\\n",
"0 9.0 22.0 26.0 ... 32.0 \n",
"1 22.0 11.0 10.0 ... 17.0 \n",
"2 0.0 3.0 4.0 ... 3.0 \n",
"3 14.0 9.0 11.0 ... 32.0 \n",
"4 NaN NaN NaN ... 48.0 \n",
"5 5.0 17.0 24.0 ... 16.0 \n",
"6 NaN NaN NaN ... 2.0 \n",
"7 53.0 37.0 20.0 ... 64.0 \n",
"8 35.0 15.0 14.0 ... 34.0 \n",
"9 4.0 13.0 9.0 ... 25.0 \n",
"10 1.0 NaN NaN ... 11.0 \n",
"11 3.0 6.0 9.0 ... 8.0 \n",
"12 5.0 3.0 17.0 ... 5.0 \n",
"13 NaN NaN NaN ... 14.0 \n",
"14 34.0 22.0 17.0 ... 36.0 \n",
"15 18.0 21.0 33.0 ... 27.0 \n",
"16 39.0 195.0 62.0 ... 14.0 \n",
"17 15.0 9.0 17.0 ... 5.0 \n",
"18 32.0 11.0 6.0 ... 37.0 \n",
"19 NaN NaN NaN ... 4.0 \n",
"20 74.0 17.0 38.0 ... 43.0 \n",
"21 16.0 15.0 22.0 ... 12.0 \n",
"22 12.0 64.0 9.0 ... 11.0 \n",
"23 15.0 8.0 50.0 ... 85.0 \n",
"24 NaN NaN NaN ... 32.0 \n",
"25 8.0 5.0 12.0 ... 16.0 \n",
"26 17.0 33.0 43.0 ... 19.0 \n",
"27 NaN NaN NaN ... 11.0 \n",
"28 20.0 10.0 48.0 ... 23.0 \n",
"29 11.0 9.0 5.0 ... 11.0 \n",
"... ... ... ... ... ... \n",
"145033 NaN NaN NaN ... 19.0 \n",
"145034 NaN NaN NaN ... 3.0 \n",
"145035 NaN NaN NaN ... 10.0 \n",
"145036 NaN NaN NaN ... 1.0 \n",
"145037 NaN NaN NaN ... 18.0 \n",
"145038 NaN NaN NaN ... 7.0 \n",
"145039 NaN NaN NaN ... 14.0 \n",
"145040 NaN NaN NaN ... 3.0 \n",
"145041 NaN NaN NaN ... 10.0 \n",
"145042 NaN NaN NaN ... 1.0 \n",
"145043 NaN NaN NaN ... 4.0 \n",
"145044 NaN NaN NaN ... 2.0 \n",
"145045 NaN NaN NaN ... 2.0 \n",
"145046 NaN NaN NaN ... 6.0 \n",
"145047 NaN NaN NaN ... 5.0 \n",
"145048 NaN NaN NaN ... 26.0 \n",
"145049 NaN NaN NaN ... 6.0 \n",
"145050 NaN NaN NaN ... 1.0 \n",
"145051 NaN NaN NaN ... 22.0 \n",
"145052 NaN NaN NaN ... 5.0 \n",
"145053 NaN NaN NaN ... 13.0 \n",
"145054 NaN NaN NaN ... 8.0 \n",
"145055 NaN NaN NaN ... 1.0 \n",
"145056 NaN NaN NaN ... 21.0 \n",
"145057 NaN NaN NaN ... 0.0 \n",
"145058 NaN NaN NaN ... NaN \n",
"145059 NaN NaN NaN ... NaN \n",
"145060 NaN NaN NaN ... NaN \n",
"145061 NaN NaN NaN ... NaN \n",
"145062 NaN NaN NaN ... NaN \n",
"\n",
" 2016-12-23 2016-12-24 2016-12-25 2016-12-26 2016-12-27 \\\n",
"0 63.0 15.0 26.0 14.0 20.0 \n",
"1 42.0 28.0 15.0 9.0 30.0 \n",
"2 1.0 1.0 7.0 4.0 4.0 \n",
"3 10.0 26.0 27.0 16.0 11.0 \n",
"4 9.0 25.0 13.0 3.0 11.0 \n",
"5 27.0 8.0 17.0 32.0 19.0 \n",
"6 7.0 33.0 8.0 11.0 4.0 \n",
"7 35.0 35.0 28.0 20.0 23.0 \n",
"8 105.0 72.0 36.0 33.0 30.0 \n",
"9 17.0 22.0 29.0 30.0 29.0 \n",
"10 38.0 85.0 79.0 30.0 14.0 \n",
"11 17.0 18.0 48.0 19.0 14.0 \n",
"12 4.0 4.0 5.0 2.0 9.0 \n",
"13 13.0 14.0 17.0 19.0 56.0 \n",
"14 36.0 46.0 42.0 40.0 40.0 \n",
"15 40.0 26.0 30.0 68.0 31.0 \n",
"16 24.0 35.0 34.0 24.0 34.0 \n",
"17 14.0 8.0 11.0 8.0 24.0 \n",
"18 12.0 7.0 11.0 13.0 17.0 \n",
"19 26.0 10.0 5.0 5.0 11.0 \n",
"20 23.0 52.0 60.0 14.0 19.0 \n",
"21 18.0 23.0 17.0 20.0 19.0 \n",
"22 13.0 20.0 30.0 16.0 24.0 \n",
"23 63.0 80.0 29.0 37.0 40.0 \n",
"24 56.0 39.0 65.0 78.0 143.0 \n",
"25 13.0 14.0 15.0 14.0 21.0 \n",
"26 17.0 14.0 17.0 28.0 23.0 \n",
"27 8.0 6.0 7.0 2.0 11.0 \n",
"28 35.0 53.0 35.0 20.0 29.0 \n",
"29 20.0 9.0 13.0 7.0 17.0 \n",
"... ... ... ... ... ... \n",
"145033 40.0 22.0 28.0 13.0 53.0 \n",
"145034 9.0 1.0 9.0 5.0 5.0 \n",
"145035 5.0 14.0 8.0 54.0 18.0 \n",
"145036 1.0 2.0 5.0 5.0 1.0 \n",
"145037 8.0 3.0 4.0 31.0 5.0 \n",
"145038 23.0 28.0 31.0 26.0 12.0 \n",
"145039 46.0 51.0 11.0 11.0 14.0 \n",
"145040 4.0 13.0 10.0 2.0 4.0 \n",
"145041 9.0 8.0 37.0 7.0 5.0 \n",
"145042 2.0 23.0 11.0 4.0 25.0 \n",
"145043 7.0 3.0 4.0 4.0 2.0 \n",
"145044 7.0 8.0 20.0 27.0 11.0 \n",
"145045 1.0 2.0 0.0 0.0 1.0 \n",
"145046 4.0 4.0 6.0 5.0 5.0 \n",
"145047 3.0 10.0 1.0 6.0 22.0 \n",
"145048 25.0 7.0 5.0 8.0 25.0 \n",
"145049 35.0 10.0 3.0 4.0 1.0 \n",
"145050 2.0 7.0 2.0 3.0 2.0 \n",
"145051 74.0 222.0 2.0 16.0 21.0 \n",
"145052 83.0 44.0 36.0 9.0 4.0 \n",
"145053 7.0 6.0 3.0 3.0 8.0 \n",
"145054 9.0 9.0 19.0 17.0 7.0 \n",
"145055 2.0 1.0 1.0 3.0 4.0 \n",
"145056 NaN NaN NaN NaN NaN \n",
"145057 0.0 NaN NaN NaN NaN \n",
"145058 NaN NaN NaN 13.0 12.0 \n",
"145059 NaN NaN NaN NaN NaN \n",
"145060 NaN NaN NaN NaN NaN \n",
"145061 NaN NaN NaN NaN NaN \n",
"145062 NaN NaN NaN NaN NaN \n",
"\n",
" 2016-12-28 2016-12-29 2016-12-30 2016-12-31 \n",
"0 22.0 19.0 18.0 20.0 \n",
"1 52.0 45.0 26.0 20.0 \n",
"2 6.0 3.0 4.0 17.0 \n",
"3 17.0 19.0 10.0 11.0 \n",
"4 27.0 13.0 36.0 10.0 \n",
"5 23.0 17.0 17.0 50.0 \n",
"6 15.0 6.0 8.0 6.0 \n",
"7 32.0 39.0 32.0 17.0 \n",
"8 36.0 38.0 31.0 97.0 \n",
"9 35.0 44.0 26.0 41.0 \n",
"10 10.0 38.0 12.0 51.0 \n",
"11 9.0 23.0 11.0 7.0 \n",
"12 7.0 4.0 5.0 0.0 \n",
"13 21.0 13.0 21.0 11.0 \n",
"14 66.0 43.0 38.0 74.0 \n",
"15 77.0 42.0 111.0 37.0 \n",
"16 28.0 44.0 12.0 31.0 \n",
"17 10.0 15.0 12.0 11.0 \n",
"18 13.0 12.0 9.0 8.0 \n",
"19 10.0 4.0 8.0 6.0 \n",
"20 38.0 30.0 21.0 24.0 \n",
"21 20.0 38.0 21.0 16.0 \n",
"22 47.0 26.0 13.0 13.0 \n",
"23 104.0 39.0 32.0 34.0 \n",
"24 96.0 63.0 28.0 75.0 \n",
"25 15.0 16.0 15.0 28.0 \n",
"26 32.0 37.0 42.0 60.0 \n",
"27 11.0 29.0 12.0 14.0 \n",
"28 40.0 28.0 39.0 75.0 \n",
"29 13.0 20.0 19.0 13.0 \n",
"... ... ... ... ... \n",
"145033 12.0 32.0 11.0 62.0 \n",
"145034 5.0 8.0 5.0 2.0 \n",
"145035 46.0 70.0 15.0 6.0 \n",
"145036 0.0 1.0 1.0 1.0 \n",
"145037 1.0 2.0 0.0 2.0 \n",
"145038 13.0 12.0 9.0 10.0 \n",
"145039 26.0 13.0 12.0 7.0 \n",
"145040 4.0 3.0 1.0 2.0 \n",
"145041 9.0 7.0 10.0 4.0 \n",
"145042 2.0 14.0 2.0 14.0 \n",
"145043 4.0 5.0 2.0 2.0 \n",
"145044 7.0 17.0 13.0 40.0 \n",
"145045 1.0 1.0 0.0 1.0 \n",
"145046 13.0 7.0 11.0 7.0 \n",
"145047 34.0 1.0 3.0 29.0 \n",
"145048 2.0 8.0 3.0 1.0 \n",
"145049 31.0 27.0 9.0 135.0 \n",
"145050 18.0 40.0 1.0 42.0 \n",
"145051 7.0 34.0 37.0 42.0 \n",
"145052 17.0 6.0 11.0 5.0 \n",
"145053 21.0 14.0 24.0 37.0 \n",
"145054 13.0 12.0 31.0 11.0 \n",
"145055 2.0 4.0 4.0 3.0 \n",
"145056 NaN NaN NaN 51.0 \n",
"145057 NaN NaN NaN NaN \n",
"145058 13.0 3.0 5.0 10.0 \n",
"145059 NaN NaN NaN NaN \n",
"145060 NaN NaN NaN NaN \n",
"145061 NaN NaN NaN NaN \n",
"145062 NaN NaN NaN NaN \n",
"\n",
"[145063 rows x 551 columns]"
]
},
"execution_count": 3,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df_train"
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {},
"outputs": [],
"source": [
"df_train_0 = df_train.fillna(0) #Fillna，反正分不出是0還是na就通通當成0"
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {},
"outputs": [],
"source": [
"page_list = df_train['Page'].tolist()"
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"page_list.sort() #排序page才方便跟test欄位比較"
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"['!vote_en.wikipedia.org_all-access_all-agents',\n",
" '!vote_en.wikipedia.org_all-access_spider',\n",
" '!vote_en.wikipedia.org_desktop_all-agents',\n",
" '\"Awaken,_My_Love!\"_en.wikipedia.org_all-access_all-agents',\n",
" '\"Awaken,_My_Love!\"_en.wikipedia.org_all-access_spider',\n",
" '\"Awaken,_My_Love!\"_en.wikipedia.org_desktop_all-agents',\n",
" '\"European_Society_for_Clinical_Investigation\"_en.wikipedia.org_all-access_all-agents',\n",
" '\"European_Society_for_Clinical_Investigation\"_en.wikipedia.org_all-access_spider',\n",
" '\"European_Society_for_Clinical_Investigation\"_en.wikipedia.org_desktop_all-agents',\n",
" '\"Heroes\"_(David_Bowie_album)_en.wikipedia.org_all-access_all-agents',\n",
" '\"Heroes\"_(David_Bowie_album)_en.wikipedia.org_all-access_spider',\n",
" '\"Heroes\"_(David_Bowie_album)_en.wikipedia.org_desktop_all-agents',\n",
" '\"Keep_me_logged_in\"_extended_to_one_year_www.mediawiki.org_all-access_all-agents',\n",
" '\"Keep_me_logged_in\"_extended_to_one_year_www.mediawiki.org_all-access_spider',\n",
" '\"Keep_me_logged_in\"_extended_to_one_year_www.mediawiki.org_desktop_all-agents',\n",
" '\"Keep_me_logged_in\"_extended_to_one_year_www.mediawiki.org_mobile-web_all-agents',\n",
" '\"Weird_Al\"_Yankovic_en.wikipedia.org_all-access_all-agents',\n",
" '\"Weird_Al\"_Yankovic_en.wikipedia.org_all-access_spider',\n",
" '\"Weird_Al\"_Yankovic_en.wikipedia.org_desktop_all-agents',\n",
" '%s_www.mediawiki.org_mobile-web_all-agents',\n",
" \"'Tis_the_Season_(Vince_Gill_and_Olivia_Newton-John_album)_en.wikipedia.org_all-access_all-agents\",\n",
" \"'Tis_the_Season_(Vince_Gill_and_Olivia_Newton-John_album)_en.wikipedia.org_all-access_spider\",\n",
" \"'Tis_the_Season_(Vince_Gill_and_Olivia_Newton-John_album)_en.wikipedia.org_mobile-web_all-agents\",\n",
" \"'Tis_the_Season_en.wikipedia.org_all-access_all-agents\",\n",
" \"'Tis_the_Season_en.wikipedia.org_all-access_spider\",\n",
" \"'Tis_the_Season_en.wikipedia.org_desktop_all-agents\",\n",
" \"'Tis_the_Season_en.wikipedia.org_mobile-web_all-agents\",\n",
" '(1)_Ceres_de.wikipedia.org_desktop_all-agents',\n",
" '(236984)_Astier_fr.wikipedia.org_all-access_all-agents',\n",
" '(236984)_Astier_fr.wikipedia.org_all-access_spider',\n",
" '(236984)_Astier_fr.wikipedia.org_mobile-web_all-agents',\n",
" '(500)_Days_of_Summer_es.wikipedia.org_mobile-web_all-agents',\n",
" '...The_Stories_We_Could_Tell_en.wikipedia.org_all-access_all-agents',\n",
" '.bn_ru.wikipedia.org_desktop_all-agents',\n",
" '.ca_ru.wikipedia.org_desktop_all-agents',\n",
" '.odc_en.wikipedia.org_all-access_all-agents',\n",
" '.xxx_en.wikipedia.org_all-access_all-agents',\n",
" '.xxx_en.wikipedia.org_all-access_spider',\n",
" '.xxx_en.wikipedia.org_mobile-web_all-agents',\n",
" '0.999…_zh.wikipedia.org_all-access_all-agents',\n",
" '0.999…_zh.wikipedia.org_all-access_spider',\n",
" '0.999…_zh.wikipedia.org_desktop_all-agents',\n",
" '0.999…_zh.wikipedia.org_mobile-web_all-agents',\n",
" '007:_Координаты_«Скайфолл»_ru.wikipedia.org_mobile-web_all-agents',\n",
" '007:_Спектр_ru.wikipedia.org_all-access_all-agents',\n",
" '007:_Спектр_ru.wikipedia.org_all-access_spider',\n",
" '007:_Спектр_ru.wikipedia.org_desktop_all-agents',\n",
" '007:_Спектр_ru.wikipedia.org_mobile-web_all-agents',\n",
" '007_Spectre_fr.wikipedia.org_all-access_all-agents',\n",
" '007_Spectre_fr.wikipedia.org_all-access_spider',\n",
" '007_Spectre_fr.wikipedia.org_desktop_all-agents',\n",
" '007_Spectre_fr.wikipedia.org_mobile-web_all-agents',\n",
" '007_スカイフォール_ja.wikipedia.org_desktop_all-agents',\n",
" '007_スペクター_ja.wikipedia.org_all-access_all-agents',\n",
" '007_スペクター_ja.wikipedia.org_all-access_spider',\n",
" '007_スペクター_ja.wikipedia.org_desktop_all-agents',\n",
" '007_スペクター_ja.wikipedia.org_mobile-web_all-agents',\n",
" '007：惡魔四伏_zh.wikipedia.org_all-access_all-agents',\n",
" '007：惡魔四伏_zh.wikipedia.org_all-access_spider',\n",
" '007：惡魔四伏_zh.wikipedia.org_desktop_all-agents',\n",
" '007：惡魔四伏_zh.wikipedia.org_mobile-web_all-agents',\n",
" '052D型导弹驱逐舰_zh.wikipedia.org_desktop_all-agents',\n",
" '0_en.wikipedia.org_desktop_all-agents',\n",
" '0_ja.wikipedia.org_desktop_all-agents',\n",
" '1%的可能性_(2016年電視劇)_zh.wikipedia.org_all-access_all-agents',\n",
" '1%的可能性_(2016年電視劇)_zh.wikipedia.org_all-access_spider',\n",
" '1%的可能性_(2016年電視劇)_zh.wikipedia.org_desktop_all-agents',\n",
" '1%的可能性_(2016年電視劇)_zh.wikipedia.org_mobile-web_all-agents',\n",
" '1%的可能性_zh.wikipedia.org_desktop_all-agents',\n",
" '1+1_(фильм)_ru.wikipedia.org_all-access_all-agents',\n",
" '1+1_(фильм)_ru.wikipedia.org_all-access_spider',\n",
" '1+1_(фильм)_ru.wikipedia.org_desktop_all-agents',\n",
" '1+1_(фильм)_ru.wikipedia.org_mobile-web_all-agents',\n",
" '1._April_de.wikipedia.org_desktop_all-agents',\n",
" '1._August_de.wikipedia.org_desktop_all-agents',\n",
" '1._Dezember_de.wikipedia.org_desktop_all-agents',\n",
" '1._FC_Nürnberg_de.wikipedia.org_all-access_all-agents',\n",
" '1._FC_Nürnberg_de.wikipedia.org_all-access_spider',\n",
" '1._FC_Nürnberg_de.wikipedia.org_mobile-web_all-agents',\n",
" '1._Februar_de.wikipedia.org_desktop_all-agents',\n",
" '1._Juli_de.wikipedia.org_desktop_all-agents',\n",
" '1._Juni_de.wikipedia.org_desktop_all-agents',\n",
" '1._Mai_de.wikipedia.org_all-access_all-agents',\n",
" '1._Mai_de.wikipedia.org_all-access_spider',\n",
" '1._Mai_de.wikipedia.org_desktop_all-agents',\n",
" '1._Mai_de.wikipedia.org_mobile-web_all-agents',\n",
" '1._März_de.wikipedia.org_desktop_all-agents',\n",
" '1._September_de.wikipedia.org_desktop_all-agents',\n",
" '10,000_metres_en.wikipedia.org_mobile-web_all-agents',\n",
" '10._August_de.wikipedia.org_desktop_all-agents',\n",
" '10._Februar_de.wikipedia.org_desktop_all-agents',\n",
" '10._Januar_de.wikipedia.org_desktop_all-agents',\n",
" '10._Juni_de.wikipedia.org_desktop_all-agents',\n",
" '10._Mai_de.wikipedia.org_desktop_all-agents',\n",
" '10._März_de.wikipedia.org_desktop_all-agents',\n",
" '10._November_de.wikipedia.org_desktop_all-agents',\n",
" '10._Oktober_de.wikipedia.org_desktop_all-agents',\n",
" '100-Meter-Lauf_de.wikipedia.org_all-access_all-agents',\n",
" '100-Meter-Lauf_de.wikipedia.org_all-access_spider',\n",
" '100-Meter-Lauf_de.wikipedia.org_desktop_all-agents',\n",
" '100-Meter-Lauf_de.wikipedia.org_mobile-web_all-agents',\n",
" '100_(телесериал)_ru.wikipedia.org_all-access_all-agents',\n",
" '100_(телесериал)_ru.wikipedia.org_all-access_spider',\n",
" '100_(телесериал)_ru.wikipedia.org_desktop_all-agents',\n",
" '100_(телесериал)_ru.wikipedia.org_mobile-web_all-agents',\n",
" '100_metres_en.wikipedia.org_all-access_all-agents',\n",
" '100_metres_en.wikipedia.org_all-access_spider',\n",
" '100_metres_en.wikipedia.org_desktop_all-agents',\n",
" '100_metres_en.wikipedia.org_mobile-web_all-agents',\n",
" '100_metros_es.wikipedia.org_all-access_all-agents',\n",
" '100_metros_es.wikipedia.org_all-access_spider',\n",
" '100_metros_es.wikipedia.org_desktop_all-agents',\n",
" '100_metros_es.wikipedia.org_mobile-web_all-agents',\n",
" '100_mètres_(athlétisme)_fr.wikipedia.org_all-access_all-agents',\n",
" '100_mètres_(athlétisme)_fr.wikipedia.org_all-access_spider',\n",
" '100_mètres_(athlétisme)_fr.wikipedia.org_desktop_all-agents',\n",
" '100_mètres_(athlétisme)_fr.wikipedia.org_mobile-web_all-agents',\n",
" '100_лучших_героев_комиксов_по_версии_IGN_ru.wikipedia.org_all-access_all-agents',\n",
" '100_лучших_героев_комиксов_по_версии_IGN_ru.wikipedia.org_all-access_spider',\n",
" '100_лучших_героев_комиксов_по_версии_IGN_ru.wikipedia.org_desktop_all-agents',\n",
" '100_лучших_героев_комиксов_по_версии_IGN_ru.wikipedia.org_mobile-web_all-agents',\n",
" '100_лучших_злодеев_комиксов_по_версии_IGN_ru.wikipedia.org_all-access_all-agents',\n",
" '100_лучших_злодеев_комиксов_по_версии_IGN_ru.wikipedia.org_all-access_spider',\n",
" '100_лучших_злодеев_комиксов_по_версии_IGN_ru.wikipedia.org_mobile-web_all-agents',\n",
" '100_лучших_книг_всех_времён_ru.wikipedia.org_all-access_all-agents',\n",
" '100_лучших_книг_всех_времён_ru.wikipedia.org_all-access_spider',\n",
" '100_лучших_книг_всех_времён_ru.wikipedia.org_desktop_all-agents',\n",
" '100_лучших_книг_всех_времён_ru.wikipedia.org_mobile-web_all-agents',\n",
" '100メートル競走_ja.wikipedia.org_all-access_all-agents',\n",
" '100メートル競走_ja.wikipedia.org_all-access_spider',\n",
" '100メートル競走_ja.wikipedia.org_desktop_all-agents',\n",
" '100メートル競走_ja.wikipedia.org_mobile-web_all-agents',\n",
" '100毛_zh.wikipedia.org_all-access_all-agents',\n",
" '100毛_zh.wikipedia.org_all-access_spider',\n",
" '100毛_zh.wikipedia.org_desktop_all-agents',\n",
" '100毛_zh.wikipedia.org_mobile-web_all-agents',\n",
" '100米賽跑_zh.wikipedia.org_all-access_all-agents',\n",
" '100米賽跑_zh.wikipedia.org_all-access_spider',\n",
" '100米賽跑_zh.wikipedia.org_mobile-web_all-agents',\n",
" '101st_Military_Intelligence_Battalion_en.wikipedia.org_all-access_all-agents',\n",
" '10_000_mètres_fr.wikipedia.org_desktop_all-agents',\n",
" '10_Cloverfield_Lane_de.wikipedia.org_all-access_all-agents',\n",
" '10_Cloverfield_Lane_de.wikipedia.org_all-access_spider',\n",
" '10_Cloverfield_Lane_de.wikipedia.org_desktop_all-agents',\n",
" '10_Cloverfield_Lane_de.wikipedia.org_mobile-web_all-agents',\n",
" '10_Cloverfield_Lane_en.wikipedia.org_all-access_all-agents',\n",
" '10_Cloverfield_Lane_en.wikipedia.org_all-access_spider',\n",
" '10_Cloverfield_Lane_en.wikipedia.org_desktop_all-agents',\n",
" '10_Cloverfield_Lane_en.wikipedia.org_mobile-web_all-agents',\n",
" '10_Cloverfield_Lane_es.wikipedia.org_all-access_all-agents',\n",
" '10_Cloverfield_Lane_es.wikipedia.org_all-access_spider',\n",
" '10_Cloverfield_Lane_es.wikipedia.org_desktop_all-agents',\n",
" '10_Cloverfield_Lane_es.wikipedia.org_mobile-web_all-agents',\n",
" '10_Cloverfield_Lane_fr.wikipedia.org_all-access_all-agents',\n",
" '10_Cloverfield_Lane_fr.wikipedia.org_all-access_spider',\n",
" '10_Cloverfield_Lane_fr.wikipedia.org_desktop_all-agents',\n",
" '10_Cloverfield_Lane_fr.wikipedia.org_mobile-web_all-agents',\n",
" '10_Dinge,_die_ich_an_Dir_hasse_de.wikipedia.org_mobile-web_all-agents',\n",
" '10_Gigabit_Ethernet_en.wikipedia.org_desktop_all-agents',\n",
" '10th_National_Congress_of_the_Communist_Party_of_China_en.wikipedia.org_all-access_all-agents',\n",
" '10式戦車_ja.wikipedia.org_desktop_all-agents',\n",
" '10月10日_ja.wikipedia.org_desktop_all-agents',\n",
" '10月11日_ja.wikipedia.org_desktop_all-agents',\n",
" '10月12日_ja.wikipedia.org_desktop_all-agents',\n",
" '10月13日_ja.wikipedia.org_desktop_all-agents',\n",
" '10月14日_ja.wikipedia.org_desktop_all-agents',\n",
" '10月17日_ja.wikipedia.org_desktop_all-agents',\n",
" '10月18日_ja.wikipedia.org_desktop_all-agents',\n",
" '10月19日_ja.wikipedia.org_desktop_all-agents',\n",
" '10月20日_ja.wikipedia.org_desktop_all-agents',\n",
" '10月21日_ja.wikipedia.org_desktop_all-agents',\n",
" '10月24日_ja.wikipedia.org_desktop_all-agents',\n",
" '10月25日_ja.wikipedia.org_desktop_all-agents',\n",
" '10月26日_ja.wikipedia.org_desktop_all-agents',\n",
" '10月27日_ja.wikipedia.org_desktop_all-agents',\n",
" '10月28日_ja.wikipedia.org_desktop_all-agents',\n",
" '10月31日_ja.wikipedia.org_desktop_all-agents',\n",
" '10月31日_zh.wikipedia.org_all-access_all-agents',\n",
" '10月31日_zh.wikipedia.org_all-access_spider',\n",
" '10月31日_zh.wikipedia.org_desktop_all-agents',\n",
" '10月31日_zh.wikipedia.org_mobile-web_all-agents',\n",
" '10月4日_ja.wikipedia.org_desktop_all-agents',\n",
" '10月5日_ja.wikipedia.org_desktop_all-agents',\n",
" '10月6日_ja.wikipedia.org_desktop_all-agents',\n",
" '10月7日_ja.wikipedia.org_desktop_all-agents',\n",
" '10月8日_ja.wikipedia.org_desktop_all-agents',\n",
" '11.22.63_en.wikipedia.org_all-access_all-agents',\n",
" '11.22.63_en.wikipedia.org_all-access_spider',\n",
" '11.22.63_en.wikipedia.org_desktop_all-agents',\n",
" '11.22.63_en.wikipedia.org_mobile-web_all-agents',\n",
" '11.22.63_ru.wikipedia.org_all-access_all-agents',\n",
" '11.22.63_ru.wikipedia.org_all-access_spider',\n",
" '11.22.63_ru.wikipedia.org_desktop_all-agents',\n",
" '11._April_de.wikipedia.org_desktop_all-agents',\n",
" '11._August_de.wikipedia.org_desktop_all-agents',\n",
" '11._Februar_de.wikipedia.org_desktop_all-agents',\n",
" '11._Januar_de.wikipedia.org_desktop_all-agents',\n",
" '11._Juli_de.wikipedia.org_desktop_all-agents',\n",
" '11._Mai_de.wikipedia.org_desktop_all-agents',\n",
" '11._März_de.wikipedia.org_desktop_all-agents',\n",
" '11._November_de.wikipedia.org_all-access_all-agents',\n",
" '11._November_de.wikipedia.org_all-access_spider',\n",
" '11._November_de.wikipedia.org_desktop_all-agents',\n",
" '11._Oktober_de.wikipedia.org_desktop_all-agents',\n",
" '11/22/63_en.wikipedia.org_all-access_all-agents',\n",
" '11/22/63_en.wikipedia.org_all-access_spider',\n",
" '11/22/63_en.wikipedia.org_mobile-web_all-agents',\n",
" '11/22/63_ru.wikipedia.org_all-access_all-agents',\n",
" '11/22/63_ru.wikipedia.org_all-access_spider',\n",
" '11/22/63_ru.wikipedia.org_desktop_all-agents',\n",
" '112_de_Ocean_Avenue_(Amityville)_es.wikipedia.org_all-access_all-agents',\n",
" '112_de_Ocean_Avenue_(Amityville)_es.wikipedia.org_all-access_spider',\n",
" '112_de_Ocean_Avenue_(Amityville)_es.wikipedia.org_mobile-web_all-agents',\n",
" '115th_United_States_Congress_en.wikipedia.org_desktop_all-agents',\n",
" '11_novembre_fr.wikipedia.org_all-access_all-agents',\n",
" '11_novembre_fr.wikipedia.org_all-access_spider',\n",
" '11_novembre_fr.wikipedia.org_mobile-web_all-agents',\n",
" '11人もいる!_ja.wikipedia.org_mobile-web_all-agents',\n",
" '11月10日_ja.wikipedia.org_desktop_all-agents',\n",
" '11月11日_ja.wikipedia.org_all-access_all-agents',\n",
" '11月11日_ja.wikipedia.org_all-access_spider',\n",
" '11月11日_ja.wikipedia.org_desktop_all-agents',\n",
" '11月11日_ja.wikipedia.org_mobile-web_all-agents',\n",
" '11月12日_ja.wikipedia.org_desktop_all-agents',\n",
" '11月14日_ja.wikipedia.org_desktop_all-agents',\n",
" '11月15日_ja.wikipedia.org_desktop_all-agents',\n",
" '11月16日_ja.wikipedia.org_desktop_all-agents',\n",
" '11月17日_ja.wikipedia.org_desktop_all-agents',\n",
" '11月18日_ja.wikipedia.org_desktop_all-agents',\n",
" '11月1日_ja.wikipedia.org_desktop_all-agents',\n",
" '11月21日_ja.wikipedia.org_desktop_all-agents',\n",
" '11月22日_ja.wikipedia.org_desktop_all-agents',\n",
" '11月23日_ja.wikipedia.org_desktop_all-agents',\n",
" '11月24日_ja.wikipedia.org_desktop_all-agents',\n",
" '11月25日_ja.wikipedia.org_desktop_all-agents',\n",
" '11月26日_ja.wikipedia.org_desktop_all-agents',\n",
" '11月27日_ja.wikipedia.org_desktop_all-agents',\n",
" '11月28日_ja.wikipedia.org_desktop_all-agents',\n",
" '11月29日_ja.wikipedia.org_desktop_all-agents',\n",
" '11月2日_ja.wikipedia.org_desktop_all-agents',\n",
" '11月30日_ja.wikipedia.org_desktop_all-agents',\n",
" '11月3日_ja.wikipedia.org_desktop_all-agents',\n",
" '11月4日_ja.wikipedia.org_desktop_all-agents',\n",
" '11月7日_ja.wikipedia.org_desktop_all-agents',\n",
" '11月8日_ja.wikipedia.org_desktop_all-agents',\n",
" '11月9日_ja.wikipedia.org_desktop_all-agents',\n",
" '11月_ja.wikipedia.org_all-access_all-agents',\n",
" '11月_ja.wikipedia.org_all-access_spider',\n",
" '11月_ja.wikipedia.org_desktop_all-agents',\n",
" '12-часовой_формат_времени_ru.wikipedia.org_all-access_all-agents',\n",
" '12-часовой_формат_времени_ru.wikipedia.org_all-access_spider',\n",
" '12-часовой_формат_времени_ru.wikipedia.org_desktop_all-agents',\n",
" '12-часовой_формат_времени_ru.wikipedia.org_mobile-web_all-agents',\n",
" '12._April_de.wikipedia.org_desktop_all-agents',\n",
" '12._August_de.wikipedia.org_desktop_all-agents',\n",
" '12._Dezember_de.wikipedia.org_desktop_all-agents',\n",
" '12._Februar_de.wikipedia.org_desktop_all-agents',\n",
" '12._Januar_de.wikipedia.org_desktop_all-agents',\n",
" '12._Juli_de.wikipedia.org_desktop_all-agents',\n",
" '12._Mai_de.wikipedia.org_desktop_all-agents',\n",
" '12._Oktober_de.wikipedia.org_desktop_all-agents',\n",
" '12._September_de.wikipedia.org_desktop_all-agents',\n",
" '127_heures_fr.wikipedia.org_mobile-web_all-agents',\n",
" '127_horas_es.wikipedia.org_all-access_all-agents',\n",
" '127_horas_es.wikipedia.org_all-access_spider',\n",
" '127_horas_es.wikipedia.org_mobile-web_all-agents',\n",
" '12_Monkeys_(série_télévisée)_fr.wikipedia.org_desktop_all-agents',\n",
" '12_Monkeys_de.wikipedia.org_all-access_all-agents',\n",
" '12_Monkeys_de.wikipedia.org_all-access_spider',\n",
" '12_Monkeys_de.wikipedia.org_mobile-web_all-agents',\n",
" '12_Years_a_Slave_de.wikipedia.org_all-access_all-agents',\n",
" '12_Years_a_Slave_de.wikipedia.org_all-access_spider',\n",
" '12_Years_a_Slave_de.wikipedia.org_desktop_all-agents',\n",
" '12_Years_a_Slave_de.wikipedia.org_mobile-web_all-agents',\n",
" '12_años_de_esclavitud_es.wikipedia.org_mobile-web_all-agents',\n",
" '12_de_octubre_es.wikipedia.org_all-access_all-agents',\n",
" '12_de_octubre_es.wikipedia.org_all-access_spider',\n",
" '12_de_octubre_es.wikipedia.org_desktop_all-agents',\n",
" '12_de_octubre_es.wikipedia.org_mobile-web_all-agents',\n",
" '12_июня_ru.wikipedia.org_all-access_all-agents',\n",
" '12_июня_ru.wikipedia.org_all-access_spider',\n",
" '12_июня_ru.wikipedia.org_desktop_all-agents',\n",
" '12_июня_ru.wikipedia.org_mobile-web_all-agents',\n",
" '12th_Arkansas_Infantry_Regiment_en.wikipedia.org_all-access_all-agents',\n",
" '12式地対艦誘導弾_ja.wikipedia.org_all-access_all-agents',\n",
" '12式地対艦誘導弾_ja.wikipedia.org_all-access_spider',\n",
" '12式地対艦誘導弾_ja.wikipedia.org_desktop_all-agents',\n",
" '12月12日_ja.wikipedia.org_desktop_all-agents',\n",
" '12月13日_ja.wikipedia.org_desktop_all-agents',\n",
" '12月14日_ja.wikipedia.org_desktop_all-agents',\n",
" '12月15日_ja.wikipedia.org_desktop_all-agents',\n",
" '12月16日_ja.wikipedia.org_desktop_all-agents',\n",
" '12月19日_ja.wikipedia.org_desktop_all-agents',\n",
" '12月20日_ja.wikipedia.org_desktop_all-agents',\n",
" '12月21日_ja.wikipedia.org_desktop_all-agents',\n",
" '12月22日_ja.wikipedia.org_desktop_all-agents',\n",
" '12月23日_ja.wikipedia.org_desktop_all-agents',\n",
" '12月24日_ja.wikipedia.org_desktop_all-agents',\n",
" '12月25日_ja.wikipedia.org_desktop_all-agents',\n",
" '12月26日_ja.wikipedia.org_desktop_all-agents',\n",
" '12月27日_ja.wikipedia.org_desktop_all-agents',\n",
" '12月28日_ja.wikipedia.org_desktop_all-agents',\n",
" '12月2日_ja.wikipedia.org_desktop_all-agents',\n",
" '12月5日_ja.wikipedia.org_desktop_all-agents',\n",
" '12月6日_ja.wikipedia.org_desktop_all-agents',\n",
" '12月7日_ja.wikipedia.org_desktop_all-agents',\n",
" '12月8日_ja.wikipedia.org_desktop_all-agents',\n",
" '12月9日_ja.wikipedia.org_desktop_all-agents',\n",
" '12歳。_ja.wikipedia.org_all-access_all-agents',\n",
" '12歳。_ja.wikipedia.org_all-access_spider',\n",
" '12歳。_ja.wikipedia.org_desktop_all-agents',\n",
" '12歳。_ja.wikipedia.org_mobile-web_all-agents',\n",
" '13._April_de.wikipedia.org_desktop_all-agents',\n",
" '13._Dezember_de.wikipedia.org_desktop_all-agents',\n",
" '13._Februar_de.wikipedia.org_desktop_all-agents',\n",
" '13._Januar_de.wikipedia.org_desktop_all-agents',\n",
" '13._Juli_de.wikipedia.org_desktop_all-agents',\n",
" '13._Juni_de.wikipedia.org_desktop_all-agents',\n",
" '13._Mai_de.wikipedia.org_desktop_all-agents',\n",
" '13._Oktober_de.wikipedia.org_desktop_all-agents',\n",
" '13._September_de.wikipedia.org_desktop_all-agents',\n",
" '13_(Film)_de.wikipedia.org_mobile-web_all-agents',\n",
" '13_Hours:_The_Secret_Soldiers_of_Benghazi_en.wikipedia.org_all-access_all-agents',\n",
" '13_Hours:_The_Secret_Soldiers_of_Benghazi_en.wikipedia.org_all-access_spider',\n",
" '13_Hours:_The_Secret_Soldiers_of_Benghazi_en.wikipedia.org_desktop_all-agents',\n",
" '13_Hours:_The_Secret_Soldiers_of_Benghazi_en.wikipedia.org_mobile-web_all-agents',\n",
" '13_de_septiembre_es.wikipedia.org_all-access_all-agents',\n",
" '13_de_septiembre_es.wikipedia.org_all-access_spider',\n",
" '13_de_septiembre_es.wikipedia.org_desktop_all-agents',\n",
" '13_de_septiembre_es.wikipedia.org_mobile-web_all-agents',\n",
" '13小時：班加西的秘密士兵_zh.wikipedia.org_all-access_all-agents',\n",
" '13小時：班加西的秘密士兵_zh.wikipedia.org_all-access_spider',\n",
" '13小時：班加西的秘密士兵_zh.wikipedia.org_desktop_all-agents',\n",
" '13小時：班加西的秘密士兵_zh.wikipedia.org_mobile-web_all-agents',\n",
" '13日の金曜日_ja.wikipedia.org_all-access_all-agents',\n",
" '13日の金曜日_ja.wikipedia.org_all-access_spider',\n",
" '13日の金曜日_ja.wikipedia.org_desktop_all-agents',\n",
" '13日の金曜日_ja.wikipedia.org_mobile-web_all-agents',\n",
" '13號星期五_zh.wikipedia.org_mobile-web_all-agents',\n",
" '14._April_de.wikipedia.org_desktop_all-agents',\n",
" '14._Dezember_de.wikipedia.org_desktop_all-agents',\n",
" '14._Februar_de.wikipedia.org_all-access_all-agents',\n",
" '14._Februar_de.wikipedia.org_all-access_spider',\n",
" '14._Februar_de.wikipedia.org_desktop_all-agents',\n",
" '14._Januar_de.wikipedia.org_desktop_all-agents',\n",
" '14._Juli_de.wikipedia.org_desktop_all-agents',\n",
" '14._Juni_de.wikipedia.org_desktop_all-agents',\n",
" '14._März_de.wikipedia.org_desktop_all-agents',\n",
" '14._November_de.wikipedia.org_desktop_all-agents',\n",
" '14._Oktober_de.wikipedia.org_desktop_all-agents',\n",
" '14._September_de.wikipedia.org_desktop_all-agents',\n",
" '14/88_ru.wikipedia.org_all-access_all-agents',\n",
" '14/88_ru.wikipedia.org_all-access_spider',\n",
" '14/88_ru.wikipedia.org_mobile-web_all-agents',\n",
" '14627_Emilkowalski_en.wikipedia.org_all-access_all-agents',\n",
" '14_de_febrero_es.wikipedia.org_all-access_all-agents',\n",
" '14_de_febrero_es.wikipedia.org_all-access_spider',\n",
" '14_de_febrero_es.wikipedia.org_desktop_all-agents',\n",
" '14_de_febrero_es.wikipedia.org_mobile-web_all-agents',\n",
" '14_февраля_ru.wikipedia.org_all-access_all-agents',\n",
" '14_февраля_ru.wikipedia.org_all-access_spider',\n",
" '14_февраля_ru.wikipedia.org_desktop_all-agents',\n",
" '14_февраля_ru.wikipedia.org_mobile-web_all-agents',\n",
" '14th_Antisubmarine_Squadron_en.wikipedia.org_all-access_all-agents',\n",
" '14th_Armored_en.wikipedia.org_all-access_all-agents',\n",
" '14th_Virginia_Infantry_en.wikipedia.org_all-access_all-agents',\n",
" '15._April_de.wikipedia.org_desktop_all-agents',\n",
" '15._August_de.wikipedia.org_desktop_all-agents',\n",
" '15._Dezember_de.wikipedia.org_desktop_all-agents',\n",
" '15._Februar_de.wikipedia.org_desktop_all-agents',\n",
" '15._Januar_de.wikipedia.org_desktop_all-agents',\n",
" '15._Juli_de.wikipedia.org_desktop_all-agents',\n",
" '15._Juni_de.wikipedia.org_desktop_all-agents',\n",
" '15._März_de.wikipedia.org_desktop_all-agents',\n",
" '15._November_de.wikipedia.org_desktop_all-agents',\n",
" '15._September_de.wikipedia.org_desktop_all-agents',\n",
" '152-мм_гаубица-пушка_образца_1937_года_(МЛ-20)_ru.wikipedia.org_desktop_all-agents',\n",
" '1551_en.wikipedia.org_desktop_all-agents',\n",
" '15_août_fr.wikipedia.org_all-access_all-agents',\n",
" '15_août_fr.wikipedia.org_all-access_spider',\n",
" '15_août_fr.wikipedia.org_desktop_all-agents',\n",
" '15_août_fr.wikipedia.org_mobile-web_all-agents',\n",
" '15_de_septiembre_es.wikipedia.org_all-access_all-agents',\n",
" '15_de_septiembre_es.wikipedia.org_all-access_spider',\n",
" '15_de_septiembre_es.wikipedia.org_desktop_all-agents',\n",
" '15_de_septiembre_es.wikipedia.org_mobile-web_all-agents',\n",
" '16._August_de.wikipedia.org_desktop_all-agents',\n",
" '16._Dezember_de.wikipedia.org_desktop_all-agents',\n",
" '16._Februar_de.wikipedia.org_desktop_all-agents',\n",
" '16._Januar_de.wikipedia.org_desktop_all-agents',\n",
" '16._Juni_de.wikipedia.org_desktop_all-agents',\n",
" '16._März_de.wikipedia.org_desktop_all-agents',\n",
" '16._November_de.wikipedia.org_desktop_all-agents',\n",
" '16._September_de.wikipedia.org_desktop_all-agents',\n",
" '16_de_septiembre_es.wikipedia.org_all-access_all-agents',\n",
" '16_de_septiembre_es.wikipedia.org_all-access_spider',\n",
" '16_de_septiembre_es.wikipedia.org_desktop_all-agents',\n",
" '16_de_septiembre_es.wikipedia.org_mobile-web_all-agents',\n",
" '16個夏天_zh.wikipedia.org_all-access_all-agents',\n",
" '16個夏天_zh.wikipedia.org_all-access_spider',\n",
" '16個夏天_zh.wikipedia.org_desktop_all-agents',\n",
" '16個夏天_zh.wikipedia.org_mobile-web_all-agents',\n",
" '17._August_de.wikipedia.org_desktop_all-agents',\n",
" '17._Februar_de.wikipedia.org_desktop_all-agents',\n",
" '17._Januar_de.wikipedia.org_desktop_all-agents',\n",
" '17._Juni_de.wikipedia.org_desktop_all-agents',\n",
" '17._Mai_de.wikipedia.org_desktop_all-agents',\n",
" '17._März_de.wikipedia.org_desktop_all-agents',\n",
" '17._November_de.wikipedia.org_desktop_all-agents',\n",
" '17._Oktober_de.wikipedia.org_desktop_all-agents',\n",
" '1775_in_Great_Britain_en.wikipedia.org_all-access_all-agents',\n",
" '1781_in_literature_en.wikipedia.org_all-access_all-agents',\n",
" '18._April_de.wikipedia.org_desktop_all-agents',\n",
" '18._August_de.wikipedia.org_desktop_all-agents',\n",
" '18._Februar_de.wikipedia.org_desktop_all-agents',\n",
" '18._Januar_de.wikipedia.org_desktop_all-agents',\n",
" '18._Juli_de.wikipedia.org_desktop_all-agents',\n",
" '18._Mai_de.wikipedia.org_desktop_all-agents',\n",
" '18._März_de.wikipedia.org_desktop_all-agents',\n",
" '18._November_de.wikipedia.org_desktop_all-agents',\n",
" '18._Oktober_de.wikipedia.org_desktop_all-agents',\n",
" '1876_in_literature_en.wikipedia.org_all-access_all-agents',\n",
" '187_Strassenbande_de.wikipedia.org_all-access_all-agents',\n",
" '187_Strassenbande_de.wikipedia.org_all-access_spider',\n",
" '187_Strassenbande_de.wikipedia.org_desktop_all-agents',\n",
" '187_Strassenbande_de.wikipedia.org_mobile-web_all-agents',\n",
" '1884_in_South_Africa_en.wikipedia.org_all-access_all-agents',\n",
" '1896_Summer_Olympics_en.wikipedia.org_all-access_all-agents',\n",
" '1896_Summer_Olympics_en.wikipedia.org_all-access_spider',\n",
" '1896_Summer_Olympics_en.wikipedia.org_desktop_all-agents',\n",
" '1896_Summer_Olympics_en.wikipedia.org_mobile-web_all-agents',\n",
" '18F_en.wikipedia.org_all-access_all-agents',\n",
" '18th_&_California_and_18th_&_Stout_stations_en.wikipedia.org_all-access_all-agents',\n",
" '18歲不睡_zh.wikipedia.org_all-access_all-agents',\n",
" '18歲不睡_zh.wikipedia.org_all-access_spider',\n",
" '18歲不睡_zh.wikipedia.org_desktop_all-agents',\n",
" '19._April_de.wikipedia.org_desktop_all-agents',\n",
" '19._August_de.wikipedia.org_desktop_all-agents',\n",
" '19._Dezember_de.wikipedia.org_desktop_all-agents',\n",
" '19._Februar_de.wikipedia.org_desktop_all-agents',\n",
" '19._Januar_de.wikipedia.org_desktop_all-agents',\n",
" '19._Juli_de.wikipedia.org_desktop_all-agents',\n",
" '19._Mai_de.wikipedia.org_desktop_all-agents',\n",
" '19._Oktober_de.wikipedia.org_desktop_all-agents',\n",
" '19._September_de.wikipedia.org_desktop_all-agents',\n",
" '1911_Michigan_State_Normal_Normalites_football_team_en.wikipedia.org_all-access_all-agents',\n",
" '1912_Washington_and_Lee_Generals_football_team_en.wikipedia.org_all-access_all-agents',\n",
" '1917_South_American_Championship_en.wikipedia.org_all-access_all-agents',\n",
" '1918_flu_pandemic_en.wikipedia.org_all-access_all-agents',\n",
" '1918_flu_pandemic_en.wikipedia.org_all-access_spider',\n",
" '1918_flu_pandemic_en.wikipedia.org_desktop_all-agents',\n",
" '1920_London_en.wikipedia.org_all-access_all-agents',\n",
" '1920_London_en.wikipedia.org_all-access_spider',\n",
" '1920_London_en.wikipedia.org_mobile-web_all-agents',\n",
" '1922_Kentucky_Derby_en.wikipedia.org_all-access_all-agents',\n",
" '1923_San_Pedro_Maritime_Strike_en.wikipedia.org_all-access_all-agents',\n",
" '1923_San_Pedro_Maritime_Strike_en.wikipedia.org_all-access_spider',\n",
" '1923_San_Pedro_Maritime_Strike_en.wikipedia.org_desktop_all-agents',\n",
" '1925_in_Mandatory_Palestine_en.wikipedia.org_all-access_all-agents',\n",
" '1926_Birthday_Honours_en.wikipedia.org_all-access_all-agents',\n",
" '1930_in_South_Africa_en.wikipedia.org_all-access_all-agents',\n",
" '1935_in_film_en.wikipedia.org_all-access_all-agents',\n",
" '1936_Summer_Olympics_en.wikipedia.org_all-access_all-agents',\n",
" '1936_Summer_Olympics_en.wikipedia.org_all-access_spider',\n",
" '1936_Summer_Olympics_en.wikipedia.org_desktop_all-agents',\n",
" '1936_Summer_Olympics_en.wikipedia.org_mobile-web_all-agents',\n",
" '1937_Colorado_Buffaloes_football_team_en.wikipedia.org_all-access_all-agents',\n",
" '1938_World_Archery_Championships_en.wikipedia.org_all-access_all-agents',\n",
" '1938_in_Germany_en.wikipedia.org_all-access_all-agents',\n",
" '1940_Brocklesby_mid-air_collision_en.wikipedia.org_desktop_all-agents',\n",
" '1944_(Lied)_de.wikipedia.org_all-access_all-agents',\n",
" '1944_(Lied)_de.wikipedia.org_all-access_spider',\n",
" '1944_(Lied)_de.wikipedia.org_desktop_all-agents',\n",
" '1944_(song)_en.wikipedia.org_all-access_all-agents',\n",
" '1944_(song)_en.wikipedia.org_all-access_spider',\n",
" '1944_(song)_en.wikipedia.org_desktop_all-agents',\n",
" '1944_(песня)_ru.wikipedia.org_all-access_all-agents',\n",
" '1944_(песня)_ru.wikipedia.org_all-access_spider',\n",
" '1944_(песня)_ru.wikipedia.org_desktop_all-agents',\n",
" '1944_(песня)_ru.wikipedia.org_mobile-web_all-agents',\n",
" \"1949_Women's_Western_Open_en.wikipedia.org_all-access_all-agents\",\n",
" '1950_The_Citadel_Bulldogs_football_team_en.wikipedia.org_all-access_all-agents',\n",
" '1950s_en.wikipedia.org_all-access_all-agents',\n",
" '1950s_en.wikipedia.org_all-access_spider',\n",
" '1950s_en.wikipedia.org_desktop_all-agents',\n",
" '1952_French_Grand_Prix_en.wikipedia.org_all-access_all-agents',\n",
" '1952年倫敦煙霧事件_zh.wikipedia.org_desktop_all-agents',\n",
" '1953_Adelaide_Carnival_en.wikipedia.org_all-access_all-agents',\n",
" '1953_in_Denmark_en.wikipedia.org_all-access_all-agents',\n",
" '1956_Milwaukee_Braves_season_en.wikipedia.org_all-access_all-agents',\n",
" '1960_Green_Bay_Packers_season_en.wikipedia.org_all-access_all-agents',\n",
" '1960_NCAA_Soccer_Championship_en.wikipedia.org_all-access_all-agents',\n",
" '1964年白河地震_zh.wikipedia.org_all-access_all-agents',\n",
" '1964年白河地震_zh.wikipedia.org_all-access_spider',\n",
" '1964年白河地震_zh.wikipedia.org_desktop_all-agents',\n",
" '1964年白河地震_zh.wikipedia.org_mobile-web_all-agents',\n",
" '1967年_ja.wikipedia.org_desktop_all-agents',\n",
" '1969_New_Year_Honours_en.wikipedia.org_all-access_all-agents',\n",
" \"1969_WCHA_Men's_Ice_Hockey_Tournament_en.wikipedia.org_all-access_all-agents\",\n",
" '1970_Minnesota_Golden_Gophers_football_team_en.wikipedia.org_all-access_all-agents',\n",
" '1973_All-Big_Ten_Conference_football_team_en.wikipedia.org_all-access_all-agents',\n",
" '1976_Masters_(snooker)_en.wikipedia.org_all-access_all-agents',\n",
" '1976_Summer_Olympics_en.wikipedia.org_desktop_all-agents',\n",
" '1977_Ohio_Bobcats_football_team_en.wikipedia.org_all-access_all-agents',\n",
" '1977_Rose_Bowl_en.wikipedia.org_all-access_all-agents',\n",
" '1979_Calgary_Stampeders_season_en.wikipedia.org_all-access_all-agents',\n",
" '1979_Ole_Miss_Rebels_football_team_en.wikipedia.org_all-access_all-agents',\n",
" '1980_Summer_Olympics_en.wikipedia.org_desktop_all-agents',\n",
" \"1981_NCAA_Division_II_Men's_Ice_Hockey_Tournament_en.wikipedia.org_all-access_all-agents\",\n",
" '1984_(Roman)_de.wikipedia.org_all-access_all-agents',\n",
" '1984_(Roman)_de.wikipedia.org_all-access_spider',\n",
" '1984_(Roman)_de.wikipedia.org_desktop_all-agents',\n",
" '1984_(Roman)_de.wikipedia.org_mobile-web_all-agents',\n",
" '1984_(novela)_es.wikipedia.org_desktop_all-agents',\n",
" '1984_(roman)_fr.wikipedia.org_all-access_all-agents',\n",
" '1984_(roman)_fr.wikipedia.org_all-access_spider',\n",
" '1984_(roman)_fr.wikipedia.org_desktop_all-agents',\n",
" '1984_(roman)_fr.wikipedia.org_mobile-web_all-agents',\n",
" '1984_(роман)_ru.wikipedia.org_all-access_all-agents',\n",
" '1984_(роман)_ru.wikipedia.org_all-access_spider',\n",
" '1984_(роман)_ru.wikipedia.org_desktop_all-agents',\n",
" '1984_(роман)_ru.wikipedia.org_mobile-web_all-agents',\n",
" '1984_Summer_Olympics_en.wikipedia.org_all-access_all-agents',\n",
" '1984_Summer_Olympics_en.wikipedia.org_all-access_spider',\n",
" '1984_Summer_Olympics_en.wikipedia.org_desktop_all-agents',\n",
" '1984_Summer_Olympics_en.wikipedia.org_mobile-web_all-agents',\n",
" '1986_in_the_Philippines_en.wikipedia.org_all-access_all-agents',\n",
" '1987_Japanese_Grand_Prix_en.wikipedia.org_all-access_all-agents',\n",
" \"1987_U.S._Women's_Open_Golf_Championship_en.wikipedia.org_all-access_all-agents\",\n",
" '1987_Western_Soccer_Alliance_en.wikipedia.org_all-access_all-agents',\n",
" '1988_Summer_Olympics_en.wikipedia.org_desktop_all-agents',\n",
" '1989_(Taylor_Swift_album)_en.wikipedia.org_desktop_all-agents',\n",
" '1989_Individual_Speedway_Junior_World_Championship_en.wikipedia.org_all-access_all-agents',\n",
" '1989一念間_zh.wikipedia.org_all-access_all-agents',\n",
" '1989一念間_zh.wikipedia.org_all-access_spider',\n",
" '1989一念間_zh.wikipedia.org_desktop_all-agents',\n",
" '1989一念間_zh.wikipedia.org_mobile-web_all-agents',\n",
" '1990_Riklis_Classic_en.wikipedia.org_all-access_all-agents',\n",
" '1990_airlift_of_Indians_from_Kuwait_en.wikipedia.org_all-access_all-agents',\n",
" '1990_airlift_of_Indians_from_Kuwait_en.wikipedia.org_all-access_spider',\n",
" '1990_airlift_of_Indians_from_Kuwait_en.wikipedia.org_desktop_all-agents',\n",
" '1990_airlift_of_Indians_from_Kuwait_en.wikipedia.org_mobile-web_all-agents',\n",
" '1990年中華民國總統選舉_zh.wikipedia.org_mobile-web_all-agents',\n",
" '1991_Fiesta_Bowl_en.wikipedia.org_all-access_all-agents',\n",
" '1992_Summer_Olympics_en.wikipedia.org_all-access_all-agents',\n",
" '1992_Summer_Olympics_en.wikipedia.org_all-access_spider',\n",
" '1992_Summer_Olympics_en.wikipedia.org_desktop_all-agents',\n",
" '1992_Summer_Olympics_en.wikipedia.org_mobile-web_all-agents',\n",
" \"1992_United_States_men's_Olympic_basketball_team_en.wikipedia.org_all-access_all-agents\",\n",
" \"1992_United_States_men's_Olympic_basketball_team_en.wikipedia.org_all-access_spider\",\n",
" \"1992_United_States_men's_Olympic_basketball_team_en.wikipedia.org_mobile-web_all-agents\",\n",
" '1992_es.wikipedia.org_desktop_all-agents',\n",
" '1994_NBA_draft_en.wikipedia.org_all-access_all-agents',\n",
" '1994年北一女中學生自殺事件_zh.wikipedia.org_all-access_all-agents',\n",
" '1994年北一女中學生自殺事件_zh.wikipedia.org_all-access_spider',\n",
" '1994年北一女中學生自殺事件_zh.wikipedia.org_desktop_all-agents',\n",
" '1994年北一女中學生自殺事件_zh.wikipedia.org_mobile-web_all-agents',\n",
" '1995–96_Chicago_Bulls_season_en.wikipedia.org_all-access_all-agents',\n",
" '1995–96_Chicago_Bulls_season_en.wikipedia.org_all-access_spider',\n",
" '1995–96_Chicago_Bulls_season_en.wikipedia.org_mobile-web_all-agents',\n",
" '1996_Catalan_motorcycle_Grand_Prix_en.wikipedia.org_all-access_all-agents',\n",
" '1996_Mount_Everest_disaster_en.wikipedia.org_all-access_all-agents',\n",
" '1996_Mount_Everest_disaster_en.wikipedia.org_all-access_spider',\n",
" '1996_Mount_Everest_disaster_en.wikipedia.org_mobile-web_all-agents',\n",
" '1996_Summer_Olympics_en.wikipedia.org_all-access_all-agents',\n",
" '1996_Summer_Olympics_en.wikipedia.org_all-access_spider',\n",
" '1996_Summer_Olympics_en.wikipedia.org_desktop_all-agents',\n",
" '1996_Summer_Olympics_en.wikipedia.org_mobile-web_all-agents',\n",
" '1996_Summer_Olympics_medal_table_en.wikipedia.org_desktop_all-agents',\n",
" '1996_in_music_en.wikipedia.org_all-access_all-agents',\n",
" '1996_in_music_en.wikipedia.org_all-access_spider',\n",
" '1996_in_music_en.wikipedia.org_desktop_all-agents',\n",
" '1996年中華民國總統選舉_zh.wikipedia.org_all-access_all-agents',\n",
" '1996年中華民國總統選舉_zh.wikipedia.org_all-access_spider',\n",
" '1996年中華民國總統選舉_zh.wikipedia.org_desktop_all-agents',\n",
" '1996年中華民國總統選舉_zh.wikipedia.org_mobile-web_all-agents',\n",
" '1996年珠穆朗瑪峰事故_zh.wikipedia.org_mobile-web_all-agents',\n",
" '1997_Tour_de_Langkawi_en.wikipedia.org_all-access_all-agents',\n",
" '1997_in_British_radio_en.wikipedia.org_all-access_all-agents',\n",
" '1998_FIFA_World_Cup_en.wikipedia.org_all-access_all-agents',\n",
" '1998_FIFA_World_Cup_en.wikipedia.org_all-access_spider',\n",
" '1998_FIFA_World_Cup_en.wikipedia.org_desktop_all-agents',\n",
" \"1998_FIVB_Volleyball_Women's_World_Championship_qualification_en.wikipedia.org_all-access_all-agents\",\n",
" '1998_Sacramento_Monarchs_season_en.wikipedia.org_all-access_all-agents',\n",
" '1999_(Prince_album)_en.wikipedia.org_all-access_all-agents',\n",
" '1999_(Prince_album)_en.wikipedia.org_all-access_spider',\n",
" '1999_(Prince_album)_en.wikipedia.org_desktop_all-agents',\n",
" '1999_(Prince_album)_en.wikipedia.org_mobile-web_all-agents',\n",
" '1999_AT&T_Challenge_en.wikipedia.org_all-access_all-agents',\n",
" '1999_Anaheim_Angels_season_en.wikipedia.org_all-access_all-agents',\n",
" '1999_European_Aquatics_Championships_en.wikipedia.org_all-access_all-agents',\n",
" '19_Kids_and_Counting_en.wikipedia.org_mobile-web_all-agents',\n",
" '1_de_mayo_es.wikipedia.org_all-access_all-agents',\n",
" '1_de_mayo_es.wikipedia.org_all-access_spider',\n",
" '1_de_mayo_es.wikipedia.org_mobile-web_all-agents',\n",
" '1er_mai_fr.wikipedia.org_mobile-web_all-agents',\n",
" '1er_novembre_fr.wikipedia.org_all-access_all-agents',\n",
" '1er_novembre_fr.wikipedia.org_all-access_spider',\n",
" '1er_novembre_fr.wikipedia.org_desktop_all-agents',\n",
" '1er_novembre_fr.wikipedia.org_mobile-web_all-agents',\n",
" '1世代_zh.wikipedia.org_desktop_all-agents',\n",
" '1月11日_ja.wikipedia.org_desktop_all-agents',\n",
" '1月14日_ja.wikipedia.org_desktop_all-agents',\n",
" '1月17日_ja.wikipedia.org_desktop_all-agents',\n",
" '1月19日_ja.wikipedia.org_desktop_all-agents',\n",
" '1月20日_ja.wikipedia.org_desktop_all-agents',\n",
" '1月21日_ja.wikipedia.org_desktop_all-agents',\n",
" '1月23日_ja.wikipedia.org_desktop_all-agents',\n",
" '1月26日_ja.wikipedia.org_desktop_all-agents',\n",
" '1月27日_ja.wikipedia.org_desktop_all-agents',\n",
" '1月31日_ja.wikipedia.org_desktop_all-agents',\n",
" '2.0_(film)_en.wikipedia.org_all-access_all-agents',\n",
" '2.0_(film)_en.wikipedia.org_all-access_spider',\n",
" '2.0_(film)_en.wikipedia.org_mobile-web_all-agents',\n",
" '2._August_de.wikipedia.org_desktop_all-agents',\n",
" '2._Dezember_de.wikipedia.org_desktop_all-agents',\n",
" '2._Februar_de.wikipedia.org_desktop_all-agents',\n",
" '2._Fußball-Bundesliga_2015/16_de.wikipedia.org_desktop_all-agents',\n",
" '2._Fußball-Bundesliga_2016/17_de.wikipedia.org_desktop_all-agents',\n",
" '2._Januar_de.wikipedia.org_desktop_all-agents',\n",
" '2._Juni_de.wikipedia.org_desktop_all-agents',\n",
" '2._Mai_de.wikipedia.org_desktop_all-agents',\n",
" '2._März_de.wikipedia.org_desktop_all-agents',\n",
" '2._November_de.wikipedia.org_desktop_all-agents',\n",
" '2._September_de.wikipedia.org_desktop_all-agents',\n",
" '20._April_de.wikipedia.org_all-access_all-agents',\n",
" '20._April_de.wikipedia.org_all-access_spider',\n",
" '20._April_de.wikipedia.org_desktop_all-agents',\n",
" '20._Dezember_de.wikipedia.org_desktop_all-agents',\n",
" '20._Februar_de.wikipedia.org_desktop_all-agents',\n",
" '20._Januar_de.wikipedia.org_desktop_all-agents',\n",
" '20._Juli_de.wikipedia.org_desktop_all-agents',\n",
" '20._Juni_de.wikipedia.org_desktop_all-agents',\n",
" '20._Mai_de.wikipedia.org_desktop_all-agents',\n",
" '20._Oktober_de.wikipedia.org_desktop_all-agents',\n",
" '20._September_de.wikipedia.org_desktop_all-agents',\n",
" '2000_African_Futsal_Championship_en.wikipedia.org_all-access_all-agents',\n",
" '2000_Brady_tornado_en.wikipedia.org_all-access_all-agents',\n",
" '2000_Summer_Olympics_en.wikipedia.org_all-access_all-agents',\n",
" '2000_Summer_Olympics_en.wikipedia.org_all-access_spider',\n",
" '2000_Summer_Olympics_en.wikipedia.org_desktop_all-agents',\n",
" '2000_Summer_Olympics_en.wikipedia.org_mobile-web_all-agents',\n",
" '2000_Summer_Olympics_medal_table_en.wikipedia.org_all-access_all-agents',\n",
" '2000_Summer_Olympics_medal_table_en.wikipedia.org_all-access_spider',\n",
" '2000_Summer_Olympics_medal_table_en.wikipedia.org_desktop_all-agents',\n",
" '2000_Summer_Olympics_medal_table_en.wikipedia.org_mobile-web_all-agents',\n",
" '2000年アメリカ合衆国大統領選挙_ja.wikipedia.org_desktop_all-agents',\n",
" '2000年中華民國總統選舉_zh.wikipedia.org_all-access_all-agents',\n",
" '2000年中華民國總統選舉_zh.wikipedia.org_all-access_spider',\n",
" '2000年中華民國總統選舉_zh.wikipedia.org_desktop_all-agents',\n",
" '2000年中華民國總統選舉_zh.wikipedia.org_mobile-web_all-agents',\n",
" \"2001,_l'Odyssée_de_l'espace_fr.wikipedia.org_all-access_all-agents\",\n",
" \"2001,_l'Odyssée_de_l'espace_fr.wikipedia.org_all-access_spider\",\n",
" \"2001,_l'Odyssée_de_l'espace_fr.wikipedia.org_desktop_all-agents\",\n",
" \"2001,_l'Odyssée_de_l'espace_fr.wikipedia.org_mobile-web_all-agents\",\n",
" '2001:_Odyssee_im_Weltraum_de.wikipedia.org_all-access_all-agents',\n",
" '2001:_Odyssee_im_Weltraum_de.wikipedia.org_all-access_spider',\n",
" '2001:_Odyssee_im_Weltraum_de.wikipedia.org_desktop_all-agents',\n",
" '2001:_Odyssee_im_Weltraum_de.wikipedia.org_mobile-web_all-agents',\n",
" '2001_ELMS_at_Jarama_en.wikipedia.org_all-access_all-agents',\n",
" '2001_term_United_States_Supreme_Court_opinions_of_Anthony_Kennedy_en.wikipedia.org_all-access_all-agents',\n",
" '2002_European_Judo_Championships_en.wikipedia.org_all-access_all-agents',\n",
" '2002_FIFA_World_Cup_en.wikipedia.org_all-access_all-agents',\n",
" '2002_FIFA_World_Cup_en.wikipedia.org_all-access_spider',\n",
" '2002_FIFA_World_Cup_en.wikipedia.org_desktop_all-agents',\n",
" '2002_Georgia_Southern_Eagles_football_team_en.wikipedia.org_all-access_all-agents',\n",
" '2002_Miami_Hurricanes_football_team_en.wikipedia.org_all-access_all-agents',\n",
" '2003_Boeing_727-223_disappearance_en.wikipedia.org_all-access_all-agents',\n",
" '2003_Boeing_727-223_disappearance_en.wikipedia.org_all-access_spider',\n",
" '2003_Boeing_727-223_disappearance_en.wikipedia.org_desktop_all-agents',\n",
" '2003_Boeing_727-223_disappearance_en.wikipedia.org_mobile-web_all-agents',\n",
" '2003_Tonga_Major_League_en.wikipedia.org_all-access_all-agents',\n",
" '2003_invasion_of_Iraq_en.wikipedia.org_desktop_all-agents',\n",
" '2004_Harvard–Yale_prank_en.wikipedia.org_all-access_all-agents',\n",
" '2004_Harvard–Yale_prank_en.wikipedia.org_all-access_spider',\n",
" '2004_Harvard–Yale_prank_en.wikipedia.org_desktop_all-agents',\n",
" '2004_Harvard–Yale_prank_en.wikipedia.org_mobile-web_all-agents',\n",
" '2004_Summer_Olympics_en.wikipedia.org_all-access_all-agents',\n",
" '2004_Summer_Olympics_en.wikipedia.org_all-access_spider',\n",
" '2004_Summer_Olympics_en.wikipedia.org_desktop_all-agents',\n",
" '2004_Summer_Olympics_en.wikipedia.org_mobile-web_all-agents',\n",
" '2004_Summer_Olympics_medal_table_en.wikipedia.org_all-access_all-agents',\n",
" '2004_Summer_Olympics_medal_table_en.wikipedia.org_all-access_spider',\n",
" '2004_Summer_Olympics_medal_table_en.wikipedia.org_desktop_all-agents',\n",
" '2004_Summer_Olympics_medal_table_en.wikipedia.org_mobile-web_all-agents',\n",
" \"2004_United_States_men's_Olympic_basketball_team_en.wikipedia.org_all-access_all-agents\",\n",
" \"2004_United_States_men's_Olympic_basketball_team_en.wikipedia.org_all-access_spider\",\n",
" \"2004_United_States_men's_Olympic_basketball_team_en.wikipedia.org_mobile-web_all-agents\",\n",
" '2004年中華民國總統選舉_zh.wikipedia.org_all-access_all-agents',\n",
" '2004年中華民國總統選舉_zh.wikipedia.org_all-access_spider',\n",
" '2004年中華民國總統選舉_zh.wikipedia.org_desktop_all-agents',\n",
" '2004年中華民國總統選舉_zh.wikipedia.org_mobile-web_all-agents',\n",
" '2004年印度洋大地震_zh.wikipedia.org_all-access_all-agents',\n",
" '2004年印度洋大地震_zh.wikipedia.org_all-access_spider',\n",
" '2004年印度洋大地震_zh.wikipedia.org_desktop_all-agents',\n",
" '2004年夏季奥林匹克运动会_zh.wikipedia.org_all-access_all-agents',\n",
" '2004年夏季奥林匹克运动会_zh.wikipedia.org_all-access_spider',\n",
" '2004年夏季奥林匹克运动会_zh.wikipedia.org_desktop_all-agents',\n",
" '2004年夏季奥林匹克运动会_zh.wikipedia.org_mobile-web_all-agents',\n",
" '2005_HEW_Cyclassics_en.wikipedia.org_all-access_all-agents',\n",
" '2005_世界物理年_zh.wikipedia.org_all-access_all-agents',\n",
" '2005_世界物理年_zh.wikipedia.org_all-access_spider',\n",
" '2005_世界物理年_zh.wikipedia.org_desktop_all-agents',\n",
" '2005年中華民國國民大會代表選舉_zh.wikipedia.org_all-access_all-agents',\n",
" '2005年中華民國國民大會代表選舉_zh.wikipedia.org_all-access_spider',\n",
" '2005年中華民國國民大會代表選舉_zh.wikipedia.org_mobile-web_all-agents',\n",
" '2006_FIFA_World_Cup_en.wikipedia.org_all-access_all-agents',\n",
" '2006_FIFA_World_Cup_en.wikipedia.org_all-access_spider',\n",
" '2006_FIFA_World_Cup_en.wikipedia.org_desktop_all-agents',\n",
" '2006_FIFA_World_Cup_en.wikipedia.org_mobile-web_all-agents',\n",
" '2006_in_Malaysia_en.wikipedia.org_all-access_all-agents',\n",
" '2007_Champs_Sports_Bowl_en.wikipedia.org_all-access_all-agents',\n",
" '2007_College_Baseball_All-America_Team_en.wikipedia.org_all-access_all-agents',\n",
" '2007_UNAF_U-17_Tournament_en.wikipedia.org_all-access_all-agents',\n",
" '2007_tuberculosis_scare_en.wikipedia.org_desktop_all-agents',\n",
" '2007年_ja.wikipedia.org_all-access_all-agents',\n",
" '2007年_ja.wikipedia.org_all-access_spider',\n",
" '2007年_ja.wikipedia.org_desktop_all-agents',\n",
" '2008_FAI_Cup_en.wikipedia.org_all-access_all-agents',\n",
" '2008_Florida_Atlantic_Owls_football_team_en.wikipedia.org_all-access_all-agents',\n",
" \"2008_IIHF_Men's_InLine_Hockey_World_Championship_en.wikipedia.org_all-access_all-agents\",\n",
" '2008_Noida_double_murder_case_en.wikipedia.org_all-access_all-agents',\n",
" '2008_Noida_double_murder_case_en.wikipedia.org_all-access_spider',\n",
" '2008_Noida_double_murder_case_en.wikipedia.org_mobile-web_all-agents',\n",
" '2008_Oita_Trinita_season_en.wikipedia.org_all-access_all-agents',\n",
" '2008_Rally_GB_en.wikipedia.org_all-access_all-agents',\n",
" '2008_Summer_Olympics_en.wikipedia.org_all-access_all-agents',\n",
" '2008_Summer_Olympics_en.wikipedia.org_all-access_spider',\n",
" '2008_Summer_Olympics_en.wikipedia.org_desktop_all-agents',\n",
" '2008_Summer_Olympics_en.wikipedia.org_mobile-web_all-agents',\n",
" '2008_Summer_Olympics_medal_table_en.wikipedia.org_all-access_all-agents',\n",
" '2008_Summer_Olympics_medal_table_en.wikipedia.org_all-access_spider',\n",
" '2008_Summer_Olympics_medal_table_en.wikipedia.org_desktop_all-agents',\n",
" '2008_Summer_Olympics_medal_table_en.wikipedia.org_mobile-web_all-agents',\n",
" '2008_US_Open_Series_en.wikipedia.org_all-access_all-agents',\n",
" '2008_in_Maximum_Fighting_Championship_en.wikipedia.org_all-access_all-agents',\n",
" '2008年アメリカ合衆国大統領選挙_ja.wikipedia.org_desktop_all-agents',\n",
" '2008年中華民國立法委員選舉_zh.wikipedia.org_all-access_all-agents',\n",
" '2008年中華民國立法委員選舉_zh.wikipedia.org_all-access_spider',\n",
" '2008年中華民國立法委員選舉_zh.wikipedia.org_desktop_all-agents',\n",
" '2008年中華民國立法委員選舉_zh.wikipedia.org_mobile-web_all-agents',\n",
" '2008年中華民國總統選舉_zh.wikipedia.org_all-access_all-agents',\n",
" '2008年中華民國總統選舉_zh.wikipedia.org_all-access_spider',\n",
" '2008年中華民國總統選舉_zh.wikipedia.org_desktop_all-agents',\n",
" '2008年中華民國總統選舉_zh.wikipedia.org_mobile-web_all-agents',\n",
" '2008年夏季奥林匹克运动会_zh.wikipedia.org_all-access_all-agents',\n",
" '2008年夏季奥林匹克运动会_zh.wikipedia.org_all-access_spider',\n",
" '2008年夏季奥林匹克运动会_zh.wikipedia.org_desktop_all-agents',\n",
" '2008年夏季奥林匹克运动会_zh.wikipedia.org_mobile-web_all-agents',\n",
" '2008年夏季奥林匹克运动会奖牌榜_zh.wikipedia.org_all-access_all-agents',\n",
" '2008年夏季奥林匹克运动会奖牌榜_zh.wikipedia.org_all-access_spider',\n",
" '2008年夏季奥林匹克运动会奖牌榜_zh.wikipedia.org_desktop_all-agents',\n",
" '2008年夏季奥林匹克运动会奖牌榜_zh.wikipedia.org_mobile-web_all-agents',\n",
" '2009_Denmark_Super_Series_en.wikipedia.org_all-access_all-agents',\n",
" '2009_European_U17_Badminton_Championships_en.wikipedia.org_all-access_all-agents',\n",
" '2009_Ulster_Senior_Football_Championship_en.wikipedia.org_all-access_all-agents',\n",
" '2009_southeast_Queensland_oil_spill_en.wikipedia.org_all-access_all-agents',\n",
" '2009年NBA選秀_zh.wikipedia.org_all-access_all-agents',\n",
" '2009年NBA選秀_zh.wikipedia.org_all-access_spider',\n",
" '2009年NBA選秀_zh.wikipedia.org_desktop_all-agents',\n",
" '2010_Atlantic_Hockey_Tournament_en.wikipedia.org_all-access_all-agents',\n",
" '2010_Copiapó_mining_accident_en.wikipedia.org_mobile-web_all-agents',\n",
" '2010_FIFA_World_Cup_en.wikipedia.org_all-access_all-agents',\n",
" '2010_FIFA_World_Cup_en.wikipedia.org_all-access_spider',\n",
" '2010_FIFA_World_Cup_en.wikipedia.org_desktop_all-agents',\n",
" '2010_FIFA_World_Cup_en.wikipedia.org_mobile-web_all-agents',\n",
" \"2010_FIVB_Volleyball_Men's_World_Championship_squads_en.wikipedia.org_all-access_all-agents\",\n",
" '2010_UCI_Road_World_Championships_en.wikipedia.org_all-access_all-agents',\n",
" '2010年墨西哥灣漏油事故_zh.wikipedia.org_all-access_all-agents',\n",
" '2010年墨西哥灣漏油事故_zh.wikipedia.org_all-access_spider',\n",
" '2010年墨西哥灣漏油事故_zh.wikipedia.org_desktop_all-agents',\n",
" '2010年墨西哥灣漏油事故_zh.wikipedia.org_mobile-web_all-agents',\n",
" '2011_Telavi_Open_en.wikipedia.org_all-access_all-agents',\n",
" '2011_in_Japanese_television_en.wikipedia.org_all-access_all-agents',\n",
" '2011年日本东北地方太平洋近海地震_zh.wikipedia.org_all-access_all-agents',\n",
" '2011年日本东北地方太平洋近海地震_zh.wikipedia.org_all-access_spider',\n",
" '2011年日本东北地方太平洋近海地震_zh.wikipedia.org_desktop_all-agents',\n",
" '2011年日本东北地方太平洋近海地震_zh.wikipedia.org_mobile-web_all-agents',\n",
" '2011年金三角中国船员遇袭案_zh.wikipedia.org_all-access_all-agents',\n",
" '2011年金三角中国船员遇袭案_zh.wikipedia.org_all-access_spider',\n",
" '2011年金三角中国船员遇袭案_zh.wikipedia.org_desktop_all-agents',\n",
" '2011年金三角中国船员遇袭案_zh.wikipedia.org_mobile-web_all-agents',\n",
" '2012_(Film)_de.wikipedia.org_all-access_all-agents',\n",
" '2012_(Film)_de.wikipedia.org_all-access_spider',\n",
" '2012_(Film)_de.wikipedia.org_mobile-web_all-agents',\n",
" '2012_(film)_fr.wikipedia.org_all-access_all-agents',\n",
" '2012_(film)_fr.wikipedia.org_all-access_spider',\n",
" '2012_(film)_fr.wikipedia.org_mobile-web_all-agents',\n",
" '2012_Benghazi_attack_en.wikipedia.org_all-access_all-agents',\n",
" '2012_Benghazi_attack_en.wikipedia.org_all-access_spider',\n",
" '2012_Benghazi_attack_en.wikipedia.org_desktop_all-agents',\n",
" '2012_Benghazi_attack_en.wikipedia.org_mobile-web_all-agents',\n",
" \"2012_Men's_World_Junior_Squash_Championships_en.wikipedia.org_all-access_all-agents\",\n",
" '2012_Munster_Senior_Football_Championship_en.wikipedia.org_all-access_all-agents',\n",
" '2012_Summer_Olympics_en.wikipedia.org_all-access_all-agents',\n",
" '2012_Summer_Olympics_en.wikipedia.org_all-access_spider',\n",
" '2012_Summer_Olympics_en.wikipedia.org_desktop_all-agents',\n",
" '2012_Summer_Olympics_en.wikipedia.org_mobile-web_all-agents',\n",
" '2012_Summer_Olympics_medal_table_en.wikipedia.org_all-access_all-agents',\n",
" '2012_Summer_Olympics_medal_table_en.wikipedia.org_all-access_spider',\n",
" '2012_Summer_Olympics_medal_table_en.wikipedia.org_desktop_all-agents',\n",
" '2012_Summer_Olympics_medal_table_en.wikipedia.org_mobile-web_all-agents',\n",
" \"2012_Summit_League_Men's_Basketball_Tournament_en.wikipedia.org_all-access_all-agents\",\n",
" \"2012_Women's_Softball_World_Championship_en.wikipedia.org_all-access_all-agents\",\n",
" '2012_in_Iran_en.wikipedia.org_all-access_all-agents',\n",
" '2012年Running_Man節目列表_zh.wikipedia.org_all-access_all-agents',\n",
" '2012年Running_Man節目列表_zh.wikipedia.org_all-access_spider',\n",
" '2012年Running_Man節目列表_zh.wikipedia.org_desktop_all-agents',\n",
" '2012年アメリカ合衆国大統領選挙_ja.wikipedia.org_all-access_all-agents',\n",
" '2012年アメリカ合衆国大統領選挙_ja.wikipedia.org_all-access_spider',\n",
" '2012年アメリカ合衆国大統領選挙_ja.wikipedia.org_desktop_all-agents',\n",
" '2012年中華民國立法委員選舉_zh.wikipedia.org_all-access_all-agents',\n",
" '2012年中華民國立法委員選舉_zh.wikipedia.org_all-access_spider',\n",
" '2012年中華民國立法委員選舉_zh.wikipedia.org_desktop_all-agents',\n",
" '2012年中華民國立法委員選舉_zh.wikipedia.org_mobile-web_all-agents',\n",
" '2012年中華民國總統選舉_zh.wikipedia.org_all-access_all-agents',\n",
" '2012年中華民國總統選舉_zh.wikipedia.org_all-access_spider',\n",
" '2012年中華民國總統選舉_zh.wikipedia.org_desktop_all-agents',\n",
" '2012年中華民國總統選舉_zh.wikipedia.org_mobile-web_all-agents',\n",
" '2012年夏季奥林匹克运动会_zh.wikipedia.org_all-access_all-agents',\n",
" '2012年夏季奥林匹克运动会_zh.wikipedia.org_all-access_spider',\n",
" '2012年夏季奥林匹克运动会_zh.wikipedia.org_desktop_all-agents',\n",
" '2012年夏季奥林匹克运动会_zh.wikipedia.org_mobile-web_all-agents',\n",
" '2012年夏季奥林匹克运动会奖牌榜_zh.wikipedia.org_all-access_all-agents',\n",
" '2012年夏季奥林匹克运动会奖牌榜_zh.wikipedia.org_all-access_spider',\n",
" '2012年夏季奥林匹克运动会奖牌榜_zh.wikipedia.org_desktop_all-agents',\n",
" '2012年夏季奥林匹克运动会奖牌榜_zh.wikipedia.org_mobile-web_all-agents',\n",
" '2012年班加西攻擊事件_zh.wikipedia.org_all-access_all-agents',\n",
" '2012年班加西攻擊事件_zh.wikipedia.org_all-access_spider',\n",
" '2012年班加西攻擊事件_zh.wikipedia.org_desktop_all-agents',\n",
" '2012年班加西攻擊事件_zh.wikipedia.org_mobile-web_all-agents',\n",
" '2012年美國總統選舉_zh.wikipedia.org_all-access_all-agents',\n",
" '2012年美國總統選舉_zh.wikipedia.org_all-access_spider',\n",
" '2012年美國總統選舉_zh.wikipedia.org_desktop_all-agents',\n",
" '2012年美國總統選舉_zh.wikipedia.org_mobile-web_all-agents',\n",
" '2012年香港立法會選舉_zh.wikipedia.org_all-access_all-agents',\n",
" '2012年香港立法會選舉_zh.wikipedia.org_all-access_spider',\n",
" '2012年香港立法會選舉_zh.wikipedia.org_desktop_all-agents',\n",
" '2012年香港立法會選舉_zh.wikipedia.org_mobile-web_all-agents',\n",
" '2013_Bengali_blog_blackout_en.wikipedia.org_all-access_all-agents',\n",
" '2013_CEMAC_Cup_en.wikipedia.org_all-access_all-agents',\n",
" '2013_Estonia_national_football_team_results_en.wikipedia.org_all-access_all-agents',\n",
" '2013_Fast5_Netball_World_Series_en.wikipedia.org_all-access_all-agents',\n",
" '2013_Pacific_Mini_Games_en.wikipedia.org_all-access_all-agents',\n",
" '2013_Southern_Illinois_Salukis_football_team_en.wikipedia.org_all-access_all-agents',\n",
" '2013_St_Helens_season_en.wikipedia.org_all-access_all-agents',\n",
" '2013年Running_Man節目列表_zh.wikipedia.org_all-access_all-agents',\n",
" '2013年Running_Man節目列表_zh.wikipedia.org_all-access_spider',\n",
" '2013年Running_Man節目列表_zh.wikipedia.org_desktop_all-agents',\n",
" '2013年Running_Man節目列表_zh.wikipedia.org_mobile-web_all-agents',\n",
" '2013年波士頓馬拉松爆炸案_zh.wikipedia.org_all-access_all-agents',\n",
" '2013年波士頓馬拉松爆炸案_zh.wikipedia.org_all-access_spider',\n",
" '2013年波士頓馬拉松爆炸案_zh.wikipedia.org_desktop_all-agents',\n",
" '2013年波士頓馬拉松爆炸案_zh.wikipedia.org_mobile-web_all-agents',\n",
" '2014_American_Indoor_Football_season_en.wikipedia.org_all-access_all-agents',\n",
" '2014_FIFA_World_Cup_en.wikipedia.org_all-access_all-agents',\n",
" '2014_FIFA_World_Cup_en.wikipedia.org_all-access_spider',\n",
" '2014_FIFA_World_Cup_en.wikipedia.org_desktop_all-agents',\n",
" '2014_FIFA_World_Cup_en.wikipedia.org_mobile-web_all-agents',\n",
" '2014_Great_Alaska_Shootout_en.wikipedia.org_all-access_all-agents',\n",
" '2014_Maui_Invitational_Tournament_en.wikipedia.org_all-access_all-agents',\n",
" '2014_Winter_Olympics_en.wikipedia.org_all-access_all-agents',\n",
" '2014_Winter_Olympics_en.wikipedia.org_all-access_spider',\n",
" '2014_Winter_Olympics_en.wikipedia.org_desktop_all-agents',\n",
" '2014_Winter_Olympics_en.wikipedia.org_mobile-web_all-agents',\n",
" \"2014_Women's_European_Amateur_Boxing_Championships_en.wikipedia.org_all-access_all-agents\",\n",
" '2014年Running_Man節目列表_zh.wikipedia.org_all-access_all-agents',\n",
" '2014年Running_Man節目列表_zh.wikipedia.org_all-access_spider',\n",
" '2014年Running_Man節目列表_zh.wikipedia.org_desktop_all-agents',\n",
" '2014年Running_Man節目列表_zh.wikipedia.org_mobile-web_all-agents',\n",
" '2014年世界盃足球賽_zh.wikipedia.org_all-access_all-agents',\n",
" '2014年世界盃足球賽_zh.wikipedia.org_all-access_spider',\n",
" '2014年世界盃足球賽_zh.wikipedia.org_desktop_all-agents',\n",
" '2014年世界盃足球賽_zh.wikipedia.org_mobile-web_all-agents',\n",
" '2014年中華民國地方公職人員選舉_zh.wikipedia.org_all-access_all-agents',\n",
" '2014年中華民國地方公職人員選舉_zh.wikipedia.org_all-access_spider',\n",
" '2014年中華民國地方公職人員選舉_zh.wikipedia.org_desktop_all-agents',\n",
" '2014年中華民國直轄市長及縣市長選舉_zh.wikipedia.org_all-access_all-agents',\n",
" '2014年中華民國直轄市長及縣市長選舉_zh.wikipedia.org_all-access_spider',\n",
" '2014年中華民國直轄市長及縣市長選舉_zh.wikipedia.org_desktop_all-agents',\n",
" '2014年中華民國直轄市長及縣市長選舉_zh.wikipedia.org_mobile-web_all-agents',\n",
" '2014年台北捷運隨機殺人事件_zh.wikipedia.org_all-access_all-agents',\n",
" '2014年台北捷運隨機殺人事件_zh.wikipedia.org_all-access_spider',\n",
" '2014年台北捷運隨機殺人事件_zh.wikipedia.org_desktop_all-agents',\n",
" '2014年台北捷運隨機殺人事件_zh.wikipedia.org_mobile-web_all-agents',\n",
" '2014年東京都知事選挙_ja.wikipedia.org_all-access_all-agents',\n",
" '2014年東京都知事選挙_ja.wikipedia.org_all-access_spider',\n",
" '2014年東京都知事選挙_ja.wikipedia.org_desktop_all-agents',\n",
" '2014年東京都知事選挙_ja.wikipedia.org_mobile-web_all-agents',\n",
" '2014年臺北市長選舉爭議事件_zh.wikipedia.org_all-access_all-agents',\n",
" '2014年臺北市長選舉爭議事件_zh.wikipedia.org_all-access_spider',\n",
" '2014年臺北市長選舉爭議事件_zh.wikipedia.org_mobile-web_all-agents',\n",
" '2014年韓国フェリー転覆事故_ja.wikipedia.org_all-access_all-agents',\n",
" '2014年韓国フェリー転覆事故_ja.wikipedia.org_all-access_spider',\n",
" '2014年韓国フェリー転覆事故_ja.wikipedia.org_desktop_all-agents',\n",
" '2014年韓国フェリー転覆事故_ja.wikipedia.org_mobile-web_all-agents',\n",
" '2015-16_NBA赛季_zh.wikipedia.org_mobile-web_all-agents',\n",
" '2015_24_Hours_of_Le_Mans_en.wikipedia.org_desktop_all-agents',\n",
" '2015_Autumn_Classic_International_en.wikipedia.org_all-access_all-agents',\n",
" '2015_CFU_Club_Championship_en.wikipedia.org_all-access_all-agents',\n",
" '2015_College_Football_Playoff_National_Championship_en.wikipedia.org_mobile-web_all-agents',\n",
" '2015_Copa_América_en.wikipedia.org_all-access_all-agents',\n",
" '2015_Copa_América_en.wikipedia.org_all-access_spider',\n",
" '2015_Copa_América_en.wikipedia.org_desktop_all-agents',\n",
" '2015_Copa_América_en.wikipedia.org_mobile-web_all-agents',\n",
" '2015_NBA_Finals_en.wikipedia.org_all-access_all-agents',\n",
" '2015_NBA_Finals_en.wikipedia.org_all-access_spider',\n",
" '2015_NBA_Finals_en.wikipedia.org_desktop_all-agents',\n",
" '2015_NBA_Finals_en.wikipedia.org_mobile-web_all-agents',\n",
" \"2015_NCAA_Men's_Division_I_Basketball_Tournament_en.wikipedia.org_all-access_all-agents\",\n",
" \"2015_NCAA_Men's_Division_I_Basketball_Tournament_en.wikipedia.org_all-access_spider\",\n",
" \"2015_NCAA_Men's_Division_I_Basketball_Tournament_en.wikipedia.org_desktop_all-agents\",\n",
" \"2015_NCAA_Men's_Division_I_Basketball_Tournament_en.wikipedia.org_mobile-web_all-agents\",\n",
" '2015_NFL_season_en.wikipedia.org_desktop_all-agents',\n",
" '2015_San_Bernardino_attack_en.wikipedia.org_desktop_all-agents',\n",
" '2015_WTA_Finals_en.wikipedia.org_all-access_all-agents',\n",
" '2015_es.wikipedia.org_all-access_all-agents',\n",
" '2015_es.wikipedia.org_all-access_spider',\n",
" '2015_es.wikipedia.org_desktop_all-agents',\n",
" '2015_fr.wikipedia.org_all-access_all-agents',\n",
" '2015_fr.wikipedia.org_all-access_spider',\n",
" '2015_fr.wikipedia.org_desktop_all-agents',\n",
" '2015_in_film_en.wikipedia.org_all-access_all-agents',\n",
" '2015_in_film_en.wikipedia.org_all-access_spider',\n",
" '2015_in_film_en.wikipedia.org_desktop_all-agents',\n",
" '2015_in_film_en.wikipedia.org_mobile-web_all-agents',\n",
" '2015_год_ru.wikipedia.org_desktop_all-agents',\n",
" '2015_год_в_кино_ru.wikipedia.org_all-access_all-agents',\n",
" '2015_год_в_кино_ru.wikipedia.org_all-access_spider',\n",
" '2015_год_в_кино_ru.wikipedia.org_desktop_all-agents',\n",
" '2015–16_Football_League_Championship_en.wikipedia.org_desktop_all-agents',\n",
" '2015–16_La_Liga_en.wikipedia.org_all-access_all-agents',\n",
" '2015–16_La_Liga_en.wikipedia.org_all-access_spider',\n",
" '2015–16_La_Liga_en.wikipedia.org_desktop_all-agents',\n",
" '2015–16_NBA_season_en.wikipedia.org_desktop_all-agents',\n",
" '2015–16_Premier_League_en.wikipedia.org_all-access_all-agents',\n",
" '2015–16_Premier_League_en.wikipedia.org_all-access_spider',\n",
" '2015–16_Premier_League_en.wikipedia.org_desktop_all-agents',\n",
" '2015–16_Premier_League_en.wikipedia.org_mobile-web_all-agents',\n",
" '2015–16_UEFA_Champions_League_en.wikipedia.org_all-access_all-agents',\n",
" '2015–16_UEFA_Champions_League_en.wikipedia.org_all-access_spider',\n",
" '2015–16_UEFA_Champions_League_en.wikipedia.org_desktop_all-agents',\n",
" '2015–16_UEFA_Champions_League_en.wikipedia.org_mobile-web_all-agents',\n",
" '2015–16_UEFA_Europa_League_en.wikipedia.org_desktop_all-agents',\n",
" '2015年Running_Man節目列表_zh.wikipedia.org_all-access_all-agents',\n",
" '2015年Running_Man節目列表_zh.wikipedia.org_all-access_spider',\n",
" '2015年Running_Man節目列表_zh.wikipedia.org_desktop_all-agents',\n",
" '2015年Running_Man節目列表_zh.wikipedia.org_mobile-web_all-agents',\n",
" '2015年_ja.wikipedia.org_desktop_all-agents',\n",
" '2015年八仙樂園派對粉塵爆炸事故_zh.wikipedia.org_all-access_all-agents',\n",
" '2015年八仙樂園派對粉塵爆炸事故_zh.wikipedia.org_all-access_spider',\n",
" '2015年八仙樂園派對粉塵爆炸事故_zh.wikipedia.org_desktop_all-agents',\n",
" '2015年八仙樂園派對粉塵爆炸事故_zh.wikipedia.org_mobile-web_all-agents',\n",
" '2015年度叱咤樂壇流行榜頒獎典禮得獎名單_zh.wikipedia.org_all-access_all-agents',\n",
" '2015年度叱咤樂壇流行榜頒獎典禮得獎名單_zh.wikipedia.org_all-access_spider',\n",
" '2015年度叱咤樂壇流行榜頒獎典禮得獎名單_zh.wikipedia.org_desktop_all-agents',\n",
" '2015年度叱咤樂壇流行榜頒獎典禮得獎名單_zh.wikipedia.org_mobile-web_all-agents',\n",
" '2015年度新人選手選択会議_(日本プロ野球)_ja.wikipedia.org_desktop_all-agents',\n",
" '2015年臺北市文化國小隨機殺人事件_zh.wikipedia.org_all-access_all-agents',\n",
" '2015年臺北市文化國小隨機殺人事件_zh.wikipedia.org_all-access_spider',\n",
" '2015年臺北市文化國小隨機殺人事件_zh.wikipedia.org_desktop_all-agents',\n",
" '2015年華潔洗滌罷工事件_zh.wikipedia.org_all-access_all-agents',\n",
" '2015年華潔洗滌罷工事件_zh.wikipedia.org_all-access_spider',\n",
" '2015年華潔洗滌罷工事件_zh.wikipedia.org_desktop_all-agents',\n",
" '2015年電影列表_zh.wikipedia.org_all-access_all-agents',\n",
" '2015年電影列表_zh.wikipedia.org_all-access_spider',\n",
" '2015年電影列表_zh.wikipedia.org_desktop_all-agents',\n",
" '2015年電影列表_zh.wikipedia.org_mobile-web_all-agents',\n",
" '2016_AFC_U-23_Championship_en.wikipedia.org_all-access_all-agents',\n",
" '2016_AFC_U-23_Championship_en.wikipedia.org_all-access_spider',\n",
" '2016_AFC_U-23_Championship_en.wikipedia.org_desktop_all-agents',\n",
" '2016_ATP_World_Tour_en.wikipedia.org_desktop_all-agents',\n",
" '2016_Africa_Women_Cup_of_Nations_en.wikipedia.org_mobile-web_all-agents',\n",
" '2016_African_Nations_Championship_en.wikipedia.org_all-access_all-agents',\n",
" '2016_African_Nations_Championship_en.wikipedia.org_all-access_spider',\n",
" '2016_African_Nations_Championship_en.wikipedia.org_mobile-web_all-agents',\n",
" '2016_Asia_Cup_en.wikipedia.org_all-access_all-agents',\n",
" '2016_Asia_Cup_en.wikipedia.org_all-access_spider',\n",
" '2016_Asia_Cup_en.wikipedia.org_desktop_all-agents',\n",
" '2016_Asia_Cup_en.wikipedia.org_mobile-web_all-agents',\n",
" '2016_Atlantic_hurricane_season_en.wikipedia.org_all-access_all-agents',\n",
" '2016_Atlantic_hurricane_season_en.wikipedia.org_all-access_spider',\n",
" '2016_Atlantic_hurricane_season_en.wikipedia.org_desktop_all-agents',\n",
" '2016_Atlantic_hurricane_season_en.wikipedia.org_mobile-web_all-agents',\n",
" '2016_Australian_Open_en.wikipedia.org_all-access_all-agents',\n",
" '2016_Australian_Open_en.wikipedia.org_all-access_spider',\n",
" '2016_Australian_Open_en.wikipedia.org_desktop_all-agents',\n",
" '2016_Australian_Open_en.wikipedia.org_mobile-web_all-agents',\n",
" \"2016_Australian_Open_–_Men's_Singles_en.wikipedia.org_all-access_all-agents\",\n",
" \"2016_Australian_Open_–_Men's_Singles_en.wikipedia.org_all-access_spider\",\n",
" \"2016_Australian_Open_–_Men's_Singles_en.wikipedia.org_desktop_all-agents\",\n",
" \"2016_Australian_Open_–_Women's_Singles_en.wikipedia.org_all-access_all-agents\",\n",
" \"2016_Australian_Open_–_Women's_Singles_en.wikipedia.org_all-access_spider\",\n",
" \"2016_Australian_Open_–_Women's_Singles_en.wikipedia.org_desktop_all-agents\",\n",
" '2016_Berlin_attack_en.wikipedia.org_all-access_all-agents',\n",
" '2016_Berlin_attack_en.wikipedia.org_all-access_spider',\n",
" '2016_Berlin_attack_en.wikipedia.org_desktop_all-agents',\n",
" '2016_Brown_Bears_football_team_en.wikipedia.org_all-access_all-agents',\n",
" '2016_Brussels_bombings_en.wikipedia.org_all-access_all-agents',\n",
" '2016_Brussels_bombings_en.wikipedia.org_all-access_spider',\n",
" ...]"
]
},
"execution_count": 7,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"page_list"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### 可以發現到：8703780/145063 = 每一頁有60個Id，代表60天的每日數據流量 <br/>\n",
"### 分別是從1/1~3/1的每日數據流量預測項目，上傳答案就是每日數據流量預測"
]
},
{
"cell_type": "code",
"execution_count": 8,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"<class 'pandas.core.frame.DataFrame'>\n",
"RangeIndex: 8703780 entries, 0 to 8703779\n",
"Data columns (total 2 columns):\n",
"Page object\n",
"Id object\n",
"dtypes: object(2)\n",
"memory usage: 132.8+ MB\n"
]
}
],
"source": [
"df_test.info() #可以發現到：8703780/145063 = 每一頁有60個Id，分別是從1/1~3/1的每日數據流量預測項目，上傳答案就是每日數據流量預測"
]
},
{
"cell_type": "code",
"execution_count": 9,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>Page</th>\n",
" <th>Id</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>!vote_en.wikipedia.org_all-access_all-agents_2...</td>\n",
" <td>bf4edcf969af</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>!vote_en.wikipedia.org_all-access_all-agents_2...</td>\n",
" <td>929ed2bf52b9</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>!vote_en.wikipedia.org_all-access_all-agents_2...</td>\n",
" <td>ff29d0f51d5c</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>!vote_en.wikipedia.org_all-access_all-agents_2...</td>\n",
" <td>e98873359be6</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>!vote_en.wikipedia.org_all-access_all-agents_2...</td>\n",
" <td>fa012434263a</td>\n",
" </tr>\n",
" <tr>\n",
" <th>5</th>\n",
" <td>!vote_en.wikipedia.org_all-access_all-agents_2...</td>\n",
" <td>48f1e93517a2</td>\n",
" </tr>\n",
" <tr>\n",
" <th>6</th>\n",
" <td>!vote_en.wikipedia.org_all-access_all-agents_2...</td>\n",
" <td>5def418fcb36</td>\n",
" </tr>\n",
" <tr>\n",
" <th>7</th>\n",
" <td>!vote_en.wikipedia.org_all-access_all-agents_2...</td>\n",
" <td>77bd08134351</td>\n",
" </tr>\n",
" <tr>\n",
" <th>8</th>\n",
" <td>!vote_en.wikipedia.org_all-access_all-agents_2...</td>\n",
" <td>5889e6dbb16f</td>\n",
" </tr>\n",
" <tr>\n",
" <th>9</th>\n",
" <td>!vote_en.wikipedia.org_all-access_all-agents_2...</td>\n",
" <td>5f21fef1d764</td>\n",
" </tr>\n",
" <tr>\n",
" <th>10</th>\n",
" <td>!vote_en.wikipedia.org_all-access_all-agents_2...</td>\n",
" <td>6f07e1b8815a</td>\n",
" </tr>\n",
" <tr>\n",
" <th>11</th>\n",
" <td>!vote_en.wikipedia.org_all-access_all-agents_2...</td>\n",
" <td>228e54b5dea0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>12</th>\n",
" <td>!vote_en.wikipedia.org_all-access_all-agents_2...</td>\n",
" <td>da1b34963ed7</td>\n",
" </tr>\n",
" <tr>\n",
" <th>13</th>\n",
" <td>!vote_en.wikipedia.org_all-access_all-agents_2...</td>\n",
" <td>ab5ccefaa2db</td>\n",
" </tr>\n",
" <tr>\n",
" <th>14</th>\n",
" <td>!vote_en.wikipedia.org_all-access_all-agents_2...</td>\n",
" <td>cbf42873ebf1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>15</th>\n",
" <td>!vote_en.wikipedia.org_all-access_all-agents_2...</td>\n",
" <td>ac67e35ed44e</td>\n",
" </tr>\n",
" <tr>\n",
" <th>16</th>\n",
" <td>!vote_en.wikipedia.org_all-access_all-agents_2...</td>\n",
" <td>88c098aa640d</td>\n",
" </tr>\n",
" <tr>\n",
" <th>17</th>\n",
" <td>!vote_en.wikipedia.org_all-access_all-agents_2...</td>\n",
" <td>7c72842a89d1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>18</th>\n",
" <td>!vote_en.wikipedia.org_all-access_all-agents_2...</td>\n",
" <td>8ce002f2c329</td>\n",
" </tr>\n",
" <tr>\n",
" <th>19</th>\n",
" <td>!vote_en.wikipedia.org_all-access_all-agents_2...</td>\n",
" <td>5f72d9920560</td>\n",
" </tr>\n",
" <tr>\n",
" <th>20</th>\n",
" <td>!vote_en.wikipedia.org_all-access_all-agents_2...</td>\n",
" <td>f93afd7f5d9b</td>\n",
" </tr>\n",
" <tr>\n",
" <th>21</th>\n",
" <td>!vote_en.wikipedia.org_all-access_all-agents_2...</td>\n",
" <td>14011cb66f2d</td>\n",
" </tr>\n",
" <tr>\n",
" <th>22</th>\n",
" <td>!vote_en.wikipedia.org_all-access_all-agents_2...</td>\n",
" <td>0065551ac465</td>\n",
" </tr>\n",
" <tr>\n",
" <th>23</th>\n",
" <td>!vote_en.wikipedia.org_all-access_all-agents_2...</td>\n",
" <td>175f1872729e</td>\n",
" </tr>\n",
" <tr>\n",
" <th>24</th>\n",
" <td>!vote_en.wikipedia.org_all-access_all-agents_2...</td>\n",
" <td>31d756e83124</td>\n",
" </tr>\n",
" <tr>\n",
" <th>25</th>\n",
" <td>!vote_en.wikipedia.org_all-access_all-agents_2...</td>\n",
" <td>e186c2363c5e</td>\n",
" </tr>\n",
" <tr>\n",
" <th>26</th>\n",
" <td>!vote_en.wikipedia.org_all-access_all-agents_2...</td>\n",
" <td>3bce56c2b977</td>\n",
" </tr>\n",
" <tr>\n",
" <th>27</th>\n",
" <td>!vote_en.wikipedia.org_all-access_all-agents_2...</td>\n",
" <td>d497981dce77</td>\n",
" </tr>\n",
" <tr>\n",
" <th>28</th>\n",
" <td>!vote_en.wikipedia.org_all-access_all-agents_2...</td>\n",
" <td>c813cec10548</td>\n",
" </tr>\n",
" <tr>\n",
" <th>29</th>\n",
" <td>!vote_en.wikipedia.org_all-access_all-agents_2...</td>\n",
" <td>5123e0ed62c9</td>\n",
" </tr>\n",
" <tr>\n",
" <th>...</th>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>8703750</th>\n",
" <td>龙生九子_zh.wikipedia.org_mobile-web_all-agents_20...</td>\n",
" <td>1fb8f902ad0f</td>\n",
" </tr>\n",
" <tr>\n",
" <th>8703751</th>\n",
" <td>龙生九子_zh.wikipedia.org_mobile-web_all-agents_20...</td>\n",
" <td>0107f6d7cd82</td>\n",
" </tr>\n",
" <tr>\n",
" <th>8703752</th>\n",
" <td>龙生九子_zh.wikipedia.org_mobile-web_all-agents_20...</td>\n",
" <td>30c402ed9e49</td>\n",
" </tr>\n",
" <tr>\n",
" <th>8703753</th>\n",
" <td>龙生九子_zh.wikipedia.org_mobile-web_all-agents_20...</td>\n",
" <td>935fa0168d01</td>\n",
" </tr>\n",
" <tr>\n",
" <th>8703754</th>\n",
" <td>龙生九子_zh.wikipedia.org_mobile-web_all-agents_20...</td>\n",
" <td>1140b428380e</td>\n",
" </tr>\n",
" <tr>\n",
" <th>8703755</th>\n",
" <td>龙生九子_zh.wikipedia.org_mobile-web_all-agents_20...</td>\n",
" <td>cc5eadae0d7a</td>\n",
" </tr>\n",
" <tr>\n",
" <th>8703756</th>\n",
" <td>龙生九子_zh.wikipedia.org_mobile-web_all-agents_20...</td>\n",
" <td>f923701cdb05</td>\n",
" </tr>\n",
" <tr>\n",
" <th>8703757</th>\n",
" <td>龙生九子_zh.wikipedia.org_mobile-web_all-agents_20...</td>\n",
" <td>905679a20d39</td>\n",
" </tr>\n",
" <tr>\n",
" <th>8703758</th>\n",
" <td>龙生九子_zh.wikipedia.org_mobile-web_all-agents_20...</td>\n",
" <td>642354a50690</td>\n",
" </tr>\n",
" <tr>\n",
" <th>8703759</th>\n",
" <td>龙生九子_zh.wikipedia.org_mobile-web_all-agents_20...</td>\n",
" <td>7376c63bd4c1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>8703760</th>\n",
" <td>龙生九子_zh.wikipedia.org_mobile-web_all-agents_20...</td>\n",
" <td>1f0566b71f7e</td>\n",
" </tr>\n",
" <tr>\n",
" <th>8703761</th>\n",
" <td>龙生九子_zh.wikipedia.org_mobile-web_all-agents_20...</td>\n",
" <td>938774bbb675</td>\n",
" </tr>\n",
" <tr>\n",
" <th>8703762</th>\n",
" <td>龙生九子_zh.wikipedia.org_mobile-web_all-agents_20...</td>\n",
" <td>53c046bac8cb</td>\n",
" </tr>\n",
" <tr>\n",
" <th>8703763</th>\n",
" <td>龙生九子_zh.wikipedia.org_mobile-web_all-agents_20...</td>\n",
" <td>ead2377353d3</td>\n",
" </tr>\n",
" <tr>\n",
" <th>8703764</th>\n",
" <td>龙生九子_zh.wikipedia.org_mobile-web_all-agents_20...</td>\n",
" <td>efa87c7d5160</td>\n",
" </tr>\n",
" <tr>\n",
" <th>8703765</th>\n",
" <td>龙生九子_zh.wikipedia.org_mobile-web_all-agents_20...</td>\n",
" <td>f239d6ceb17b</td>\n",
" </tr>\n",
" <tr>\n",
" <th>8703766</th>\n",
" <td>龙生九子_zh.wikipedia.org_mobile-web_all-agents_20...</td>\n",
" <td>0fef0826b1bc</td>\n",
" </tr>\n",
" <tr>\n",
" <th>8703767</th>\n",
" <td>龙生九子_zh.wikipedia.org_mobile-web_all-agents_20...</td>\n",
" <td>478d3c34b0c1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>8703768</th>\n",
" <td>龙生九子_zh.wikipedia.org_mobile-web_all-agents_20...</td>\n",
" <td>6a1b6e3028fc</td>\n",
" </tr>\n",
" <tr>\n",
" <th>8703769</th>\n",
" <td>龙生九子_zh.wikipedia.org_mobile-web_all-agents_20...</td>\n",
" <td>3b5fb022accd</td>\n",
" </tr>\n",
" <tr>\n",
" <th>8703770</th>\n",
" <td>龙生九子_zh.wikipedia.org_mobile-web_all-agents_20...</td>\n",
" <td>a4456a9d271d</td>\n",
" </tr>\n",
" <tr>\n",
" <th>8703771</th>\n",
" <td>龙生九子_zh.wikipedia.org_mobile-web_all-agents_20...</td>\n",
" <td>d43a25cf4ef2</td>\n",
" </tr>\n",
" <tr>\n",
" <th>8703772</th>\n",
" <td>龙生九子_zh.wikipedia.org_mobile-web_all-agents_20...</td>\n",
" <td>8f47d2e020cd</td>\n",
" </tr>\n",
" <tr>\n",
" <th>8703773</th>\n",
" <td>龙生九子_zh.wikipedia.org_mobile-web_all-agents_20...</td>\n",
" <td>a78af728d84b</td>\n",
" </tr>\n",
" <tr>\n",
" <th>8703774</th>\n",
" <td>龙生九子_zh.wikipedia.org_mobile-web_all-agents_20...</td>\n",
" <td>d1ba45c7ec08</td>\n",
" </tr>\n",
" <tr>\n",
" <th>8703775</th>\n",
" <td>龙生九子_zh.wikipedia.org_mobile-web_all-agents_20...</td>\n",
" <td>f69747f5ee68</td>\n",
" </tr>\n",
" <tr>\n",
" <th>8703776</th>\n",
" <td>龙生九子_zh.wikipedia.org_mobile-web_all-agents_20...</td>\n",
" <td>2489963dc503</td>\n",
" </tr>\n",
" <tr>\n",
" <th>8703777</th>\n",
" <td>龙生九子_zh.wikipedia.org_mobile-web_all-agents_20...</td>\n",
" <td>b0624c909f4c</td>\n",
" </tr>\n",
" <tr>\n",
" <th>8703778</th>\n",
" <td>龙生九子_zh.wikipedia.org_mobile-web_all-agents_20...</td>\n",
" <td>24a1dfb06c10</td>\n",
" </tr>\n",
" <tr>\n",
" <th>8703779</th>\n",
" <td>龙生九子_zh.wikipedia.org_mobile-web_all-agents_20...</td>\n",
" <td>add681d54216</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"<p>8703780 rows × 2 columns</p>\n",
"</div>"
],
"text/plain": [
" Page Id\n",
"0 !vote_en.wikipedia.org_all-access_all-agents_2... bf4edcf969af\n",
"1 !vote_en.wikipedia.org_all-access_all-agents_2... 929ed2bf52b9\n",
"2 !vote_en.wikipedia.org_all-access_all-agents_2... ff29d0f51d5c\n",
"3 !vote_en.wikipedia.org_all-access_all-agents_2... e98873359be6\n",
"4 !vote_en.wikipedia.org_all-access_all-agents_2... fa012434263a\n",
"5 !vote_en.wikipedia.org_all-access_all-agents_2... 48f1e93517a2\n",
"6 !vote_en.wikipedia.org_all-access_all-agents_2... 5def418fcb36\n",
"7 !vote_en.wikipedia.org_all-access_all-agents_2... 77bd08134351\n",
"8 !vote_en.wikipedia.org_all-access_all-agents_2... 5889e6dbb16f\n",
"9 !vote_en.wikipedia.org_all-access_all-agents_2... 5f21fef1d764\n",
"10 !vote_en.wikipedia.org_all-access_all-agents_2... 6f07e1b8815a\n",
"11 !vote_en.wikipedia.org_all-access_all-agents_2... 228e54b5dea0\n",
"12 !vote_en.wikipedia.org_all-access_all-agents_2... da1b34963ed7\n",
"13 !vote_en.wikipedia.org_all-access_all-agents_2... ab5ccefaa2db\n",
"14 !vote_en.wikipedia.org_all-access_all-agents_2... cbf42873ebf1\n",
"15 !vote_en.wikipedia.org_all-access_all-agents_2... ac67e35ed44e\n",
"16 !vote_en.wikipedia.org_all-access_all-agents_2... 88c098aa640d\n",
"17 !vote_en.wikipedia.org_all-access_all-agents_2... 7c72842a89d1\n",
"18 !vote_en.wikipedia.org_all-access_all-agents_2... 8ce002f2c329\n",
"19 !vote_en.wikipedia.org_all-access_all-agents_2... 5f72d9920560\n",
"20 !vote_en.wikipedia.org_all-access_all-agents_2... f93afd7f5d9b\n",
"21 !vote_en.wikipedia.org_all-access_all-agents_2... 14011cb66f2d\n",
"22 !vote_en.wikipedia.org_all-access_all-agents_2... 0065551ac465\n",
"23 !vote_en.wikipedia.org_all-access_all-agents_2... 175f1872729e\n",
"24 !vote_en.wikipedia.org_all-access_all-agents_2... 31d756e83124\n",
"25 !vote_en.wikipedia.org_all-access_all-agents_2... e186c2363c5e\n",
"26 !vote_en.wikipedia.org_all-access_all-agents_2... 3bce56c2b977\n",
"27 !vote_en.wikipedia.org_all-access_all-agents_2... d497981dce77\n",
"28 !vote_en.wikipedia.org_all-access_all-agents_2... c813cec10548\n",
"29 !vote_en.wikipedia.org_all-access_all-agents_2... 5123e0ed62c9\n",
"... ... ...\n",
"8703750 龙生九子_zh.wikipedia.org_mobile-web_all-agents_20... 1fb8f902ad0f\n",
"8703751 龙生九子_zh.wikipedia.org_mobile-web_all-agents_20... 0107f6d7cd82\n",
"8703752 龙生九子_zh.wikipedia.org_mobile-web_all-agents_20... 30c402ed9e49\n",
"8703753 龙生九子_zh.wikipedia.org_mobile-web_all-agents_20... 935fa0168d01\n",
"8703754 龙生九子_zh.wikipedia.org_mobile-web_all-agents_20... 1140b428380e\n",
"8703755 龙生九子_zh.wikipedia.org_mobile-web_all-agents_20... cc5eadae0d7a\n",
"8703756 龙生九子_zh.wikipedia.org_mobile-web_all-agents_20... f923701cdb05\n",
"8703757 龙生九子_zh.wikipedia.org_mobile-web_all-agents_20... 905679a20d39\n",
"8703758 龙生九子_zh.wikipedia.org_mobile-web_all-agents_20... 642354a50690\n",
"8703759 龙生九子_zh.wikipedia.org_mobile-web_all-agents_20... 7376c63bd4c1\n",
"8703760 龙生九子_zh.wikipedia.org_mobile-web_all-agents_20... 1f0566b71f7e\n",
"8703761 龙生九子_zh.wikipedia.org_mobile-web_all-agents_20... 938774bbb675\n",
"8703762 龙生九子_zh.wikipedia.org_mobile-web_all-agents_20... 53c046bac8cb\n",
"8703763 龙生九子_zh.wikipedia.org_mobile-web_all-agents_20... ead2377353d3\n",
"8703764 龙生九子_zh.wikipedia.org_mobile-web_all-agents_20... efa87c7d5160\n",
"8703765 龙生九子_zh.wikipedia.org_mobile-web_all-agents_20... f239d6ceb17b\n",
"8703766 龙生九子_zh.wikipedia.org_mobile-web_all-agents_20... 0fef0826b1bc\n",
"8703767 龙生九子_zh.wikipedia.org_mobile-web_all-agents_20... 478d3c34b0c1\n",
"8703768 龙生九子_zh.wikipedia.org_mobile-web_all-agents_20... 6a1b6e3028fc\n",
"8703769 龙生九子_zh.wikipedia.org_mobile-web_all-agents_20... 3b5fb022accd\n",
"8703770 龙生九子_zh.wikipedia.org_mobile-web_all-agents_20... a4456a9d271d\n",
"8703771 龙生九子_zh.wikipedia.org_mobile-web_all-agents_20... d43a25cf4ef2\n",
"8703772 龙生九子_zh.wikipedia.org_mobile-web_all-agents_20... 8f47d2e020cd\n",
"8703773 龙生九子_zh.wikipedia.org_mobile-web_all-agents_20... a78af728d84b\n",
"8703774 龙生九子_zh.wikipedia.org_mobile-web_all-agents_20... d1ba45c7ec08\n",
"8703775 龙生九子_zh.wikipedia.org_mobile-web_all-agents_20... f69747f5ee68\n",
"8703776 龙生九子_zh.wikipedia.org_mobile-web_all-agents_20... 2489963dc503\n",
"8703777 龙生九子_zh.wikipedia.org_mobile-web_all-agents_20... b0624c909f4c\n",
"8703778 龙生九子_zh.wikipedia.org_mobile-web_all-agents_20... 24a1dfb06c10\n",
"8703779 龙生九子_zh.wikipedia.org_mobile-web_all-agents_20... add681d54216\n",
"\n",
"[8703780 rows x 2 columns]"
]
},
"execution_count": 9,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df_test"
]
},
{
"cell_type": "code",
"execution_count": 10,
"metadata": {},
"outputs": [],
"source": [
"test_page_list = df_test['Page'].tolist()"
]
},
{
"cell_type": "code",
"execution_count": 11,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"test_page_list.sort() #排序page才方便跟test欄位比較"
]
},
{
"cell_type": "code",
"execution_count": 12,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"['!vote_en.wikipedia.org_all-access_all-agents_2017-01-01',\n",
" '!vote_en.wikipedia.org_all-access_all-agents_2017-01-02',\n",
" '!vote_en.wikipedia.org_all-access_all-agents_2017-01-03',\n",
" '!vote_en.wikipedia.org_all-access_all-agents_2017-01-04',\n",
" '!vote_en.wikipedia.org_all-access_all-agents_2017-01-05',\n",
" '!vote_en.wikipedia.org_all-access_all-agents_2017-01-06',\n",
" '!vote_en.wikipedia.org_all-access_all-agents_2017-01-07',\n",
" '!vote_en.wikipedia.org_all-access_all-agents_2017-01-08',\n",
" '!vote_en.wikipedia.org_all-access_all-agents_2017-01-09',\n",
" '!vote_en.wikipedia.org_all-access_all-agents_2017-01-10',\n",
" '!vote_en.wikipedia.org_all-access_all-agents_2017-01-11',\n",
" '!vote_en.wikipedia.org_all-access_all-agents_2017-01-12',\n",
" '!vote_en.wikipedia.org_all-access_all-agents_2017-01-13',\n",
" '!vote_en.wikipedia.org_all-access_all-agents_2017-01-14',\n",
" '!vote_en.wikipedia.org_all-access_all-agents_2017-01-15',\n",
" '!vote_en.wikipedia.org_all-access_all-agents_2017-01-16',\n",
" '!vote_en.wikipedia.org_all-access_all-agents_2017-01-17',\n",
" '!vote_en.wikipedia.org_all-access_all-agents_2017-01-18',\n",
" '!vote_en.wikipedia.org_all-access_all-agents_2017-01-19',\n",
" '!vote_en.wikipedia.org_all-access_all-agents_2017-01-20',\n",
" '!vote_en.wikipedia.org_all-access_all-agents_2017-01-21',\n",
" '!vote_en.wikipedia.org_all-access_all-agents_2017-01-22',\n",
" '!vote_en.wikipedia.org_all-access_all-agents_2017-01-23',\n",
" '!vote_en.wikipedia.org_all-access_all-agents_2017-01-24',\n",
" '!vote_en.wikipedia.org_all-access_all-agents_2017-01-25',\n",
" '!vote_en.wikipedia.org_all-access_all-agents_2017-01-26',\n",
" '!vote_en.wikipedia.org_all-access_all-agents_2017-01-27',\n",
" '!vote_en.wikipedia.org_all-access_all-agents_2017-01-28',\n",
" '!vote_en.wikipedia.org_all-access_all-agents_2017-01-29',\n",
" '!vote_en.wikipedia.org_all-access_all-agents_2017-01-30',\n",
" '!vote_en.wikipedia.org_all-access_all-agents_2017-01-31',\n",
" '!vote_en.wikipedia.org_all-access_all-agents_2017-02-01',\n",
" '!vote_en.wikipedia.org_all-access_all-agents_2017-02-02',\n",
" '!vote_en.wikipedia.org_all-access_all-agents_2017-02-03',\n",
" '!vote_en.wikipedia.org_all-access_all-agents_2017-02-04',\n",
" '!vote_en.wikipedia.org_all-access_all-agents_2017-02-05',\n",
" '!vote_en.wikipedia.org_all-access_all-agents_2017-02-06',\n",
" '!vote_en.wikipedia.org_all-access_all-agents_2017-02-07',\n",
" '!vote_en.wikipedia.org_all-access_all-agents_2017-02-08',\n",
" '!vote_en.wikipedia.org_all-access_all-agents_2017-02-09',\n",
" '!vote_en.wikipedia.org_all-access_all-agents_2017-02-10',\n",
" '!vote_en.wikipedia.org_all-access_all-agents_2017-02-11',\n",
" '!vote_en.wikipedia.org_all-access_all-agents_2017-02-12',\n",
" '!vote_en.wikipedia.org_all-access_all-agents_2017-02-13',\n",
" '!vote_en.wikipedia.org_all-access_all-agents_2017-02-14',\n",
" '!vote_en.wikipedia.org_all-access_all-agents_2017-02-15',\n",
" '!vote_en.wikipedia.org_all-access_all-agents_2017-02-16',\n",
" '!vote_en.wikipedia.org_all-access_all-agents_2017-02-17',\n",
" '!vote_en.wikipedia.org_all-access_all-agents_2017-02-18',\n",
" '!vote_en.wikipedia.org_all-access_all-agents_2017-02-19',\n",
" '!vote_en.wikipedia.org_all-access_all-agents_2017-02-20',\n",
" '!vote_en.wikipedia.org_all-access_all-agents_2017-02-21',\n",
" '!vote_en.wikipedia.org_all-access_all-agents_2017-02-22',\n",
" '!vote_en.wikipedia.org_all-access_all-agents_2017-02-23',\n",
" '!vote_en.wikipedia.org_all-access_all-agents_2017-02-24',\n",
" '!vote_en.wikipedia.org_all-access_all-agents_2017-02-25',\n",
" '!vote_en.wikipedia.org_all-access_all-agents_2017-02-26',\n",
" '!vote_en.wikipedia.org_all-access_all-agents_2017-02-27',\n",
" '!vote_en.wikipedia.org_all-access_all-agents_2017-02-28',\n",
" '!vote_en.wikipedia.org_all-access_all-agents_2017-03-01',\n",
" '!vote_en.wikipedia.org_all-access_spider_2017-01-01',\n",
" '!vote_en.wikipedia.org_all-access_spider_2017-01-02',\n",
" '!vote_en.wikipedia.org_all-access_spider_2017-01-03',\n",
" '!vote_en.wikipedia.org_all-access_spider_2017-01-04',\n",
" '!vote_en.wikipedia.org_all-access_spider_2017-01-05',\n",
" '!vote_en.wikipedia.org_all-access_spider_2017-01-06',\n",
" '!vote_en.wikipedia.org_all-access_spider_2017-01-07',\n",
" '!vote_en.wikipedia.org_all-access_spider_2017-01-08',\n",
" '!vote_en.wikipedia.org_all-access_spider_2017-01-09',\n",
" '!vote_en.wikipedia.org_all-access_spider_2017-01-10',\n",
" '!vote_en.wikipedia.org_all-access_spider_2017-01-11',\n",
" '!vote_en.wikipedia.org_all-access_spider_2017-01-12',\n",
" '!vote_en.wikipedia.org_all-access_spider_2017-01-13',\n",
" '!vote_en.wikipedia.org_all-access_spider_2017-01-14',\n",
" '!vote_en.wikipedia.org_all-access_spider_2017-01-15',\n",
" '!vote_en.wikipedia.org_all-access_spider_2017-01-16',\n",
" '!vote_en.wikipedia.org_all-access_spider_2017-01-17',\n",
" '!vote_en.wikipedia.org_all-access_spider_2017-01-18',\n",
" '!vote_en.wikipedia.org_all-access_spider_2017-01-19',\n",
" '!vote_en.wikipedia.org_all-access_spider_2017-01-20',\n",
" '!vote_en.wikipedia.org_all-access_spider_2017-01-21',\n",
" '!vote_en.wikipedia.org_all-access_spider_2017-01-22',\n",
" '!vote_en.wikipedia.org_all-access_spider_2017-01-23',\n",
" '!vote_en.wikipedia.org_all-access_spider_2017-01-24',\n",
" '!vote_en.wikipedia.org_all-access_spider_2017-01-25',\n",
" '!vote_en.wikipedia.org_all-access_spider_2017-01-26',\n",
" '!vote_en.wikipedia.org_all-access_spider_2017-01-27',\n",
" '!vote_en.wikipedia.org_all-access_spider_2017-01-28',\n",
" '!vote_en.wikipedia.org_all-access_spider_2017-01-29',\n",
" '!vote_en.wikipedia.org_all-access_spider_2017-01-30',\n",
" '!vote_en.wikipedia.org_all-access_spider_2017-01-31',\n",
" '!vote_en.wikipedia.org_all-access_spider_2017-02-01',\n",
" '!vote_en.wikipedia.org_all-access_spider_2017-02-02',\n",
" '!vote_en.wikipedia.org_all-access_spider_2017-02-03',\n",
" '!vote_en.wikipedia.org_all-access_spider_2017-02-04',\n",
" '!vote_en.wikipedia.org_all-access_spider_2017-02-05',\n",
" '!vote_en.wikipedia.org_all-access_spider_2017-02-06',\n",
" '!vote_en.wikipedia.org_all-access_spider_2017-02-07',\n",
" '!vote_en.wikipedia.org_all-access_spider_2017-02-08',\n",
" '!vote_en.wikipedia.org_all-access_spider_2017-02-09',\n",
" '!vote_en.wikipedia.org_all-access_spider_2017-02-10',\n",
" '!vote_en.wikipedia.org_all-access_spider_2017-02-11',\n",
" '!vote_en.wikipedia.org_all-access_spider_2017-02-12',\n",
" '!vote_en.wikipedia.org_all-access_spider_2017-02-13',\n",
" '!vote_en.wikipedia.org_all-access_spider_2017-02-14',\n",
" '!vote_en.wikipedia.org_all-access_spider_2017-02-15',\n",
" '!vote_en.wikipedia.org_all-access_spider_2017-02-16',\n",
" '!vote_en.wikipedia.org_all-access_spider_2017-02-17',\n",
" '!vote_en.wikipedia.org_all-access_spider_2017-02-18',\n",
" '!vote_en.wikipedia.org_all-access_spider_2017-02-19',\n",
" '!vote_en.wikipedia.org_all-access_spider_2017-02-20',\n",
" '!vote_en.wikipedia.org_all-access_spider_2017-02-21',\n",
" '!vote_en.wikipedia.org_all-access_spider_2017-02-22',\n",
" '!vote_en.wikipedia.org_all-access_spider_2017-02-23',\n",
" '!vote_en.wikipedia.org_all-access_spider_2017-02-24',\n",
" '!vote_en.wikipedia.org_all-access_spider_2017-02-25',\n",
" '!vote_en.wikipedia.org_all-access_spider_2017-02-26',\n",
" '!vote_en.wikipedia.org_all-access_spider_2017-02-27',\n",
" '!vote_en.wikipedia.org_all-access_spider_2017-02-28',\n",
" '!vote_en.wikipedia.org_all-access_spider_2017-03-01',\n",
" '!vote_en.wikipedia.org_desktop_all-agents_2017-01-01',\n",
" '!vote_en.wikipedia.org_desktop_all-agents_2017-01-02',\n",
" '!vote_en.wikipedia.org_desktop_all-agents_2017-01-03',\n",
" '!vote_en.wikipedia.org_desktop_all-agents_2017-01-04',\n",
" '!vote_en.wikipedia.org_desktop_all-agents_2017-01-05',\n",
" '!vote_en.wikipedia.org_desktop_all-agents_2017-01-06',\n",
" '!vote_en.wikipedia.org_desktop_all-agents_2017-01-07',\n",
" '!vote_en.wikipedia.org_desktop_all-agents_2017-01-08',\n",
" '!vote_en.wikipedia.org_desktop_all-agents_2017-01-09',\n",
" '!vote_en.wikipedia.org_desktop_all-agents_2017-01-10',\n",
" '!vote_en.wikipedia.org_desktop_all-agents_2017-01-11',\n",
" '!vote_en.wikipedia.org_desktop_all-agents_2017-01-12',\n",
" '!vote_en.wikipedia.org_desktop_all-agents_2017-01-13',\n",
" '!vote_en.wikipedia.org_desktop_all-agents_2017-01-14',\n",
" '!vote_en.wikipedia.org_desktop_all-agents_2017-01-15',\n",
" '!vote_en.wikipedia.org_desktop_all-agents_2017-01-16',\n",
" '!vote_en.wikipedia.org_desktop_all-agents_2017-01-17',\n",
" '!vote_en.wikipedia.org_desktop_all-agents_2017-01-18',\n",
" '!vote_en.wikipedia.org_desktop_all-agents_2017-01-19',\n",
" '!vote_en.wikipedia.org_desktop_all-agents_2017-01-20',\n",
" '!vote_en.wikipedia.org_desktop_all-agents_2017-01-21',\n",
" '!vote_en.wikipedia.org_desktop_all-agents_2017-01-22',\n",
" '!vote_en.wikipedia.org_desktop_all-agents_2017-01-23',\n",
" '!vote_en.wikipedia.org_desktop_all-agents_2017-01-24',\n",
" '!vote_en.wikipedia.org_desktop_all-agents_2017-01-25',\n",
" '!vote_en.wikipedia.org_desktop_all-agents_2017-01-26',\n",
" '!vote_en.wikipedia.org_desktop_all-agents_2017-01-27',\n",
" '!vote_en.wikipedia.org_desktop_all-agents_2017-01-28',\n",
" '!vote_en.wikipedia.org_desktop_all-agents_2017-01-29',\n",
" '!vote_en.wikipedia.org_desktop_all-agents_2017-01-30',\n",
" '!vote_en.wikipedia.org_desktop_all-agents_2017-01-31',\n",
" '!vote_en.wikipedia.org_desktop_all-agents_2017-02-01',\n",
" '!vote_en.wikipedia.org_desktop_all-agents_2017-02-02',\n",
" '!vote_en.wikipedia.org_desktop_all-agents_2017-02-03',\n",
" '!vote_en.wikipedia.org_desktop_all-agents_2017-02-04',\n",
" '!vote_en.wikipedia.org_desktop_all-agents_2017-02-05',\n",
" '!vote_en.wikipedia.org_desktop_all-agents_2017-02-06',\n",
" '!vote_en.wikipedia.org_desktop_all-agents_2017-02-07',\n",
" '!vote_en.wikipedia.org_desktop_all-agents_2017-02-08',\n",
" '!vote_en.wikipedia.org_desktop_all-agents_2017-02-09',\n",
" '!vote_en.wikipedia.org_desktop_all-agents_2017-02-10',\n",
" '!vote_en.wikipedia.org_desktop_all-agents_2017-02-11',\n",
" '!vote_en.wikipedia.org_desktop_all-agents_2017-02-12',\n",
" '!vote_en.wikipedia.org_desktop_all-agents_2017-02-13',\n",
" '!vote_en.wikipedia.org_desktop_all-agents_2017-02-14',\n",
" '!vote_en.wikipedia.org_desktop_all-agents_2017-02-15',\n",
" '!vote_en.wikipedia.org_desktop_all-agents_2017-02-16',\n",
" '!vote_en.wikipedia.org_desktop_all-agents_2017-02-17',\n",
" '!vote_en.wikipedia.org_desktop_all-agents_2017-02-18',\n",
" '!vote_en.wikipedia.org_desktop_all-agents_2017-02-19',\n",
" '!vote_en.wikipedia.org_desktop_all-agents_2017-02-20',\n",
" '!vote_en.wikipedia.org_desktop_all-agents_2017-02-21',\n",
" '!vote_en.wikipedia.org_desktop_all-agents_2017-02-22',\n",
" '!vote_en.wikipedia.org_desktop_all-agents_2017-02-23',\n",
" '!vote_en.wikipedia.org_desktop_all-agents_2017-02-24',\n",
" '!vote_en.wikipedia.org_desktop_all-agents_2017-02-25',\n",
" '!vote_en.wikipedia.org_desktop_all-agents_2017-02-26',\n",
" '!vote_en.wikipedia.org_desktop_all-agents_2017-02-27',\n",
" '!vote_en.wikipedia.org_desktop_all-agents_2017-02-28',\n",
" '!vote_en.wikipedia.org_desktop_all-agents_2017-03-01',\n",
" '\"Awaken,_My_Love!\"_en.wikipedia.org_all-access_all-agents_2017-01-01',\n",
" '\"Awaken,_My_Love!\"_en.wikipedia.org_all-access_all-agents_2017-01-02',\n",
" '\"Awaken,_My_Love!\"_en.wikipedia.org_all-access_all-agents_2017-01-03',\n",
" '\"Awaken,_My_Love!\"_en.wikipedia.org_all-access_all-agents_2017-01-04',\n",
" '\"Awaken,_My_Love!\"_en.wikipedia.org_all-access_all-agents_2017-01-05',\n",
" '\"Awaken,_My_Love!\"_en.wikipedia.org_all-access_all-agents_2017-01-06',\n",
" '\"Awaken,_My_Love!\"_en.wikipedia.org_all-access_all-agents_2017-01-07',\n",
" '\"Awaken,_My_Love!\"_en.wikipedia.org_all-access_all-agents_2017-01-08',\n",
" '\"Awaken,_My_Love!\"_en.wikipedia.org_all-access_all-agents_2017-01-09',\n",
" '\"Awaken,_My_Love!\"_en.wikipedia.org_all-access_all-agents_2017-01-10',\n",
" '\"Awaken,_My_Love!\"_en.wikipedia.org_all-access_all-agents_2017-01-11',\n",
" '\"Awaken,_My_Love!\"_en.wikipedia.org_all-access_all-agents_2017-01-12',\n",
" '\"Awaken,_My_Love!\"_en.wikipedia.org_all-access_all-agents_2017-01-13',\n",
" '\"Awaken,_My_Love!\"_en.wikipedia.org_all-access_all-agents_2017-01-14',\n",
" '\"Awaken,_My_Love!\"_en.wikipedia.org_all-access_all-agents_2017-01-15',\n",
" '\"Awaken,_My_Love!\"_en.wikipedia.org_all-access_all-agents_2017-01-16',\n",
" '\"Awaken,_My_Love!\"_en.wikipedia.org_all-access_all-agents_2017-01-17',\n",
" '\"Awaken,_My_Love!\"_en.wikipedia.org_all-access_all-agents_2017-01-18',\n",
" '\"Awaken,_My_Love!\"_en.wikipedia.org_all-access_all-agents_2017-01-19',\n",
" '\"Awaken,_My_Love!\"_en.wikipedia.org_all-access_all-agents_2017-01-20',\n",
" '\"Awaken,_My_Love!\"_en.wikipedia.org_all-access_all-agents_2017-01-21',\n",
" '\"Awaken,_My_Love!\"_en.wikipedia.org_all-access_all-agents_2017-01-22',\n",
" '\"Awaken,_My_Love!\"_en.wikipedia.org_all-access_all-agents_2017-01-23',\n",
" '\"Awaken,_My_Love!\"_en.wikipedia.org_all-access_all-agents_2017-01-24',\n",
" '\"Awaken,_My_Love!\"_en.wikipedia.org_all-access_all-agents_2017-01-25',\n",
" '\"Awaken,_My_Love!\"_en.wikipedia.org_all-access_all-agents_2017-01-26',\n",
" '\"Awaken,_My_Love!\"_en.wikipedia.org_all-access_all-agents_2017-01-27',\n",
" '\"Awaken,_My_Love!\"_en.wikipedia.org_all-access_all-agents_2017-01-28',\n",
" '\"Awaken,_My_Love!\"_en.wikipedia.org_all-access_all-agents_2017-01-29',\n",
" '\"Awaken,_My_Love!\"_en.wikipedia.org_all-access_all-agents_2017-01-30',\n",
" '\"Awaken,_My_Love!\"_en.wikipedia.org_all-access_all-agents_2017-01-31',\n",
" '\"Awaken,_My_Love!\"_en.wikipedia.org_all-access_all-agents_2017-02-01',\n",
" '\"Awaken,_My_Love!\"_en.wikipedia.org_all-access_all-agents_2017-02-02',\n",
" '\"Awaken,_My_Love!\"_en.wikipedia.org_all-access_all-agents_2017-02-03',\n",
" '\"Awaken,_My_Love!\"_en.wikipedia.org_all-access_all-agents_2017-02-04',\n",
" '\"Awaken,_My_Love!\"_en.wikipedia.org_all-access_all-agents_2017-02-05',\n",
" '\"Awaken,_My_Love!\"_en.wikipedia.org_all-access_all-agents_2017-02-06',\n",
" '\"Awaken,_My_Love!\"_en.wikipedia.org_all-access_all-agents_2017-02-07',\n",
" '\"Awaken,_My_Love!\"_en.wikipedia.org_all-access_all-agents_2017-02-08',\n",
" '\"Awaken,_My_Love!\"_en.wikipedia.org_all-access_all-agents_2017-02-09',\n",
" '\"Awaken,_My_Love!\"_en.wikipedia.org_all-access_all-agents_2017-02-10',\n",
" '\"Awaken,_My_Love!\"_en.wikipedia.org_all-access_all-agents_2017-02-11',\n",
" '\"Awaken,_My_Love!\"_en.wikipedia.org_all-access_all-agents_2017-02-12',\n",
" '\"Awaken,_My_Love!\"_en.wikipedia.org_all-access_all-agents_2017-02-13',\n",
" '\"Awaken,_My_Love!\"_en.wikipedia.org_all-access_all-agents_2017-02-14',\n",
" '\"Awaken,_My_Love!\"_en.wikipedia.org_all-access_all-agents_2017-02-15',\n",
" '\"Awaken,_My_Love!\"_en.wikipedia.org_all-access_all-agents_2017-02-16',\n",
" '\"Awaken,_My_Love!\"_en.wikipedia.org_all-access_all-agents_2017-02-17',\n",
" '\"Awaken,_My_Love!\"_en.wikipedia.org_all-access_all-agents_2017-02-18',\n",
" '\"Awaken,_My_Love!\"_en.wikipedia.org_all-access_all-agents_2017-02-19',\n",
" '\"Awaken,_My_Love!\"_en.wikipedia.org_all-access_all-agents_2017-02-20',\n",
" '\"Awaken,_My_Love!\"_en.wikipedia.org_all-access_all-agents_2017-02-21',\n",
" '\"Awaken,_My_Love!\"_en.wikipedia.org_all-access_all-agents_2017-02-22',\n",
" '\"Awaken,_My_Love!\"_en.wikipedia.org_all-access_all-agents_2017-02-23',\n",
" '\"Awaken,_My_Love!\"_en.wikipedia.org_all-access_all-agents_2017-02-24',\n",
" '\"Awaken,_My_Love!\"_en.wikipedia.org_all-access_all-agents_2017-02-25',\n",
" '\"Awaken,_My_Love!\"_en.wikipedia.org_all-access_all-agents_2017-02-26',\n",
" '\"Awaken,_My_Love!\"_en.wikipedia.org_all-access_all-agents_2017-02-27',\n",
" '\"Awaken,_My_Love!\"_en.wikipedia.org_all-access_all-agents_2017-02-28',\n",
" '\"Awaken,_My_Love!\"_en.wikipedia.org_all-access_all-agents_2017-03-01',\n",
" '\"Awaken,_My_Love!\"_en.wikipedia.org_all-access_spider_2017-01-01',\n",
" '\"Awaken,_My_Love!\"_en.wikipedia.org_all-access_spider_2017-01-02',\n",
" '\"Awaken,_My_Love!\"_en.wikipedia.org_all-access_spider_2017-01-03',\n",
" '\"Awaken,_My_Love!\"_en.wikipedia.org_all-access_spider_2017-01-04',\n",
" '\"Awaken,_My_Love!\"_en.wikipedia.org_all-access_spider_2017-01-05',\n",
" '\"Awaken,_My_Love!\"_en.wikipedia.org_all-access_spider_2017-01-06',\n",
" '\"Awaken,_My_Love!\"_en.wikipedia.org_all-access_spider_2017-01-07',\n",
" '\"Awaken,_My_Love!\"_en.wikipedia.org_all-access_spider_2017-01-08',\n",
" '\"Awaken,_My_Love!\"_en.wikipedia.org_all-access_spider_2017-01-09',\n",
" '\"Awaken,_My_Love!\"_en.wikipedia.org_all-access_spider_2017-01-10',\n",
" '\"Awaken,_My_Love!\"_en.wikipedia.org_all-access_spider_2017-01-11',\n",
" '\"Awaken,_My_Love!\"_en.wikipedia.org_all-access_spider_2017-01-12',\n",
" '\"Awaken,_My_Love!\"_en.wikipedia.org_all-access_spider_2017-01-13',\n",
" '\"Awaken,_My_Love!\"_en.wikipedia.org_all-access_spider_2017-01-14',\n",
" '\"Awaken,_My_Love!\"_en.wikipedia.org_all-access_spider_2017-01-15',\n",
" '\"Awaken,_My_Love!\"_en.wikipedia.org_all-access_spider_2017-01-16',\n",
" '\"Awaken,_My_Love!\"_en.wikipedia.org_all-access_spider_2017-01-17',\n",
" '\"Awaken,_My_Love!\"_en.wikipedia.org_all-access_spider_2017-01-18',\n",
" '\"Awaken,_My_Love!\"_en.wikipedia.org_all-access_spider_2017-01-19',\n",
" '\"Awaken,_My_Love!\"_en.wikipedia.org_all-access_spider_2017-01-20',\n",
" '\"Awaken,_My_Love!\"_en.wikipedia.org_all-access_spider_2017-01-21',\n",
" '\"Awaken,_My_Love!\"_en.wikipedia.org_all-access_spider_2017-01-22',\n",
" '\"Awaken,_My_Love!\"_en.wikipedia.org_all-access_spider_2017-01-23',\n",
" '\"Awaken,_My_Love!\"_en.wikipedia.org_all-access_spider_2017-01-24',\n",
" '\"Awaken,_My_Love!\"_en.wikipedia.org_all-access_spider_2017-01-25',\n",
" '\"Awaken,_My_Love!\"_en.wikipedia.org_all-access_spider_2017-01-26',\n",
" '\"Awaken,_My_Love!\"_en.wikipedia.org_all-access_spider_2017-01-27',\n",
" '\"Awaken,_My_Love!\"_en.wikipedia.org_all-access_spider_2017-01-28',\n",
" '\"Awaken,_My_Love!\"_en.wikipedia.org_all-access_spider_2017-01-29',\n",
" '\"Awaken,_My_Love!\"_en.wikipedia.org_all-access_spider_2017-01-30',\n",
" '\"Awaken,_My_Love!\"_en.wikipedia.org_all-access_spider_2017-01-31',\n",
" '\"Awaken,_My_Love!\"_en.wikipedia.org_all-access_spider_2017-02-01',\n",
" '\"Awaken,_My_Love!\"_en.wikipedia.org_all-access_spider_2017-02-02',\n",
" '\"Awaken,_My_Love!\"_en.wikipedia.org_all-access_spider_2017-02-03',\n",
" '\"Awaken,_My_Love!\"_en.wikipedia.org_all-access_spider_2017-02-04',\n",
" '\"Awaken,_My_Love!\"_en.wikipedia.org_all-access_spider_2017-02-05',\n",
" '\"Awaken,_My_Love!\"_en.wikipedia.org_all-access_spider_2017-02-06',\n",
" '\"Awaken,_My_Love!\"_en.wikipedia.org_all-access_spider_2017-02-07',\n",
" '\"Awaken,_My_Love!\"_en.wikipedia.org_all-access_spider_2017-02-08',\n",
" '\"Awaken,_My_Love!\"_en.wikipedia.org_all-access_spider_2017-02-09',\n",
" '\"Awaken,_My_Love!\"_en.wikipedia.org_all-access_spider_2017-02-10',\n",
" '\"Awaken,_My_Love!\"_en.wikipedia.org_all-access_spider_2017-02-11',\n",
" '\"Awaken,_My_Love!\"_en.wikipedia.org_all-access_spider_2017-02-12',\n",
" '\"Awaken,_My_Love!\"_en.wikipedia.org_all-access_spider_2017-02-13',\n",
" '\"Awaken,_My_Love!\"_en.wikipedia.org_all-access_spider_2017-02-14',\n",
" '\"Awaken,_My_Love!\"_en.wikipedia.org_all-access_spider_2017-02-15',\n",
" '\"Awaken,_My_Love!\"_en.wikipedia.org_all-access_spider_2017-02-16',\n",
" '\"Awaken,_My_Love!\"_en.wikipedia.org_all-access_spider_2017-02-17',\n",
" '\"Awaken,_My_Love!\"_en.wikipedia.org_all-access_spider_2017-02-18',\n",
" '\"Awaken,_My_Love!\"_en.wikipedia.org_all-access_spider_2017-02-19',\n",
" '\"Awaken,_My_Love!\"_en.wikipedia.org_all-access_spider_2017-02-20',\n",
" '\"Awaken,_My_Love!\"_en.wikipedia.org_all-access_spider_2017-02-21',\n",
" '\"Awaken,_My_Love!\"_en.wikipedia.org_all-access_spider_2017-02-22',\n",
" '\"Awaken,_My_Love!\"_en.wikipedia.org_all-access_spider_2017-02-23',\n",
" '\"Awaken,_My_Love!\"_en.wikipedia.org_all-access_spider_2017-02-24',\n",
" '\"Awaken,_My_Love!\"_en.wikipedia.org_all-access_spider_2017-02-25',\n",
" '\"Awaken,_My_Love!\"_en.wikipedia.org_all-access_spider_2017-02-26',\n",
" '\"Awaken,_My_Love!\"_en.wikipedia.org_all-access_spider_2017-02-27',\n",
" '\"Awaken,_My_Love!\"_en.wikipedia.org_all-access_spider_2017-02-28',\n",
" '\"Awaken,_My_Love!\"_en.wikipedia.org_all-access_spider_2017-03-01',\n",
" '\"Awaken,_My_Love!\"_en.wikipedia.org_desktop_all-agents_2017-01-01',\n",
" '\"Awaken,_My_Love!\"_en.wikipedia.org_desktop_all-agents_2017-01-02',\n",
" '\"Awaken,_My_Love!\"_en.wikipedia.org_desktop_all-agents_2017-01-03',\n",
" '\"Awaken,_My_Love!\"_en.wikipedia.org_desktop_all-agents_2017-01-04',\n",
" '\"Awaken,_My_Love!\"_en.wikipedia.org_desktop_all-agents_2017-01-05',\n",
" '\"Awaken,_My_Love!\"_en.wikipedia.org_desktop_all-agents_2017-01-06',\n",
" '\"Awaken,_My_Love!\"_en.wikipedia.org_desktop_all-agents_2017-01-07',\n",
" '\"Awaken,_My_Love!\"_en.wikipedia.org_desktop_all-agents_2017-01-08',\n",
" '\"Awaken,_My_Love!\"_en.wikipedia.org_desktop_all-agents_2017-01-09',\n",
" '\"Awaken,_My_Love!\"_en.wikipedia.org_desktop_all-agents_2017-01-10',\n",
" '\"Awaken,_My_Love!\"_en.wikipedia.org_desktop_all-agents_2017-01-11',\n",
" '\"Awaken,_My_Love!\"_en.wikipedia.org_desktop_all-agents_2017-01-12',\n",
" '\"Awaken,_My_Love!\"_en.wikipedia.org_desktop_all-agents_2017-01-13',\n",
" '\"Awaken,_My_Love!\"_en.wikipedia.org_desktop_all-agents_2017-01-14',\n",
" '\"Awaken,_My_Love!\"_en.wikipedia.org_desktop_all-agents_2017-01-15',\n",
" '\"Awaken,_My_Love!\"_en.wikipedia.org_desktop_all-agents_2017-01-16',\n",
" '\"Awaken,_My_Love!\"_en.wikipedia.org_desktop_all-agents_2017-01-17',\n",
" '\"Awaken,_My_Love!\"_en.wikipedia.org_desktop_all-agents_2017-01-18',\n",
" '\"Awaken,_My_Love!\"_en.wikipedia.org_desktop_all-agents_2017-01-19',\n",
" '\"Awaken,_My_Love!\"_en.wikipedia.org_desktop_all-agents_2017-01-20',\n",
" '\"Awaken,_My_Love!\"_en.wikipedia.org_desktop_all-agents_2017-01-21',\n",
" '\"Awaken,_My_Love!\"_en.wikipedia.org_desktop_all-agents_2017-01-22',\n",
" '\"Awaken,_My_Love!\"_en.wikipedia.org_desktop_all-agents_2017-01-23',\n",
" '\"Awaken,_My_Love!\"_en.wikipedia.org_desktop_all-agents_2017-01-24',\n",
" '\"Awaken,_My_Love!\"_en.wikipedia.org_desktop_all-agents_2017-01-25',\n",
" '\"Awaken,_My_Love!\"_en.wikipedia.org_desktop_all-agents_2017-01-26',\n",
" '\"Awaken,_My_Love!\"_en.wikipedia.org_desktop_all-agents_2017-01-27',\n",
" '\"Awaken,_My_Love!\"_en.wikipedia.org_desktop_all-agents_2017-01-28',\n",
" '\"Awaken,_My_Love!\"_en.wikipedia.org_desktop_all-agents_2017-01-29',\n",
" '\"Awaken,_My_Love!\"_en.wikipedia.org_desktop_all-agents_2017-01-30',\n",
" '\"Awaken,_My_Love!\"_en.wikipedia.org_desktop_all-agents_2017-01-31',\n",
" '\"Awaken,_My_Love!\"_en.wikipedia.org_desktop_all-agents_2017-02-01',\n",
" '\"Awaken,_My_Love!\"_en.wikipedia.org_desktop_all-agents_2017-02-02',\n",
" '\"Awaken,_My_Love!\"_en.wikipedia.org_desktop_all-agents_2017-02-03',\n",
" '\"Awaken,_My_Love!\"_en.wikipedia.org_desktop_all-agents_2017-02-04',\n",
" '\"Awaken,_My_Love!\"_en.wikipedia.org_desktop_all-agents_2017-02-05',\n",
" '\"Awaken,_My_Love!\"_en.wikipedia.org_desktop_all-agents_2017-02-06',\n",
" '\"Awaken,_My_Love!\"_en.wikipedia.org_desktop_all-agents_2017-02-07',\n",
" '\"Awaken,_My_Love!\"_en.wikipedia.org_desktop_all-agents_2017-02-08',\n",
" '\"Awaken,_My_Love!\"_en.wikipedia.org_desktop_all-agents_2017-02-09',\n",
" '\"Awaken,_My_Love!\"_en.wikipedia.org_desktop_all-agents_2017-02-10',\n",
" '\"Awaken,_My_Love!\"_en.wikipedia.org_desktop_all-agents_2017-02-11',\n",
" '\"Awaken,_My_Love!\"_en.wikipedia.org_desktop_all-agents_2017-02-12',\n",
" '\"Awaken,_My_Love!\"_en.wikipedia.org_desktop_all-agents_2017-02-13',\n",
" '\"Awaken,_My_Love!\"_en.wikipedia.org_desktop_all-agents_2017-02-14',\n",
" '\"Awaken,_My_Love!\"_en.wikipedia.org_desktop_all-agents_2017-02-15',\n",
" '\"Awaken,_My_Love!\"_en.wikipedia.org_desktop_all-agents_2017-02-16',\n",
" '\"Awaken,_My_Love!\"_en.wikipedia.org_desktop_all-agents_2017-02-17',\n",
" '\"Awaken,_My_Love!\"_en.wikipedia.org_desktop_all-agents_2017-02-18',\n",
" '\"Awaken,_My_Love!\"_en.wikipedia.org_desktop_all-agents_2017-02-19',\n",
" '\"Awaken,_My_Love!\"_en.wikipedia.org_desktop_all-agents_2017-02-20',\n",
" '\"Awaken,_My_Love!\"_en.wikipedia.org_desktop_all-agents_2017-02-21',\n",
" '\"Awaken,_My_Love!\"_en.wikipedia.org_desktop_all-agents_2017-02-22',\n",
" '\"Awaken,_My_Love!\"_en.wikipedia.org_desktop_all-agents_2017-02-23',\n",
" '\"Awaken,_My_Love!\"_en.wikipedia.org_desktop_all-agents_2017-02-24',\n",
" '\"Awaken,_My_Love!\"_en.wikipedia.org_desktop_all-agents_2017-02-25',\n",
" '\"Awaken,_My_Love!\"_en.wikipedia.org_desktop_all-agents_2017-02-26',\n",
" '\"Awaken,_My_Love!\"_en.wikipedia.org_desktop_all-agents_2017-02-27',\n",
" '\"Awaken,_My_Love!\"_en.wikipedia.org_desktop_all-agents_2017-02-28',\n",
" '\"Awaken,_My_Love!\"_en.wikipedia.org_desktop_all-agents_2017-03-01',\n",
" '\"European_Society_for_Clinical_Investigation\"_en.wikipedia.org_all-access_all-agents_2017-01-01',\n",
" '\"European_Society_for_Clinical_Investigation\"_en.wikipedia.org_all-access_all-agents_2017-01-02',\n",
" '\"European_Society_for_Clinical_Investigation\"_en.wikipedia.org_all-access_all-agents_2017-01-03',\n",
" '\"European_Society_for_Clinical_Investigation\"_en.wikipedia.org_all-access_all-agents_2017-01-04',\n",
" '\"European_Society_for_Clinical_Investigation\"_en.wikipedia.org_all-access_all-agents_2017-01-05',\n",
" '\"European_Society_for_Clinical_Investigation\"_en.wikipedia.org_all-access_all-agents_2017-01-06',\n",
" '\"European_Society_for_Clinical_Investigation\"_en.wikipedia.org_all-access_all-agents_2017-01-07',\n",
" '\"European_Society_for_Clinical_Investigation\"_en.wikipedia.org_all-access_all-agents_2017-01-08',\n",
" '\"European_Society_for_Clinical_Investigation\"_en.wikipedia.org_all-access_all-agents_2017-01-09',\n",
" '\"European_Society_for_Clinical_Investigation\"_en.wikipedia.org_all-access_all-agents_2017-01-10',\n",
" '\"European_Society_for_Clinical_Investigation\"_en.wikipedia.org_all-access_all-agents_2017-01-11',\n",
" '\"European_Society_for_Clinical_Investigation\"_en.wikipedia.org_all-access_all-agents_2017-01-12',\n",
" '\"European_Society_for_Clinical_Investigation\"_en.wikipedia.org_all-access_all-agents_2017-01-13',\n",
" '\"European_Society_for_Clinical_Investigation\"_en.wikipedia.org_all-access_all-agents_2017-01-14',\n",
" '\"European_Society_for_Clinical_Investigation\"_en.wikipedia.org_all-access_all-agents_2017-01-15',\n",
" '\"European_Society_for_Clinical_Investigation\"_en.wikipedia.org_all-access_all-agents_2017-01-16',\n",
" '\"European_Society_for_Clinical_Investigation\"_en.wikipedia.org_all-access_all-agents_2017-01-17',\n",
" '\"European_Society_for_Clinical_Investigation\"_en.wikipedia.org_all-access_all-agents_2017-01-18',\n",
" '\"European_Society_for_Clinical_Investigation\"_en.wikipedia.org_all-access_all-agents_2017-01-19',\n",
" '\"European_Society_for_Clinical_Investigation\"_en.wikipedia.org_all-access_all-agents_2017-01-20',\n",
" '\"European_Society_for_Clinical_Investigation\"_en.wikipedia.org_all-access_all-agents_2017-01-21',\n",
" '\"European_Society_for_Clinical_Investigation\"_en.wikipedia.org_all-access_all-agents_2017-01-22',\n",
" '\"European_Society_for_Clinical_Investigation\"_en.wikipedia.org_all-access_all-agents_2017-01-23',\n",
" '\"European_Society_for_Clinical_Investigation\"_en.wikipedia.org_all-access_all-agents_2017-01-24',\n",
" '\"European_Society_for_Clinical_Investigation\"_en.wikipedia.org_all-access_all-agents_2017-01-25',\n",
" '\"European_Society_for_Clinical_Investigation\"_en.wikipedia.org_all-access_all-agents_2017-01-26',\n",
" '\"European_Society_for_Clinical_Investigation\"_en.wikipedia.org_all-access_all-agents_2017-01-27',\n",
" '\"European_Society_for_Clinical_Investigation\"_en.wikipedia.org_all-access_all-agents_2017-01-28',\n",
" '\"European_Society_for_Clinical_Investigation\"_en.wikipedia.org_all-access_all-agents_2017-01-29',\n",
" '\"European_Society_for_Clinical_Investigation\"_en.wikipedia.org_all-access_all-agents_2017-01-30',\n",
" '\"European_Society_for_Clinical_Investigation\"_en.wikipedia.org_all-access_all-agents_2017-01-31',\n",
" '\"European_Society_for_Clinical_Investigation\"_en.wikipedia.org_all-access_all-agents_2017-02-01',\n",
" '\"European_Society_for_Clinical_Investigation\"_en.wikipedia.org_all-access_all-agents_2017-02-02',\n",
" '\"European_Society_for_Clinical_Investigation\"_en.wikipedia.org_all-access_all-agents_2017-02-03',\n",
" '\"European_Society_for_Clinical_Investigation\"_en.wikipedia.org_all-access_all-agents_2017-02-04',\n",
" '\"European_Society_for_Clinical_Investigation\"_en.wikipedia.org_all-access_all-agents_2017-02-05',\n",
" '\"European_Society_for_Clinical_Investigation\"_en.wikipedia.org_all-access_all-agents_2017-02-06',\n",
" '\"European_Society_for_Clinical_Investigation\"_en.wikipedia.org_all-access_all-agents_2017-02-07',\n",
" '\"European_Society_for_Clinical_Investigation\"_en.wikipedia.org_all-access_all-agents_2017-02-08',\n",
" '\"European_Society_for_Clinical_Investigation\"_en.wikipedia.org_all-access_all-agents_2017-02-09',\n",
" '\"European_Society_for_Clinical_Investigation\"_en.wikipedia.org_all-access_all-agents_2017-02-10',\n",
" '\"European_Society_for_Clinical_Investigation\"_en.wikipedia.org_all-access_all-agents_2017-02-11',\n",
" '\"European_Society_for_Clinical_Investigation\"_en.wikipedia.org_all-access_all-agents_2017-02-12',\n",
" '\"European_Society_for_Clinical_Investigation\"_en.wikipedia.org_all-access_all-agents_2017-02-13',\n",
" '\"European_Society_for_Clinical_Investigation\"_en.wikipedia.org_all-access_all-agents_2017-02-14',\n",
" '\"European_Society_for_Clinical_Investigation\"_en.wikipedia.org_all-access_all-agents_2017-02-15',\n",
" '\"European_Society_for_Clinical_Investigation\"_en.wikipedia.org_all-access_all-agents_2017-02-16',\n",
" '\"European_Society_for_Clinical_Investigation\"_en.wikipedia.org_all-access_all-agents_2017-02-17',\n",
" '\"European_Society_for_Clinical_Investigation\"_en.wikipedia.org_all-access_all-agents_2017-02-18',\n",
" '\"European_Society_for_Clinical_Investigation\"_en.wikipedia.org_all-access_all-agents_2017-02-19',\n",
" '\"European_Society_for_Clinical_Investigation\"_en.wikipedia.org_all-access_all-agents_2017-02-20',\n",
" '\"European_Society_for_Clinical_Investigation\"_en.wikipedia.org_all-access_all-agents_2017-02-21',\n",
" '\"European_Society_for_Clinical_Investigation\"_en.wikipedia.org_all-access_all-agents_2017-02-22',\n",
" '\"European_Society_for_Clinical_Investigation\"_en.wikipedia.org_all-access_all-agents_2017-02-23',\n",
" '\"European_Society_for_Clinical_Investigation\"_en.wikipedia.org_all-access_all-agents_2017-02-24',\n",
" '\"European_Society_for_Clinical_Investigation\"_en.wikipedia.org_all-access_all-agents_2017-02-25',\n",
" '\"European_Society_for_Clinical_Investigation\"_en.wikipedia.org_all-access_all-agents_2017-02-26',\n",
" '\"European_Society_for_Clinical_Investigation\"_en.wikipedia.org_all-access_all-agents_2017-02-27',\n",
" '\"European_Society_for_Clinical_Investigation\"_en.wikipedia.org_all-access_all-agents_2017-02-28',\n",
" '\"European_Society_for_Clinical_Investigation\"_en.wikipedia.org_all-access_all-agents_2017-03-01',\n",
" '\"European_Society_for_Clinical_Investigation\"_en.wikipedia.org_all-access_spider_2017-01-01',\n",
" '\"European_Society_for_Clinical_Investigation\"_en.wikipedia.org_all-access_spider_2017-01-02',\n",
" '\"European_Society_for_Clinical_Investigation\"_en.wikipedia.org_all-access_spider_2017-01-03',\n",
" '\"European_Society_for_Clinical_Investigation\"_en.wikipedia.org_all-access_spider_2017-01-04',\n",
" '\"European_Society_for_Clinical_Investigation\"_en.wikipedia.org_all-access_spider_2017-01-05',\n",
" '\"European_Society_for_Clinical_Investigation\"_en.wikipedia.org_all-access_spider_2017-01-06',\n",
" '\"European_Society_for_Clinical_Investigation\"_en.wikipedia.org_all-access_spider_2017-01-07',\n",
" '\"European_Society_for_Clinical_Investigation\"_en.wikipedia.org_all-access_spider_2017-01-08',\n",
" '\"European_Society_for_Clinical_Investigation\"_en.wikipedia.org_all-access_spider_2017-01-09',\n",
" '\"European_Society_for_Clinical_Investigation\"_en.wikipedia.org_all-access_spider_2017-01-10',\n",
" '\"European_Society_for_Clinical_Investigation\"_en.wikipedia.org_all-access_spider_2017-01-11',\n",
" '\"European_Society_for_Clinical_Investigation\"_en.wikipedia.org_all-access_spider_2017-01-12',\n",
" '\"European_Society_for_Clinical_Investigation\"_en.wikipedia.org_all-access_spider_2017-01-13',\n",
" '\"European_Society_for_Clinical_Investigation\"_en.wikipedia.org_all-access_spider_2017-01-14',\n",
" '\"European_Society_for_Clinical_Investigation\"_en.wikipedia.org_all-access_spider_2017-01-15',\n",
" '\"European_Society_for_Clinical_Investigation\"_en.wikipedia.org_all-access_spider_2017-01-16',\n",
" '\"European_Society_for_Clinical_Investigation\"_en.wikipedia.org_all-access_spider_2017-01-17',\n",
" '\"European_Society_for_Clinical_Investigation\"_en.wikipedia.org_all-access_spider_2017-01-18',\n",
" '\"European_Society_for_Clinical_Investigation\"_en.wikipedia.org_all-access_spider_2017-01-19',\n",
" '\"European_Society_for_Clinical_Investigation\"_en.wikipedia.org_all-access_spider_2017-01-20',\n",
" '\"European_Society_for_Clinical_Investigation\"_en.wikipedia.org_all-access_spider_2017-01-21',\n",
" '\"European_Society_for_Clinical_Investigation\"_en.wikipedia.org_all-access_spider_2017-01-22',\n",
" '\"European_Society_for_Clinical_Investigation\"_en.wikipedia.org_all-access_spider_2017-01-23',\n",
" '\"European_Society_for_Clinical_Investigation\"_en.wikipedia.org_all-access_spider_2017-01-24',\n",
" '\"European_Society_for_Clinical_Investigation\"_en.wikipedia.org_all-access_spider_2017-01-25',\n",
" '\"European_Society_for_Clinical_Investigation\"_en.wikipedia.org_all-access_spider_2017-01-26',\n",
" '\"European_Society_for_Clinical_Investigation\"_en.wikipedia.org_all-access_spider_2017-01-27',\n",
" '\"European_Society_for_Clinical_Investigation\"_en.wikipedia.org_all-access_spider_2017-01-28',\n",
" '\"European_Society_for_Clinical_Investigation\"_en.wikipedia.org_all-access_spider_2017-01-29',\n",
" '\"European_Society_for_Clinical_Investigation\"_en.wikipedia.org_all-access_spider_2017-01-30',\n",
" '\"European_Society_for_Clinical_Investigation\"_en.wikipedia.org_all-access_spider_2017-01-31',\n",
" '\"European_Society_for_Clinical_Investigation\"_en.wikipedia.org_all-access_spider_2017-02-01',\n",
" '\"European_Society_for_Clinical_Investigation\"_en.wikipedia.org_all-access_spider_2017-02-02',\n",
" '\"European_Society_for_Clinical_Investigation\"_en.wikipedia.org_all-access_spider_2017-02-03',\n",
" '\"European_Society_for_Clinical_Investigation\"_en.wikipedia.org_all-access_spider_2017-02-04',\n",
" '\"European_Society_for_Clinical_Investigation\"_en.wikipedia.org_all-access_spider_2017-02-05',\n",
" '\"European_Society_for_Clinical_Investigation\"_en.wikipedia.org_all-access_spider_2017-02-06',\n",
" '\"European_Society_for_Clinical_Investigation\"_en.wikipedia.org_all-access_spider_2017-02-07',\n",
" '\"European_Society_for_Clinical_Investigation\"_en.wikipedia.org_all-access_spider_2017-02-08',\n",
" '\"European_Society_for_Clinical_Investigation\"_en.wikipedia.org_all-access_spider_2017-02-09',\n",
" '\"European_Society_for_Clinical_Investigation\"_en.wikipedia.org_all-access_spider_2017-02-10',\n",
" '\"European_Society_for_Clinical_Investigation\"_en.wikipedia.org_all-access_spider_2017-02-11',\n",
" '\"European_Society_for_Clinical_Investigation\"_en.wikipedia.org_all-access_spider_2017-02-12',\n",
" '\"European_Society_for_Clinical_Investigation\"_en.wikipedia.org_all-access_spider_2017-02-13',\n",
" '\"European_Society_for_Clinical_Investigation\"_en.wikipedia.org_all-access_spider_2017-02-14',\n",
" '\"European_Society_for_Clinical_Investigation\"_en.wikipedia.org_all-access_spider_2017-02-15',\n",
" '\"European_Society_for_Clinical_Investigation\"_en.wikipedia.org_all-access_spider_2017-02-16',\n",
" '\"European_Society_for_Clinical_Investigation\"_en.wikipedia.org_all-access_spider_2017-02-17',\n",
" '\"European_Society_for_Clinical_Investigation\"_en.wikipedia.org_all-access_spider_2017-02-18',\n",
" '\"European_Society_for_Clinical_Investigation\"_en.wikipedia.org_all-access_spider_2017-02-19',\n",
" '\"European_Society_for_Clinical_Investigation\"_en.wikipedia.org_all-access_spider_2017-02-20',\n",
" '\"European_Society_for_Clinical_Investigation\"_en.wikipedia.org_all-access_spider_2017-02-21',\n",
" '\"European_Society_for_Clinical_Investigation\"_en.wikipedia.org_all-access_spider_2017-02-22',\n",
" '\"European_Society_for_Clinical_Investigation\"_en.wikipedia.org_all-access_spider_2017-02-23',\n",
" '\"European_Society_for_Clinical_Investigation\"_en.wikipedia.org_all-access_spider_2017-02-24',\n",
" '\"European_Society_for_Clinical_Investigation\"_en.wikipedia.org_all-access_spider_2017-02-25',\n",
" '\"European_Society_for_Clinical_Investigation\"_en.wikipedia.org_all-access_spider_2017-02-26',\n",
" '\"European_Society_for_Clinical_Investigation\"_en.wikipedia.org_all-access_spider_2017-02-27',\n",
" '\"European_Society_for_Clinical_Investigation\"_en.wikipedia.org_all-access_spider_2017-02-28',\n",
" '\"European_Society_for_Clinical_Investigation\"_en.wikipedia.org_all-access_spider_2017-03-01',\n",
" '\"European_Society_for_Clinical_Investigation\"_en.wikipedia.org_desktop_all-agents_2017-01-01',\n",
" '\"European_Society_for_Clinical_Investigation\"_en.wikipedia.org_desktop_all-agents_2017-01-02',\n",
" '\"European_Society_for_Clinical_Investigation\"_en.wikipedia.org_desktop_all-agents_2017-01-03',\n",
" '\"European_Society_for_Clinical_Investigation\"_en.wikipedia.org_desktop_all-agents_2017-01-04',\n",
" '\"European_Society_for_Clinical_Investigation\"_en.wikipedia.org_desktop_all-agents_2017-01-05',\n",
" '\"European_Society_for_Clinical_Investigation\"_en.wikipedia.org_desktop_all-agents_2017-01-06',\n",
" '\"European_Society_for_Clinical_Investigation\"_en.wikipedia.org_desktop_all-agents_2017-01-07',\n",
" '\"European_Society_for_Clinical_Investigation\"_en.wikipedia.org_desktop_all-agents_2017-01-08',\n",
" '\"European_Society_for_Clinical_Investigation\"_en.wikipedia.org_desktop_all-agents_2017-01-09',\n",
" '\"European_Society_for_Clinical_Investigation\"_en.wikipedia.org_desktop_all-agents_2017-01-10',\n",
" '\"European_Society_for_Clinical_Investigation\"_en.wikipedia.org_desktop_all-agents_2017-01-11',\n",
" '\"European_Society_for_Clinical_Investigation\"_en.wikipedia.org_desktop_all-agents_2017-01-12',\n",
" '\"European_Society_for_Clinical_Investigation\"_en.wikipedia.org_desktop_all-agents_2017-01-13',\n",
" '\"European_Society_for_Clinical_Investigation\"_en.wikipedia.org_desktop_all-agents_2017-01-14',\n",
" '\"European_Society_for_Clinical_Investigation\"_en.wikipedia.org_desktop_all-agents_2017-01-15',\n",
" '\"European_Society_for_Clinical_Investigation\"_en.wikipedia.org_desktop_all-agents_2017-01-16',\n",
" '\"European_Society_for_Clinical_Investigation\"_en.wikipedia.org_desktop_all-agents_2017-01-17',\n",
" '\"European_Society_for_Clinical_Investigation\"_en.wikipedia.org_desktop_all-agents_2017-01-18',\n",
" '\"European_Society_for_Clinical_Investigation\"_en.wikipedia.org_desktop_all-agents_2017-01-19',\n",
" '\"European_Society_for_Clinical_Investigation\"_en.wikipedia.org_desktop_all-agents_2017-01-20',\n",
" '\"European_Society_for_Clinical_Investigation\"_en.wikipedia.org_desktop_all-agents_2017-01-21',\n",
" '\"European_Society_for_Clinical_Investigation\"_en.wikipedia.org_desktop_all-agents_2017-01-22',\n",
" '\"European_Society_for_Clinical_Investigation\"_en.wikipedia.org_desktop_all-agents_2017-01-23',\n",
" '\"European_Society_for_Clinical_Investigation\"_en.wikipedia.org_desktop_all-agents_2017-01-24',\n",
" '\"European_Society_for_Clinical_Investigation\"_en.wikipedia.org_desktop_all-agents_2017-01-25',\n",
" '\"European_Society_for_Clinical_Investigation\"_en.wikipedia.org_desktop_all-agents_2017-01-26',\n",
" '\"European_Society_for_Clinical_Investigation\"_en.wikipedia.org_desktop_all-agents_2017-01-27',\n",
" '\"European_Society_for_Clinical_Investigation\"_en.wikipedia.org_desktop_all-agents_2017-01-28',\n",
" '\"European_Society_for_Clinical_Investigation\"_en.wikipedia.org_desktop_all-agents_2017-01-29',\n",
" '\"European_Society_for_Clinical_Investigation\"_en.wikipedia.org_desktop_all-agents_2017-01-30',\n",
" '\"European_Society_for_Clinical_Investigation\"_en.wikipedia.org_desktop_all-agents_2017-01-31',\n",
" '\"European_Society_for_Clinical_Investigation\"_en.wikipedia.org_desktop_all-agents_2017-02-01',\n",
" '\"European_Society_for_Clinical_Investigation\"_en.wikipedia.org_desktop_all-agents_2017-02-02',\n",
" '\"European_Society_for_Clinical_Investigation\"_en.wikipedia.org_desktop_all-agents_2017-02-03',\n",
" '\"European_Society_for_Clinical_Investigation\"_en.wikipedia.org_desktop_all-agents_2017-02-04',\n",
" '\"European_Society_for_Clinical_Investigation\"_en.wikipedia.org_desktop_all-agents_2017-02-05',\n",
" '\"European_Society_for_Clinical_Investigation\"_en.wikipedia.org_desktop_all-agents_2017-02-06',\n",
" '\"European_Society_for_Clinical_Investigation\"_en.wikipedia.org_desktop_all-agents_2017-02-07',\n",
" '\"European_Society_for_Clinical_Investigation\"_en.wikipedia.org_desktop_all-agents_2017-02-08',\n",
" '\"European_Society_for_Clinical_Investigation\"_en.wikipedia.org_desktop_all-agents_2017-02-09',\n",
" '\"European_Society_for_Clinical_Investigation\"_en.wikipedia.org_desktop_all-agents_2017-02-10',\n",
" '\"European_Society_for_Clinical_Investigation\"_en.wikipedia.org_desktop_all-agents_2017-02-11',\n",
" '\"European_Society_for_Clinical_Investigation\"_en.wikipedia.org_desktop_all-agents_2017-02-12',\n",
" '\"European_Society_for_Clinical_Investigation\"_en.wikipedia.org_desktop_all-agents_2017-02-13',\n",
" '\"European_Society_for_Clinical_Investigation\"_en.wikipedia.org_desktop_all-agents_2017-02-14',\n",
" '\"European_Society_for_Clinical_Investigation\"_en.wikipedia.org_desktop_all-agents_2017-02-15',\n",
" '\"European_Society_for_Clinical_Investigation\"_en.wikipedia.org_desktop_all-agents_2017-02-16',\n",
" '\"European_Society_for_Clinical_Investigation\"_en.wikipedia.org_desktop_all-agents_2017-02-17',\n",
" '\"European_Society_for_Clinical_Investigation\"_en.wikipedia.org_desktop_all-agents_2017-02-18',\n",
" '\"European_Society_for_Clinical_Investigation\"_en.wikipedia.org_desktop_all-agents_2017-02-19',\n",
" '\"European_Society_for_Clinical_Investigation\"_en.wikipedia.org_desktop_all-agents_2017-02-20',\n",
" '\"European_Society_for_Clinical_Investigation\"_en.wikipedia.org_desktop_all-agents_2017-02-21',\n",
" '\"European_Society_for_Clinical_Investigation\"_en.wikipedia.org_desktop_all-agents_2017-02-22',\n",
" '\"European_Society_for_Clinical_Investigation\"_en.wikipedia.org_desktop_all-agents_2017-02-23',\n",
" '\"European_Society_for_Clinical_Investigation\"_en.wikipedia.org_desktop_all-agents_2017-02-24',\n",
" '\"European_Society_for_Clinical_Investigation\"_en.wikipedia.org_desktop_all-agents_2017-02-25',\n",
" '\"European_Society_for_Clinical_Investigation\"_en.wikipedia.org_desktop_all-agents_2017-02-26',\n",
" '\"European_Society_for_Clinical_Investigation\"_en.wikipedia.org_desktop_all-agents_2017-02-27',\n",
" '\"European_Society_for_Clinical_Investigation\"_en.wikipedia.org_desktop_all-agents_2017-02-28',\n",
" '\"European_Society_for_Clinical_Investigation\"_en.wikipedia.org_desktop_all-agents_2017-03-01',\n",
" '\"Heroes\"_(David_Bowie_album)_en.wikipedia.org_all-access_all-agents_2017-01-01',\n",
" '\"Heroes\"_(David_Bowie_album)_en.wikipedia.org_all-access_all-agents_2017-01-02',\n",
" '\"Heroes\"_(David_Bowie_album)_en.wikipedia.org_all-access_all-agents_2017-01-03',\n",
" '\"Heroes\"_(David_Bowie_album)_en.wikipedia.org_all-access_all-agents_2017-01-04',\n",
" '\"Heroes\"_(David_Bowie_album)_en.wikipedia.org_all-access_all-agents_2017-01-05',\n",
" '\"Heroes\"_(David_Bowie_album)_en.wikipedia.org_all-access_all-agents_2017-01-06',\n",
" '\"Heroes\"_(David_Bowie_album)_en.wikipedia.org_all-access_all-agents_2017-01-07',\n",
" '\"Heroes\"_(David_Bowie_album)_en.wikipedia.org_all-access_all-agents_2017-01-08',\n",
" '\"Heroes\"_(David_Bowie_album)_en.wikipedia.org_all-access_all-agents_2017-01-09',\n",
" '\"Heroes\"_(David_Bowie_album)_en.wikipedia.org_all-access_all-agents_2017-01-10',\n",
" '\"Heroes\"_(David_Bowie_album)_en.wikipedia.org_all-access_all-agents_2017-01-11',\n",
" '\"Heroes\"_(David_Bowie_album)_en.wikipedia.org_all-access_all-agents_2017-01-12',\n",
" '\"Heroes\"_(David_Bowie_album)_en.wikipedia.org_all-access_all-agents_2017-01-13',\n",
" '\"Heroes\"_(David_Bowie_album)_en.wikipedia.org_all-access_all-agents_2017-01-14',\n",
" '\"Heroes\"_(David_Bowie_album)_en.wikipedia.org_all-access_all-agents_2017-01-15',\n",
" '\"Heroes\"_(David_Bowie_album)_en.wikipedia.org_all-access_all-agents_2017-01-16',\n",
" '\"Heroes\"_(David_Bowie_album)_en.wikipedia.org_all-access_all-agents_2017-01-17',\n",
" '\"Heroes\"_(David_Bowie_album)_en.wikipedia.org_all-access_all-agents_2017-01-18',\n",
" '\"Heroes\"_(David_Bowie_album)_en.wikipedia.org_all-access_all-agents_2017-01-19',\n",
" '\"Heroes\"_(David_Bowie_album)_en.wikipedia.org_all-access_all-agents_2017-01-20',\n",
" '\"Heroes\"_(David_Bowie_album)_en.wikipedia.org_all-access_all-agents_2017-01-21',\n",
" '\"Heroes\"_(David_Bowie_album)_en.wikipedia.org_all-access_all-agents_2017-01-22',\n",
" '\"Heroes\"_(David_Bowie_album)_en.wikipedia.org_all-access_all-agents_2017-01-23',\n",
" '\"Heroes\"_(David_Bowie_album)_en.wikipedia.org_all-access_all-agents_2017-01-24',\n",
" '\"Heroes\"_(David_Bowie_album)_en.wikipedia.org_all-access_all-agents_2017-01-25',\n",
" '\"Heroes\"_(David_Bowie_album)_en.wikipedia.org_all-access_all-agents_2017-01-26',\n",
" '\"Heroes\"_(David_Bowie_album)_en.wikipedia.org_all-access_all-agents_2017-01-27',\n",
" '\"Heroes\"_(David_Bowie_album)_en.wikipedia.org_all-access_all-agents_2017-01-28',\n",
" '\"Heroes\"_(David_Bowie_album)_en.wikipedia.org_all-access_all-agents_2017-01-29',\n",
" '\"Heroes\"_(David_Bowie_album)_en.wikipedia.org_all-access_all-agents_2017-01-30',\n",
" '\"Heroes\"_(David_Bowie_album)_en.wikipedia.org_all-access_all-agents_2017-01-31',\n",
" '\"Heroes\"_(David_Bowie_album)_en.wikipedia.org_all-access_all-agents_2017-02-01',\n",
" '\"Heroes\"_(David_Bowie_album)_en.wikipedia.org_all-access_all-agents_2017-02-02',\n",
" '\"Heroes\"_(David_Bowie_album)_en.wikipedia.org_all-access_all-agents_2017-02-03',\n",
" '\"Heroes\"_(David_Bowie_album)_en.wikipedia.org_all-access_all-agents_2017-02-04',\n",
" '\"Heroes\"_(David_Bowie_album)_en.wikipedia.org_all-access_all-agents_2017-02-05',\n",
" '\"Heroes\"_(David_Bowie_album)_en.wikipedia.org_all-access_all-agents_2017-02-06',\n",
" '\"Heroes\"_(David_Bowie_album)_en.wikipedia.org_all-access_all-agents_2017-02-07',\n",
" '\"Heroes\"_(David_Bowie_album)_en.wikipedia.org_all-access_all-agents_2017-02-08',\n",
" '\"Heroes\"_(David_Bowie_album)_en.wikipedia.org_all-access_all-agents_2017-02-09',\n",
" '\"Heroes\"_(David_Bowie_album)_en.wikipedia.org_all-access_all-agents_2017-02-10',\n",
" '\"Heroes\"_(David_Bowie_album)_en.wikipedia.org_all-access_all-agents_2017-02-11',\n",
" '\"Heroes\"_(David_Bowie_album)_en.wikipedia.org_all-access_all-agents_2017-02-12',\n",
" '\"Heroes\"_(David_Bowie_album)_en.wikipedia.org_all-access_all-agents_2017-02-13',\n",
" '\"Heroes\"_(David_Bowie_album)_en.wikipedia.org_all-access_all-agents_2017-02-14',\n",
" '\"Heroes\"_(David_Bowie_album)_en.wikipedia.org_all-access_all-agents_2017-02-15',\n",
" '\"Heroes\"_(David_Bowie_album)_en.wikipedia.org_all-access_all-agents_2017-02-16',\n",
" '\"Heroes\"_(David_Bowie_album)_en.wikipedia.org_all-access_all-agents_2017-02-17',\n",
" '\"Heroes\"_(David_Bowie_album)_en.wikipedia.org_all-access_all-agents_2017-02-18',\n",
" '\"Heroes\"_(David_Bowie_album)_en.wikipedia.org_all-access_all-agents_2017-02-19',\n",
" '\"Heroes\"_(David_Bowie_album)_en.wikipedia.org_all-access_all-agents_2017-02-20',\n",
" '\"Heroes\"_(David_Bowie_album)_en.wikipedia.org_all-access_all-agents_2017-02-21',\n",
" '\"Heroes\"_(David_Bowie_album)_en.wikipedia.org_all-access_all-agents_2017-02-22',\n",
" '\"Heroes\"_(David_Bowie_album)_en.wikipedia.org_all-access_all-agents_2017-02-23',\n",
" '\"Heroes\"_(David_Bowie_album)_en.wikipedia.org_all-access_all-agents_2017-02-24',\n",
" '\"Heroes\"_(David_Bowie_album)_en.wikipedia.org_all-access_all-agents_2017-02-25',\n",
" '\"Heroes\"_(David_Bowie_album)_en.wikipedia.org_all-access_all-agents_2017-02-26',\n",
" '\"Heroes\"_(David_Bowie_album)_en.wikipedia.org_all-access_all-agents_2017-02-27',\n",
" '\"Heroes\"_(David_Bowie_album)_en.wikipedia.org_all-access_all-agents_2017-02-28',\n",
" '\"Heroes\"_(David_Bowie_album)_en.wikipedia.org_all-access_all-agents_2017-03-01',\n",
" '\"Heroes\"_(David_Bowie_album)_en.wikipedia.org_all-access_spider_2017-01-01',\n",
" '\"Heroes\"_(David_Bowie_album)_en.wikipedia.org_all-access_spider_2017-01-02',\n",
" '\"Heroes\"_(David_Bowie_album)_en.wikipedia.org_all-access_spider_2017-01-03',\n",
" '\"Heroes\"_(David_Bowie_album)_en.wikipedia.org_all-access_spider_2017-01-04',\n",
" '\"Heroes\"_(David_Bowie_album)_en.wikipedia.org_all-access_spider_2017-01-05',\n",
" '\"Heroes\"_(David_Bowie_album)_en.wikipedia.org_all-access_spider_2017-01-06',\n",
" '\"Heroes\"_(David_Bowie_album)_en.wikipedia.org_all-access_spider_2017-01-07',\n",
" '\"Heroes\"_(David_Bowie_album)_en.wikipedia.org_all-access_spider_2017-01-08',\n",
" '\"Heroes\"_(David_Bowie_album)_en.wikipedia.org_all-access_spider_2017-01-09',\n",
" '\"Heroes\"_(David_Bowie_album)_en.wikipedia.org_all-access_spider_2017-01-10',\n",
" '\"Heroes\"_(David_Bowie_album)_en.wikipedia.org_all-access_spider_2017-01-11',\n",
" '\"Heroes\"_(David_Bowie_album)_en.wikipedia.org_all-access_spider_2017-01-12',\n",
" '\"Heroes\"_(David_Bowie_album)_en.wikipedia.org_all-access_spider_2017-01-13',\n",
" '\"Heroes\"_(David_Bowie_album)_en.wikipedia.org_all-access_spider_2017-01-14',\n",
" '\"Heroes\"_(David_Bowie_album)_en.wikipedia.org_all-access_spider_2017-01-15',\n",
" '\"Heroes\"_(David_Bowie_album)_en.wikipedia.org_all-access_spider_2017-01-16',\n",
" '\"Heroes\"_(David_Bowie_album)_en.wikipedia.org_all-access_spider_2017-01-17',\n",
" '\"Heroes\"_(David_Bowie_album)_en.wikipedia.org_all-access_spider_2017-01-18',\n",
" '\"Heroes\"_(David_Bowie_album)_en.wikipedia.org_all-access_spider_2017-01-19',\n",
" '\"Heroes\"_(David_Bowie_album)_en.wikipedia.org_all-access_spider_2017-01-20',\n",
" '\"Heroes\"_(David_Bowie_album)_en.wikipedia.org_all-access_spider_2017-01-21',\n",
" '\"Heroes\"_(David_Bowie_album)_en.wikipedia.org_all-access_spider_2017-01-22',\n",
" '\"Heroes\"_(David_Bowie_album)_en.wikipedia.org_all-access_spider_2017-01-23',\n",
" '\"Heroes\"_(David_Bowie_album)_en.wikipedia.org_all-access_spider_2017-01-24',\n",
" '\"Heroes\"_(David_Bowie_album)_en.wikipedia.org_all-access_spider_2017-01-25',\n",
" '\"Heroes\"_(David_Bowie_album)_en.wikipedia.org_all-access_spider_2017-01-26',\n",
" '\"Heroes\"_(David_Bowie_album)_en.wikipedia.org_all-access_spider_2017-01-27',\n",
" '\"Heroes\"_(David_Bowie_album)_en.wikipedia.org_all-access_spider_2017-01-28',\n",
" '\"Heroes\"_(David_Bowie_album)_en.wikipedia.org_all-access_spider_2017-01-29',\n",
" '\"Heroes\"_(David_Bowie_album)_en.wikipedia.org_all-access_spider_2017-01-30',\n",
" '\"Heroes\"_(David_Bowie_album)_en.wikipedia.org_all-access_spider_2017-01-31',\n",
" '\"Heroes\"_(David_Bowie_album)_en.wikipedia.org_all-access_spider_2017-02-01',\n",
" '\"Heroes\"_(David_Bowie_album)_en.wikipedia.org_all-access_spider_2017-02-02',\n",
" '\"Heroes\"_(David_Bowie_album)_en.wikipedia.org_all-access_spider_2017-02-03',\n",
" '\"Heroes\"_(David_Bowie_album)_en.wikipedia.org_all-access_spider_2017-02-04',\n",
" '\"Heroes\"_(David_Bowie_album)_en.wikipedia.org_all-access_spider_2017-02-05',\n",
" '\"Heroes\"_(David_Bowie_album)_en.wikipedia.org_all-access_spider_2017-02-06',\n",
" '\"Heroes\"_(David_Bowie_album)_en.wikipedia.org_all-access_spider_2017-02-07',\n",
" '\"Heroes\"_(David_Bowie_album)_en.wikipedia.org_all-access_spider_2017-02-08',\n",
" '\"Heroes\"_(David_Bowie_album)_en.wikipedia.org_all-access_spider_2017-02-09',\n",
" '\"Heroes\"_(David_Bowie_album)_en.wikipedia.org_all-access_spider_2017-02-10',\n",
" '\"Heroes\"_(David_Bowie_album)_en.wikipedia.org_all-access_spider_2017-02-11',\n",
" '\"Heroes\"_(David_Bowie_album)_en.wikipedia.org_all-access_spider_2017-02-12',\n",
" '\"Heroes\"_(David_Bowie_album)_en.wikipedia.org_all-access_spider_2017-02-13',\n",
" '\"Heroes\"_(David_Bowie_album)_en.wikipedia.org_all-access_spider_2017-02-14',\n",
" '\"Heroes\"_(David_Bowie_album)_en.wikipedia.org_all-access_spider_2017-02-15',\n",
" '\"Heroes\"_(David_Bowie_album)_en.wikipedia.org_all-access_spider_2017-02-16',\n",
" '\"Heroes\"_(David_Bowie_album)_en.wikipedia.org_all-access_spider_2017-02-17',\n",
" '\"Heroes\"_(David_Bowie_album)_en.wikipedia.org_all-access_spider_2017-02-18',\n",
" '\"Heroes\"_(David_Bowie_album)_en.wikipedia.org_all-access_spider_2017-02-19',\n",
" '\"Heroes\"_(David_Bowie_album)_en.wikipedia.org_all-access_spider_2017-02-20',\n",
" '\"Heroes\"_(David_Bowie_album)_en.wikipedia.org_all-access_spider_2017-02-21',\n",
" '\"Heroes\"_(David_Bowie_album)_en.wikipedia.org_all-access_spider_2017-02-22',\n",
" '\"Heroes\"_(David_Bowie_album)_en.wikipedia.org_all-access_spider_2017-02-23',\n",
" '\"Heroes\"_(David_Bowie_album)_en.wikipedia.org_all-access_spider_2017-02-24',\n",
" '\"Heroes\"_(David_Bowie_album)_en.wikipedia.org_all-access_spider_2017-02-25',\n",
" '\"Heroes\"_(David_Bowie_album)_en.wikipedia.org_all-access_spider_2017-02-26',\n",
" '\"Heroes\"_(David_Bowie_album)_en.wikipedia.org_all-access_spider_2017-02-27',\n",
" '\"Heroes\"_(David_Bowie_album)_en.wikipedia.org_all-access_spider_2017-02-28',\n",
" '\"Heroes\"_(David_Bowie_album)_en.wikipedia.org_all-access_spider_2017-03-01',\n",
" '\"Heroes\"_(David_Bowie_album)_en.wikipedia.org_desktop_all-agents_2017-01-01',\n",
" '\"Heroes\"_(David_Bowie_album)_en.wikipedia.org_desktop_all-agents_2017-01-02',\n",
" '\"Heroes\"_(David_Bowie_album)_en.wikipedia.org_desktop_all-agents_2017-01-03',\n",
" '\"Heroes\"_(David_Bowie_album)_en.wikipedia.org_desktop_all-agents_2017-01-04',\n",
" '\"Heroes\"_(David_Bowie_album)_en.wikipedia.org_desktop_all-agents_2017-01-05',\n",
" '\"Heroes\"_(David_Bowie_album)_en.wikipedia.org_desktop_all-agents_2017-01-06',\n",
" '\"Heroes\"_(David_Bowie_album)_en.wikipedia.org_desktop_all-agents_2017-01-07',\n",
" '\"Heroes\"_(David_Bowie_album)_en.wikipedia.org_desktop_all-agents_2017-01-08',\n",
" '\"Heroes\"_(David_Bowie_album)_en.wikipedia.org_desktop_all-agents_2017-01-09',\n",
" '\"Heroes\"_(David_Bowie_album)_en.wikipedia.org_desktop_all-agents_2017-01-10',\n",
" '\"Heroes\"_(David_Bowie_album)_en.wikipedia.org_desktop_all-agents_2017-01-11',\n",
" '\"Heroes\"_(David_Bowie_album)_en.wikipedia.org_desktop_all-agents_2017-01-12',\n",
" '\"Heroes\"_(David_Bowie_album)_en.wikipedia.org_desktop_all-agents_2017-01-13',\n",
" '\"Heroes\"_(David_Bowie_album)_en.wikipedia.org_desktop_all-agents_2017-01-14',\n",
" '\"Heroes\"_(David_Bowie_album)_en.wikipedia.org_desktop_all-agents_2017-01-15',\n",
" '\"Heroes\"_(David_Bowie_album)_en.wikipedia.org_desktop_all-agents_2017-01-16',\n",
" '\"Heroes\"_(David_Bowie_album)_en.wikipedia.org_desktop_all-agents_2017-01-17',\n",
" '\"Heroes\"_(David_Bowie_album)_en.wikipedia.org_desktop_all-agents_2017-01-18',\n",
" '\"Heroes\"_(David_Bowie_album)_en.wikipedia.org_desktop_all-agents_2017-01-19',\n",
" '\"Heroes\"_(David_Bowie_album)_en.wikipedia.org_desktop_all-agents_2017-01-20',\n",
" '\"Heroes\"_(David_Bowie_album)_en.wikipedia.org_desktop_all-agents_2017-01-21',\n",
" '\"Heroes\"_(David_Bowie_album)_en.wikipedia.org_desktop_all-agents_2017-01-22',\n",
" '\"Heroes\"_(David_Bowie_album)_en.wikipedia.org_desktop_all-agents_2017-01-23',\n",
" '\"Heroes\"_(David_Bowie_album)_en.wikipedia.org_desktop_all-agents_2017-01-24',\n",
" '\"Heroes\"_(David_Bowie_album)_en.wikipedia.org_desktop_all-agents_2017-01-25',\n",
" '\"Heroes\"_(David_Bowie_album)_en.wikipedia.org_desktop_all-agents_2017-01-26',\n",
" '\"Heroes\"_(David_Bowie_album)_en.wikipedia.org_desktop_all-agents_2017-01-27',\n",
" '\"Heroes\"_(David_Bowie_album)_en.wikipedia.org_desktop_all-agents_2017-01-28',\n",
" '\"Heroes\"_(David_Bowie_album)_en.wikipedia.org_desktop_all-agents_2017-01-29',\n",
" '\"Heroes\"_(David_Bowie_album)_en.wikipedia.org_desktop_all-agents_2017-01-30',\n",
" '\"Heroes\"_(David_Bowie_album)_en.wikipedia.org_desktop_all-agents_2017-01-31',\n",
" '\"Heroes\"_(David_Bowie_album)_en.wikipedia.org_desktop_all-agents_2017-02-01',\n",
" '\"Heroes\"_(David_Bowie_album)_en.wikipedia.org_desktop_all-agents_2017-02-02',\n",
" '\"Heroes\"_(David_Bowie_album)_en.wikipedia.org_desktop_all-agents_2017-02-03',\n",
" '\"Heroes\"_(David_Bowie_album)_en.wikipedia.org_desktop_all-agents_2017-02-04',\n",
" '\"Heroes\"_(David_Bowie_album)_en.wikipedia.org_desktop_all-agents_2017-02-05',\n",
" '\"Heroes\"_(David_Bowie_album)_en.wikipedia.org_desktop_all-agents_2017-02-06',\n",
" '\"Heroes\"_(David_Bowie_album)_en.wikipedia.org_desktop_all-agents_2017-02-07',\n",
" '\"Heroes\"_(David_Bowie_album)_en.wikipedia.org_desktop_all-agents_2017-02-08',\n",
" '\"Heroes\"_(David_Bowie_album)_en.wikipedia.org_desktop_all-agents_2017-02-09',\n",
" '\"Heroes\"_(David_Bowie_album)_en.wikipedia.org_desktop_all-agents_2017-02-10',\n",
" '\"Heroes\"_(David_Bowie_album)_en.wikipedia.org_desktop_all-agents_2017-02-11',\n",
" '\"Heroes\"_(David_Bowie_album)_en.wikipedia.org_desktop_all-agents_2017-02-12',\n",
" '\"Heroes\"_(David_Bowie_album)_en.wikipedia.org_desktop_all-agents_2017-02-13',\n",
" '\"Heroes\"_(David_Bowie_album)_en.wikipedia.org_desktop_all-agents_2017-02-14',\n",
" '\"Heroes\"_(David_Bowie_album)_en.wikipedia.org_desktop_all-agents_2017-02-15',\n",
" '\"Heroes\"_(David_Bowie_album)_en.wikipedia.org_desktop_all-agents_2017-02-16',\n",
" '\"Heroes\"_(David_Bowie_album)_en.wikipedia.org_desktop_all-agents_2017-02-17',\n",
" '\"Heroes\"_(David_Bowie_album)_en.wikipedia.org_desktop_all-agents_2017-02-18',\n",
" '\"Heroes\"_(David_Bowie_album)_en.wikipedia.org_desktop_all-agents_2017-02-19',\n",
" '\"Heroes\"_(David_Bowie_album)_en.wikipedia.org_desktop_all-agents_2017-02-20',\n",
" '\"Heroes\"_(David_Bowie_album)_en.wikipedia.org_desktop_all-agents_2017-02-21',\n",
" '\"Heroes\"_(David_Bowie_album)_en.wikipedia.org_desktop_all-agents_2017-02-22',\n",
" '\"Heroes\"_(David_Bowie_album)_en.wikipedia.org_desktop_all-agents_2017-02-23',\n",
" '\"Heroes\"_(David_Bowie_album)_en.wikipedia.org_desktop_all-agents_2017-02-24',\n",
" '\"Heroes\"_(David_Bowie_album)_en.wikipedia.org_desktop_all-agents_2017-02-25',\n",
" '\"Heroes\"_(David_Bowie_album)_en.wikipedia.org_desktop_all-agents_2017-02-26',\n",
" '\"Heroes\"_(David_Bowie_album)_en.wikipedia.org_desktop_all-agents_2017-02-27',\n",
" '\"Heroes\"_(David_Bowie_album)_en.wikipedia.org_desktop_all-agents_2017-02-28',\n",
" '\"Heroes\"_(David_Bowie_album)_en.wikipedia.org_desktop_all-agents_2017-03-01',\n",
" '\"Keep_me_logged_in\"_extended_to_one_year_www.mediawiki.org_all-access_all-agents_2017-01-01',\n",
" '\"Keep_me_logged_in\"_extended_to_one_year_www.mediawiki.org_all-access_all-agents_2017-01-02',\n",
" '\"Keep_me_logged_in\"_extended_to_one_year_www.mediawiki.org_all-access_all-agents_2017-01-03',\n",
" '\"Keep_me_logged_in\"_extended_to_one_year_www.mediawiki.org_all-access_all-agents_2017-01-04',\n",
" '\"Keep_me_logged_in\"_extended_to_one_year_www.mediawiki.org_all-access_all-agents_2017-01-05',\n",
" '\"Keep_me_logged_in\"_extended_to_one_year_www.mediawiki.org_all-access_all-agents_2017-01-06',\n",
" '\"Keep_me_logged_in\"_extended_to_one_year_www.mediawiki.org_all-access_all-agents_2017-01-07',\n",
" '\"Keep_me_logged_in\"_extended_to_one_year_www.mediawiki.org_all-access_all-agents_2017-01-08',\n",
" '\"Keep_me_logged_in\"_extended_to_one_year_www.mediawiki.org_all-access_all-agents_2017-01-09',\n",
" '\"Keep_me_logged_in\"_extended_to_one_year_www.mediawiki.org_all-access_all-agents_2017-01-10',\n",
" '\"Keep_me_logged_in\"_extended_to_one_year_www.mediawiki.org_all-access_all-agents_2017-01-11',\n",
" '\"Keep_me_logged_in\"_extended_to_one_year_www.mediawiki.org_all-access_all-agents_2017-01-12',\n",
" '\"Keep_me_logged_in\"_extended_to_one_year_www.mediawiki.org_all-access_all-agents_2017-01-13',\n",
" '\"Keep_me_logged_in\"_extended_to_one_year_www.mediawiki.org_all-access_all-agents_2017-01-14',\n",
" '\"Keep_me_logged_in\"_extended_to_one_year_www.mediawiki.org_all-access_all-agents_2017-01-15',\n",
" '\"Keep_me_logged_in\"_extended_to_one_year_www.mediawiki.org_all-access_all-agents_2017-01-16',\n",
" '\"Keep_me_logged_in\"_extended_to_one_year_www.mediawiki.org_all-access_all-agents_2017-01-17',\n",
" '\"Keep_me_logged_in\"_extended_to_one_year_www.mediawiki.org_all-access_all-agents_2017-01-18',\n",
" '\"Keep_me_logged_in\"_extended_to_one_year_www.mediawiki.org_all-access_all-agents_2017-01-19',\n",
" '\"Keep_me_logged_in\"_extended_to_one_year_www.mediawiki.org_all-access_all-agents_2017-01-20',\n",
" '\"Keep_me_logged_in\"_extended_to_one_year_www.mediawiki.org_all-access_all-agents_2017-01-21',\n",
" '\"Keep_me_logged_in\"_extended_to_one_year_www.mediawiki.org_all-access_all-agents_2017-01-22',\n",
" '\"Keep_me_logged_in\"_extended_to_one_year_www.mediawiki.org_all-access_all-agents_2017-01-23',\n",
" '\"Keep_me_logged_in\"_extended_to_one_year_www.mediawiki.org_all-access_all-agents_2017-01-24',\n",
" '\"Keep_me_logged_in\"_extended_to_one_year_www.mediawiki.org_all-access_all-agents_2017-01-25',\n",
" '\"Keep_me_logged_in\"_extended_to_one_year_www.mediawiki.org_all-access_all-agents_2017-01-26',\n",
" '\"Keep_me_logged_in\"_extended_to_one_year_www.mediawiki.org_all-access_all-agents_2017-01-27',\n",
" '\"Keep_me_logged_in\"_extended_to_one_year_www.mediawiki.org_all-access_all-agents_2017-01-28',\n",
" '\"Keep_me_logged_in\"_extended_to_one_year_www.mediawiki.org_all-access_all-agents_2017-01-29',\n",
" '\"Keep_me_logged_in\"_extended_to_one_year_www.mediawiki.org_all-access_all-agents_2017-01-30',\n",
" '\"Keep_me_logged_in\"_extended_to_one_year_www.mediawiki.org_all-access_all-agents_2017-01-31',\n",
" '\"Keep_me_logged_in\"_extended_to_one_year_www.mediawiki.org_all-access_all-agents_2017-02-01',\n",
" '\"Keep_me_logged_in\"_extended_to_one_year_www.mediawiki.org_all-access_all-agents_2017-02-02',\n",
" '\"Keep_me_logged_in\"_extended_to_one_year_www.mediawiki.org_all-access_all-agents_2017-02-03',\n",
" '\"Keep_me_logged_in\"_extended_to_one_year_www.mediawiki.org_all-access_all-agents_2017-02-04',\n",
" '\"Keep_me_logged_in\"_extended_to_one_year_www.mediawiki.org_all-access_all-agents_2017-02-05',\n",
" '\"Keep_me_logged_in\"_extended_to_one_year_www.mediawiki.org_all-access_all-agents_2017-02-06',\n",
" '\"Keep_me_logged_in\"_extended_to_one_year_www.mediawiki.org_all-access_all-agents_2017-02-07',\n",
" '\"Keep_me_logged_in\"_extended_to_one_year_www.mediawiki.org_all-access_all-agents_2017-02-08',\n",
" '\"Keep_me_logged_in\"_extended_to_one_year_www.mediawiki.org_all-access_all-agents_2017-02-09',\n",
" '\"Keep_me_logged_in\"_extended_to_one_year_www.mediawiki.org_all-access_all-agents_2017-02-10',\n",
" '\"Keep_me_logged_in\"_extended_to_one_year_www.mediawiki.org_all-access_all-agents_2017-02-11',\n",
" '\"Keep_me_logged_in\"_extended_to_one_year_www.mediawiki.org_all-access_all-agents_2017-02-12',\n",
" '\"Keep_me_logged_in\"_extended_to_one_year_www.mediawiki.org_all-access_all-agents_2017-02-13',\n",
" '\"Keep_me_logged_in\"_extended_to_one_year_www.mediawiki.org_all-access_all-agents_2017-02-14',\n",
" '\"Keep_me_logged_in\"_extended_to_one_year_www.mediawiki.org_all-access_all-agents_2017-02-15',\n",
" '\"Keep_me_logged_in\"_extended_to_one_year_www.mediawiki.org_all-access_all-agents_2017-02-16',\n",
" '\"Keep_me_logged_in\"_extended_to_one_year_www.mediawiki.org_all-access_all-agents_2017-02-17',\n",
" '\"Keep_me_logged_in\"_extended_to_one_year_www.mediawiki.org_all-access_all-agents_2017-02-18',\n",
" '\"Keep_me_logged_in\"_extended_to_one_year_www.mediawiki.org_all-access_all-agents_2017-02-19',\n",
" '\"Keep_me_logged_in\"_extended_to_one_year_www.mediawiki.org_all-access_all-agents_2017-02-20',\n",
" '\"Keep_me_logged_in\"_extended_to_one_year_www.mediawiki.org_all-access_all-agents_2017-02-21',\n",
" '\"Keep_me_logged_in\"_extended_to_one_year_www.mediawiki.org_all-access_all-agents_2017-02-22',\n",
" '\"Keep_me_logged_in\"_extended_to_one_year_www.mediawiki.org_all-access_all-agents_2017-02-23',\n",
" '\"Keep_me_logged_in\"_extended_to_one_year_www.mediawiki.org_all-access_all-agents_2017-02-24',\n",
" '\"Keep_me_logged_in\"_extended_to_one_year_www.mediawiki.org_all-access_all-agents_2017-02-25',\n",
" '\"Keep_me_logged_in\"_extended_to_one_year_www.mediawiki.org_all-access_all-agents_2017-02-26',\n",
" '\"Keep_me_logged_in\"_extended_to_one_year_www.mediawiki.org_all-access_all-agents_2017-02-27',\n",
" '\"Keep_me_logged_in\"_extended_to_one_year_www.mediawiki.org_all-access_all-agents_2017-02-28',\n",
" '\"Keep_me_logged_in\"_extended_to_one_year_www.mediawiki.org_all-access_all-agents_2017-03-01',\n",
" '\"Keep_me_logged_in\"_extended_to_one_year_www.mediawiki.org_all-access_spider_2017-01-01',\n",
" '\"Keep_me_logged_in\"_extended_to_one_year_www.mediawiki.org_all-access_spider_2017-01-02',\n",
" '\"Keep_me_logged_in\"_extended_to_one_year_www.mediawiki.org_all-access_spider_2017-01-03',\n",
" '\"Keep_me_logged_in\"_extended_to_one_year_www.mediawiki.org_all-access_spider_2017-01-04',\n",
" '\"Keep_me_logged_in\"_extended_to_one_year_www.mediawiki.org_all-access_spider_2017-01-05',\n",
" '\"Keep_me_logged_in\"_extended_to_one_year_www.mediawiki.org_all-access_spider_2017-01-06',\n",
" '\"Keep_me_logged_in\"_extended_to_one_year_www.mediawiki.org_all-access_spider_2017-01-07',\n",
" '\"Keep_me_logged_in\"_extended_to_one_year_www.mediawiki.org_all-access_spider_2017-01-08',\n",
" '\"Keep_me_logged_in\"_extended_to_one_year_www.mediawiki.org_all-access_spider_2017-01-09',\n",
" '\"Keep_me_logged_in\"_extended_to_one_year_www.mediawiki.org_all-access_spider_2017-01-10',\n",
" '\"Keep_me_logged_in\"_extended_to_one_year_www.mediawiki.org_all-access_spider_2017-01-11',\n",
" '\"Keep_me_logged_in\"_extended_to_one_year_www.mediawiki.org_all-access_spider_2017-01-12',\n",
" '\"Keep_me_logged_in\"_extended_to_one_year_www.mediawiki.org_all-access_spider_2017-01-13',\n",
" '\"Keep_me_logged_in\"_extended_to_one_year_www.mediawiki.org_all-access_spider_2017-01-14',\n",
" '\"Keep_me_logged_in\"_extended_to_one_year_www.mediawiki.org_all-access_spider_2017-01-15',\n",
" '\"Keep_me_logged_in\"_extended_to_one_year_www.mediawiki.org_all-access_spider_2017-01-16',\n",
" '\"Keep_me_logged_in\"_extended_to_one_year_www.mediawiki.org_all-access_spider_2017-01-17',\n",
" '\"Keep_me_logged_in\"_extended_to_one_year_www.mediawiki.org_all-access_spider_2017-01-18',\n",
" '\"Keep_me_logged_in\"_extended_to_one_year_www.mediawiki.org_all-access_spider_2017-01-19',\n",
" '\"Keep_me_logged_in\"_extended_to_one_year_www.mediawiki.org_all-access_spider_2017-01-20',\n",
" '\"Keep_me_logged_in\"_extended_to_one_year_www.mediawiki.org_all-access_spider_2017-01-21',\n",
" '\"Keep_me_logged_in\"_extended_to_one_year_www.mediawiki.org_all-access_spider_2017-01-22',\n",
" '\"Keep_me_logged_in\"_extended_to_one_year_www.mediawiki.org_all-access_spider_2017-01-23',\n",
" '\"Keep_me_logged_in\"_extended_to_one_year_www.mediawiki.org_all-access_spider_2017-01-24',\n",
" '\"Keep_me_logged_in\"_extended_to_one_year_www.mediawiki.org_all-access_spider_2017-01-25',\n",
" '\"Keep_me_logged_in\"_extended_to_one_year_www.mediawiki.org_all-access_spider_2017-01-26',\n",
" '\"Keep_me_logged_in\"_extended_to_one_year_www.mediawiki.org_all-access_spider_2017-01-27',\n",
" '\"Keep_me_logged_in\"_extended_to_one_year_www.mediawiki.org_all-access_spider_2017-01-28',\n",
" '\"Keep_me_logged_in\"_extended_to_one_year_www.mediawiki.org_all-access_spider_2017-01-29',\n",
" '\"Keep_me_logged_in\"_extended_to_one_year_www.mediawiki.org_all-access_spider_2017-01-30',\n",
" '\"Keep_me_logged_in\"_extended_to_one_year_www.mediawiki.org_all-access_spider_2017-01-31',\n",
" '\"Keep_me_logged_in\"_extended_to_one_year_www.mediawiki.org_all-access_spider_2017-02-01',\n",
" '\"Keep_me_logged_in\"_extended_to_one_year_www.mediawiki.org_all-access_spider_2017-02-02',\n",
" '\"Keep_me_logged_in\"_extended_to_one_year_www.mediawiki.org_all-access_spider_2017-02-03',\n",
" '\"Keep_me_logged_in\"_extended_to_one_year_www.mediawiki.org_all-access_spider_2017-02-04',\n",
" '\"Keep_me_logged_in\"_extended_to_one_year_www.mediawiki.org_all-access_spider_2017-02-05',\n",
" '\"Keep_me_logged_in\"_extended_to_one_year_www.mediawiki.org_all-access_spider_2017-02-06',\n",
" '\"Keep_me_logged_in\"_extended_to_one_year_www.mediawiki.org_all-access_spider_2017-02-07',\n",
" '\"Keep_me_logged_in\"_extended_to_one_year_www.mediawiki.org_all-access_spider_2017-02-08',\n",
" '\"Keep_me_logged_in\"_extended_to_one_year_www.mediawiki.org_all-access_spider_2017-02-09',\n",
" '\"Keep_me_logged_in\"_extended_to_one_year_www.mediawiki.org_all-access_spider_2017-02-10',\n",
" '\"Keep_me_logged_in\"_extended_to_one_year_www.mediawiki.org_all-access_spider_2017-02-11',\n",
" '\"Keep_me_logged_in\"_extended_to_one_year_www.mediawiki.org_all-access_spider_2017-02-12',\n",
" '\"Keep_me_logged_in\"_extended_to_one_year_www.mediawiki.org_all-access_spider_2017-02-13',\n",
" '\"Keep_me_logged_in\"_extended_to_one_year_www.mediawiki.org_all-access_spider_2017-02-14',\n",
" '\"Keep_me_logged_in\"_extended_to_one_year_www.mediawiki.org_all-access_spider_2017-02-15',\n",
" '\"Keep_me_logged_in\"_extended_to_one_year_www.mediawiki.org_all-access_spider_2017-02-16',\n",
" '\"Keep_me_logged_in\"_extended_to_one_year_www.mediawiki.org_all-access_spider_2017-02-17',\n",
" '\"Keep_me_logged_in\"_extended_to_one_year_www.mediawiki.org_all-access_spider_2017-02-18',\n",
" '\"Keep_me_logged_in\"_extended_to_one_year_www.mediawiki.org_all-access_spider_2017-02-19',\n",
" '\"Keep_me_logged_in\"_extended_to_one_year_www.mediawiki.org_all-access_spider_2017-02-20',\n",
" '\"Keep_me_logged_in\"_extended_to_one_year_www.mediawiki.org_all-access_spider_2017-02-21',\n",
" '\"Keep_me_logged_in\"_extended_to_one_year_www.mediawiki.org_all-access_spider_2017-02-22',\n",
" '\"Keep_me_logged_in\"_extended_to_one_year_www.mediawiki.org_all-access_spider_2017-02-23',\n",
" '\"Keep_me_logged_in\"_extended_to_one_year_www.mediawiki.org_all-access_spider_2017-02-24',\n",
" '\"Keep_me_logged_in\"_extended_to_one_year_www.mediawiki.org_all-access_spider_2017-02-25',\n",
" '\"Keep_me_logged_in\"_extended_to_one_year_www.mediawiki.org_all-access_spider_2017-02-26',\n",
" '\"Keep_me_logged_in\"_extended_to_one_year_www.mediawiki.org_all-access_spider_2017-02-27',\n",
" '\"Keep_me_logged_in\"_extended_to_one_year_www.mediawiki.org_all-access_spider_2017-02-28',\n",
" '\"Keep_me_logged_in\"_extended_to_one_year_www.mediawiki.org_all-access_spider_2017-03-01',\n",
" '\"Keep_me_logged_in\"_extended_to_one_year_www.mediawiki.org_desktop_all-agents_2017-01-01',\n",
" '\"Keep_me_logged_in\"_extended_to_one_year_www.mediawiki.org_desktop_all-agents_2017-01-02',\n",
" '\"Keep_me_logged_in\"_extended_to_one_year_www.mediawiki.org_desktop_all-agents_2017-01-03',\n",
" '\"Keep_me_logged_in\"_extended_to_one_year_www.mediawiki.org_desktop_all-agents_2017-01-04',\n",
" '\"Keep_me_logged_in\"_extended_to_one_year_www.mediawiki.org_desktop_all-agents_2017-01-05',\n",
" '\"Keep_me_logged_in\"_extended_to_one_year_www.mediawiki.org_desktop_all-agents_2017-01-06',\n",
" '\"Keep_me_logged_in\"_extended_to_one_year_www.mediawiki.org_desktop_all-agents_2017-01-07',\n",
" '\"Keep_me_logged_in\"_extended_to_one_year_www.mediawiki.org_desktop_all-agents_2017-01-08',\n",
" '\"Keep_me_logged_in\"_extended_to_one_year_www.mediawiki.org_desktop_all-agents_2017-01-09',\n",
" '\"Keep_me_logged_in\"_extended_to_one_year_www.mediawiki.org_desktop_all-agents_2017-01-10',\n",
" '\"Keep_me_logged_in\"_extended_to_one_year_www.mediawiki.org_desktop_all-agents_2017-01-11',\n",
" '\"Keep_me_logged_in\"_extended_to_one_year_www.mediawiki.org_desktop_all-agents_2017-01-12',\n",
" '\"Keep_me_logged_in\"_extended_to_one_year_www.mediawiki.org_desktop_all-agents_2017-01-13',\n",
" '\"Keep_me_logged_in\"_extended_to_one_year_www.mediawiki.org_desktop_all-agents_2017-01-14',\n",
" '\"Keep_me_logged_in\"_extended_to_one_year_www.mediawiki.org_desktop_all-agents_2017-01-15',\n",
" '\"Keep_me_logged_in\"_extended_to_one_year_www.mediawiki.org_desktop_all-agents_2017-01-16',\n",
" '\"Keep_me_logged_in\"_extended_to_one_year_www.mediawiki.org_desktop_all-agents_2017-01-17',\n",
" '\"Keep_me_logged_in\"_extended_to_one_year_www.mediawiki.org_desktop_all-agents_2017-01-18',\n",
" '\"Keep_me_logged_in\"_extended_to_one_year_www.mediawiki.org_desktop_all-agents_2017-01-19',\n",
" '\"Keep_me_logged_in\"_extended_to_one_year_www.mediawiki.org_desktop_all-agents_2017-01-20',\n",
" '\"Keep_me_logged_in\"_extended_to_one_year_www.mediawiki.org_desktop_all-agents_2017-01-21',\n",
" '\"Keep_me_logged_in\"_extended_to_one_year_www.mediawiki.org_desktop_all-agents_2017-01-22',\n",
" '\"Keep_me_logged_in\"_extended_to_one_year_www.mediawiki.org_desktop_all-agents_2017-01-23',\n",
" '\"Keep_me_logged_in\"_extended_to_one_year_www.mediawiki.org_desktop_all-agents_2017-01-24',\n",
" '\"Keep_me_logged_in\"_extended_to_one_year_www.mediawiki.org_desktop_all-agents_2017-01-25',\n",
" '\"Keep_me_logged_in\"_extended_to_one_year_www.mediawiki.org_desktop_all-agents_2017-01-26',\n",
" '\"Keep_me_logged_in\"_extended_to_one_year_www.mediawiki.org_desktop_all-agents_2017-01-27',\n",
" '\"Keep_me_logged_in\"_extended_to_one_year_www.mediawiki.org_desktop_all-agents_2017-01-28',\n",
" '\"Keep_me_logged_in\"_extended_to_one_year_www.mediawiki.org_desktop_all-agents_2017-01-29',\n",
" '\"Keep_me_logged_in\"_extended_to_one_year_www.mediawiki.org_desktop_all-agents_2017-01-30',\n",
" '\"Keep_me_logged_in\"_extended_to_one_year_www.mediawiki.org_desktop_all-agents_2017-01-31',\n",
" '\"Keep_me_logged_in\"_extended_to_one_year_www.mediawiki.org_desktop_all-agents_2017-02-01',\n",
" '\"Keep_me_logged_in\"_extended_to_one_year_www.mediawiki.org_desktop_all-agents_2017-02-02',\n",
" '\"Keep_me_logged_in\"_extended_to_one_year_www.mediawiki.org_desktop_all-agents_2017-02-03',\n",
" '\"Keep_me_logged_in\"_extended_to_one_year_www.mediawiki.org_desktop_all-agents_2017-02-04',\n",
" '\"Keep_me_logged_in\"_extended_to_one_year_www.mediawiki.org_desktop_all-agents_2017-02-05',\n",
" '\"Keep_me_logged_in\"_extended_to_one_year_www.mediawiki.org_desktop_all-agents_2017-02-06',\n",
" '\"Keep_me_logged_in\"_extended_to_one_year_www.mediawiki.org_desktop_all-agents_2017-02-07',\n",
" '\"Keep_me_logged_in\"_extended_to_one_year_www.mediawiki.org_desktop_all-agents_2017-02-08',\n",
" '\"Keep_me_logged_in\"_extended_to_one_year_www.mediawiki.org_desktop_all-agents_2017-02-09',\n",
" '\"Keep_me_logged_in\"_extended_to_one_year_www.mediawiki.org_desktop_all-agents_2017-02-10',\n",
" '\"Keep_me_logged_in\"_extended_to_one_year_www.mediawiki.org_desktop_all-agents_2017-02-11',\n",
" '\"Keep_me_logged_in\"_extended_to_one_year_www.mediawiki.org_desktop_all-agents_2017-02-12',\n",
" '\"Keep_me_logged_in\"_extended_to_one_year_www.mediawiki.org_desktop_all-agents_2017-02-13',\n",
" '\"Keep_me_logged_in\"_extended_to_one_year_www.mediawiki.org_desktop_all-agents_2017-02-14',\n",
" '\"Keep_me_logged_in\"_extended_to_one_year_www.mediawiki.org_desktop_all-agents_2017-02-15',\n",
" '\"Keep_me_logged_in\"_extended_to_one_year_www.mediawiki.org_desktop_all-agents_2017-02-16',\n",
" '\"Keep_me_logged_in\"_extended_to_one_year_www.mediawiki.org_desktop_all-agents_2017-02-17',\n",
" '\"Keep_me_logged_in\"_extended_to_one_year_www.mediawiki.org_desktop_all-agents_2017-02-18',\n",
" '\"Keep_me_logged_in\"_extended_to_one_year_www.mediawiki.org_desktop_all-agents_2017-02-19',\n",
" '\"Keep_me_logged_in\"_extended_to_one_year_www.mediawiki.org_desktop_all-agents_2017-02-20',\n",
" '\"Keep_me_logged_in\"_extended_to_one_year_www.mediawiki.org_desktop_all-agents_2017-02-21',\n",
" '\"Keep_me_logged_in\"_extended_to_one_year_www.mediawiki.org_desktop_all-agents_2017-02-22',\n",
" '\"Keep_me_logged_in\"_extended_to_one_year_www.mediawiki.org_desktop_all-agents_2017-02-23',\n",
" '\"Keep_me_logged_in\"_extended_to_one_year_www.mediawiki.org_desktop_all-agents_2017-02-24',\n",
" '\"Keep_me_logged_in\"_extended_to_one_year_www.mediawiki.org_desktop_all-agents_2017-02-25',\n",
" '\"Keep_me_logged_in\"_extended_to_one_year_www.mediawiki.org_desktop_all-agents_2017-02-26',\n",
" '\"Keep_me_logged_in\"_extended_to_one_year_www.mediawiki.org_desktop_all-agents_2017-02-27',\n",
" '\"Keep_me_logged_in\"_extended_to_one_year_www.mediawiki.org_desktop_all-agents_2017-02-28',\n",
" '\"Keep_me_logged_in\"_extended_to_one_year_www.mediawiki.org_desktop_all-agents_2017-03-01',\n",
" '\"Keep_me_logged_in\"_extended_to_one_year_www.mediawiki.org_mobile-web_all-agents_2017-01-01',\n",
" '\"Keep_me_logged_in\"_extended_to_one_year_www.mediawiki.org_mobile-web_all-agents_2017-01-02',\n",
" '\"Keep_me_logged_in\"_extended_to_one_year_www.mediawiki.org_mobile-web_all-agents_2017-01-03',\n",
" '\"Keep_me_logged_in\"_extended_to_one_year_www.mediawiki.org_mobile-web_all-agents_2017-01-04',\n",
" '\"Keep_me_logged_in\"_extended_to_one_year_www.mediawiki.org_mobile-web_all-agents_2017-01-05',\n",
" '\"Keep_me_logged_in\"_extended_to_one_year_www.mediawiki.org_mobile-web_all-agents_2017-01-06',\n",
" '\"Keep_me_logged_in\"_extended_to_one_year_www.mediawiki.org_mobile-web_all-agents_2017-01-07',\n",
" '\"Keep_me_logged_in\"_extended_to_one_year_www.mediawiki.org_mobile-web_all-agents_2017-01-08',\n",
" '\"Keep_me_logged_in\"_extended_to_one_year_www.mediawiki.org_mobile-web_all-agents_2017-01-09',\n",
" '\"Keep_me_logged_in\"_extended_to_one_year_www.mediawiki.org_mobile-web_all-agents_2017-01-10',\n",
" '\"Keep_me_logged_in\"_extended_to_one_year_www.mediawiki.org_mobile-web_all-agents_2017-01-11',\n",
" '\"Keep_me_logged_in\"_extended_to_one_year_www.mediawiki.org_mobile-web_all-agents_2017-01-12',\n",
" '\"Keep_me_logged_in\"_extended_to_one_year_www.mediawiki.org_mobile-web_all-agents_2017-01-13',\n",
" '\"Keep_me_logged_in\"_extended_to_one_year_www.mediawiki.org_mobile-web_all-agents_2017-01-14',\n",
" '\"Keep_me_logged_in\"_extended_to_one_year_www.mediawiki.org_mobile-web_all-agents_2017-01-15',\n",
" '\"Keep_me_logged_in\"_extended_to_one_year_www.mediawiki.org_mobile-web_all-agents_2017-01-16',\n",
" '\"Keep_me_logged_in\"_extended_to_one_year_www.mediawiki.org_mobile-web_all-agents_2017-01-17',\n",
" '\"Keep_me_logged_in\"_extended_to_one_year_www.mediawiki.org_mobile-web_all-agents_2017-01-18',\n",
" '\"Keep_me_logged_in\"_extended_to_one_year_www.mediawiki.org_mobile-web_all-agents_2017-01-19',\n",
" '\"Keep_me_logged_in\"_extended_to_one_year_www.mediawiki.org_mobile-web_all-agents_2017-01-20',\n",
" '\"Keep_me_logged_in\"_extended_to_one_year_www.mediawiki.org_mobile-web_all-agents_2017-01-21',\n",
" '\"Keep_me_logged_in\"_extended_to_one_year_www.mediawiki.org_mobile-web_all-agents_2017-01-22',\n",
" '\"Keep_me_logged_in\"_extended_to_one_year_www.mediawiki.org_mobile-web_all-agents_2017-01-23',\n",
" '\"Keep_me_logged_in\"_extended_to_one_year_www.mediawiki.org_mobile-web_all-agents_2017-01-24',\n",
" '\"Keep_me_logged_in\"_extended_to_one_year_www.mediawiki.org_mobile-web_all-agents_2017-01-25',\n",
" '\"Keep_me_logged_in\"_extended_to_one_year_www.mediawiki.org_mobile-web_all-agents_2017-01-26',\n",
" '\"Keep_me_logged_in\"_extended_to_one_year_www.mediawiki.org_mobile-web_all-agents_2017-01-27',\n",
" '\"Keep_me_logged_in\"_extended_to_one_year_www.mediawiki.org_mobile-web_all-agents_2017-01-28',\n",
" '\"Keep_me_logged_in\"_extended_to_one_year_www.mediawiki.org_mobile-web_all-agents_2017-01-29',\n",
" '\"Keep_me_logged_in\"_extended_to_one_year_www.mediawiki.org_mobile-web_all-agents_2017-01-30',\n",
" '\"Keep_me_logged_in\"_extended_to_one_year_www.mediawiki.org_mobile-web_all-agents_2017-01-31',\n",
" '\"Keep_me_logged_in\"_extended_to_one_year_www.mediawiki.org_mobile-web_all-agents_2017-02-01',\n",
" '\"Keep_me_logged_in\"_extended_to_one_year_www.mediawiki.org_mobile-web_all-agents_2017-02-02',\n",
" '\"Keep_me_logged_in\"_extended_to_one_year_www.mediawiki.org_mobile-web_all-agents_2017-02-03',\n",
" '\"Keep_me_logged_in\"_extended_to_one_year_www.mediawiki.org_mobile-web_all-agents_2017-02-04',\n",
" '\"Keep_me_logged_in\"_extended_to_one_year_www.mediawiki.org_mobile-web_all-agents_2017-02-05',\n",
" '\"Keep_me_logged_in\"_extended_to_one_year_www.mediawiki.org_mobile-web_all-agents_2017-02-06',\n",
" '\"Keep_me_logged_in\"_extended_to_one_year_www.mediawiki.org_mobile-web_all-agents_2017-02-07',\n",
" '\"Keep_me_logged_in\"_extended_to_one_year_www.mediawiki.org_mobile-web_all-agents_2017-02-08',\n",
" '\"Keep_me_logged_in\"_extended_to_one_year_www.mediawiki.org_mobile-web_all-agents_2017-02-09',\n",
" '\"Keep_me_logged_in\"_extended_to_one_year_www.mediawiki.org_mobile-web_all-agents_2017-02-10',\n",
" '\"Keep_me_logged_in\"_extended_to_one_year_www.mediawiki.org_mobile-web_all-agents_2017-02-11',\n",
" '\"Keep_me_logged_in\"_extended_to_one_year_www.mediawiki.org_mobile-web_all-agents_2017-02-12',\n",
" '\"Keep_me_logged_in\"_extended_to_one_year_www.mediawiki.org_mobile-web_all-agents_2017-02-13',\n",
" '\"Keep_me_logged_in\"_extended_to_one_year_www.mediawiki.org_mobile-web_all-agents_2017-02-14',\n",
" '\"Keep_me_logged_in\"_extended_to_one_year_www.mediawiki.org_mobile-web_all-agents_2017-02-15',\n",
" '\"Keep_me_logged_in\"_extended_to_one_year_www.mediawiki.org_mobile-web_all-agents_2017-02-16',\n",
" '\"Keep_me_logged_in\"_extended_to_one_year_www.mediawiki.org_mobile-web_all-agents_2017-02-17',\n",
" '\"Keep_me_logged_in\"_extended_to_one_year_www.mediawiki.org_mobile-web_all-agents_2017-02-18',\n",
" '\"Keep_me_logged_in\"_extended_to_one_year_www.mediawiki.org_mobile-web_all-agents_2017-02-19',\n",
" '\"Keep_me_logged_in\"_extended_to_one_year_www.mediawiki.org_mobile-web_all-agents_2017-02-20',\n",
" '\"Keep_me_logged_in\"_extended_to_one_year_www.mediawiki.org_mobile-web_all-agents_2017-02-21',\n",
" '\"Keep_me_logged_in\"_extended_to_one_year_www.mediawiki.org_mobile-web_all-agents_2017-02-22',\n",
" '\"Keep_me_logged_in\"_extended_to_one_year_www.mediawiki.org_mobile-web_all-agents_2017-02-23',\n",
" '\"Keep_me_logged_in\"_extended_to_one_year_www.mediawiki.org_mobile-web_all-agents_2017-02-24',\n",
" '\"Keep_me_logged_in\"_extended_to_one_year_www.mediawiki.org_mobile-web_all-agents_2017-02-25',\n",
" '\"Keep_me_logged_in\"_extended_to_one_year_www.mediawiki.org_mobile-web_all-agents_2017-02-26',\n",
" '\"Keep_me_logged_in\"_extended_to_one_year_www.mediawiki.org_mobile-web_all-agents_2017-02-27',\n",
" '\"Keep_me_logged_in\"_extended_to_one_year_www.mediawiki.org_mobile-web_all-agents_2017-02-28',\n",
" '\"Keep_me_logged_in\"_extended_to_one_year_www.mediawiki.org_mobile-web_all-agents_2017-03-01',\n",
" '\"Weird_Al\"_Yankovic_en.wikipedia.org_all-access_all-agents_2017-01-01',\n",
" '\"Weird_Al\"_Yankovic_en.wikipedia.org_all-access_all-agents_2017-01-02',\n",
" '\"Weird_Al\"_Yankovic_en.wikipedia.org_all-access_all-agents_2017-01-03',\n",
" '\"Weird_Al\"_Yankovic_en.wikipedia.org_all-access_all-agents_2017-01-04',\n",
" '\"Weird_Al\"_Yankovic_en.wikipedia.org_all-access_all-agents_2017-01-05',\n",
" '\"Weird_Al\"_Yankovic_en.wikipedia.org_all-access_all-agents_2017-01-06',\n",
" '\"Weird_Al\"_Yankovic_en.wikipedia.org_all-access_all-agents_2017-01-07',\n",
" '\"Weird_Al\"_Yankovic_en.wikipedia.org_all-access_all-agents_2017-01-08',\n",
" '\"Weird_Al\"_Yankovic_en.wikipedia.org_all-access_all-agents_2017-01-09',\n",
" '\"Weird_Al\"_Yankovic_en.wikipedia.org_all-access_all-agents_2017-01-10',\n",
" '\"Weird_Al\"_Yankovic_en.wikipedia.org_all-access_all-agents_2017-01-11',\n",
" '\"Weird_Al\"_Yankovic_en.wikipedia.org_all-access_all-agents_2017-01-12',\n",
" '\"Weird_Al\"_Yankovic_en.wikipedia.org_all-access_all-agents_2017-01-13',\n",
" '\"Weird_Al\"_Yankovic_en.wikipedia.org_all-access_all-agents_2017-01-14',\n",
" '\"Weird_Al\"_Yankovic_en.wikipedia.org_all-access_all-agents_2017-01-15',\n",
" '\"Weird_Al\"_Yankovic_en.wikipedia.org_all-access_all-agents_2017-01-16',\n",
" '\"Weird_Al\"_Yankovic_en.wikipedia.org_all-access_all-agents_2017-01-17',\n",
" '\"Weird_Al\"_Yankovic_en.wikipedia.org_all-access_all-agents_2017-01-18',\n",
" '\"Weird_Al\"_Yankovic_en.wikipedia.org_all-access_all-agents_2017-01-19',\n",
" '\"Weird_Al\"_Yankovic_en.wikipedia.org_all-access_all-agents_2017-01-20',\n",
" '\"Weird_Al\"_Yankovic_en.wikipedia.org_all-access_all-agents_2017-01-21',\n",
" '\"Weird_Al\"_Yankovic_en.wikipedia.org_all-access_all-agents_2017-01-22',\n",
" '\"Weird_Al\"_Yankovic_en.wikipedia.org_all-access_all-agents_2017-01-23',\n",
" '\"Weird_Al\"_Yankovic_en.wikipedia.org_all-access_all-agents_2017-01-24',\n",
" '\"Weird_Al\"_Yankovic_en.wikipedia.org_all-access_all-agents_2017-01-25',\n",
" '\"Weird_Al\"_Yankovic_en.wikipedia.org_all-access_all-agents_2017-01-26',\n",
" '\"Weird_Al\"_Yankovic_en.wikipedia.org_all-access_all-agents_2017-01-27',\n",
" '\"Weird_Al\"_Yankovic_en.wikipedia.org_all-access_all-agents_2017-01-28',\n",
" '\"Weird_Al\"_Yankovic_en.wikipedia.org_all-access_all-agents_2017-01-29',\n",
" '\"Weird_Al\"_Yankovic_en.wikipedia.org_all-access_all-agents_2017-01-30',\n",
" '\"Weird_Al\"_Yankovic_en.wikipedia.org_all-access_all-agents_2017-01-31',\n",
" '\"Weird_Al\"_Yankovic_en.wikipedia.org_all-access_all-agents_2017-02-01',\n",
" '\"Weird_Al\"_Yankovic_en.wikipedia.org_all-access_all-agents_2017-02-02',\n",
" '\"Weird_Al\"_Yankovic_en.wikipedia.org_all-access_all-agents_2017-02-03',\n",
" '\"Weird_Al\"_Yankovic_en.wikipedia.org_all-access_all-agents_2017-02-04',\n",
" '\"Weird_Al\"_Yankovic_en.wikipedia.org_all-access_all-agents_2017-02-05',\n",
" '\"Weird_Al\"_Yankovic_en.wikipedia.org_all-access_all-agents_2017-02-06',\n",
" '\"Weird_Al\"_Yankovic_en.wikipedia.org_all-access_all-agents_2017-02-07',\n",
" '\"Weird_Al\"_Yankovic_en.wikipedia.org_all-access_all-agents_2017-02-08',\n",
" '\"Weird_Al\"_Yankovic_en.wikipedia.org_all-access_all-agents_2017-02-09',\n",
" ...]"
]
},
"execution_count": 12,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"test_page_list #可以發現該有的欄位一模一樣"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## 把ID跟df_train上的page對在一起(先排序好)，這樣才好ML並且放到df_ans當中"
]
},
{
"cell_type": "code",
"execution_count": 13,
"metadata": {},
"outputs": [],
"source": [
"df_train_0_order = df_train_0.sort_values('Page') #排序好"
]
},
{
"cell_type": "code",
"execution_count": 14,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>Page</th>\n",
" <th>2015-07-01</th>\n",
" <th>2015-07-02</th>\n",
" <th>2015-07-03</th>\n",
" <th>2015-07-04</th>\n",
" <th>2015-07-05</th>\n",
" <th>2015-07-06</th>\n",
" <th>2015-07-07</th>\n",
" <th>2015-07-08</th>\n",
" <th>2015-07-09</th>\n",
" <th>...</th>\n",
" <th>2016-12-22</th>\n",
" <th>2016-12-23</th>\n",
" <th>2016-12-24</th>\n",
" <th>2016-12-25</th>\n",
" <th>2016-12-26</th>\n",
" <th>2016-12-27</th>\n",
" <th>2016-12-28</th>\n",
" <th>2016-12-29</th>\n",
" <th>2016-12-30</th>\n",
" <th>2016-12-31</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>37206</th>\n",
" <td>!vote_en.wikipedia.org_all-access_all-agents</td>\n",
" <td>3.0</td>\n",
" <td>4.0</td>\n",
" <td>7.0</td>\n",
" <td>4.0</td>\n",
" <td>4.0</td>\n",
" <td>2.0</td>\n",
" <td>3.0</td>\n",
" <td>7.0</td>\n",
" <td>2.0</td>\n",
" <td>...</td>\n",
" <td>3.0</td>\n",
" <td>1.0</td>\n",
" <td>6.0</td>\n",
" <td>3.0</td>\n",
" <td>1.0</td>\n",
" <td>1.0</td>\n",
" <td>4.0</td>\n",
" <td>3.0</td>\n",
" <td>1.0</td>\n",
" <td>1.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>32275</th>\n",
" <td>!vote_en.wikipedia.org_all-access_spider</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>1.0</td>\n",
" <td>2.0</td>\n",
" <td>0.0</td>\n",
" <td>1.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>2.0</td>\n",
" <td>...</td>\n",
" <td>2.0</td>\n",
" <td>0.0</td>\n",
" <td>2.0</td>\n",
" <td>2.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>1.0</td>\n",
" <td>1.0</td>\n",
" <td>1.0</td>\n",
" <td>0.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>8357</th>\n",
" <td>!vote_en.wikipedia.org_desktop_all-agents</td>\n",
" <td>3.0</td>\n",
" <td>4.0</td>\n",
" <td>7.0</td>\n",
" <td>4.0</td>\n",
" <td>4.0</td>\n",
" <td>2.0</td>\n",
" <td>3.0</td>\n",
" <td>7.0</td>\n",
" <td>2.0</td>\n",
" <td>...</td>\n",
" <td>3.0</td>\n",
" <td>1.0</td>\n",
" <td>6.0</td>\n",
" <td>3.0</td>\n",
" <td>1.0</td>\n",
" <td>1.0</td>\n",
" <td>3.0</td>\n",
" <td>3.0</td>\n",
" <td>1.0</td>\n",
" <td>0.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>37207</th>\n",
" <td>\"Awaken,_My_Love!\"_en.wikipedia.org_all-access...</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>...</td>\n",
" <td>4923.0</td>\n",
" <td>5074.0</td>\n",
" <td>4149.0</td>\n",
" <td>4209.0</td>\n",
" <td>3783.0</td>\n",
" <td>3994.0</td>\n",
" <td>3910.0</td>\n",
" <td>4006.0</td>\n",
" <td>3841.0</td>\n",
" <td>3517.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>32276</th>\n",
" <td>\"Awaken,_My_Love!\"_en.wikipedia.org_all-access...</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>...</td>\n",
" <td>89.0</td>\n",
" <td>12.0</td>\n",
" <td>63.0</td>\n",
" <td>15.0</td>\n",
" <td>38.0</td>\n",
" <td>14.0</td>\n",
" <td>59.0</td>\n",
" <td>40.0</td>\n",
" <td>35.0</td>\n",
" <td>45.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>8358</th>\n",
" <td>\"Awaken,_My_Love!\"_en.wikipedia.org_desktop_al...</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>...</td>\n",
" <td>2898.0</td>\n",
" <td>2676.0</td>\n",
" <td>2032.0</td>\n",
" <td>2294.0</td>\n",
" <td>2074.0</td>\n",
" <td>2175.0</td>\n",
" <td>2156.0</td>\n",
" <td>2302.0</td>\n",
" <td>2098.0</td>\n",
" <td>1770.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>37208</th>\n",
" <td>\"European_Society_for_Clinical_Investigation\"_...</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>1.0</td>\n",
" <td>...</td>\n",
" <td>1.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>1.0</td>\n",
" <td>2.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>32277</th>\n",
" <td>\"European_Society_for_Clinical_Investigation\"_...</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>...</td>\n",
" <td>1.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>1.0</td>\n",
" <td>0.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>8359</th>\n",
" <td>\"European_Society_for_Clinical_Investigation\"_...</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>1.0</td>\n",
" <td>...</td>\n",
" <td>1.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>1.0</td>\n",
" <td>2.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>39669</th>\n",
" <td>\"Heroes\"_(David_Bowie_album)_en.wikipedia.org_...</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>...</td>\n",
" <td>731.0</td>\n",
" <td>679.0</td>\n",
" <td>625.0</td>\n",
" <td>595.0</td>\n",
" <td>767.0</td>\n",
" <td>986.0</td>\n",
" <td>838.0</td>\n",
" <td>962.0</td>\n",
" <td>993.0</td>\n",
" <td>881.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>35987</th>\n",
" <td>\"Heroes\"_(David_Bowie_album)_en.wikipedia.org_...</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>...</td>\n",
" <td>26.0</td>\n",
" <td>22.0</td>\n",
" <td>13.0</td>\n",
" <td>11.0</td>\n",
" <td>10.0</td>\n",
" <td>11.0</td>\n",
" <td>6.0</td>\n",
" <td>42.0</td>\n",
" <td>8.0</td>\n",
" <td>14.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>12170</th>\n",
" <td>\"Heroes\"_(David_Bowie_album)_en.wikipedia.org_...</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>...</td>\n",
" <td>483.0</td>\n",
" <td>462.0</td>\n",
" <td>371.0</td>\n",
" <td>349.0</td>\n",
" <td>475.0</td>\n",
" <td>578.0</td>\n",
" <td>547.0</td>\n",
" <td>651.0</td>\n",
" <td>659.0</td>\n",
" <td>549.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>19611</th>\n",
" <td>\"Keep_me_logged_in\"_extended_to_one_year_www.m...</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>...</td>\n",
" <td>17.0</td>\n",
" <td>18.0</td>\n",
" <td>11.0</td>\n",
" <td>15.0</td>\n",
" <td>11.0</td>\n",
" <td>15.0</td>\n",
" <td>10.0</td>\n",
" <td>14.0</td>\n",
" <td>7.0</td>\n",
" <td>12.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>82986</th>\n",
" <td>\"Keep_me_logged_in\"_extended_to_one_year_www.m...</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>...</td>\n",
" <td>1.0</td>\n",
" <td>1.0</td>\n",
" <td>2.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>1.0</td>\n",
" <td>0.0</td>\n",
" <td>1.0</td>\n",
" <td>1.0</td>\n",
" <td>1.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>42124</th>\n",
" <td>\"Keep_me_logged_in\"_extended_to_one_year_www.m...</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>...</td>\n",
" <td>10.0</td>\n",
" <td>15.0</td>\n",
" <td>6.0</td>\n",
" <td>11.0</td>\n",
" <td>6.0</td>\n",
" <td>11.0</td>\n",
" <td>6.0</td>\n",
" <td>10.0</td>\n",
" <td>3.0</td>\n",
" <td>7.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>23341</th>\n",
" <td>\"Keep_me_logged_in\"_extended_to_one_year_www.m...</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>...</td>\n",
" <td>7.0</td>\n",
" <td>3.0</td>\n",
" <td>5.0</td>\n",
" <td>4.0</td>\n",
" <td>5.0</td>\n",
" <td>4.0</td>\n",
" <td>4.0</td>\n",
" <td>4.0</td>\n",
" <td>4.0</td>\n",
" <td>5.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>37209</th>\n",
" <td>\"Weird_Al\"_Yankovic_en.wikipedia.org_all-acces...</td>\n",
" <td>3966.0</td>\n",
" <td>27052.0</td>\n",
" <td>3867.0</td>\n",
" <td>3117.0</td>\n",
" <td>3578.0</td>\n",
" <td>3331.0</td>\n",
" <td>3041.0</td>\n",
" <td>3538.0</td>\n",
" <td>3298.0</td>\n",
" <td>...</td>\n",
" <td>2512.0</td>\n",
" <td>2422.0</td>\n",
" <td>2193.0</td>\n",
" <td>2209.0</td>\n",
" <td>2248.0</td>\n",
" <td>3144.0</td>\n",
" <td>2835.0</td>\n",
" <td>3084.0</td>\n",
" <td>2754.0</td>\n",
" <td>2969.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>32278</th>\n",
" <td>\"Weird_Al\"_Yankovic_en.wikipedia.org_all-acces...</td>\n",
" <td>144.0</td>\n",
" <td>91.0</td>\n",
" <td>44.0</td>\n",
" <td>40.0</td>\n",
" <td>29.0</td>\n",
" <td>25.0</td>\n",
" <td>49.0</td>\n",
" <td>97.0</td>\n",
" <td>239.0</td>\n",
" <td>...</td>\n",
" <td>61.0</td>\n",
" <td>66.0</td>\n",
" <td>76.0</td>\n",
" <td>47.0</td>\n",
" <td>62.0</td>\n",
" <td>61.0</td>\n",
" <td>62.0</td>\n",
" <td>525.0</td>\n",
" <td>52.0</td>\n",
" <td>79.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>8360</th>\n",
" <td>\"Weird_Al\"_Yankovic_en.wikipedia.org_desktop_a...</td>\n",
" <td>2403.0</td>\n",
" <td>20136.0</td>\n",
" <td>1850.0</td>\n",
" <td>1432.0</td>\n",
" <td>1351.0</td>\n",
" <td>1792.0</td>\n",
" <td>1710.0</td>\n",
" <td>2039.0</td>\n",
" <td>1957.0</td>\n",
" <td>...</td>\n",
" <td>1401.0</td>\n",
" <td>1238.0</td>\n",
" <td>976.0</td>\n",
" <td>810.0</td>\n",
" <td>989.0</td>\n",
" <td>1332.0</td>\n",
" <td>1243.0</td>\n",
" <td>1681.0</td>\n",
" <td>1303.0</td>\n",
" <td>1098.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>22945</th>\n",
" <td>%s_www.mediawiki.org_mobile-web_all-agents</td>\n",
" <td>0.0</td>\n",
" <td>1.0</td>\n",
" <td>3.0</td>\n",
" <td>0.0</td>\n",
" <td>1.0</td>\n",
" <td>2.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>1.0</td>\n",
" <td>...</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>39670</th>\n",
" <td>'Tis_the_Season_(Vince_Gill_and_Olivia_Newton-...</td>\n",
" <td>15.0</td>\n",
" <td>22.0</td>\n",
" <td>30.0</td>\n",
" <td>21.0</td>\n",
" <td>13.0</td>\n",
" <td>10.0</td>\n",
" <td>15.0</td>\n",
" <td>28.0</td>\n",
" <td>16.0</td>\n",
" <td>...</td>\n",
" <td>6763.0</td>\n",
" <td>125432.0</td>\n",
" <td>167369.0</td>\n",
" <td>187423.0</td>\n",
" <td>47504.0</td>\n",
" <td>7357.0</td>\n",
" <td>5348.0</td>\n",
" <td>3917.0</td>\n",
" <td>3104.0</td>\n",
" <td>2506.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>34746</th>\n",
" <td>'Tis_the_Season_(Vince_Gill_and_Olivia_Newton-...</td>\n",
" <td>9.0</td>\n",
" <td>11.0</td>\n",
" <td>9.0</td>\n",
" <td>7.0</td>\n",
" <td>6.0</td>\n",
" <td>4.0</td>\n",
" <td>4.0</td>\n",
" <td>5.0</td>\n",
" <td>8.0</td>\n",
" <td>...</td>\n",
" <td>25.0</td>\n",
" <td>335.0</td>\n",
" <td>126.0</td>\n",
" <td>92.0</td>\n",
" <td>74.0</td>\n",
" <td>47.0</td>\n",
" <td>36.0</td>\n",
" <td>19.0</td>\n",
" <td>15.0</td>\n",
" <td>41.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>75165</th>\n",
" <td>'Tis_the_Season_(Vince_Gill_and_Olivia_Newton-...</td>\n",
" <td>9.0</td>\n",
" <td>4.0</td>\n",
" <td>13.0</td>\n",
" <td>11.0</td>\n",
" <td>2.0</td>\n",
" <td>2.0</td>\n",
" <td>6.0</td>\n",
" <td>7.0</td>\n",
" <td>3.0</td>\n",
" <td>...</td>\n",
" <td>4632.0</td>\n",
" <td>82417.0</td>\n",
" <td>141056.0</td>\n",
" <td>173163.0</td>\n",
" <td>45088.0</td>\n",
" <td>6971.0</td>\n",
" <td>5065.0</td>\n",
" <td>3687.0</td>\n",
" <td>2959.0</td>\n",
" <td>2358.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>37210</th>\n",
" <td>'Tis_the_Season_en.wikipedia.org_all-access_al...</td>\n",
" <td>34.0</td>\n",
" <td>30.0</td>\n",
" <td>32.0</td>\n",
" <td>33.0</td>\n",
" <td>17.0</td>\n",
" <td>39.0</td>\n",
" <td>31.0</td>\n",
" <td>23.0</td>\n",
" <td>28.0</td>\n",
" <td>...</td>\n",
" <td>35128.0</td>\n",
" <td>740695.0</td>\n",
" <td>790629.0</td>\n",
" <td>637395.0</td>\n",
" <td>116636.0</td>\n",
" <td>20534.0</td>\n",
" <td>12626.0</td>\n",
" <td>9160.0</td>\n",
" <td>6866.0</td>\n",
" <td>5282.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>34745</th>\n",
" <td>'Tis_the_Season_en.wikipedia.org_all-access_sp...</td>\n",
" <td>6.0</td>\n",
" <td>5.0</td>\n",
" <td>2.0</td>\n",
" <td>9.0</td>\n",
" <td>2.0</td>\n",
" <td>4.0</td>\n",
" <td>4.0</td>\n",
" <td>3.0</td>\n",
" <td>3.0</td>\n",
" <td>...</td>\n",
" <td>162.0</td>\n",
" <td>521.0</td>\n",
" <td>326.0</td>\n",
" <td>175.0</td>\n",
" <td>265.0</td>\n",
" <td>40.0</td>\n",
" <td>44.0</td>\n",
" <td>81.0</td>\n",
" <td>72.0</td>\n",
" <td>22.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>10893</th>\n",
" <td>'Tis_the_Season_en.wikipedia.org_desktop_all-a...</td>\n",
" <td>29.0</td>\n",
" <td>17.0</td>\n",
" <td>10.0</td>\n",
" <td>16.0</td>\n",
" <td>11.0</td>\n",
" <td>16.0</td>\n",
" <td>17.0</td>\n",
" <td>8.0</td>\n",
" <td>20.0</td>\n",
" <td>...</td>\n",
" <td>11646.0</td>\n",
" <td>201406.0</td>\n",
" <td>103427.0</td>\n",
" <td>53036.0</td>\n",
" <td>7560.0</td>\n",
" <td>1175.0</td>\n",
" <td>830.0</td>\n",
" <td>669.0</td>\n",
" <td>502.0</td>\n",
" <td>321.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>72749</th>\n",
" <td>'Tis_the_Season_en.wikipedia.org_mobile-web_al...</td>\n",
" <td>5.0</td>\n",
" <td>13.0</td>\n",
" <td>21.0</td>\n",
" <td>17.0</td>\n",
" <td>6.0</td>\n",
" <td>23.0</td>\n",
" <td>14.0</td>\n",
" <td>15.0</td>\n",
" <td>8.0</td>\n",
" <td>...</td>\n",
" <td>23331.0</td>\n",
" <td>536455.0</td>\n",
" <td>682925.0</td>\n",
" <td>580620.0</td>\n",
" <td>108080.0</td>\n",
" <td>19087.0</td>\n",
" <td>11590.0</td>\n",
" <td>8346.0</td>\n",
" <td>6245.0</td>\n",
" <td>4837.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>64839</th>\n",
" <td>(1)_Ceres_de.wikipedia.org_desktop_all-agents</td>\n",
" <td>404.0</td>\n",
" <td>380.0</td>\n",
" <td>293.0</td>\n",
" <td>229.0</td>\n",
" <td>224.0</td>\n",
" <td>398.0</td>\n",
" <td>401.0</td>\n",
" <td>380.0</td>\n",
" <td>397.0</td>\n",
" <td>...</td>\n",
" <td>170.0</td>\n",
" <td>98.0</td>\n",
" <td>84.0</td>\n",
" <td>108.0</td>\n",
" <td>123.0</td>\n",
" <td>151.0</td>\n",
" <td>179.0</td>\n",
" <td>173.0</td>\n",
" <td>152.0</td>\n",
" <td>137.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>23466</th>\n",
" <td>(236984)_Astier_fr.wikipedia.org_all-access_al...</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>...</td>\n",
" <td>49.0</td>\n",
" <td>48.0</td>\n",
" <td>35.0</td>\n",
" <td>42.0</td>\n",
" <td>26.0</td>\n",
" <td>126.0</td>\n",
" <td>40.0</td>\n",
" <td>45.0</td>\n",
" <td>48.0</td>\n",
" <td>33.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>129577</th>\n",
" <td>(236984)_Astier_fr.wikipedia.org_all-access_sp...</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>...</td>\n",
" <td>2.0</td>\n",
" <td>3.0</td>\n",
" <td>3.0</td>\n",
" <td>4.0</td>\n",
" <td>3.0</td>\n",
" <td>6.0</td>\n",
" <td>3.0</td>\n",
" <td>5.0</td>\n",
" <td>6.0</td>\n",
" <td>7.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>...</th>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3471</th>\n",
" <td>龍千玉_zh.wikipedia.org_all-access_spider</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>...</td>\n",
" <td>1.0</td>\n",
" <td>11.0</td>\n",
" <td>3.0</td>\n",
" <td>6.0</td>\n",
" <td>8.0</td>\n",
" <td>5.0</td>\n",
" <td>4.0</td>\n",
" <td>5.0</td>\n",
" <td>6.0</td>\n",
" <td>7.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>63993</th>\n",
" <td>龍千玉_zh.wikipedia.org_desktop_all-agents</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>1.0</td>\n",
" <td>0.0</td>\n",
" <td>...</td>\n",
" <td>42.0</td>\n",
" <td>36.0</td>\n",
" <td>25.0</td>\n",
" <td>48.0</td>\n",
" <td>54.0</td>\n",
" <td>37.0</td>\n",
" <td>25.0</td>\n",
" <td>21.0</td>\n",
" <td>40.0</td>\n",
" <td>28.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>108626</th>\n",
" <td>龍千玉_zh.wikipedia.org_mobile-web_all-agents</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>...</td>\n",
" <td>31.0</td>\n",
" <td>28.0</td>\n",
" <td>30.0</td>\n",
" <td>48.0</td>\n",
" <td>14.0</td>\n",
" <td>20.0</td>\n",
" <td>20.0</td>\n",
" <td>12.0</td>\n",
" <td>17.0</td>\n",
" <td>30.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>28920</th>\n",
" <td>龍應台_zh.wikipedia.org_all-access_all-agents</td>\n",
" <td>217.0</td>\n",
" <td>252.0</td>\n",
" <td>225.0</td>\n",
" <td>203.0</td>\n",
" <td>206.0</td>\n",
" <td>211.0</td>\n",
" <td>224.0</td>\n",
" <td>219.0</td>\n",
" <td>212.0</td>\n",
" <td>...</td>\n",
" <td>1442.0</td>\n",
" <td>1502.0</td>\n",
" <td>715.0</td>\n",
" <td>660.0</td>\n",
" <td>670.0</td>\n",
" <td>580.0</td>\n",
" <td>570.0</td>\n",
" <td>441.0</td>\n",
" <td>371.0</td>\n",
" <td>325.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>972</th>\n",
" <td>龍應台_zh.wikipedia.org_all-access_spider</td>\n",
" <td>0.0</td>\n",
" <td>5.0</td>\n",
" <td>2.0</td>\n",
" <td>7.0</td>\n",
" <td>9.0</td>\n",
" <td>6.0</td>\n",
" <td>9.0</td>\n",
" <td>9.0</td>\n",
" <td>10.0</td>\n",
" <td>...</td>\n",
" <td>26.0</td>\n",
" <td>52.0</td>\n",
" <td>20.0</td>\n",
" <td>34.0</td>\n",
" <td>19.0</td>\n",
" <td>18.0</td>\n",
" <td>19.0</td>\n",
" <td>15.0</td>\n",
" <td>18.0</td>\n",
" <td>19.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>62211</th>\n",
" <td>龍應台_zh.wikipedia.org_desktop_all-agents</td>\n",
" <td>136.0</td>\n",
" <td>169.0</td>\n",
" <td>148.0</td>\n",
" <td>128.0</td>\n",
" <td>135.0</td>\n",
" <td>149.0</td>\n",
" <td>174.0</td>\n",
" <td>165.0</td>\n",
" <td>152.0</td>\n",
" <td>...</td>\n",
" <td>1043.0</td>\n",
" <td>1117.0</td>\n",
" <td>493.0</td>\n",
" <td>441.0</td>\n",
" <td>448.0</td>\n",
" <td>397.0</td>\n",
" <td>364.0</td>\n",
" <td>289.0</td>\n",
" <td>234.0</td>\n",
" <td>193.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>107130</th>\n",
" <td>龍抬頭_zh.wikipedia.org_mobile-web_all-agents</td>\n",
" <td>3.0</td>\n",
" <td>0.0</td>\n",
" <td>4.0</td>\n",
" <td>3.0</td>\n",
" <td>19.0</td>\n",
" <td>5.0</td>\n",
" <td>2.0</td>\n",
" <td>3.0</td>\n",
" <td>2.0</td>\n",
" <td>...</td>\n",
" <td>7.0</td>\n",
" <td>7.0</td>\n",
" <td>6.0</td>\n",
" <td>5.0</td>\n",
" <td>10.0</td>\n",
" <td>23.0</td>\n",
" <td>12.0</td>\n",
" <td>10.0</td>\n",
" <td>10.0</td>\n",
" <td>16.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>29237</th>\n",
" <td>龍涎香_zh.wikipedia.org_all-access_all-agents</td>\n",
" <td>88.0</td>\n",
" <td>65.0</td>\n",
" <td>46.0</td>\n",
" <td>61.0</td>\n",
" <td>96.0</td>\n",
" <td>308.0</td>\n",
" <td>4705.0</td>\n",
" <td>862.0</td>\n",
" <td>135.0</td>\n",
" <td>...</td>\n",
" <td>114.0</td>\n",
" <td>96.0</td>\n",
" <td>98.0</td>\n",
" <td>121.0</td>\n",
" <td>95.0</td>\n",
" <td>324.0</td>\n",
" <td>220.0</td>\n",
" <td>99.0</td>\n",
" <td>90.0</td>\n",
" <td>77.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1286</th>\n",
" <td>龍涎香_zh.wikipedia.org_all-access_spider</td>\n",
" <td>1.0</td>\n",
" <td>1.0</td>\n",
" <td>1.0</td>\n",
" <td>4.0</td>\n",
" <td>5.0</td>\n",
" <td>1.0</td>\n",
" <td>17.0</td>\n",
" <td>5.0</td>\n",
" <td>3.0</td>\n",
" <td>...</td>\n",
" <td>7.0</td>\n",
" <td>7.0</td>\n",
" <td>10.0</td>\n",
" <td>9.0</td>\n",
" <td>3.0</td>\n",
" <td>14.0</td>\n",
" <td>17.0</td>\n",
" <td>6.0</td>\n",
" <td>4.0</td>\n",
" <td>5.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>61849</th>\n",
" <td>龍涎香_zh.wikipedia.org_desktop_all-agents</td>\n",
" <td>58.0</td>\n",
" <td>46.0</td>\n",
" <td>29.0</td>\n",
" <td>32.0</td>\n",
" <td>51.0</td>\n",
" <td>140.0</td>\n",
" <td>1822.0</td>\n",
" <td>290.0</td>\n",
" <td>75.0</td>\n",
" <td>...</td>\n",
" <td>54.0</td>\n",
" <td>51.0</td>\n",
" <td>33.0</td>\n",
" <td>64.0</td>\n",
" <td>53.0</td>\n",
" <td>220.0</td>\n",
" <td>118.0</td>\n",
" <td>61.0</td>\n",
" <td>51.0</td>\n",
" <td>42.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>31445</th>\n",
" <td>龍珠超_zh.wikipedia.org_all-access_all-agents</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>...</td>\n",
" <td>1842.0</td>\n",
" <td>1405.0</td>\n",
" <td>1487.0</td>\n",
" <td>2581.0</td>\n",
" <td>1771.0</td>\n",
" <td>1543.0</td>\n",
" <td>1188.0</td>\n",
" <td>1054.0</td>\n",
" <td>1347.0</td>\n",
" <td>1292.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3479</th>\n",
" <td>龍珠超_zh.wikipedia.org_all-access_spider</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>...</td>\n",
" <td>481.0</td>\n",
" <td>215.0</td>\n",
" <td>137.0</td>\n",
" <td>221.0</td>\n",
" <td>113.0</td>\n",
" <td>107.0</td>\n",
" <td>48.0</td>\n",
" <td>31.0</td>\n",
" <td>188.0</td>\n",
" <td>113.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>64003</th>\n",
" <td>龍珠超_zh.wikipedia.org_desktop_all-agents</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>...</td>\n",
" <td>1461.0</td>\n",
" <td>1025.0</td>\n",
" <td>1029.0</td>\n",
" <td>1864.0</td>\n",
" <td>1265.0</td>\n",
" <td>1036.0</td>\n",
" <td>852.0</td>\n",
" <td>701.0</td>\n",
" <td>992.0</td>\n",
" <td>868.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>108638</th>\n",
" <td>龍珠超_zh.wikipedia.org_mobile-web_all-agents</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>...</td>\n",
" <td>371.0</td>\n",
" <td>372.0</td>\n",
" <td>447.0</td>\n",
" <td>710.0</td>\n",
" <td>495.0</td>\n",
" <td>498.0</td>\n",
" <td>331.0</td>\n",
" <td>349.0</td>\n",
" <td>348.0</td>\n",
" <td>419.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>30467</th>\n",
" <td>龐茲騙局_zh.wikipedia.org_all-access_all-agents</td>\n",
" <td>263.0</td>\n",
" <td>626.0</td>\n",
" <td>371.0</td>\n",
" <td>417.0</td>\n",
" <td>281.0</td>\n",
" <td>302.0</td>\n",
" <td>514.0</td>\n",
" <td>386.0</td>\n",
" <td>317.0</td>\n",
" <td>...</td>\n",
" <td>269.0</td>\n",
" <td>219.0</td>\n",
" <td>176.0</td>\n",
" <td>211.0</td>\n",
" <td>235.0</td>\n",
" <td>230.0</td>\n",
" <td>239.0</td>\n",
" <td>254.0</td>\n",
" <td>238.0</td>\n",
" <td>124.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2503</th>\n",
" <td>龐茲騙局_zh.wikipedia.org_all-access_spider</td>\n",
" <td>5.0</td>\n",
" <td>12.0</td>\n",
" <td>1.0</td>\n",
" <td>10.0</td>\n",
" <td>11.0</td>\n",
" <td>3.0</td>\n",
" <td>3.0</td>\n",
" <td>5.0</td>\n",
" <td>8.0</td>\n",
" <td>...</td>\n",
" <td>16.0</td>\n",
" <td>14.0</td>\n",
" <td>22.0</td>\n",
" <td>18.0</td>\n",
" <td>10.0</td>\n",
" <td>7.0</td>\n",
" <td>16.0</td>\n",
" <td>8.0</td>\n",
" <td>10.0</td>\n",
" <td>9.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>63028</th>\n",
" <td>龐茲騙局_zh.wikipedia.org_desktop_all-agents</td>\n",
" <td>198.0</td>\n",
" <td>463.0</td>\n",
" <td>286.0</td>\n",
" <td>210.0</td>\n",
" <td>186.0</td>\n",
" <td>225.0</td>\n",
" <td>363.0</td>\n",
" <td>295.0</td>\n",
" <td>202.0</td>\n",
" <td>...</td>\n",
" <td>176.0</td>\n",
" <td>133.0</td>\n",
" <td>87.0</td>\n",
" <td>120.0</td>\n",
" <td>142.0</td>\n",
" <td>158.0</td>\n",
" <td>147.0</td>\n",
" <td>160.0</td>\n",
" <td>138.0</td>\n",
" <td>66.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>29887</th>\n",
" <td>龔嘉欣_zh.wikipedia.org_all-access_all-agents</td>\n",
" <td>294.0</td>\n",
" <td>397.0</td>\n",
" <td>486.0</td>\n",
" <td>416.0</td>\n",
" <td>431.0</td>\n",
" <td>404.0</td>\n",
" <td>389.0</td>\n",
" <td>366.0</td>\n",
" <td>373.0</td>\n",
" <td>...</td>\n",
" <td>706.0</td>\n",
" <td>600.0</td>\n",
" <td>703.0</td>\n",
" <td>570.0</td>\n",
" <td>654.0</td>\n",
" <td>569.0</td>\n",
" <td>484.0</td>\n",
" <td>507.0</td>\n",
" <td>377.0</td>\n",
" <td>616.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1925</th>\n",
" <td>龔嘉欣_zh.wikipedia.org_all-access_spider</td>\n",
" <td>32.0</td>\n",
" <td>6.0</td>\n",
" <td>16.0</td>\n",
" <td>25.0</td>\n",
" <td>21.0</td>\n",
" <td>9.0</td>\n",
" <td>36.0</td>\n",
" <td>71.0</td>\n",
" <td>82.0</td>\n",
" <td>...</td>\n",
" <td>20.0</td>\n",
" <td>50.0</td>\n",
" <td>120.0</td>\n",
" <td>41.0</td>\n",
" <td>75.0</td>\n",
" <td>18.0</td>\n",
" <td>37.0</td>\n",
" <td>62.0</td>\n",
" <td>13.0</td>\n",
" <td>22.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>62487</th>\n",
" <td>龔嘉欣_zh.wikipedia.org_desktop_all-agents</td>\n",
" <td>166.0</td>\n",
" <td>226.0</td>\n",
" <td>283.0</td>\n",
" <td>281.0</td>\n",
" <td>248.0</td>\n",
" <td>264.0</td>\n",
" <td>257.0</td>\n",
" <td>241.0</td>\n",
" <td>275.0</td>\n",
" <td>...</td>\n",
" <td>366.0</td>\n",
" <td>298.0</td>\n",
" <td>409.0</td>\n",
" <td>243.0</td>\n",
" <td>318.0</td>\n",
" <td>259.0</td>\n",
" <td>231.0</td>\n",
" <td>275.0</td>\n",
" <td>200.0</td>\n",
" <td>267.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>107041</th>\n",
" <td>龔嘉欣_zh.wikipedia.org_mobile-web_all-agents</td>\n",
" <td>125.0</td>\n",
" <td>166.0</td>\n",
" <td>199.0</td>\n",
" <td>134.0</td>\n",
" <td>179.0</td>\n",
" <td>139.0</td>\n",
" <td>125.0</td>\n",
" <td>124.0</td>\n",
" <td>94.0</td>\n",
" <td>...</td>\n",
" <td>332.0</td>\n",
" <td>291.0</td>\n",
" <td>291.0</td>\n",
" <td>323.0</td>\n",
" <td>331.0</td>\n",
" <td>304.0</td>\n",
" <td>252.0</td>\n",
" <td>229.0</td>\n",
" <td>171.0</td>\n",
" <td>343.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>63079</th>\n",
" <td>龔照勝_zh.wikipedia.org_desktop_all-agents</td>\n",
" <td>10.0</td>\n",
" <td>5.0</td>\n",
" <td>4.0</td>\n",
" <td>25.0</td>\n",
" <td>6.0</td>\n",
" <td>5.0</td>\n",
" <td>5.0</td>\n",
" <td>1.0</td>\n",
" <td>4.0</td>\n",
" <td>...</td>\n",
" <td>18.0</td>\n",
" <td>9.0</td>\n",
" <td>8.0</td>\n",
" <td>7.0</td>\n",
" <td>7.0</td>\n",
" <td>8.0</td>\n",
" <td>6.0</td>\n",
" <td>16.0</td>\n",
" <td>45.0</td>\n",
" <td>12.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>108083</th>\n",
" <td>龙生九子_zh.wikipedia.org_mobile-web_all-agents</td>\n",
" <td>95.0</td>\n",
" <td>86.0</td>\n",
" <td>87.0</td>\n",
" <td>86.0</td>\n",
" <td>81.0</td>\n",
" <td>90.0</td>\n",
" <td>89.0</td>\n",
" <td>104.0</td>\n",
" <td>67.0</td>\n",
" <td>...</td>\n",
" <td>284.0</td>\n",
" <td>229.0</td>\n",
" <td>304.0</td>\n",
" <td>339.0</td>\n",
" <td>308.0</td>\n",
" <td>324.0</td>\n",
" <td>303.0</td>\n",
" <td>455.0</td>\n",
" <td>555.0</td>\n",
" <td>739.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>46228</th>\n",
" <td>대문_commons.wikimedia.org_all-access_all-agents</td>\n",
" <td>165.0</td>\n",
" <td>196.0</td>\n",
" <td>181.0</td>\n",
" <td>167.0</td>\n",
" <td>131.0</td>\n",
" <td>170.0</td>\n",
" <td>231.0</td>\n",
" <td>169.0</td>\n",
" <td>134.0</td>\n",
" <td>...</td>\n",
" <td>185.0</td>\n",
" <td>137.0</td>\n",
" <td>146.0</td>\n",
" <td>139.0</td>\n",
" <td>206.0</td>\n",
" <td>183.0</td>\n",
" <td>172.0</td>\n",
" <td>183.0</td>\n",
" <td>142.0</td>\n",
" <td>148.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>15739</th>\n",
" <td>대문_commons.wikimedia.org_all-access_spider</td>\n",
" <td>20.0</td>\n",
" <td>27.0</td>\n",
" <td>11.0</td>\n",
" <td>25.0</td>\n",
" <td>14.0</td>\n",
" <td>11.0</td>\n",
" <td>12.0</td>\n",
" <td>20.0</td>\n",
" <td>21.0</td>\n",
" <td>...</td>\n",
" <td>19.0</td>\n",
" <td>18.0</td>\n",
" <td>16.0</td>\n",
" <td>34.0</td>\n",
" <td>53.0</td>\n",
" <td>26.0</td>\n",
" <td>18.0</td>\n",
" <td>26.0</td>\n",
" <td>29.0</td>\n",
" <td>37.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>82846</th>\n",
" <td>대문_commons.wikimedia.org_desktop_all-agents</td>\n",
" <td>154.0</td>\n",
" <td>182.0</td>\n",
" <td>168.0</td>\n",
" <td>153.0</td>\n",
" <td>92.0</td>\n",
" <td>157.0</td>\n",
" <td>224.0</td>\n",
" <td>155.0</td>\n",
" <td>128.0</td>\n",
" <td>...</td>\n",
" <td>174.0</td>\n",
" <td>127.0</td>\n",
" <td>134.0</td>\n",
" <td>132.0</td>\n",
" <td>192.0</td>\n",
" <td>165.0</td>\n",
" <td>159.0</td>\n",
" <td>172.0</td>\n",
" <td>130.0</td>\n",
" <td>137.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>121217</th>\n",
" <td>［Alexandros］_ja.wikipedia.org_all-access_all-a...</td>\n",
" <td>5438.0</td>\n",
" <td>5966.0</td>\n",
" <td>5727.0</td>\n",
" <td>7036.0</td>\n",
" <td>7078.0</td>\n",
" <td>5009.0</td>\n",
" <td>4559.0</td>\n",
" <td>4544.0</td>\n",
" <td>4191.0</td>\n",
" <td>...</td>\n",
" <td>2645.0</td>\n",
" <td>3340.0</td>\n",
" <td>3299.0</td>\n",
" <td>3269.0</td>\n",
" <td>2838.0</td>\n",
" <td>3149.0</td>\n",
" <td>3268.0</td>\n",
" <td>4228.0</td>\n",
" <td>3585.0</td>\n",
" <td>4179.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>134478</th>\n",
" <td>［Alexandros］_ja.wikipedia.org_all-access_spider</td>\n",
" <td>4.0</td>\n",
" <td>4.0</td>\n",
" <td>3.0</td>\n",
" <td>6.0</td>\n",
" <td>9.0</td>\n",
" <td>4.0</td>\n",
" <td>9.0</td>\n",
" <td>7.0</td>\n",
" <td>5.0</td>\n",
" <td>...</td>\n",
" <td>198.0</td>\n",
" <td>250.0</td>\n",
" <td>198.0</td>\n",
" <td>226.0</td>\n",
" <td>180.0</td>\n",
" <td>193.0</td>\n",
" <td>233.0</td>\n",
" <td>277.0</td>\n",
" <td>226.0</td>\n",
" <td>287.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>87354</th>\n",
" <td>［Alexandros］_ja.wikipedia.org_desktop_all-agents</td>\n",
" <td>1077.0</td>\n",
" <td>1210.0</td>\n",
" <td>1080.0</td>\n",
" <td>1285.0</td>\n",
" <td>1296.0</td>\n",
" <td>1105.0</td>\n",
" <td>969.0</td>\n",
" <td>1004.0</td>\n",
" <td>985.0</td>\n",
" <td>...</td>\n",
" <td>734.0</td>\n",
" <td>771.0</td>\n",
" <td>790.0</td>\n",
" <td>850.0</td>\n",
" <td>762.0</td>\n",
" <td>804.0</td>\n",
" <td>805.0</td>\n",
" <td>904.0</td>\n",
" <td>786.0</td>\n",
" <td>761.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>58147</th>\n",
" <td>［Alexandros］_ja.wikipedia.org_mobile-web_all-a...</td>\n",
" <td>4345.0</td>\n",
" <td>4737.0</td>\n",
" <td>4633.0</td>\n",
" <td>5737.0</td>\n",
" <td>5765.0</td>\n",
" <td>3889.0</td>\n",
" <td>3579.0</td>\n",
" <td>3530.0</td>\n",
" <td>3199.0</td>\n",
" <td>...</td>\n",
" <td>1900.0</td>\n",
" <td>2559.0</td>\n",
" <td>2500.0</td>\n",
" <td>2409.0</td>\n",
" <td>2067.0</td>\n",
" <td>2336.0</td>\n",
" <td>2455.0</td>\n",
" <td>3302.0</td>\n",
" <td>2789.0</td>\n",
" <td>3410.0</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"<p>145063 rows × 551 columns</p>\n",
"</div>"
],
"text/plain": [
" Page 2015-07-01 \\\n",
"37206 !vote_en.wikipedia.org_all-access_all-agents 3.0 \n",
"32275 !vote_en.wikipedia.org_all-access_spider 0.0 \n",
"8357 !vote_en.wikipedia.org_desktop_all-agents 3.0 \n",
"37207 \"Awaken,_My_Love!\"_en.wikipedia.org_all-access... 0.0 \n",
"32276 \"Awaken,_My_Love!\"_en.wikipedia.org_all-access... 0.0 \n",
"8358 \"Awaken,_My_Love!\"_en.wikipedia.org_desktop_al... 0.0 \n",
"37208 \"European_Society_for_Clinical_Investigation\"_... 0.0 \n",
"32277 \"European_Society_for_Clinical_Investigation\"_... 0.0 \n",
"8359 \"European_Society_for_Clinical_Investigation\"_... 0.0 \n",
"39669 \"Heroes\"_(David_Bowie_album)_en.wikipedia.org_... 0.0 \n",
"35987 \"Heroes\"_(David_Bowie_album)_en.wikipedia.org_... 0.0 \n",
"12170 \"Heroes\"_(David_Bowie_album)_en.wikipedia.org_... 0.0 \n",
"19611 \"Keep_me_logged_in\"_extended_to_one_year_www.m... 0.0 \n",
"82986 \"Keep_me_logged_in\"_extended_to_one_year_www.m... 0.0 \n",
"42124 \"Keep_me_logged_in\"_extended_to_one_year_www.m... 0.0 \n",
"23341 \"Keep_me_logged_in\"_extended_to_one_year_www.m... 0.0 \n",
"37209 \"Weird_Al\"_Yankovic_en.wikipedia.org_all-acces... 3966.0 \n",
"32278 \"Weird_Al\"_Yankovic_en.wikipedia.org_all-acces... 144.0 \n",
"8360 \"Weird_Al\"_Yankovic_en.wikipedia.org_desktop_a... 2403.0 \n",
"22945 %s_www.mediawiki.org_mobile-web_all-agents 0.0 \n",
"39670 'Tis_the_Season_(Vince_Gill_and_Olivia_Newton-... 15.0 \n",
"34746 'Tis_the_Season_(Vince_Gill_and_Olivia_Newton-... 9.0 \n",
"75165 'Tis_the_Season_(Vince_Gill_and_Olivia_Newton-... 9.0 \n",
"37210 'Tis_the_Season_en.wikipedia.org_all-access_al... 34.0 \n",
"34745 'Tis_the_Season_en.wikipedia.org_all-access_sp... 6.0 \n",
"10893 'Tis_the_Season_en.wikipedia.org_desktop_all-a... 29.0 \n",
"72749 'Tis_the_Season_en.wikipedia.org_mobile-web_al... 5.0 \n",
"64839 (1)_Ceres_de.wikipedia.org_desktop_all-agents 404.0 \n",
"23466 (236984)_Astier_fr.wikipedia.org_all-access_al... 0.0 \n",
"129577 (236984)_Astier_fr.wikipedia.org_all-access_sp... 0.0 \n",
"... ... ... \n",
"3471 龍千玉_zh.wikipedia.org_all-access_spider 0.0 \n",
"63993 龍千玉_zh.wikipedia.org_desktop_all-agents 0.0 \n",
"108626 龍千玉_zh.wikipedia.org_mobile-web_all-agents 0.0 \n",
"28920 龍應台_zh.wikipedia.org_all-access_all-agents 217.0 \n",
"972 龍應台_zh.wikipedia.org_all-access_spider 0.0 \n",
"62211 龍應台_zh.wikipedia.org_desktop_all-agents 136.0 \n",
"107130 龍抬頭_zh.wikipedia.org_mobile-web_all-agents 3.0 \n",
"29237 龍涎香_zh.wikipedia.org_all-access_all-agents 88.0 \n",
"1286 龍涎香_zh.wikipedia.org_all-access_spider 1.0 \n",
"61849 龍涎香_zh.wikipedia.org_desktop_all-agents 58.0 \n",
"31445 龍珠超_zh.wikipedia.org_all-access_all-agents 0.0 \n",
"3479 龍珠超_zh.wikipedia.org_all-access_spider 0.0 \n",
"64003 龍珠超_zh.wikipedia.org_desktop_all-agents 0.0 \n",
"108638 龍珠超_zh.wikipedia.org_mobile-web_all-agents 0.0 \n",
"30467 龐茲騙局_zh.wikipedia.org_all-access_all-agents 263.0 \n",
"2503 龐茲騙局_zh.wikipedia.org_all-access_spider 5.0 \n",
"63028 龐茲騙局_zh.wikipedia.org_desktop_all-agents 198.0 \n",
"29887 龔嘉欣_zh.wikipedia.org_all-access_all-agents 294.0 \n",
"1925 龔嘉欣_zh.wikipedia.org_all-access_spider 32.0 \n",
"62487 龔嘉欣_zh.wikipedia.org_desktop_all-agents 166.0 \n",
"107041 龔嘉欣_zh.wikipedia.org_mobile-web_all-agents 125.0 \n",
"63079 龔照勝_zh.wikipedia.org_desktop_all-agents 10.0 \n",
"108083 龙生九子_zh.wikipedia.org_mobile-web_all-agents 95.0 \n",
"46228 대문_commons.wikimedia.org_all-access_all-agents 165.0 \n",
"15739 대문_commons.wikimedia.org_all-access_spider 20.0 \n",
"82846 대문_commons.wikimedia.org_desktop_all-agents 154.0 \n",
"121217 ［Alexandros］_ja.wikipedia.org_all-access_all-a... 5438.0 \n",
"134478 ［Alexandros］_ja.wikipedia.org_all-access_spider 4.0 \n",
"87354 ［Alexandros］_ja.wikipedia.org_desktop_all-agents 1077.0 \n",
"58147 ［Alexandros］_ja.wikipedia.org_mobile-web_all-a... 4345.0 \n",
"\n",
" 2015-07-02 2015-07-03 2015-07-04 2015-07-05 2015-07-06 \\\n",
"37206 4.0 7.0 4.0 4.0 2.0 \n",
"32275 0.0 1.0 2.0 0.0 1.0 \n",
"8357 4.0 7.0 4.0 4.0 2.0 \n",
"37207 0.0 0.0 0.0 0.0 0.0 \n",
"32276 0.0 0.0 0.0 0.0 0.0 \n",
"8358 0.0 0.0 0.0 0.0 0.0 \n",
"37208 0.0 0.0 0.0 0.0 0.0 \n",
"32277 0.0 0.0 0.0 0.0 0.0 \n",
"8359 0.0 0.0 0.0 0.0 0.0 \n",
"39669 0.0 0.0 0.0 0.0 0.0 \n",
"35987 0.0 0.0 0.0 0.0 0.0 \n",
"12170 0.0 0.0 0.0 0.0 0.0 \n",
"19611 0.0 0.0 0.0 0.0 0.0 \n",
"82986 0.0 0.0 0.0 0.0 0.0 \n",
"42124 0.0 0.0 0.0 0.0 0.0 \n",
"23341 0.0 0.0 0.0 0.0 0.0 \n",
"37209 27052.0 3867.0 3117.0 3578.0 3331.0 \n",
"32278 91.0 44.0 40.0 29.0 25.0 \n",
"8360 20136.0 1850.0 1432.0 1351.0 1792.0 \n",
"22945 1.0 3.0 0.0 1.0 2.0 \n",
"39670 22.0 30.0 21.0 13.0 10.0 \n",
"34746 11.0 9.0 7.0 6.0 4.0 \n",
"75165 4.0 13.0 11.0 2.0 2.0 \n",
"37210 30.0 32.0 33.0 17.0 39.0 \n",
"34745 5.0 2.0 9.0 2.0 4.0 \n",
"10893 17.0 10.0 16.0 11.0 16.0 \n",
"72749 13.0 21.0 17.0 6.0 23.0 \n",
"64839 380.0 293.0 229.0 224.0 398.0 \n",
"23466 0.0 0.0 0.0 0.0 0.0 \n",
"129577 0.0 0.0 0.0 0.0 0.0 \n",
"... ... ... ... ... ... \n",
"3471 0.0 0.0 0.0 0.0 0.0 \n",
"63993 0.0 0.0 0.0 0.0 0.0 \n",
"108626 0.0 0.0 0.0 0.0 0.0 \n",
"28920 252.0 225.0 203.0 206.0 211.0 \n",
"972 5.0 2.0 7.0 9.0 6.0 \n",
"62211 169.0 148.0 128.0 135.0 149.0 \n",
"107130 0.0 4.0 3.0 19.0 5.0 \n",
"29237 65.0 46.0 61.0 96.0 308.0 \n",
"1286 1.0 1.0 4.0 5.0 1.0 \n",
"61849 46.0 29.0 32.0 51.0 140.0 \n",
"31445 0.0 0.0 0.0 0.0 0.0 \n",
"3479 0.0 0.0 0.0 0.0 0.0 \n",
"64003 0.0 0.0 0.0 0.0 0.0 \n",
"108638 0.0 0.0 0.0 0.0 0.0 \n",
"30467 626.0 371.0 417.0 281.0 302.0 \n",
"2503 12.0 1.0 10.0 11.0 3.0 \n",
"63028 463.0 286.0 210.0 186.0 225.0 \n",
"29887 397.0 486.0 416.0 431.0 404.0 \n",
"1925 6.0 16.0 25.0 21.0 9.0 \n",
"62487 226.0 283.0 281.0 248.0 264.0 \n",
"107041 166.0 199.0 134.0 179.0 139.0 \n",
"63079 5.0 4.0 25.0 6.0 5.0 \n",
"108083 86.0 87.0 86.0 81.0 90.0 \n",
"46228 196.0 181.0 167.0 131.0 170.0 \n",
"15739 27.0 11.0 25.0 14.0 11.0 \n",
"82846 182.0 168.0 153.0 92.0 157.0 \n",
"121217 5966.0 5727.0 7036.0 7078.0 5009.0 \n",
"134478 4.0 3.0 6.0 9.0 4.0 \n",
"87354 1210.0 1080.0 1285.0 1296.0 1105.0 \n",
"58147 4737.0 4633.0 5737.0 5765.0 3889.0 \n",
"\n",
" 2015-07-07 2015-07-08 2015-07-09 ... 2016-12-22 \\\n",
"37206 3.0 7.0 2.0 ... 3.0 \n",
"32275 0.0 0.0 2.0 ... 2.0 \n",
"8357 3.0 7.0 2.0 ... 3.0 \n",
"37207 0.0 0.0 0.0 ... 4923.0 \n",
"32276 0.0 0.0 0.0 ... 89.0 \n",
"8358 0.0 0.0 0.0 ... 2898.0 \n",
"37208 0.0 0.0 1.0 ... 1.0 \n",
"32277 0.0 0.0 0.0 ... 1.0 \n",
"8359 0.0 0.0 1.0 ... 1.0 \n",
"39669 0.0 0.0 0.0 ... 731.0 \n",
"35987 0.0 0.0 0.0 ... 26.0 \n",
"12170 0.0 0.0 0.0 ... 483.0 \n",
"19611 0.0 0.0 0.0 ... 17.0 \n",
"82986 0.0 0.0 0.0 ... 1.0 \n",
"42124 0.0 0.0 0.0 ... 10.0 \n",
"23341 0.0 0.0 0.0 ... 7.0 \n",
"37209 3041.0 3538.0 3298.0 ... 2512.0 \n",
"32278 49.0 97.0 239.0 ... 61.0 \n",
"8360 1710.0 2039.0 1957.0 ... 1401.0 \n",
"22945 0.0 0.0 1.0 ... 0.0 \n",
"39670 15.0 28.0 16.0 ... 6763.0 \n",
"34746 4.0 5.0 8.0 ... 25.0 \n",
"75165 6.0 7.0 3.0 ... 4632.0 \n",
"37210 31.0 23.0 28.0 ... 35128.0 \n",
"34745 4.0 3.0 3.0 ... 162.0 \n",
"10893 17.0 8.0 20.0 ... 11646.0 \n",
"72749 14.0 15.0 8.0 ... 23331.0 \n",
"64839 401.0 380.0 397.0 ... 170.0 \n",
"23466 0.0 0.0 0.0 ... 49.0 \n",
"129577 0.0 0.0 0.0 ... 2.0 \n",
"... ... ... ... ... ... \n",
"3471 0.0 0.0 0.0 ... 1.0 \n",
"63993 0.0 1.0 0.0 ... 42.0 \n",
"108626 0.0 0.0 0.0 ... 31.0 \n",
"28920 224.0 219.0 212.0 ... 1442.0 \n",
"972 9.0 9.0 10.0 ... 26.0 \n",
"62211 174.0 165.0 152.0 ... 1043.0 \n",
"107130 2.0 3.0 2.0 ... 7.0 \n",
"29237 4705.0 862.0 135.0 ... 114.0 \n",
"1286 17.0 5.0 3.0 ... 7.0 \n",
"61849 1822.0 290.0 75.0 ... 54.0 \n",
"31445 0.0 0.0 0.0 ... 1842.0 \n",
"3479 0.0 0.0 0.0 ... 481.0 \n",
"64003 0.0 0.0 0.0 ... 1461.0 \n",
"108638 0.0 0.0 0.0 ... 371.0 \n",
"30467 514.0 386.0 317.0 ... 269.0 \n",
"2503 3.0 5.0 8.0 ... 16.0 \n",
"63028 363.0 295.0 202.0 ... 176.0 \n",
"29887 389.0 366.0 373.0 ... 706.0 \n",
"1925 36.0 71.0 82.0 ... 20.0 \n",
"62487 257.0 241.0 275.0 ... 366.0 \n",
"107041 125.0 124.0 94.0 ... 332.0 \n",
"63079 5.0 1.0 4.0 ... 18.0 \n",
"108083 89.0 104.0 67.0 ... 284.0 \n",
"46228 231.0 169.0 134.0 ... 185.0 \n",
"15739 12.0 20.0 21.0 ... 19.0 \n",
"82846 224.0 155.0 128.0 ... 174.0 \n",
"121217 4559.0 4544.0 4191.0 ... 2645.0 \n",
"134478 9.0 7.0 5.0 ... 198.0 \n",
"87354 969.0 1004.0 985.0 ... 734.0 \n",
"58147 3579.0 3530.0 3199.0 ... 1900.0 \n",
"\n",
" 2016-12-23 2016-12-24 2016-12-25 2016-12-26 2016-12-27 \\\n",
"37206 1.0 6.0 3.0 1.0 1.0 \n",
"32275 0.0 2.0 2.0 0.0 0.0 \n",
"8357 1.0 6.0 3.0 1.0 1.0 \n",
"37207 5074.0 4149.0 4209.0 3783.0 3994.0 \n",
"32276 12.0 63.0 15.0 38.0 14.0 \n",
"8358 2676.0 2032.0 2294.0 2074.0 2175.0 \n",
"37208 0.0 0.0 0.0 0.0 0.0 \n",
"32277 0.0 0.0 0.0 0.0 0.0 \n",
"8359 0.0 0.0 0.0 0.0 0.0 \n",
"39669 679.0 625.0 595.0 767.0 986.0 \n",
"35987 22.0 13.0 11.0 10.0 11.0 \n",
"12170 462.0 371.0 349.0 475.0 578.0 \n",
"19611 18.0 11.0 15.0 11.0 15.0 \n",
"82986 1.0 2.0 0.0 0.0 1.0 \n",
"42124 15.0 6.0 11.0 6.0 11.0 \n",
"23341 3.0 5.0 4.0 5.0 4.0 \n",
"37209 2422.0 2193.0 2209.0 2248.0 3144.0 \n",
"32278 66.0 76.0 47.0 62.0 61.0 \n",
"8360 1238.0 976.0 810.0 989.0 1332.0 \n",
"22945 0.0 0.0 0.0 0.0 0.0 \n",
"39670 125432.0 167369.0 187423.0 47504.0 7357.0 \n",
"34746 335.0 126.0 92.0 74.0 47.0 \n",
"75165 82417.0 141056.0 173163.0 45088.0 6971.0 \n",
"37210 740695.0 790629.0 637395.0 116636.0 20534.0 \n",
"34745 521.0 326.0 175.0 265.0 40.0 \n",
"10893 201406.0 103427.0 53036.0 7560.0 1175.0 \n",
"72749 536455.0 682925.0 580620.0 108080.0 19087.0 \n",
"64839 98.0 84.0 108.0 123.0 151.0 \n",
"23466 48.0 35.0 42.0 26.0 126.0 \n",
"129577 3.0 3.0 4.0 3.0 6.0 \n",
"... ... ... ... ... ... \n",
"3471 11.0 3.0 6.0 8.0 5.0 \n",
"63993 36.0 25.0 48.0 54.0 37.0 \n",
"108626 28.0 30.0 48.0 14.0 20.0 \n",
"28920 1502.0 715.0 660.0 670.0 580.0 \n",
"972 52.0 20.0 34.0 19.0 18.0 \n",
"62211 1117.0 493.0 441.0 448.0 397.0 \n",
"107130 7.0 6.0 5.0 10.0 23.0 \n",
"29237 96.0 98.0 121.0 95.0 324.0 \n",
"1286 7.0 10.0 9.0 3.0 14.0 \n",
"61849 51.0 33.0 64.0 53.0 220.0 \n",
"31445 1405.0 1487.0 2581.0 1771.0 1543.0 \n",
"3479 215.0 137.0 221.0 113.0 107.0 \n",
"64003 1025.0 1029.0 1864.0 1265.0 1036.0 \n",
"108638 372.0 447.0 710.0 495.0 498.0 \n",
"30467 219.0 176.0 211.0 235.0 230.0 \n",
"2503 14.0 22.0 18.0 10.0 7.0 \n",
"63028 133.0 87.0 120.0 142.0 158.0 \n",
"29887 600.0 703.0 570.0 654.0 569.0 \n",
"1925 50.0 120.0 41.0 75.0 18.0 \n",
"62487 298.0 409.0 243.0 318.0 259.0 \n",
"107041 291.0 291.0 323.0 331.0 304.0 \n",
"63079 9.0 8.0 7.0 7.0 8.0 \n",
"108083 229.0 304.0 339.0 308.0 324.0 \n",
"46228 137.0 146.0 139.0 206.0 183.0 \n",
"15739 18.0 16.0 34.0 53.0 26.0 \n",
"82846 127.0 134.0 132.0 192.0 165.0 \n",
"121217 3340.0 3299.0 3269.0 2838.0 3149.0 \n",
"134478 250.0 198.0 226.0 180.0 193.0 \n",
"87354 771.0 790.0 850.0 762.0 804.0 \n",
"58147 2559.0 2500.0 2409.0 2067.0 2336.0 \n",
"\n",
" 2016-12-28 2016-12-29 2016-12-30 2016-12-31 \n",
"37206 4.0 3.0 1.0 1.0 \n",
"32275 1.0 1.0 1.0 0.0 \n",
"8357 3.0 3.0 1.0 0.0 \n",
"37207 3910.0 4006.0 3841.0 3517.0 \n",
"32276 59.0 40.0 35.0 45.0 \n",
"8358 2156.0 2302.0 2098.0 1770.0 \n",
"37208 0.0 0.0 1.0 2.0 \n",
"32277 0.0 0.0 1.0 0.0 \n",
"8359 0.0 0.0 1.0 2.0 \n",
"39669 838.0 962.0 993.0 881.0 \n",
"35987 6.0 42.0 8.0 14.0 \n",
"12170 547.0 651.0 659.0 549.0 \n",
"19611 10.0 14.0 7.0 12.0 \n",
"82986 0.0 1.0 1.0 1.0 \n",
"42124 6.0 10.0 3.0 7.0 \n",
"23341 4.0 4.0 4.0 5.0 \n",
"37209 2835.0 3084.0 2754.0 2969.0 \n",
"32278 62.0 525.0 52.0 79.0 \n",
"8360 1243.0 1681.0 1303.0 1098.0 \n",
"22945 0.0 0.0 0.0 0.0 \n",
"39670 5348.0 3917.0 3104.0 2506.0 \n",
"34746 36.0 19.0 15.0 41.0 \n",
"75165 5065.0 3687.0 2959.0 2358.0 \n",
"37210 12626.0 9160.0 6866.0 5282.0 \n",
"34745 44.0 81.0 72.0 22.0 \n",
"10893 830.0 669.0 502.0 321.0 \n",
"72749 11590.0 8346.0 6245.0 4837.0 \n",
"64839 179.0 173.0 152.0 137.0 \n",
"23466 40.0 45.0 48.0 33.0 \n",
"129577 3.0 5.0 6.0 7.0 \n",
"... ... ... ... ... \n",
"3471 4.0 5.0 6.0 7.0 \n",
"63993 25.0 21.0 40.0 28.0 \n",
"108626 20.0 12.0 17.0 30.0 \n",
"28920 570.0 441.0 371.0 325.0 \n",
"972 19.0 15.0 18.0 19.0 \n",
"62211 364.0 289.0 234.0 193.0 \n",
"107130 12.0 10.0 10.0 16.0 \n",
"29237 220.0 99.0 90.0 77.0 \n",
"1286 17.0 6.0 4.0 5.0 \n",
"61849 118.0 61.0 51.0 42.0 \n",
"31445 1188.0 1054.0 1347.0 1292.0 \n",
"3479 48.0 31.0 188.0 113.0 \n",
"64003 852.0 701.0 992.0 868.0 \n",
"108638 331.0 349.0 348.0 419.0 \n",
"30467 239.0 254.0 238.0 124.0 \n",
"2503 16.0 8.0 10.0 9.0 \n",
"63028 147.0 160.0 138.0 66.0 \n",
"29887 484.0 507.0 377.0 616.0 \n",
"1925 37.0 62.0 13.0 22.0 \n",
"62487 231.0 275.0 200.0 267.0 \n",
"107041 252.0 229.0 171.0 343.0 \n",
"63079 6.0 16.0 45.0 12.0 \n",
"108083 303.0 455.0 555.0 739.0 \n",
"46228 172.0 183.0 142.0 148.0 \n",
"15739 18.0 26.0 29.0 37.0 \n",
"82846 159.0 172.0 130.0 137.0 \n",
"121217 3268.0 4228.0 3585.0 4179.0 \n",
"134478 233.0 277.0 226.0 287.0 \n",
"87354 805.0 904.0 786.0 761.0 \n",
"58147 2455.0 3302.0 2789.0 3410.0 \n",
"\n",
"[145063 rows x 551 columns]"
]
},
"execution_count": 14,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df_train_0_order"
]
},
{
"cell_type": "code",
"execution_count": 15,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"df_test_order = df_test.sort_values('Page')"
]
},
{
"cell_type": "code",
"execution_count": 16,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>Page</th>\n",
" <th>Id</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>!vote_en.wikipedia.org_all-access_all-agents_2...</td>\n",
" <td>bf4edcf969af</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>!vote_en.wikipedia.org_all-access_all-agents_2...</td>\n",
" <td>929ed2bf52b9</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>!vote_en.wikipedia.org_all-access_all-agents_2...</td>\n",
" <td>ff29d0f51d5c</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>!vote_en.wikipedia.org_all-access_all-agents_2...</td>\n",
" <td>e98873359be6</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>!vote_en.wikipedia.org_all-access_all-agents_2...</td>\n",
" <td>fa012434263a</td>\n",
" </tr>\n",
" <tr>\n",
" <th>5</th>\n",
" <td>!vote_en.wikipedia.org_all-access_all-agents_2...</td>\n",
" <td>48f1e93517a2</td>\n",
" </tr>\n",
" <tr>\n",
" <th>6</th>\n",
" <td>!vote_en.wikipedia.org_all-access_all-agents_2...</td>\n",
" <td>5def418fcb36</td>\n",
" </tr>\n",
" <tr>\n",
" <th>7</th>\n",
" <td>!vote_en.wikipedia.org_all-access_all-agents_2...</td>\n",
" <td>77bd08134351</td>\n",
" </tr>\n",
" <tr>\n",
" <th>8</th>\n",
" <td>!vote_en.wikipedia.org_all-access_all-agents_2...</td>\n",
" <td>5889e6dbb16f</td>\n",
" </tr>\n",
" <tr>\n",
" <th>9</th>\n",
" <td>!vote_en.wikipedia.org_all-access_all-agents_2...</td>\n",
" <td>5f21fef1d764</td>\n",
" </tr>\n",
" <tr>\n",
" <th>10</th>\n",
" <td>!vote_en.wikipedia.org_all-access_all-agents_2...</td>\n",
" <td>6f07e1b8815a</td>\n",
" </tr>\n",
" <tr>\n",
" <th>11</th>\n",
" <td>!vote_en.wikipedia.org_all-access_all-agents_2...</td>\n",
" <td>228e54b5dea0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>12</th>\n",
" <td>!vote_en.wikipedia.org_all-access_all-agents_2...</td>\n",
" <td>da1b34963ed7</td>\n",
" </tr>\n",
" <tr>\n",
" <th>13</th>\n",
" <td>!vote_en.wikipedia.org_all-access_all-agents_2...</td>\n",
" <td>ab5ccefaa2db</td>\n",
" </tr>\n",
" <tr>\n",
" <th>14</th>\n",
" <td>!vote_en.wikipedia.org_all-access_all-agents_2...</td>\n",
" <td>cbf42873ebf1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>15</th>\n",
" <td>!vote_en.wikipedia.org_all-access_all-agents_2...</td>\n",
" <td>ac67e35ed44e</td>\n",
" </tr>\n",
" <tr>\n",
" <th>16</th>\n",
" <td>!vote_en.wikipedia.org_all-access_all-agents_2...</td>\n",
" <td>88c098aa640d</td>\n",
" </tr>\n",
" <tr>\n",
" <th>17</th>\n",
" <td>!vote_en.wikipedia.org_all-access_all-agents_2...</td>\n",
" <td>7c72842a89d1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>18</th>\n",
" <td>!vote_en.wikipedia.org_all-access_all-agents_2...</td>\n",
" <td>8ce002f2c329</td>\n",
" </tr>\n",
" <tr>\n",
" <th>19</th>\n",
" <td>!vote_en.wikipedia.org_all-access_all-agents_2...</td>\n",
" <td>5f72d9920560</td>\n",
" </tr>\n",
" <tr>\n",
" <th>20</th>\n",
" <td>!vote_en.wikipedia.org_all-access_all-agents_2...</td>\n",
" <td>f93afd7f5d9b</td>\n",
" </tr>\n",
" <tr>\n",
" <th>21</th>\n",
" <td>!vote_en.wikipedia.org_all-access_all-agents_2...</td>\n",
" <td>14011cb66f2d</td>\n",
" </tr>\n",
" <tr>\n",
" <th>22</th>\n",
" <td>!vote_en.wikipedia.org_all-access_all-agents_2...</td>\n",
" <td>0065551ac465</td>\n",
" </tr>\n",
" <tr>\n",
" <th>23</th>\n",
" <td>!vote_en.wikipedia.org_all-access_all-agents_2...</td>\n",
" <td>175f1872729e</td>\n",
" </tr>\n",
" <tr>\n",
" <th>24</th>\n",
" <td>!vote_en.wikipedia.org_all-access_all-agents_2...</td>\n",
" <td>31d756e83124</td>\n",
" </tr>\n",
" <tr>\n",
" <th>25</th>\n",
" <td>!vote_en.wikipedia.org_all-access_all-agents_2...</td>\n",
" <td>e186c2363c5e</td>\n",
" </tr>\n",
" <tr>\n",
" <th>26</th>\n",
" <td>!vote_en.wikipedia.org_all-access_all-agents_2...</td>\n",
" <td>3bce56c2b977</td>\n",
" </tr>\n",
" <tr>\n",
" <th>27</th>\n",
" <td>!vote_en.wikipedia.org_all-access_all-agents_2...</td>\n",
" <td>d497981dce77</td>\n",
" </tr>\n",
" <tr>\n",
" <th>28</th>\n",
" <td>!vote_en.wikipedia.org_all-access_all-agents_2...</td>\n",
" <td>c813cec10548</td>\n",
" </tr>\n",
" <tr>\n",
" <th>29</th>\n",
" <td>!vote_en.wikipedia.org_all-access_all-agents_2...</td>\n",
" <td>5123e0ed62c9</td>\n",
" </tr>\n",
" <tr>\n",
" <th>...</th>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3330</th>\n",
" <td>［Alexandros］_ja.wikipedia.org_mobile-web_all-a...</td>\n",
" <td>a49077fb4250</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3331</th>\n",
" <td>［Alexandros］_ja.wikipedia.org_mobile-web_all-a...</td>\n",
" <td>b41e15bdce94</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3332</th>\n",
" <td>［Alexandros］_ja.wikipedia.org_mobile-web_all-a...</td>\n",
" <td>20befbc08a48</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3333</th>\n",
" <td>［Alexandros］_ja.wikipedia.org_mobile-web_all-a...</td>\n",
" <td>aed89c02c2bf</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3334</th>\n",
" <td>［Alexandros］_ja.wikipedia.org_mobile-web_all-a...</td>\n",
" <td>752fa508765f</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3335</th>\n",
" <td>［Alexandros］_ja.wikipedia.org_mobile-web_all-a...</td>\n",
" <td>b87fa58711d4</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3336</th>\n",
" <td>［Alexandros］_ja.wikipedia.org_mobile-web_all-a...</td>\n",
" <td>babdf0ab9c17</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3337</th>\n",
" <td>［Alexandros］_ja.wikipedia.org_mobile-web_all-a...</td>\n",
" <td>a31ff39e65b2</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3338</th>\n",
" <td>［Alexandros］_ja.wikipedia.org_mobile-web_all-a...</td>\n",
" <td>f6f755a4784f</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3339</th>\n",
" <td>［Alexandros］_ja.wikipedia.org_mobile-web_all-a...</td>\n",
" <td>07373bc81000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3340</th>\n",
" <td>［Alexandros］_ja.wikipedia.org_mobile-web_all-a...</td>\n",
" <td>1a716410ef10</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3341</th>\n",
" <td>［Alexandros］_ja.wikipedia.org_mobile-web_all-a...</td>\n",
" <td>1c981138b1d0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3342</th>\n",
" <td>［Alexandros］_ja.wikipedia.org_mobile-web_all-a...</td>\n",
" <td>835e9921004f</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3343</th>\n",
" <td>［Alexandros］_ja.wikipedia.org_mobile-web_all-a...</td>\n",
" <td>e80d82f38da3</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3344</th>\n",
" <td>［Alexandros］_ja.wikipedia.org_mobile-web_all-a...</td>\n",
" <td>c5eb03cebdf0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3345</th>\n",
" <td>［Alexandros］_ja.wikipedia.org_mobile-web_all-a...</td>\n",
" <td>7abc0d37b8df</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3346</th>\n",
" <td>［Alexandros］_ja.wikipedia.org_mobile-web_all-a...</td>\n",
" <td>e27c1d7bca11</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3347</th>\n",
" <td>［Alexandros］_ja.wikipedia.org_mobile-web_all-a...</td>\n",
" <td>07a407b27394</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3348</th>\n",
" <td>［Alexandros］_ja.wikipedia.org_mobile-web_all-a...</td>\n",
" <td>43f0e83c0d8f</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3349</th>\n",
" <td>［Alexandros］_ja.wikipedia.org_mobile-web_all-a...</td>\n",
" <td>7ff0d300f855</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3350</th>\n",
" <td>［Alexandros］_ja.wikipedia.org_mobile-web_all-a...</td>\n",
" <td>fec442127f20</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3351</th>\n",
" <td>［Alexandros］_ja.wikipedia.org_mobile-web_all-a...</td>\n",
" <td>9cab502f0c43</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3352</th>\n",
" <td>［Alexandros］_ja.wikipedia.org_mobile-web_all-a...</td>\n",
" <td>f8c721fdecde</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3353</th>\n",
" <td>［Alexandros］_ja.wikipedia.org_mobile-web_all-a...</td>\n",
" <td>5837c7aa7bba</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3354</th>\n",
" <td>［Alexandros］_ja.wikipedia.org_mobile-web_all-a...</td>\n",
" <td>857c39e7e7ea</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3355</th>\n",
" <td>［Alexandros］_ja.wikipedia.org_mobile-web_all-a...</td>\n",
" <td>4424f444c1e4</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3356</th>\n",
" <td>［Alexandros］_ja.wikipedia.org_mobile-web_all-a...</td>\n",
" <td>c1c570aa56ff</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3357</th>\n",
" <td>［Alexandros］_ja.wikipedia.org_mobile-web_all-a...</td>\n",
" <td>b00b33e14c90</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3358</th>\n",
" <td>［Alexandros］_ja.wikipedia.org_mobile-web_all-a...</td>\n",
" <td>20f0042d160c</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3359</th>\n",
" <td>［Alexandros］_ja.wikipedia.org_mobile-web_all-a...</td>\n",
" <td>75133adcc863</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"<p>8703780 rows × 2 columns</p>\n",
"</div>"
],
"text/plain": [
" Page Id\n",
"0 !vote_en.wikipedia.org_all-access_all-agents_2... bf4edcf969af\n",
"1 !vote_en.wikipedia.org_all-access_all-agents_2... 929ed2bf52b9\n",
"2 !vote_en.wikipedia.org_all-access_all-agents_2... ff29d0f51d5c\n",
"3 !vote_en.wikipedia.org_all-access_all-agents_2... e98873359be6\n",
"4 !vote_en.wikipedia.org_all-access_all-agents_2... fa012434263a\n",
"5 !vote_en.wikipedia.org_all-access_all-agents_2... 48f1e93517a2\n",
"6 !vote_en.wikipedia.org_all-access_all-agents_2... 5def418fcb36\n",
"7 !vote_en.wikipedia.org_all-access_all-agents_2... 77bd08134351\n",
"8 !vote_en.wikipedia.org_all-access_all-agents_2... 5889e6dbb16f\n",
"9 !vote_en.wikipedia.org_all-access_all-agents_2... 5f21fef1d764\n",
"10 !vote_en.wikipedia.org_all-access_all-agents_2... 6f07e1b8815a\n",
"11 !vote_en.wikipedia.org_all-access_all-agents_2... 228e54b5dea0\n",
"12 !vote_en.wikipedia.org_all-access_all-agents_2... da1b34963ed7\n",
"13 !vote_en.wikipedia.org_all-access_all-agents_2... ab5ccefaa2db\n",
"14 !vote_en.wikipedia.org_all-access_all-agents_2... cbf42873ebf1\n",
"15 !vote_en.wikipedia.org_all-access_all-agents_2... ac67e35ed44e\n",
"16 !vote_en.wikipedia.org_all-access_all-agents_2... 88c098aa640d\n",
"17 !vote_en.wikipedia.org_all-access_all-agents_2... 7c72842a89d1\n",
"18 !vote_en.wikipedia.org_all-access_all-agents_2... 8ce002f2c329\n",
"19 !vote_en.wikipedia.org_all-access_all-agents_2... 5f72d9920560\n",
"20 !vote_en.wikipedia.org_all-access_all-agents_2... f93afd7f5d9b\n",
"21 !vote_en.wikipedia.org_all-access_all-agents_2... 14011cb66f2d\n",
"22 !vote_en.wikipedia.org_all-access_all-agents_2... 0065551ac465\n",
"23 !vote_en.wikipedia.org_all-access_all-agents_2... 175f1872729e\n",
"24 !vote_en.wikipedia.org_all-access_all-agents_2... 31d756e83124\n",
"25 !vote_en.wikipedia.org_all-access_all-agents_2... e186c2363c5e\n",
"26 !vote_en.wikipedia.org_all-access_all-agents_2... 3bce56c2b977\n",
"27 !vote_en.wikipedia.org_all-access_all-agents_2... d497981dce77\n",
"28 !vote_en.wikipedia.org_all-access_all-agents_2... c813cec10548\n",
"29 !vote_en.wikipedia.org_all-access_all-agents_2... 5123e0ed62c9\n",
"... ... ...\n",
"3330 ［Alexandros］_ja.wikipedia.org_mobile-web_all-a... a49077fb4250\n",
"3331 ［Alexandros］_ja.wikipedia.org_mobile-web_all-a... b41e15bdce94\n",
"3332 ［Alexandros］_ja.wikipedia.org_mobile-web_all-a... 20befbc08a48\n",
"3333 ［Alexandros］_ja.wikipedia.org_mobile-web_all-a... aed89c02c2bf\n",
"3334 ［Alexandros］_ja.wikipedia.org_mobile-web_all-a... 752fa508765f\n",
"3335 ［Alexandros］_ja.wikipedia.org_mobile-web_all-a... b87fa58711d4\n",
"3336 ［Alexandros］_ja.wikipedia.org_mobile-web_all-a... babdf0ab9c17\n",
"3337 ［Alexandros］_ja.wikipedia.org_mobile-web_all-a... a31ff39e65b2\n",
"3338 ［Alexandros］_ja.wikipedia.org_mobile-web_all-a... f6f755a4784f\n",
"3339 ［Alexandros］_ja.wikipedia.org_mobile-web_all-a... 07373bc81000\n",
"3340 ［Alexandros］_ja.wikipedia.org_mobile-web_all-a... 1a716410ef10\n",
"3341 ［Alexandros］_ja.wikipedia.org_mobile-web_all-a... 1c981138b1d0\n",
"3342 ［Alexandros］_ja.wikipedia.org_mobile-web_all-a... 835e9921004f\n",
"3343 ［Alexandros］_ja.wikipedia.org_mobile-web_all-a... e80d82f38da3\n",
"3344 ［Alexandros］_ja.wikipedia.org_mobile-web_all-a... c5eb03cebdf0\n",
"3345 ［Alexandros］_ja.wikipedia.org_mobile-web_all-a... 7abc0d37b8df\n",
"3346 ［Alexandros］_ja.wikipedia.org_mobile-web_all-a... e27c1d7bca11\n",
"3347 ［Alexandros］_ja.wikipedia.org_mobile-web_all-a... 07a407b27394\n",
"3348 ［Alexandros］_ja.wikipedia.org_mobile-web_all-a... 43f0e83c0d8f\n",
"3349 ［Alexandros］_ja.wikipedia.org_mobile-web_all-a... 7ff0d300f855\n",
"3350 ［Alexandros］_ja.wikipedia.org_mobile-web_all-a... fec442127f20\n",
"3351 ［Alexandros］_ja.wikipedia.org_mobile-web_all-a... 9cab502f0c43\n",
"3352 ［Alexandros］_ja.wikipedia.org_mobile-web_all-a... f8c721fdecde\n",
"3353 ［Alexandros］_ja.wikipedia.org_mobile-web_all-a... 5837c7aa7bba\n",
"3354 ［Alexandros］_ja.wikipedia.org_mobile-web_all-a... 857c39e7e7ea\n",
"3355 ［Alexandros］_ja.wikipedia.org_mobile-web_all-a... 4424f444c1e4\n",
"3356 ［Alexandros］_ja.wikipedia.org_mobile-web_all-a... c1c570aa56ff\n",
"3357 ［Alexandros］_ja.wikipedia.org_mobile-web_all-a... b00b33e14c90\n",
"3358 ［Alexandros］_ja.wikipedia.org_mobile-web_all-a... 20f0042d160c\n",
"3359 ［Alexandros］_ja.wikipedia.org_mobile-web_all-a... 75133adcc863\n",
"\n",
"[8703780 rows x 2 columns]"
]
},
"execution_count": 16,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df_test_order"
]
},
{
"cell_type": "code",
"execution_count": 17,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"<class 'pandas.core.frame.DataFrame'>\n",
"RangeIndex: 8703780 entries, 0 to 8703779\n",
"Data columns (total 2 columns):\n",
"Id object\n",
"Visits int64\n",
"dtypes: int64(1), object(1)\n",
"memory usage: 132.8+ MB\n"
]
}
],
"source": [
"df_ans.info()"
]
},
{
"cell_type": "code",
"execution_count": 17,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>Id</th>\n",
" <th>Visits</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>bf4edcf969af</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>929ed2bf52b9</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>ff29d0f51d5c</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>e98873359be6</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>fa012434263a</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>5</th>\n",
" <td>48f1e93517a2</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>6</th>\n",
" <td>5def418fcb36</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>7</th>\n",
" <td>77bd08134351</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>8</th>\n",
" <td>5889e6dbb16f</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>9</th>\n",
" <td>5f21fef1d764</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>10</th>\n",
" <td>6f07e1b8815a</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>11</th>\n",
" <td>228e54b5dea0</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>12</th>\n",
" <td>da1b34963ed7</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>13</th>\n",
" <td>ab5ccefaa2db</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>14</th>\n",
" <td>cbf42873ebf1</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>15</th>\n",
" <td>ac67e35ed44e</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>16</th>\n",
" <td>88c098aa640d</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>17</th>\n",
" <td>7c72842a89d1</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>18</th>\n",
" <td>8ce002f2c329</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>19</th>\n",
" <td>5f72d9920560</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>20</th>\n",
" <td>f93afd7f5d9b</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>21</th>\n",
" <td>14011cb66f2d</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>22</th>\n",
" <td>0065551ac465</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>23</th>\n",
" <td>175f1872729e</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>24</th>\n",
" <td>31d756e83124</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>25</th>\n",
" <td>e186c2363c5e</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>26</th>\n",
" <td>3bce56c2b977</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>27</th>\n",
" <td>d497981dce77</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>28</th>\n",
" <td>c813cec10548</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>29</th>\n",
" <td>5123e0ed62c9</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>...</th>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>8703750</th>\n",
" <td>1fb8f902ad0f</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>8703751</th>\n",
" <td>0107f6d7cd82</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>8703752</th>\n",
" <td>30c402ed9e49</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>8703753</th>\n",
" <td>935fa0168d01</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>8703754</th>\n",
" <td>1140b428380e</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>8703755</th>\n",
" <td>cc5eadae0d7a</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>8703756</th>\n",
" <td>f923701cdb05</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>8703757</th>\n",
" <td>905679a20d39</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>8703758</th>\n",
" <td>642354a50690</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>8703759</th>\n",
" <td>7376c63bd4c1</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>8703760</th>\n",
" <td>1f0566b71f7e</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>8703761</th>\n",
" <td>938774bbb675</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>8703762</th>\n",
" <td>53c046bac8cb</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>8703763</th>\n",
" <td>ead2377353d3</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>8703764</th>\n",
" <td>efa87c7d5160</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>8703765</th>\n",
" <td>f239d6ceb17b</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>8703766</th>\n",
" <td>0fef0826b1bc</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>8703767</th>\n",
" <td>478d3c34b0c1</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>8703768</th>\n",
" <td>6a1b6e3028fc</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>8703769</th>\n",
" <td>3b5fb022accd</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>8703770</th>\n",
" <td>a4456a9d271d</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>8703771</th>\n",
" <td>d43a25cf4ef2</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>8703772</th>\n",
" <td>8f47d2e020cd</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>8703773</th>\n",
" <td>a78af728d84b</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>8703774</th>\n",
" <td>d1ba45c7ec08</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>8703775</th>\n",
" <td>f69747f5ee68</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>8703776</th>\n",
" <td>2489963dc503</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>8703777</th>\n",
" <td>b0624c909f4c</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>8703778</th>\n",
" <td>24a1dfb06c10</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>8703779</th>\n",
" <td>add681d54216</td>\n",
" <td>0</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"<p>8703780 rows × 2 columns</p>\n",
"</div>"
],
"text/plain": [
" Id Visits\n",
"0 bf4edcf969af 0\n",
"1 929ed2bf52b9 0\n",
"2 ff29d0f51d5c 0\n",
"3 e98873359be6 0\n",
"4 fa012434263a 0\n",
"5 48f1e93517a2 0\n",
"6 5def418fcb36 0\n",
"7 77bd08134351 0\n",
"8 5889e6dbb16f 0\n",
"9 5f21fef1d764 0\n",
"10 6f07e1b8815a 0\n",
"11 228e54b5dea0 0\n",
"12 da1b34963ed7 0\n",
"13 ab5ccefaa2db 0\n",
"14 cbf42873ebf1 0\n",
"15 ac67e35ed44e 0\n",
"16 88c098aa640d 0\n",
"17 7c72842a89d1 0\n",
"18 8ce002f2c329 0\n",
"19 5f72d9920560 0\n",
"20 f93afd7f5d9b 0\n",
"21 14011cb66f2d 0\n",
"22 0065551ac465 0\n",
"23 175f1872729e 0\n",
"24 31d756e83124 0\n",
"25 e186c2363c5e 0\n",
"26 3bce56c2b977 0\n",
"27 d497981dce77 0\n",
"28 c813cec10548 0\n",
"29 5123e0ed62c9 0\n",
"... ... ...\n",
"8703750 1fb8f902ad0f 0\n",
"8703751 0107f6d7cd82 0\n",
"8703752 30c402ed9e49 0\n",
"8703753 935fa0168d01 0\n",
"8703754 1140b428380e 0\n",
"8703755 cc5eadae0d7a 0\n",
"8703756 f923701cdb05 0\n",
"8703757 905679a20d39 0\n",
"8703758 642354a50690 0\n",
"8703759 7376c63bd4c1 0\n",
"8703760 1f0566b71f7e 0\n",
"8703761 938774bbb675 0\n",
"8703762 53c046bac8cb 0\n",
"8703763 ead2377353d3 0\n",
"8703764 efa87c7d5160 0\n",
"8703765 f239d6ceb17b 0\n",
"8703766 0fef0826b1bc 0\n",
"8703767 478d3c34b0c1 0\n",
"8703768 6a1b6e3028fc 0\n",
"8703769 3b5fb022accd 0\n",
"8703770 a4456a9d271d 0\n",
"8703771 d43a25cf4ef2 0\n",
"8703772 8f47d2e020cd 0\n",
"8703773 a78af728d84b 0\n",
"8703774 d1ba45c7ec08 0\n",
"8703775 f69747f5ee68 0\n",
"8703776 2489963dc503 0\n",
"8703777 b0624c909f4c 0\n",
"8703778 24a1dfb06c10 0\n",
"8703779 add681d54216 0\n",
"\n",
"[8703780 rows x 2 columns]"
]
},
"execution_count": 17,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df_ans \n",
"#不過有個問題是這個Id可是沒有排序過的呢\n",
"#到時候按照page_list_order填上答案之後，在使用col=0的那一欄數字重新排位正確答案順序吧～"
]
},
{
"cell_type": "markdown",
"metadata": {
"collapsed": true
},
"source": [
"## 目標就是把有order每一組的60天預測數據做出來，然後fit到對應的id格子去"
]
},
{
"cell_type": "code",
"execution_count": 18,
"metadata": {},
"outputs": [],
"source": [
"list_train_order_value = df_train_0_order.values[:,1:] #除掉page那個欄位，取得純數字"
]
},
{
"cell_type": "code",
"execution_count": 19,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"array([[3.0, 4.0, 7.0, ..., 3.0, 1.0, 1.0],\n",
" [0.0, 0.0, 1.0, ..., 1.0, 1.0, 0.0],\n",
" [3.0, 4.0, 7.0, ..., 3.0, 1.0, 0.0],\n",
" ..., \n",
" [4.0, 4.0, 3.0, ..., 277.0, 226.0, 287.0],\n",
" [1077.0, 1210.0, 1080.0, ..., 904.0, 786.0, 761.0],\n",
" [4345.0, 4737.0, 4633.0, ..., 3302.0, 2789.0, 3410.0]], dtype=object)"
]
},
"execution_count": 19,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"list_train_order_value"
]
},
{
"cell_type": "code",
"execution_count": 24,
"metadata": {},
"outputs": [],
"source": [
"list_train_order_value = list_train_order_value.astype(int)"
]
},
{
"cell_type": "code",
"execution_count": 25,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"array([[ 3, 4, 7, ..., 3, 1, 1],\n",
" [ 0, 0, 1, ..., 1, 1, 0],\n",
" [ 3, 4, 7, ..., 3, 1, 0],\n",
" ..., \n",
" [ 4, 4, 3, ..., 277, 226, 287],\n",
" [1077, 1210, 1080, ..., 904, 786, 761],\n",
" [4345, 4737, 4633, ..., 3302, 2789, 3410]])"
]
},
"execution_count": 25,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"list_train_order_value "
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# 先跑一個Page的模型看看，取前面10天預測後面1天"
]
},
{
"cell_type": "code",
"execution_count": 32,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"page_data = list_train_order_value[0]"
]
},
{
"cell_type": "code",
"execution_count": 35,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"array([ 3, 4, 7, 4, 4, 2, 3, 7, 2, 0, 3, 1, 2, 2, 3, 2, 1,\n",
" 1, 1, 0, 6, 8, 5, 1, 2, 1, 2, 10, 4, 6, 4, 2, 1, 4,\n",
" 1, 2, 0, 0, 1, 0, 2, 3, 3, 1, 3, 3, 4, 1, 0, 3, 3,\n",
" 7, 4, 6, 14, 6, 5, 4, 7, 3, 2, 1, 3, 3, 2, 1, 1, 4,\n",
" 6, 3, 1, 3, 1, 3, 3, 1, 4, 4, 4, 5, 1, 3, 6, 3, 2,\n",
" 4, 4, 3, 3, 3, 2, 1, 0, 4, 7, 0, 6, 2, 2, 3, 2, 1,\n",
" 3, 1, 2, 1, 1, 4, 1, 6, 5, 3, 3, 9, 4, 6, 7, 8, 2,\n",
" 2, 4, 8, 6, 5, 5, 4, 2, 3, 5, 8, 7, 4, 10, 4, 1, 1,\n",
" 2, 14, 2, 4, 0, 5, 8, 9, 3, 4, 3, 5, 3, 3, 3, 1, 2,\n",
" 2, 2, 5, 2, 2, 1, 2, 2, 9, 3, 4, 4, 0, 4, 1, 3, 4,\n",
" 0, 3, 3, 1, 1, 2, 6, 4, 4, 10, 2, 4, 0, 3, 3, 2, 2,\n",
" 2, 3, 1, 1, 1, 4, 3, 0, 1, 2, 3, 2, 1, 1, 2, 2, 2,\n",
" 1, 2, 4, 2, 8, 4, 5, 6, 3, 4, 2, 9, 1, 11, 4, 2, 3,\n",
" 3, 1, 3, 2, 3, 1, 1, 1, 3, 3, 2, 4, 3, 1, 2, 1, 3,\n",
" 1, 4, 5, 4, 4, 0, 1, 4, 6, 5, 3, 5, 4, 6, 0, 3, 1,\n",
" 1, 2, 3, 3, 8, 6, 4, 13, 1, 12, 2, 9, 3, 8, 3, 3, 4,\n",
" 6, 4, 1, 33, 22, 3, 3, 8, 1, 4, 4, 6, 2, 4, 1, 2, 4,\n",
" 3, 3, 4, 2, 2, 1, 4, 2, 4, 4, 4, 3, 3, 3, 4, 3, 2,\n",
" 1, 0, 0, 2, 1, 4, 2, 2, 1, 3, 3, 3, 2, 2, 3, 1, 7,\n",
" 1, 4, 1, 2, 6, 6, 1, 3, 3, 4, 5, 2, 3, 3, 5, 10, 4,\n",
" 1, 6, 3, 3, 4, 3, 2, 2, 6, 0, 9, 2, 1, 5, 5, 5, 4,\n",
" 2, 6, 4, 1, 2, 2, 3, 3, 5, 5, 1, 3, 3, 3, 4, 2, 7,\n",
" 2, 0, 1, 2, 3, 2, 3, 2, 2, 2, 3, 3, 5, 1, 1, 10, 1,\n",
" 3, 6, 9, 3, 2, 1, 4, 8, 13, 3, 1, 6, 2, 3, 0, 4, 4,\n",
" 1, 1, 0, 4, 9, 1, 2, 3, 4, 3, 4, 2, 3, 8, 4, 3, 4,\n",
" 6, 2, 2, 9, 3, 3, 6, 3, 3, 4, 0, 2, 5, 1, 3, 3, 2,\n",
" 5, 1, 3, 5, 2, 5, 0, 3, 3, 1, 2, 7, 5, 2, 11, 3, 3,\n",
" 11, 7, 2, 5, 3, 1, 3, 4, 4, 1, 1, 3, 1, 1, 5, 3, 5,\n",
" 2, 15, 1, 5, 4, 5, 2, 2, 2, 5, 6, 3, 7, 1, 5, 4, 4,\n",
" 8, 5, 6, 6, 4, 4, 4, 2, 2, 4, 8, 3, 2, 1, 4, 3, 3,\n",
" 2, 2, 4, 2, 5, 4, 6, 7, 6, 1, 3, 3, 2, 2, 2, 1, 2,\n",
" 0, 2, 2, 3, 1, 2, 5, 3, 3, 2, 1, 3, 2, 3, 1, 6, 3,\n",
" 1, 1, 4, 3, 1, 1])"
]
},
"execution_count": 35,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"page_data"
]
},
{
"cell_type": "code",
"execution_count": 38,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"(550,)"
]
},
"execution_count": 38,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"page_data.shape"
]
},
{
"cell_type": "code",
"execution_count": 52,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"page_data_label = page_data[10:]"
]
},
{
"cell_type": "code",
"execution_count": 53,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"(540,)"
]
},
"execution_count": 53,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"page_data_label.shape"
]
},
{
"cell_type": "code",
"execution_count": 65,
"metadata": {},
"outputs": [],
"source": [
"train_feature = []\n",
"for i in range(540):\n",
" train_feature.append(page_data[i:i+10])"
]
},
{
"cell_type": "code",
"execution_count": 66,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"[array([3, 4, 7, 4, 4, 2, 3, 7, 2, 0]),\n",
" array([4, 7, 4, 4, 2, 3, 7, 2, 0, 3]),\n",
" array([7, 4, 4, 2, 3, 7, 2, 0, 3, 1]),\n",
" array([4, 4, 2, 3, 7, 2, 0, 3, 1, 2]),\n",
" array([4, 2, 3, 7, 2, 0, 3, 1, 2, 2]),\n",
" array([2, 3, 7, 2, 0, 3, 1, 2, 2, 3]),\n",
" array([3, 7, 2, 0, 3, 1, 2, 2, 3, 2]),\n",
" array([7, 2, 0, 3, 1, 2, 2, 3, 2, 1]),\n",
" array([2, 0, 3, 1, 2, 2, 3, 2, 1, 1]),\n",
" array([0, 3, 1, 2, 2, 3, 2, 1, 1, 1]),\n",
" array([3, 1, 2, 2, 3, 2, 1, 1, 1, 0]),\n",
" array([1, 2, 2, 3, 2, 1, 1, 1, 0, 6]),\n",
" array([2, 2, 3, 2, 1, 1, 1, 0, 6, 8]),\n",
" array([2, 3, 2, 1, 1, 1, 0, 6, 8, 5]),\n",
" array([3, 2, 1, 1, 1, 0, 6, 8, 5, 1]),\n",
" array([2, 1, 1, 1, 0, 6, 8, 5, 1, 2]),\n",
" array([1, 1, 1, 0, 6, 8, 5, 1, 2, 1]),\n",
" array([1, 1, 0, 6, 8, 5, 1, 2, 1, 2]),\n",
" array([ 1, 0, 6, 8, 5, 1, 2, 1, 2, 10]),\n",
" array([ 0, 6, 8, 5, 1, 2, 1, 2, 10, 4]),\n",
" array([ 6, 8, 5, 1, 2, 1, 2, 10, 4, 6]),\n",
" array([ 8, 5, 1, 2, 1, 2, 10, 4, 6, 4]),\n",
" array([ 5, 1, 2, 1, 2, 10, 4, 6, 4, 2]),\n",
" array([ 1, 2, 1, 2, 10, 4, 6, 4, 2, 1]),\n",
" array([ 2, 1, 2, 10, 4, 6, 4, 2, 1, 4]),\n",
" array([ 1, 2, 10, 4, 6, 4, 2, 1, 4, 1]),\n",
" array([ 2, 10, 4, 6, 4, 2, 1, 4, 1, 2]),\n",
" array([10, 4, 6, 4, 2, 1, 4, 1, 2, 0]),\n",
" array([4, 6, 4, 2, 1, 4, 1, 2, 0, 0]),\n",
" array([6, 4, 2, 1, 4, 1, 2, 0, 0, 1]),\n",
" array([4, 2, 1, 4, 1, 2, 0, 0, 1, 0]),\n",
" array([2, 1, 4, 1, 2, 0, 0, 1, 0, 2]),\n",
" array([1, 4, 1, 2, 0, 0, 1, 0, 2, 3]),\n",
" array([4, 1, 2, 0, 0, 1, 0, 2, 3, 3]),\n",
" array([1, 2, 0, 0, 1, 0, 2, 3, 3, 1]),\n",
" array([2, 0, 0, 1, 0, 2, 3, 3, 1, 3]),\n",
" array([0, 0, 1, 0, 2, 3, 3, 1, 3, 3]),\n",
" array([0, 1, 0, 2, 3, 3, 1, 3, 3, 4]),\n",
" array([1, 0, 2, 3, 3, 1, 3, 3, 4, 1]),\n",
" array([0, 2, 3, 3, 1, 3, 3, 4, 1, 0]),\n",
" array([2, 3, 3, 1, 3, 3, 4, 1, 0, 3]),\n",
" array([3, 3, 1, 3, 3, 4, 1, 0, 3, 3]),\n",
" array([3, 1, 3, 3, 4, 1, 0, 3, 3, 7]),\n",
" array([1, 3, 3, 4, 1, 0, 3, 3, 7, 4]),\n",
" array([3, 3, 4, 1, 0, 3, 3, 7, 4, 6]),\n",
" array([ 3, 4, 1, 0, 3, 3, 7, 4, 6, 14]),\n",
" array([ 4, 1, 0, 3, 3, 7, 4, 6, 14, 6]),\n",
" array([ 1, 0, 3, 3, 7, 4, 6, 14, 6, 5]),\n",
" array([ 0, 3, 3, 7, 4, 6, 14, 6, 5, 4]),\n",
" array([ 3, 3, 7, 4, 6, 14, 6, 5, 4, 7]),\n",
" array([ 3, 7, 4, 6, 14, 6, 5, 4, 7, 3]),\n",
" array([ 7, 4, 6, 14, 6, 5, 4, 7, 3, 2]),\n",
" array([ 4, 6, 14, 6, 5, 4, 7, 3, 2, 1]),\n",
" array([ 6, 14, 6, 5, 4, 7, 3, 2, 1, 3]),\n",
" array([14, 6, 5, 4, 7, 3, 2, 1, 3, 3]),\n",
" array([6, 5, 4, 7, 3, 2, 1, 3, 3, 2]),\n",
" array([5, 4, 7, 3, 2, 1, 3, 3, 2, 1]),\n",
" array([4, 7, 3, 2, 1, 3, 3, 2, 1, 1]),\n",
" array([7, 3, 2, 1, 3, 3, 2, 1, 1, 4]),\n",
" array([3, 2, 1, 3, 3, 2, 1, 1, 4, 6]),\n",
" array([2, 1, 3, 3, 2, 1, 1, 4, 6, 3]),\n",
" array([1, 3, 3, 2, 1, 1, 4, 6, 3, 1]),\n",
" array([3, 3, 2, 1, 1, 4, 6, 3, 1, 3]),\n",
" array([3, 2, 1, 1, 4, 6, 3, 1, 3, 1]),\n",
" array([2, 1, 1, 4, 6, 3, 1, 3, 1, 3]),\n",
" array([1, 1, 4, 6, 3, 1, 3, 1, 3, 3]),\n",
" array([1, 4, 6, 3, 1, 3, 1, 3, 3, 1]),\n",
" array([4, 6, 3, 1, 3, 1, 3, 3, 1, 4]),\n",
" array([6, 3, 1, 3, 1, 3, 3, 1, 4, 4]),\n",
" array([3, 1, 3, 1, 3, 3, 1, 4, 4, 4]),\n",
" array([1, 3, 1, 3, 3, 1, 4, 4, 4, 5]),\n",
" array([3, 1, 3, 3, 1, 4, 4, 4, 5, 1]),\n",
" array([1, 3, 3, 1, 4, 4, 4, 5, 1, 3]),\n",
" array([3, 3, 1, 4, 4, 4, 5, 1, 3, 6]),\n",
" array([3, 1, 4, 4, 4, 5, 1, 3, 6, 3]),\n",
" array([1, 4, 4, 4, 5, 1, 3, 6, 3, 2]),\n",
" array([4, 4, 4, 5, 1, 3, 6, 3, 2, 4]),\n",
" array([4, 4, 5, 1, 3, 6, 3, 2, 4, 4]),\n",
" array([4, 5, 1, 3, 6, 3, 2, 4, 4, 3]),\n",
" array([5, 1, 3, 6, 3, 2, 4, 4, 3, 3]),\n",
" array([1, 3, 6, 3, 2, 4, 4, 3, 3, 3]),\n",
" array([3, 6, 3, 2, 4, 4, 3, 3, 3, 2]),\n",
" array([6, 3, 2, 4, 4, 3, 3, 3, 2, 1]),\n",
" array([3, 2, 4, 4, 3, 3, 3, 2, 1, 0]),\n",
" array([2, 4, 4, 3, 3, 3, 2, 1, 0, 4]),\n",
" array([4, 4, 3, 3, 3, 2, 1, 0, 4, 7]),\n",
" array([4, 3, 3, 3, 2, 1, 0, 4, 7, 0]),\n",
" array([3, 3, 3, 2, 1, 0, 4, 7, 0, 6]),\n",
" array([3, 3, 2, 1, 0, 4, 7, 0, 6, 2]),\n",
" array([3, 2, 1, 0, 4, 7, 0, 6, 2, 2]),\n",
" array([2, 1, 0, 4, 7, 0, 6, 2, 2, 3]),\n",
" array([1, 0, 4, 7, 0, 6, 2, 2, 3, 2]),\n",
" array([0, 4, 7, 0, 6, 2, 2, 3, 2, 1]),\n",
" array([4, 7, 0, 6, 2, 2, 3, 2, 1, 3]),\n",
" array([7, 0, 6, 2, 2, 3, 2, 1, 3, 1]),\n",
" array([0, 6, 2, 2, 3, 2, 1, 3, 1, 2]),\n",
" array([6, 2, 2, 3, 2, 1, 3, 1, 2, 1]),\n",
" array([2, 2, 3, 2, 1, 3, 1, 2, 1, 1]),\n",
" array([2, 3, 2, 1, 3, 1, 2, 1, 1, 4]),\n",
" array([3, 2, 1, 3, 1, 2, 1, 1, 4, 1]),\n",
" array([2, 1, 3, 1, 2, 1, 1, 4, 1, 6]),\n",
" array([1, 3, 1, 2, 1, 1, 4, 1, 6, 5]),\n",
" array([3, 1, 2, 1, 1, 4, 1, 6, 5, 3]),\n",
" array([1, 2, 1, 1, 4, 1, 6, 5, 3, 3]),\n",
" array([2, 1, 1, 4, 1, 6, 5, 3, 3, 9]),\n",
" array([1, 1, 4, 1, 6, 5, 3, 3, 9, 4]),\n",
" array([1, 4, 1, 6, 5, 3, 3, 9, 4, 6]),\n",
" array([4, 1, 6, 5, 3, 3, 9, 4, 6, 7]),\n",
" array([1, 6, 5, 3, 3, 9, 4, 6, 7, 8]),\n",
" array([6, 5, 3, 3, 9, 4, 6, 7, 8, 2]),\n",
" array([5, 3, 3, 9, 4, 6, 7, 8, 2, 2]),\n",
" array([3, 3, 9, 4, 6, 7, 8, 2, 2, 4]),\n",
" array([3, 9, 4, 6, 7, 8, 2, 2, 4, 8]),\n",
" array([9, 4, 6, 7, 8, 2, 2, 4, 8, 6]),\n",
" array([4, 6, 7, 8, 2, 2, 4, 8, 6, 5]),\n",
" array([6, 7, 8, 2, 2, 4, 8, 6, 5, 5]),\n",
" array([7, 8, 2, 2, 4, 8, 6, 5, 5, 4]),\n",
" array([8, 2, 2, 4, 8, 6, 5, 5, 4, 2]),\n",
" array([2, 2, 4, 8, 6, 5, 5, 4, 2, 3]),\n",
" array([2, 4, 8, 6, 5, 5, 4, 2, 3, 5]),\n",
" array([4, 8, 6, 5, 5, 4, 2, 3, 5, 8]),\n",
" array([8, 6, 5, 5, 4, 2, 3, 5, 8, 7]),\n",
" array([6, 5, 5, 4, 2, 3, 5, 8, 7, 4]),\n",
" array([ 5, 5, 4, 2, 3, 5, 8, 7, 4, 10]),\n",
" array([ 5, 4, 2, 3, 5, 8, 7, 4, 10, 4]),\n",
" array([ 4, 2, 3, 5, 8, 7, 4, 10, 4, 1]),\n",
" array([ 2, 3, 5, 8, 7, 4, 10, 4, 1, 1]),\n",
" array([ 3, 5, 8, 7, 4, 10, 4, 1, 1, 2]),\n",
" array([ 5, 8, 7, 4, 10, 4, 1, 1, 2, 14]),\n",
" array([ 8, 7, 4, 10, 4, 1, 1, 2, 14, 2]),\n",
" array([ 7, 4, 10, 4, 1, 1, 2, 14, 2, 4]),\n",
" array([ 4, 10, 4, 1, 1, 2, 14, 2, 4, 0]),\n",
" array([10, 4, 1, 1, 2, 14, 2, 4, 0, 5]),\n",
" array([ 4, 1, 1, 2, 14, 2, 4, 0, 5, 8]),\n",
" array([ 1, 1, 2, 14, 2, 4, 0, 5, 8, 9]),\n",
" array([ 1, 2, 14, 2, 4, 0, 5, 8, 9, 3]),\n",
" array([ 2, 14, 2, 4, 0, 5, 8, 9, 3, 4]),\n",
" array([14, 2, 4, 0, 5, 8, 9, 3, 4, 3]),\n",
" array([2, 4, 0, 5, 8, 9, 3, 4, 3, 5]),\n",
" array([4, 0, 5, 8, 9, 3, 4, 3, 5, 3]),\n",
" array([0, 5, 8, 9, 3, 4, 3, 5, 3, 3]),\n",
" array([5, 8, 9, 3, 4, 3, 5, 3, 3, 3]),\n",
" array([8, 9, 3, 4, 3, 5, 3, 3, 3, 1]),\n",
" array([9, 3, 4, 3, 5, 3, 3, 3, 1, 2]),\n",
" array([3, 4, 3, 5, 3, 3, 3, 1, 2, 2]),\n",
" array([4, 3, 5, 3, 3, 3, 1, 2, 2, 2]),\n",
" array([3, 5, 3, 3, 3, 1, 2, 2, 2, 5]),\n",
" array([5, 3, 3, 3, 1, 2, 2, 2, 5, 2]),\n",
" array([3, 3, 3, 1, 2, 2, 2, 5, 2, 2]),\n",
" array([3, 3, 1, 2, 2, 2, 5, 2, 2, 1]),\n",
" array([3, 1, 2, 2, 2, 5, 2, 2, 1, 2]),\n",
" array([1, 2, 2, 2, 5, 2, 2, 1, 2, 2]),\n",
" array([2, 2, 2, 5, 2, 2, 1, 2, 2, 9]),\n",
" array([2, 2, 5, 2, 2, 1, 2, 2, 9, 3]),\n",
" array([2, 5, 2, 2, 1, 2, 2, 9, 3, 4]),\n",
" array([5, 2, 2, 1, 2, 2, 9, 3, 4, 4]),\n",
" array([2, 2, 1, 2, 2, 9, 3, 4, 4, 0]),\n",
" array([2, 1, 2, 2, 9, 3, 4, 4, 0, 4]),\n",
" array([1, 2, 2, 9, 3, 4, 4, 0, 4, 1]),\n",
" array([2, 2, 9, 3, 4, 4, 0, 4, 1, 3]),\n",
" array([2, 9, 3, 4, 4, 0, 4, 1, 3, 4]),\n",
" array([9, 3, 4, 4, 0, 4, 1, 3, 4, 0]),\n",
" array([3, 4, 4, 0, 4, 1, 3, 4, 0, 3]),\n",
" array([4, 4, 0, 4, 1, 3, 4, 0, 3, 3]),\n",
" array([4, 0, 4, 1, 3, 4, 0, 3, 3, 1]),\n",
" array([0, 4, 1, 3, 4, 0, 3, 3, 1, 1]),\n",
" array([4, 1, 3, 4, 0, 3, 3, 1, 1, 2]),\n",
" array([1, 3, 4, 0, 3, 3, 1, 1, 2, 6]),\n",
" array([3, 4, 0, 3, 3, 1, 1, 2, 6, 4]),\n",
" array([4, 0, 3, 3, 1, 1, 2, 6, 4, 4]),\n",
" array([ 0, 3, 3, 1, 1, 2, 6, 4, 4, 10]),\n",
" array([ 3, 3, 1, 1, 2, 6, 4, 4, 10, 2]),\n",
" array([ 3, 1, 1, 2, 6, 4, 4, 10, 2, 4]),\n",
" array([ 1, 1, 2, 6, 4, 4, 10, 2, 4, 0]),\n",
" array([ 1, 2, 6, 4, 4, 10, 2, 4, 0, 3]),\n",
" array([ 2, 6, 4, 4, 10, 2, 4, 0, 3, 3]),\n",
" array([ 6, 4, 4, 10, 2, 4, 0, 3, 3, 2]),\n",
" array([ 4, 4, 10, 2, 4, 0, 3, 3, 2, 2]),\n",
" array([ 4, 10, 2, 4, 0, 3, 3, 2, 2, 2]),\n",
" array([10, 2, 4, 0, 3, 3, 2, 2, 2, 3]),\n",
" array([2, 4, 0, 3, 3, 2, 2, 2, 3, 1]),\n",
" array([4, 0, 3, 3, 2, 2, 2, 3, 1, 1]),\n",
" array([0, 3, 3, 2, 2, 2, 3, 1, 1, 1]),\n",
" array([3, 3, 2, 2, 2, 3, 1, 1, 1, 4]),\n",
" array([3, 2, 2, 2, 3, 1, 1, 1, 4, 3]),\n",
" array([2, 2, 2, 3, 1, 1, 1, 4, 3, 0]),\n",
" array([2, 2, 3, 1, 1, 1, 4, 3, 0, 1]),\n",
" array([2, 3, 1, 1, 1, 4, 3, 0, 1, 2]),\n",
" array([3, 1, 1, 1, 4, 3, 0, 1, 2, 3]),\n",
" array([1, 1, 1, 4, 3, 0, 1, 2, 3, 2]),\n",
" array([1, 1, 4, 3, 0, 1, 2, 3, 2, 1]),\n",
" array([1, 4, 3, 0, 1, 2, 3, 2, 1, 1]),\n",
" array([4, 3, 0, 1, 2, 3, 2, 1, 1, 2]),\n",
" array([3, 0, 1, 2, 3, 2, 1, 1, 2, 2]),\n",
" array([0, 1, 2, 3, 2, 1, 1, 2, 2, 2]),\n",
" array([1, 2, 3, 2, 1, 1, 2, 2, 2, 1]),\n",
" array([2, 3, 2, 1, 1, 2, 2, 2, 1, 2]),\n",
" array([3, 2, 1, 1, 2, 2, 2, 1, 2, 4]),\n",
" array([2, 1, 1, 2, 2, 2, 1, 2, 4, 2]),\n",
" array([1, 1, 2, 2, 2, 1, 2, 4, 2, 8]),\n",
" array([1, 2, 2, 2, 1, 2, 4, 2, 8, 4]),\n",
" array([2, 2, 2, 1, 2, 4, 2, 8, 4, 5]),\n",
" array([2, 2, 1, 2, 4, 2, 8, 4, 5, 6]),\n",
" array([2, 1, 2, 4, 2, 8, 4, 5, 6, 3]),\n",
" array([1, 2, 4, 2, 8, 4, 5, 6, 3, 4]),\n",
" array([2, 4, 2, 8, 4, 5, 6, 3, 4, 2]),\n",
" array([4, 2, 8, 4, 5, 6, 3, 4, 2, 9]),\n",
" array([2, 8, 4, 5, 6, 3, 4, 2, 9, 1]),\n",
" array([ 8, 4, 5, 6, 3, 4, 2, 9, 1, 11]),\n",
" array([ 4, 5, 6, 3, 4, 2, 9, 1, 11, 4]),\n",
" array([ 5, 6, 3, 4, 2, 9, 1, 11, 4, 2]),\n",
" array([ 6, 3, 4, 2, 9, 1, 11, 4, 2, 3]),\n",
" array([ 3, 4, 2, 9, 1, 11, 4, 2, 3, 3]),\n",
" array([ 4, 2, 9, 1, 11, 4, 2, 3, 3, 1]),\n",
" array([ 2, 9, 1, 11, 4, 2, 3, 3, 1, 3]),\n",
" array([ 9, 1, 11, 4, 2, 3, 3, 1, 3, 2]),\n",
" array([ 1, 11, 4, 2, 3, 3, 1, 3, 2, 3]),\n",
" array([11, 4, 2, 3, 3, 1, 3, 2, 3, 1]),\n",
" array([4, 2, 3, 3, 1, 3, 2, 3, 1, 1]),\n",
" array([2, 3, 3, 1, 3, 2, 3, 1, 1, 1]),\n",
" array([3, 3, 1, 3, 2, 3, 1, 1, 1, 3]),\n",
" array([3, 1, 3, 2, 3, 1, 1, 1, 3, 3]),\n",
" array([1, 3, 2, 3, 1, 1, 1, 3, 3, 2]),\n",
" array([3, 2, 3, 1, 1, 1, 3, 3, 2, 4]),\n",
" array([2, 3, 1, 1, 1, 3, 3, 2, 4, 3]),\n",
" array([3, 1, 1, 1, 3, 3, 2, 4, 3, 1]),\n",
" array([1, 1, 1, 3, 3, 2, 4, 3, 1, 2]),\n",
" array([1, 1, 3, 3, 2, 4, 3, 1, 2, 1]),\n",
" array([1, 3, 3, 2, 4, 3, 1, 2, 1, 3]),\n",
" array([3, 3, 2, 4, 3, 1, 2, 1, 3, 1]),\n",
" array([3, 2, 4, 3, 1, 2, 1, 3, 1, 4]),\n",
" array([2, 4, 3, 1, 2, 1, 3, 1, 4, 5]),\n",
" array([4, 3, 1, 2, 1, 3, 1, 4, 5, 4]),\n",
" array([3, 1, 2, 1, 3, 1, 4, 5, 4, 4]),\n",
" array([1, 2, 1, 3, 1, 4, 5, 4, 4, 0]),\n",
" array([2, 1, 3, 1, 4, 5, 4, 4, 0, 1]),\n",
" array([1, 3, 1, 4, 5, 4, 4, 0, 1, 4]),\n",
" array([3, 1, 4, 5, 4, 4, 0, 1, 4, 6]),\n",
" array([1, 4, 5, 4, 4, 0, 1, 4, 6, 5]),\n",
" array([4, 5, 4, 4, 0, 1, 4, 6, 5, 3]),\n",
" array([5, 4, 4, 0, 1, 4, 6, 5, 3, 5]),\n",
" array([4, 4, 0, 1, 4, 6, 5, 3, 5, 4]),\n",
" array([4, 0, 1, 4, 6, 5, 3, 5, 4, 6]),\n",
" array([0, 1, 4, 6, 5, 3, 5, 4, 6, 0]),\n",
" array([1, 4, 6, 5, 3, 5, 4, 6, 0, 3]),\n",
" array([4, 6, 5, 3, 5, 4, 6, 0, 3, 1]),\n",
" array([6, 5, 3, 5, 4, 6, 0, 3, 1, 1]),\n",
" array([5, 3, 5, 4, 6, 0, 3, 1, 1, 2]),\n",
" array([3, 5, 4, 6, 0, 3, 1, 1, 2, 3]),\n",
" array([5, 4, 6, 0, 3, 1, 1, 2, 3, 3]),\n",
" array([4, 6, 0, 3, 1, 1, 2, 3, 3, 8]),\n",
" array([6, 0, 3, 1, 1, 2, 3, 3, 8, 6]),\n",
" array([0, 3, 1, 1, 2, 3, 3, 8, 6, 4]),\n",
" array([ 3, 1, 1, 2, 3, 3, 8, 6, 4, 13]),\n",
" array([ 1, 1, 2, 3, 3, 8, 6, 4, 13, 1]),\n",
" array([ 1, 2, 3, 3, 8, 6, 4, 13, 1, 12]),\n",
" array([ 2, 3, 3, 8, 6, 4, 13, 1, 12, 2]),\n",
" array([ 3, 3, 8, 6, 4, 13, 1, 12, 2, 9]),\n",
" array([ 3, 8, 6, 4, 13, 1, 12, 2, 9, 3]),\n",
" array([ 8, 6, 4, 13, 1, 12, 2, 9, 3, 8]),\n",
" array([ 6, 4, 13, 1, 12, 2, 9, 3, 8, 3]),\n",
" array([ 4, 13, 1, 12, 2, 9, 3, 8, 3, 3]),\n",
" array([13, 1, 12, 2, 9, 3, 8, 3, 3, 4]),\n",
" array([ 1, 12, 2, 9, 3, 8, 3, 3, 4, 6]),\n",
" array([12, 2, 9, 3, 8, 3, 3, 4, 6, 4]),\n",
" array([2, 9, 3, 8, 3, 3, 4, 6, 4, 1]),\n",
" array([ 9, 3, 8, 3, 3, 4, 6, 4, 1, 33]),\n",
" array([ 3, 8, 3, 3, 4, 6, 4, 1, 33, 22]),\n",
" array([ 8, 3, 3, 4, 6, 4, 1, 33, 22, 3]),\n",
" array([ 3, 3, 4, 6, 4, 1, 33, 22, 3, 3]),\n",
" array([ 3, 4, 6, 4, 1, 33, 22, 3, 3, 8]),\n",
" array([ 4, 6, 4, 1, 33, 22, 3, 3, 8, 1]),\n",
" array([ 6, 4, 1, 33, 22, 3, 3, 8, 1, 4]),\n",
" array([ 4, 1, 33, 22, 3, 3, 8, 1, 4, 4]),\n",
" array([ 1, 33, 22, 3, 3, 8, 1, 4, 4, 6]),\n",
" array([33, 22, 3, 3, 8, 1, 4, 4, 6, 2]),\n",
" array([22, 3, 3, 8, 1, 4, 4, 6, 2, 4]),\n",
" array([3, 3, 8, 1, 4, 4, 6, 2, 4, 1]),\n",
" array([3, 8, 1, 4, 4, 6, 2, 4, 1, 2]),\n",
" array([8, 1, 4, 4, 6, 2, 4, 1, 2, 4]),\n",
" array([1, 4, 4, 6, 2, 4, 1, 2, 4, 3]),\n",
" array([4, 4, 6, 2, 4, 1, 2, 4, 3, 3]),\n",
" array([4, 6, 2, 4, 1, 2, 4, 3, 3, 4]),\n",
" array([6, 2, 4, 1, 2, 4, 3, 3, 4, 2]),\n",
" array([2, 4, 1, 2, 4, 3, 3, 4, 2, 2]),\n",
" array([4, 1, 2, 4, 3, 3, 4, 2, 2, 1]),\n",
" array([1, 2, 4, 3, 3, 4, 2, 2, 1, 4]),\n",
" array([2, 4, 3, 3, 4, 2, 2, 1, 4, 2]),\n",
" array([4, 3, 3, 4, 2, 2, 1, 4, 2, 4]),\n",
" array([3, 3, 4, 2, 2, 1, 4, 2, 4, 4]),\n",
" array([3, 4, 2, 2, 1, 4, 2, 4, 4, 4]),\n",
" array([4, 2, 2, 1, 4, 2, 4, 4, 4, 3]),\n",
" array([2, 2, 1, 4, 2, 4, 4, 4, 3, 3]),\n",
" array([2, 1, 4, 2, 4, 4, 4, 3, 3, 3]),\n",
" array([1, 4, 2, 4, 4, 4, 3, 3, 3, 4]),\n",
" array([4, 2, 4, 4, 4, 3, 3, 3, 4, 3]),\n",
" array([2, 4, 4, 4, 3, 3, 3, 4, 3, 2]),\n",
" array([4, 4, 4, 3, 3, 3, 4, 3, 2, 1]),\n",
" array([4, 4, 3, 3, 3, 4, 3, 2, 1, 0]),\n",
" array([4, 3, 3, 3, 4, 3, 2, 1, 0, 0]),\n",
" array([3, 3, 3, 4, 3, 2, 1, 0, 0, 2]),\n",
" array([3, 3, 4, 3, 2, 1, 0, 0, 2, 1]),\n",
" array([3, 4, 3, 2, 1, 0, 0, 2, 1, 4]),\n",
" array([4, 3, 2, 1, 0, 0, 2, 1, 4, 2]),\n",
" array([3, 2, 1, 0, 0, 2, 1, 4, 2, 2]),\n",
" array([2, 1, 0, 0, 2, 1, 4, 2, 2, 1]),\n",
" array([1, 0, 0, 2, 1, 4, 2, 2, 1, 3]),\n",
" array([0, 0, 2, 1, 4, 2, 2, 1, 3, 3]),\n",
" array([0, 2, 1, 4, 2, 2, 1, 3, 3, 3]),\n",
" array([2, 1, 4, 2, 2, 1, 3, 3, 3, 2]),\n",
" array([1, 4, 2, 2, 1, 3, 3, 3, 2, 2]),\n",
" array([4, 2, 2, 1, 3, 3, 3, 2, 2, 3]),\n",
" array([2, 2, 1, 3, 3, 3, 2, 2, 3, 1]),\n",
" array([2, 1, 3, 3, 3, 2, 2, 3, 1, 7]),\n",
" array([1, 3, 3, 3, 2, 2, 3, 1, 7, 1]),\n",
" array([3, 3, 3, 2, 2, 3, 1, 7, 1, 4]),\n",
" array([3, 3, 2, 2, 3, 1, 7, 1, 4, 1]),\n",
" array([3, 2, 2, 3, 1, 7, 1, 4, 1, 2]),\n",
" array([2, 2, 3, 1, 7, 1, 4, 1, 2, 6]),\n",
" array([2, 3, 1, 7, 1, 4, 1, 2, 6, 6]),\n",
" array([3, 1, 7, 1, 4, 1, 2, 6, 6, 1]),\n",
" array([1, 7, 1, 4, 1, 2, 6, 6, 1, 3]),\n",
" array([7, 1, 4, 1, 2, 6, 6, 1, 3, 3]),\n",
" array([1, 4, 1, 2, 6, 6, 1, 3, 3, 4]),\n",
" array([4, 1, 2, 6, 6, 1, 3, 3, 4, 5]),\n",
" array([1, 2, 6, 6, 1, 3, 3, 4, 5, 2]),\n",
" array([2, 6, 6, 1, 3, 3, 4, 5, 2, 3]),\n",
" array([6, 6, 1, 3, 3, 4, 5, 2, 3, 3]),\n",
" array([6, 1, 3, 3, 4, 5, 2, 3, 3, 5]),\n",
" array([ 1, 3, 3, 4, 5, 2, 3, 3, 5, 10]),\n",
" array([ 3, 3, 4, 5, 2, 3, 3, 5, 10, 4]),\n",
" array([ 3, 4, 5, 2, 3, 3, 5, 10, 4, 1]),\n",
" array([ 4, 5, 2, 3, 3, 5, 10, 4, 1, 6]),\n",
" array([ 5, 2, 3, 3, 5, 10, 4, 1, 6, 3]),\n",
" array([ 2, 3, 3, 5, 10, 4, 1, 6, 3, 3]),\n",
" array([ 3, 3, 5, 10, 4, 1, 6, 3, 3, 4]),\n",
" array([ 3, 5, 10, 4, 1, 6, 3, 3, 4, 3]),\n",
" array([ 5, 10, 4, 1, 6, 3, 3, 4, 3, 2]),\n",
" array([10, 4, 1, 6, 3, 3, 4, 3, 2, 2]),\n",
" array([4, 1, 6, 3, 3, 4, 3, 2, 2, 6]),\n",
" array([1, 6, 3, 3, 4, 3, 2, 2, 6, 0]),\n",
" array([6, 3, 3, 4, 3, 2, 2, 6, 0, 9]),\n",
" array([3, 3, 4, 3, 2, 2, 6, 0, 9, 2]),\n",
" array([3, 4, 3, 2, 2, 6, 0, 9, 2, 1]),\n",
" array([4, 3, 2, 2, 6, 0, 9, 2, 1, 5]),\n",
" array([3, 2, 2, 6, 0, 9, 2, 1, 5, 5]),\n",
" array([2, 2, 6, 0, 9, 2, 1, 5, 5, 5]),\n",
" array([2, 6, 0, 9, 2, 1, 5, 5, 5, 4]),\n",
" array([6, 0, 9, 2, 1, 5, 5, 5, 4, 2]),\n",
" array([0, 9, 2, 1, 5, 5, 5, 4, 2, 6]),\n",
" array([9, 2, 1, 5, 5, 5, 4, 2, 6, 4]),\n",
" array([2, 1, 5, 5, 5, 4, 2, 6, 4, 1]),\n",
" array([1, 5, 5, 5, 4, 2, 6, 4, 1, 2]),\n",
" array([5, 5, 5, 4, 2, 6, 4, 1, 2, 2]),\n",
" array([5, 5, 4, 2, 6, 4, 1, 2, 2, 3]),\n",
" array([5, 4, 2, 6, 4, 1, 2, 2, 3, 3]),\n",
" array([4, 2, 6, 4, 1, 2, 2, 3, 3, 5]),\n",
" array([2, 6, 4, 1, 2, 2, 3, 3, 5, 5]),\n",
" array([6, 4, 1, 2, 2, 3, 3, 5, 5, 1]),\n",
" array([4, 1, 2, 2, 3, 3, 5, 5, 1, 3]),\n",
" array([1, 2, 2, 3, 3, 5, 5, 1, 3, 3]),\n",
" array([2, 2, 3, 3, 5, 5, 1, 3, 3, 3]),\n",
" array([2, 3, 3, 5, 5, 1, 3, 3, 3, 4]),\n",
" array([3, 3, 5, 5, 1, 3, 3, 3, 4, 2]),\n",
" array([3, 5, 5, 1, 3, 3, 3, 4, 2, 7]),\n",
" array([5, 5, 1, 3, 3, 3, 4, 2, 7, 2]),\n",
" array([5, 1, 3, 3, 3, 4, 2, 7, 2, 0]),\n",
" array([1, 3, 3, 3, 4, 2, 7, 2, 0, 1]),\n",
" array([3, 3, 3, 4, 2, 7, 2, 0, 1, 2]),\n",
" array([3, 3, 4, 2, 7, 2, 0, 1, 2, 3]),\n",
" array([3, 4, 2, 7, 2, 0, 1, 2, 3, 2]),\n",
" array([4, 2, 7, 2, 0, 1, 2, 3, 2, 3]),\n",
" array([2, 7, 2, 0, 1, 2, 3, 2, 3, 2]),\n",
" array([7, 2, 0, 1, 2, 3, 2, 3, 2, 2]),\n",
" array([2, 0, 1, 2, 3, 2, 3, 2, 2, 2]),\n",
" array([0, 1, 2, 3, 2, 3, 2, 2, 2, 3]),\n",
" array([1, 2, 3, 2, 3, 2, 2, 2, 3, 3]),\n",
" array([2, 3, 2, 3, 2, 2, 2, 3, 3, 5]),\n",
" array([3, 2, 3, 2, 2, 2, 3, 3, 5, 1]),\n",
" array([2, 3, 2, 2, 2, 3, 3, 5, 1, 1]),\n",
" array([ 3, 2, 2, 2, 3, 3, 5, 1, 1, 10]),\n",
" array([ 2, 2, 2, 3, 3, 5, 1, 1, 10, 1]),\n",
" array([ 2, 2, 3, 3, 5, 1, 1, 10, 1, 3]),\n",
" array([ 2, 3, 3, 5, 1, 1, 10, 1, 3, 6]),\n",
" array([ 3, 3, 5, 1, 1, 10, 1, 3, 6, 9]),\n",
" array([ 3, 5, 1, 1, 10, 1, 3, 6, 9, 3]),\n",
" array([ 5, 1, 1, 10, 1, 3, 6, 9, 3, 2]),\n",
" array([ 1, 1, 10, 1, 3, 6, 9, 3, 2, 1]),\n",
" array([ 1, 10, 1, 3, 6, 9, 3, 2, 1, 4]),\n",
" array([10, 1, 3, 6, 9, 3, 2, 1, 4, 8]),\n",
" array([ 1, 3, 6, 9, 3, 2, 1, 4, 8, 13]),\n",
" array([ 3, 6, 9, 3, 2, 1, 4, 8, 13, 3]),\n",
" array([ 6, 9, 3, 2, 1, 4, 8, 13, 3, 1]),\n",
" array([ 9, 3, 2, 1, 4, 8, 13, 3, 1, 6]),\n",
" array([ 3, 2, 1, 4, 8, 13, 3, 1, 6, 2]),\n",
" array([ 2, 1, 4, 8, 13, 3, 1, 6, 2, 3]),\n",
" array([ 1, 4, 8, 13, 3, 1, 6, 2, 3, 0]),\n",
" array([ 4, 8, 13, 3, 1, 6, 2, 3, 0, 4]),\n",
" array([ 8, 13, 3, 1, 6, 2, 3, 0, 4, 4]),\n",
" array([13, 3, 1, 6, 2, 3, 0, 4, 4, 1]),\n",
" array([3, 1, 6, 2, 3, 0, 4, 4, 1, 1]),\n",
" array([1, 6, 2, 3, 0, 4, 4, 1, 1, 0]),\n",
" array([6, 2, 3, 0, 4, 4, 1, 1, 0, 4]),\n",
" array([2, 3, 0, 4, 4, 1, 1, 0, 4, 9]),\n",
" array([3, 0, 4, 4, 1, 1, 0, 4, 9, 1]),\n",
" array([0, 4, 4, 1, 1, 0, 4, 9, 1, 2]),\n",
" array([4, 4, 1, 1, 0, 4, 9, 1, 2, 3]),\n",
" array([4, 1, 1, 0, 4, 9, 1, 2, 3, 4]),\n",
" array([1, 1, 0, 4, 9, 1, 2, 3, 4, 3]),\n",
" array([1, 0, 4, 9, 1, 2, 3, 4, 3, 4]),\n",
" array([0, 4, 9, 1, 2, 3, 4, 3, 4, 2]),\n",
" array([4, 9, 1, 2, 3, 4, 3, 4, 2, 3]),\n",
" array([9, 1, 2, 3, 4, 3, 4, 2, 3, 8]),\n",
" array([1, 2, 3, 4, 3, 4, 2, 3, 8, 4]),\n",
" array([2, 3, 4, 3, 4, 2, 3, 8, 4, 3]),\n",
" array([3, 4, 3, 4, 2, 3, 8, 4, 3, 4]),\n",
" array([4, 3, 4, 2, 3, 8, 4, 3, 4, 6]),\n",
" array([3, 4, 2, 3, 8, 4, 3, 4, 6, 2]),\n",
" array([4, 2, 3, 8, 4, 3, 4, 6, 2, 2]),\n",
" array([2, 3, 8, 4, 3, 4, 6, 2, 2, 9]),\n",
" array([3, 8, 4, 3, 4, 6, 2, 2, 9, 3]),\n",
" array([8, 4, 3, 4, 6, 2, 2, 9, 3, 3]),\n",
" array([4, 3, 4, 6, 2, 2, 9, 3, 3, 6]),\n",
" array([3, 4, 6, 2, 2, 9, 3, 3, 6, 3]),\n",
" array([4, 6, 2, 2, 9, 3, 3, 6, 3, 3]),\n",
" array([6, 2, 2, 9, 3, 3, 6, 3, 3, 4]),\n",
" array([2, 2, 9, 3, 3, 6, 3, 3, 4, 0]),\n",
" array([2, 9, 3, 3, 6, 3, 3, 4, 0, 2]),\n",
" array([9, 3, 3, 6, 3, 3, 4, 0, 2, 5]),\n",
" array([3, 3, 6, 3, 3, 4, 0, 2, 5, 1]),\n",
" array([3, 6, 3, 3, 4, 0, 2, 5, 1, 3]),\n",
" array([6, 3, 3, 4, 0, 2, 5, 1, 3, 3]),\n",
" array([3, 3, 4, 0, 2, 5, 1, 3, 3, 2]),\n",
" array([3, 4, 0, 2, 5, 1, 3, 3, 2, 5]),\n",
" array([4, 0, 2, 5, 1, 3, 3, 2, 5, 1]),\n",
" array([0, 2, 5, 1, 3, 3, 2, 5, 1, 3]),\n",
" array([2, 5, 1, 3, 3, 2, 5, 1, 3, 5]),\n",
" array([5, 1, 3, 3, 2, 5, 1, 3, 5, 2]),\n",
" array([1, 3, 3, 2, 5, 1, 3, 5, 2, 5]),\n",
" array([3, 3, 2, 5, 1, 3, 5, 2, 5, 0]),\n",
" array([3, 2, 5, 1, 3, 5, 2, 5, 0, 3]),\n",
" array([2, 5, 1, 3, 5, 2, 5, 0, 3, 3]),\n",
" array([5, 1, 3, 5, 2, 5, 0, 3, 3, 1]),\n",
" array([1, 3, 5, 2, 5, 0, 3, 3, 1, 2]),\n",
" array([3, 5, 2, 5, 0, 3, 3, 1, 2, 7]),\n",
" array([5, 2, 5, 0, 3, 3, 1, 2, 7, 5]),\n",
" array([2, 5, 0, 3, 3, 1, 2, 7, 5, 2]),\n",
" array([ 5, 0, 3, 3, 1, 2, 7, 5, 2, 11]),\n",
" array([ 0, 3, 3, 1, 2, 7, 5, 2, 11, 3]),\n",
" array([ 3, 3, 1, 2, 7, 5, 2, 11, 3, 3]),\n",
" array([ 3, 1, 2, 7, 5, 2, 11, 3, 3, 11]),\n",
" array([ 1, 2, 7, 5, 2, 11, 3, 3, 11, 7]),\n",
" array([ 2, 7, 5, 2, 11, 3, 3, 11, 7, 2]),\n",
" array([ 7, 5, 2, 11, 3, 3, 11, 7, 2, 5]),\n",
" array([ 5, 2, 11, 3, 3, 11, 7, 2, 5, 3]),\n",
" array([ 2, 11, 3, 3, 11, 7, 2, 5, 3, 1]),\n",
" array([11, 3, 3, 11, 7, 2, 5, 3, 1, 3]),\n",
" array([ 3, 3, 11, 7, 2, 5, 3, 1, 3, 4]),\n",
" array([ 3, 11, 7, 2, 5, 3, 1, 3, 4, 4]),\n",
" array([11, 7, 2, 5, 3, 1, 3, 4, 4, 1]),\n",
" array([7, 2, 5, 3, 1, 3, 4, 4, 1, 1]),\n",
" array([2, 5, 3, 1, 3, 4, 4, 1, 1, 3]),\n",
" array([5, 3, 1, 3, 4, 4, 1, 1, 3, 1]),\n",
" array([3, 1, 3, 4, 4, 1, 1, 3, 1, 1]),\n",
" array([1, 3, 4, 4, 1, 1, 3, 1, 1, 5]),\n",
" array([3, 4, 4, 1, 1, 3, 1, 1, 5, 3]),\n",
" array([4, 4, 1, 1, 3, 1, 1, 5, 3, 5]),\n",
" array([4, 1, 1, 3, 1, 1, 5, 3, 5, 2]),\n",
" array([ 1, 1, 3, 1, 1, 5, 3, 5, 2, 15]),\n",
" array([ 1, 3, 1, 1, 5, 3, 5, 2, 15, 1]),\n",
" array([ 3, 1, 1, 5, 3, 5, 2, 15, 1, 5]),\n",
" array([ 1, 1, 5, 3, 5, 2, 15, 1, 5, 4]),\n",
" array([ 1, 5, 3, 5, 2, 15, 1, 5, 4, 5]),\n",
" array([ 5, 3, 5, 2, 15, 1, 5, 4, 5, 2]),\n",
" array([ 3, 5, 2, 15, 1, 5, 4, 5, 2, 2]),\n",
" array([ 5, 2, 15, 1, 5, 4, 5, 2, 2, 2]),\n",
" array([ 2, 15, 1, 5, 4, 5, 2, 2, 2, 5]),\n",
" array([15, 1, 5, 4, 5, 2, 2, 2, 5, 6]),\n",
" array([1, 5, 4, 5, 2, 2, 2, 5, 6, 3]),\n",
" array([5, 4, 5, 2, 2, 2, 5, 6, 3, 7]),\n",
" array([4, 5, 2, 2, 2, 5, 6, 3, 7, 1]),\n",
" array([5, 2, 2, 2, 5, 6, 3, 7, 1, 5]),\n",
" array([2, 2, 2, 5, 6, 3, 7, 1, 5, 4]),\n",
" array([2, 2, 5, 6, 3, 7, 1, 5, 4, 4]),\n",
" array([2, 5, 6, 3, 7, 1, 5, 4, 4, 8]),\n",
" array([5, 6, 3, 7, 1, 5, 4, 4, 8, 5]),\n",
" array([6, 3, 7, 1, 5, 4, 4, 8, 5, 6]),\n",
" array([3, 7, 1, 5, 4, 4, 8, 5, 6, 6]),\n",
" array([7, 1, 5, 4, 4, 8, 5, 6, 6, 4]),\n",
" array([1, 5, 4, 4, 8, 5, 6, 6, 4, 4]),\n",
" array([5, 4, 4, 8, 5, 6, 6, 4, 4, 4]),\n",
" array([4, 4, 8, 5, 6, 6, 4, 4, 4, 2]),\n",
" array([4, 8, 5, 6, 6, 4, 4, 4, 2, 2]),\n",
" array([8, 5, 6, 6, 4, 4, 4, 2, 2, 4]),\n",
" array([5, 6, 6, 4, 4, 4, 2, 2, 4, 8]),\n",
" array([6, 6, 4, 4, 4, 2, 2, 4, 8, 3]),\n",
" array([6, 4, 4, 4, 2, 2, 4, 8, 3, 2]),\n",
" array([4, 4, 4, 2, 2, 4, 8, 3, 2, 1]),\n",
" array([4, 4, 2, 2, 4, 8, 3, 2, 1, 4]),\n",
" array([4, 2, 2, 4, 8, 3, 2, 1, 4, 3]),\n",
" array([2, 2, 4, 8, 3, 2, 1, 4, 3, 3]),\n",
" array([2, 4, 8, 3, 2, 1, 4, 3, 3, 2]),\n",
" array([4, 8, 3, 2, 1, 4, 3, 3, 2, 2]),\n",
" array([8, 3, 2, 1, 4, 3, 3, 2, 2, 4]),\n",
" array([3, 2, 1, 4, 3, 3, 2, 2, 4, 2]),\n",
" array([2, 1, 4, 3, 3, 2, 2, 4, 2, 5]),\n",
" array([1, 4, 3, 3, 2, 2, 4, 2, 5, 4]),\n",
" array([4, 3, 3, 2, 2, 4, 2, 5, 4, 6]),\n",
" array([3, 3, 2, 2, 4, 2, 5, 4, 6, 7]),\n",
" array([3, 2, 2, 4, 2, 5, 4, 6, 7, 6]),\n",
" array([2, 2, 4, 2, 5, 4, 6, 7, 6, 1]),\n",
" array([2, 4, 2, 5, 4, 6, 7, 6, 1, 3]),\n",
" array([4, 2, 5, 4, 6, 7, 6, 1, 3, 3]),\n",
" array([2, 5, 4, 6, 7, 6, 1, 3, 3, 2]),\n",
" array([5, 4, 6, 7, 6, 1, 3, 3, 2, 2]),\n",
" array([4, 6, 7, 6, 1, 3, 3, 2, 2, 2]),\n",
" array([6, 7, 6, 1, 3, 3, 2, 2, 2, 1]),\n",
" array([7, 6, 1, 3, 3, 2, 2, 2, 1, 2]),\n",
" array([6, 1, 3, 3, 2, 2, 2, 1, 2, 0]),\n",
" array([1, 3, 3, 2, 2, 2, 1, 2, 0, 2]),\n",
" array([3, 3, 2, 2, 2, 1, 2, 0, 2, 2]),\n",
" array([3, 2, 2, 2, 1, 2, 0, 2, 2, 3]),\n",
" array([2, 2, 2, 1, 2, 0, 2, 2, 3, 1]),\n",
" array([2, 2, 1, 2, 0, 2, 2, 3, 1, 2]),\n",
" array([2, 1, 2, 0, 2, 2, 3, 1, 2, 5]),\n",
" array([1, 2, 0, 2, 2, 3, 1, 2, 5, 3]),\n",
" array([2, 0, 2, 2, 3, 1, 2, 5, 3, 3]),\n",
" array([0, 2, 2, 3, 1, 2, 5, 3, 3, 2]),\n",
" array([2, 2, 3, 1, 2, 5, 3, 3, 2, 1]),\n",
" array([2, 3, 1, 2, 5, 3, 3, 2, 1, 3]),\n",
" array([3, 1, 2, 5, 3, 3, 2, 1, 3, 2]),\n",
" array([1, 2, 5, 3, 3, 2, 1, 3, 2, 3]),\n",
" array([2, 5, 3, 3, 2, 1, 3, 2, 3, 1]),\n",
" array([5, 3, 3, 2, 1, 3, 2, 3, 1, 6]),\n",
" array([3, 3, 2, 1, 3, 2, 3, 1, 6, 3]),\n",
" array([3, 2, 1, 3, 2, 3, 1, 6, 3, 1]),\n",
" array([2, 1, 3, 2, 3, 1, 6, 3, 1, 1]),\n",
" array([1, 3, 2, 3, 1, 6, 3, 1, 1, 4]),\n",
" array([3, 2, 3, 1, 6, 3, 1, 1, 4, 3]),\n",
" array([2, 3, 1, 6, 3, 1, 1, 4, 3, 1])]"
]
},
"execution_count": 66,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"train_feature"
]
},
{
"cell_type": "code",
"execution_count": 69,
"metadata": {},
"outputs": [],
"source": [
"train_feature_array = np.array(train_feature)"
]
},
{
"cell_type": "code",
"execution_count": 70,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"array([[3, 4, 7, ..., 7, 2, 0],\n",
" [4, 7, 4, ..., 2, 0, 3],\n",
" [7, 4, 4, ..., 0, 3, 1],\n",
" ..., \n",
" [1, 3, 2, ..., 1, 1, 4],\n",
" [3, 2, 3, ..., 1, 4, 3],\n",
" [2, 3, 1, ..., 4, 3, 1]])"
]
},
"execution_count": 70,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"train_feature_array"
]
},
{
"cell_type": "code",
"execution_count": 71,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"540"
]
},
"execution_count": 71,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"len(train_feature_array)"
]
},
{
"cell_type": "code",
"execution_count": 181,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"_________________________________________________________________\n",
"Layer (type) Output Shape Param # \n",
"=================================================================\n",
"dense_147 (Dense) (None, 200) 2200 \n",
"_________________________________________________________________\n",
"dropout_99 (Dropout) (None, 200) 0 \n",
"_________________________________________________________________\n",
"dense_148 (Dense) (None, 200) 40200 \n",
"_________________________________________________________________\n",
"dropout_100 (Dropout) (None, 200) 0 \n",
"_________________________________________________________________\n",
"dense_149 (Dense) (None, 1) 201 \n",
"=================================================================\n",
"Total params: 42,601\n",
"Trainable params: 42,601\n",
"Non-trainable params: 0\n",
"_________________________________________________________________\n",
"None\n"
]
},
{
"data": {
"text/plain": [
"<keras.callbacks.History at 0x34b345f28>"
]
},
"execution_count": 181,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"######################### 建立模型\n",
"from keras.models import Sequential\n",
"from keras.layers import Dense\n",
"from keras.layers import Dropout\n",
"\n",
"model = Sequential() #一層一層到底，按順序\n",
"\n",
"#輸入層（隱藏層1）\n",
"model.add(Dense(units=200, \n",
" input_dim=10, \n",
" kernel_initializer='uniform', \n",
" activation='relu'))\n",
"model.add(Dropout(0.5))\n",
"\n",
"#隱藏層2，不用寫input_dim，因為就是前一層的units\n",
"model.add(Dense(units=200, \n",
" kernel_initializer='uniform', \n",
" activation='relu'))\n",
"model.add(Dropout(0.5))\n",
"\n",
"#輸出層\n",
"model.add(Dense(units=1, #輸出一個數字 \n",
" kernel_initializer='uniform'))\n",
"\n",
"print(model.summary()) #可以清楚看到model還有參數數量\n",
"\n",
"\n",
"model.compile(loss='mse', #二元用binary\n",
" optimizer='adam', metrics=['accuracy'])\n",
"\n",
"model.fit(x=train_feature_array, y=page_data_label, #上面多分割一步在keras是內建的\n",
" validation_split=0.2, epochs=20, batch_size=150, verbose=0) #verbose=2表示顯示訓練過程"
]
},
{
"cell_type": "code",
"execution_count": 152,
"metadata": {},
"outputs": [],
"source": [
"list_future = train_feature_array[-1].reshape(-1,10)\n",
"ans_list = list_future"
]
},
{
"cell_type": "code",
"execution_count": 153,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"array([2, 3, 1, 6, 3, 1, 1, 4, 3, 1, 1])"
]
},
"execution_count": 153,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"np.append(ans_list,1) #測試用"
]
},
{
"cell_type": "code",
"execution_count": 154,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"array([[2, 3, 1, 6, 3, 1, 1, 4, 3, 1]])"
]
},
"execution_count": 154,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"ans_list #其實並沒有加上去！！"
]
},
{
"cell_type": "code",
"execution_count": 155,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"array([[2, 3, 1, 6, 3, 1, 1, 4, 3, 1]])"
]
},
"execution_count": 155,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"ans_list[0:10]"
]
},
{
"cell_type": "code",
"execution_count": 156,
"metadata": {},
"outputs": [],
"source": [
"for i in range(61):\n",
" ans = model.predict(ans_list[i:i+10].reshape(-1,10))\n",
" ans_list = np.append(ans_list, ans)"
]
},
{
"cell_type": "code",
"execution_count": 157,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"array([ 2. , 3. , 1. , 6. , 3. ,\n",
" 1. , 1. , 4. , 3. , 1. ,\n",
" 2.07314897, 2.34163165, 2.23865843, 2.11607313, 1.92182565,\n",
" 1.96203339, 1.99277389, 1.95160294, 1.85660791, 1.8078903 ,\n",
" 1.83257496, 1.81137025, 1.76898634, 1.73631012, 1.71370244,\n",
" 1.69622028, 1.67314231, 1.64847136, 1.62789738, 1.61069787,\n",
" 1.59408164, 1.57588303, 1.55843711, 1.54275393, 1.52803504,\n",
" 1.51372468, 1.49985647, 1.48678863, 1.4745357 , 1.46282303,\n",
" 1.4515537 , 1.44077516, 1.43055797, 1.42085636, 1.41159749,\n",
" 1.4027617 , 1.39435053, 1.38634956, 1.3787272 , 1.37145722,\n",
" 1.36452818, 1.35792947, 1.35164583, 1.34565926, 1.33995485,\n",
" 1.33452082, 1.32934487, 1.32441378, 1.3197155 , 1.31523955,\n",
" 1.31097543, 1.30691338, 1.3030436 , 1.29935694, 1.29584455,\n",
" 1.29249907, 1.28931141, 1.28627467, 1.28338182, 1.28062582,\n",
" 1.27800035])"
]
},
"execution_count": 157,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"ans_list"
]
},
{
"cell_type": "code",
"execution_count": 158,
"metadata": {},
"outputs": [],
"source": [
"ans_list_round = np.round(ans_list)"
]
},
{
"cell_type": "code",
"execution_count": 159,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"array([ 2., 3., 1., 6., 3., 1., 1., 4., 3., 1., 2., 2., 2.,\n",
" 2., 2., 2., 2., 2., 2., 2., 2., 2., 2., 2., 2., 2.,\n",
" 2., 2., 2., 2., 2., 2., 2., 2., 2., 2., 1., 1., 1.,\n",
" 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1.,\n",
" 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1.,\n",
" 1., 1., 1., 1., 1., 1.])"
]
},
"execution_count": 159,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"ans_list_round"
]
},
{
"cell_type": "code",
"execution_count": 160,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"real_ans = ans_list_round[11:]"
]
},
{
"cell_type": "code",
"execution_count": 161,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"array([ 2., 2., 2., 2., 2., 2., 2., 2., 2., 2., 2., 2., 2.,\n",
" 2., 2., 2., 2., 2., 2., 2., 2., 2., 2., 2., 2., 1.,\n",
" 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1.,\n",
" 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1.,\n",
" 1., 1., 1., 1., 1., 1., 1., 1.])"
]
},
"execution_count": 161,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"real_ans #測試第一組的60個數字"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# 測試完畢，統整寫for迴圈"
]
},
{
"cell_type": "code",
"execution_count": 274,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Train on 430 samples, validate on 108 samples\n",
"Epoch 1/40\n",
"430/430 [==============================] - 16s - loss: 0.0144 - acc: 0.2209 - val_loss: 0.0055 - val_acc: 0.1944\n",
"Epoch 2/40\n",
"430/430 [==============================] - 0s - loss: 0.0100 - acc: 0.2209 - val_loss: 0.0067 - val_acc: 0.1944\n",
"Epoch 3/40\n",
"430/430 [==============================] - 0s - loss: 0.0106 - acc: 0.2209 - val_loss: 0.0056 - val_acc: 0.1944\n",
"Epoch 4/40\n",
"430/430 [==============================] - 0s - loss: 0.0097 - acc: 0.2209 - val_loss: 0.0057 - val_acc: 0.1944\n",
"Epoch 5/40\n",
"430/430 [==============================] - 0s - loss: 0.0099 - acc: 0.2209 - val_loss: 0.0059 - val_acc: 0.1944\n",
"Epoch 6/40\n",
"430/430 [==============================] - 0s - loss: 0.0097 - acc: 0.2209 - val_loss: 0.0055 - val_acc: 0.1944\n",
"Epoch 7/40\n",
"430/430 [==============================] - 0s - loss: 0.0096 - acc: 0.2209 - val_loss: 0.0053 - val_acc: 0.1944\n",
"Epoch 8/40\n",
"430/430 [==============================] - 0s - loss: 0.0093 - acc: 0.2209 - val_loss: 0.0054 - val_acc: 0.1944\n",
"Epoch 9/40\n",
"430/430 [==============================] - 0s - loss: 0.0097 - acc: 0.2209 - val_loss: 0.0054 - val_acc: 0.1944\n",
"Epoch 10/40\n",
"430/430 [==============================] - 0s - loss: 0.0094 - acc: 0.2209 - val_loss: 0.0054 - val_acc: 0.1944\n",
"Epoch 11/40\n",
"430/430 [==============================] - 0s - loss: 0.0092 - acc: 0.2209 - val_loss: 0.0054 - val_acc: 0.1944\n",
"Epoch 12/40\n",
"430/430 [==============================] - 0s - loss: 0.0095 - acc: 0.2209 - val_loss: 0.0055 - val_acc: 0.1944\n",
"Epoch 13/40\n",
"430/430 [==============================] - 0s - loss: 0.0094 - acc: 0.2209 - val_loss: 0.0054 - val_acc: 0.1944\n",
"Epoch 14/40\n",
"430/430 [==============================] - 0s - loss: 0.0093 - acc: 0.2209 - val_loss: 0.0054 - val_acc: 0.1944\n",
"Epoch 15/40\n",
"430/430 [==============================] - 0s - loss: 0.0095 - acc: 0.2209 - val_loss: 0.0054 - val_acc: 0.1944\n",
"Epoch 16/40\n",
"430/430 [==============================] - 0s - loss: 0.0094 - acc: 0.2209 - val_loss: 0.0054 - val_acc: 0.1944\n",
"Epoch 17/40\n",
"430/430 [==============================] - 0s - loss: 0.0098 - acc: 0.2209 - val_loss: 0.0055 - val_acc: 0.1944\n",
"Epoch 18/40\n",
"430/430 [==============================] - 0s - loss: 0.0093 - acc: 0.2209 - val_loss: 0.0055 - val_acc: 0.1944\n",
"Epoch 19/40\n",
"430/430 [==============================] - 0s - loss: 0.0093 - acc: 0.2209 - val_loss: 0.0054 - val_acc: 0.1944\n",
"Epoch 20/40\n",
"430/430 [==============================] - 0s - loss: 0.0095 - acc: 0.2209 - val_loss: 0.0053 - val_acc: 0.1944\n",
"Epoch 21/40\n",
"430/430 [==============================] - 0s - loss: 0.0093 - acc: 0.2209 - val_loss: 0.0053 - val_acc: 0.1944\n",
"Epoch 22/40\n",
"430/430 [==============================] - 0s - loss: 0.0094 - acc: 0.2209 - val_loss: 0.0054 - val_acc: 0.1944\n",
"Epoch 23/40\n",
"430/430 [==============================] - 0s - loss: 0.0095 - acc: 0.2209 - val_loss: 0.0053 - val_acc: 0.1944\n",
"Epoch 24/40\n",
"430/430 [==============================] - 0s - loss: 0.0094 - acc: 0.2209 - val_loss: 0.0053 - val_acc: 0.1944\n",
"Epoch 25/40\n",
"430/430 [==============================] - 0s - loss: 0.0093 - acc: 0.2209 - val_loss: 0.0053 - val_acc: 0.1944\n",
"Epoch 26/40\n",
"430/430 [==============================] - 0s - loss: 0.0096 - acc: 0.2209 - val_loss: 0.0053 - val_acc: 0.1944\n",
"Epoch 27/40\n",
"430/430 [==============================] - 0s - loss: 0.0095 - acc: 0.2209 - val_loss: 0.0054 - val_acc: 0.1944\n",
"Epoch 28/40\n",
"430/430 [==============================] - 0s - loss: 0.0095 - acc: 0.2209 - val_loss: 0.0053 - val_acc: 0.1944\n",
"Epoch 29/40\n",
"430/430 [==============================] - 0s - loss: 0.0092 - acc: 0.2209 - val_loss: 0.0054 - val_acc: 0.1944\n",
"Epoch 30/40\n",
"430/430 [==============================] - 0s - loss: 0.0096 - acc: 0.2209 - val_loss: 0.0055 - val_acc: 0.1944\n",
"Epoch 31/40\n",
"430/430 [==============================] - 0s - loss: 0.0093 - acc: 0.2209 - val_loss: 0.0053 - val_acc: 0.1944\n",
"Epoch 32/40\n",
"430/430 [==============================] - 0s - loss: 0.0091 - acc: 0.2209 - val_loss: 0.0054 - val_acc: 0.1944\n",
"Epoch 33/40\n",
"430/430 [==============================] - 0s - loss: 0.0096 - acc: 0.2209 - val_loss: 0.0054 - val_acc: 0.1944\n",
"Epoch 34/40\n",
"430/430 [==============================] - 0s - loss: 0.0095 - acc: 0.2209 - val_loss: 0.0053 - val_acc: 0.1944\n",
"Epoch 35/40\n",
"430/430 [==============================] - 0s - loss: 0.0093 - acc: 0.2209 - val_loss: 0.0054 - val_acc: 0.1944\n",
"Epoch 36/40\n",
"430/430 [==============================] - 0s - loss: 0.0095 - acc: 0.2209 - val_loss: 0.0055 - val_acc: 0.1944\n",
"Epoch 37/40\n",
"430/430 [==============================] - 0s - loss: 0.0094 - acc: 0.2209 - val_loss: 0.0055 - val_acc: 0.1944\n",
"Epoch 38/40\n",
"430/430 [==============================] - 0s - loss: 0.0095 - acc: 0.2209 - val_loss: 0.0054 - val_acc: 0.1944\n",
"Epoch 39/40\n",
"430/430 [==============================] - 0s - loss: 0.0093 - acc: 0.2209 - val_loss: 0.0055 - val_acc: 0.1944\n",
"Epoch 40/40\n",
"430/430 [==============================] - 0s - loss: 0.0093 - acc: 0.2209 - val_loss: 0.0055 - val_acc: 0.1944\n",
"case completed: 1\n",
"Train on 432 samples, validate on 108 samples\n",
"Epoch 1/40\n",
"432/432 [==============================] - 11s - loss: 0.0146 - acc: 0.0718 - val_loss: 0.0056 - val_acc: 0.0370\n",
"Epoch 2/40\n",
"432/432 [==============================] - 0s - loss: 0.0082 - acc: 0.0718 - val_loss: 0.0056 - val_acc: 0.0370\n",
"Epoch 3/40\n",
"432/432 [==============================] - 0s - loss: 0.0086 - acc: 0.0718 - val_loss: 0.0052 - val_acc: 0.0370\n",
"Epoch 4/40\n",
"432/432 [==============================] - 0s - loss: 0.0080 - acc: 0.0718 - val_loss: 0.0048 - val_acc: 0.0370\n",
"Epoch 5/40\n",
"432/432 [==============================] - 0s - loss: 0.0077 - acc: 0.0718 - val_loss: 0.0050 - val_acc: 0.0370\n",
"Epoch 6/40\n",
"432/432 [==============================] - 0s - loss: 0.0078 - acc: 0.0718 - val_loss: 0.0049 - val_acc: 0.0370\n",
"Epoch 7/40\n",
"432/432 [==============================] - 0s - loss: 0.0076 - acc: 0.0718 - val_loss: 0.0047 - val_acc: 0.0370\n",
"Epoch 8/40\n",
"432/432 [==============================] - 0s - loss: 0.0076 - acc: 0.0718 - val_loss: 0.0048 - val_acc: 0.0370\n",
"Epoch 9/40\n",
"432/432 [==============================] - 0s - loss: 0.0075 - acc: 0.0718 - val_loss: 0.0047 - val_acc: 0.0370\n",
"Epoch 10/40\n",
"432/432 [==============================] - 0s - loss: 0.0073 - acc: 0.0718 - val_loss: 0.0047 - val_acc: 0.0370\n",
"Epoch 11/40\n",
"432/432 [==============================] - 0s - loss: 0.0073 - acc: 0.0718 - val_loss: 0.0047 - val_acc: 0.0370\n",
"Epoch 12/40\n",
"432/432 [==============================] - 0s - loss: 0.0073 - acc: 0.0718 - val_loss: 0.0047 - val_acc: 0.0370\n",
"Epoch 13/40\n",
"432/432 [==============================] - 0s - loss: 0.0073 - acc: 0.0718 - val_loss: 0.0048 - val_acc: 0.0370\n",
"Epoch 14/40\n",
"432/432 [==============================] - 0s - loss: 0.0076 - acc: 0.0718 - val_loss: 0.0048 - val_acc: 0.0370\n",
"Epoch 15/40\n",
"432/432 [==============================] - 0s - loss: 0.0074 - acc: 0.0718 - val_loss: 0.0048 - val_acc: 0.0370\n",
"Epoch 16/40\n",
"432/432 [==============================] - 0s - loss: 0.0073 - acc: 0.0718 - val_loss: 0.0048 - val_acc: 0.0370\n",
"Epoch 17/40\n",
"432/432 [==============================] - 0s - loss: 0.0076 - acc: 0.0718 - val_loss: 0.0048 - val_acc: 0.0370\n",
"Epoch 18/40\n",
"432/432 [==============================] - 0s - loss: 0.0073 - acc: 0.0718 - val_loss: 0.0047 - val_acc: 0.0370\n",
"Epoch 19/40\n",
"432/432 [==============================] - 0s - loss: 0.0072 - acc: 0.0718 - val_loss: 0.0047 - val_acc: 0.0370\n",
"Epoch 20/40\n",
"432/432 [==============================] - 0s - loss: 0.0077 - acc: 0.0718 - val_loss: 0.0048 - val_acc: 0.0370\n",
"Epoch 21/40\n",
"432/432 [==============================] - 0s - loss: 0.0075 - acc: 0.0718 - val_loss: 0.0047 - val_acc: 0.0370\n",
"Epoch 22/40\n",
"432/432 [==============================] - 0s - loss: 0.0073 - acc: 0.0718 - val_loss: 0.0047 - val_acc: 0.0370\n",
"Epoch 23/40\n",
"432/432 [==============================] - 0s - loss: 0.0078 - acc: 0.0718 - val_loss: 0.0047 - val_acc: 0.0370\n",
"Epoch 24/40\n",
"432/432 [==============================] - 0s - loss: 0.0077 - acc: 0.0718 - val_loss: 0.0047 - val_acc: 0.0370\n",
"Epoch 25/40\n",
"432/432 [==============================] - 0s - loss: 0.0073 - acc: 0.0718 - val_loss: 0.0047 - val_acc: 0.0370\n",
"Epoch 26/40\n",
"432/432 [==============================] - 0s - loss: 0.0077 - acc: 0.0718 - val_loss: 0.0047 - val_acc: 0.0370\n",
"Epoch 27/40\n",
"432/432 [==============================] - 0s - loss: 0.0074 - acc: 0.0718 - val_loss: 0.0047 - val_acc: 0.0370\n",
"Epoch 28/40\n",
"432/432 [==============================] - 0s - loss: 0.0076 - acc: 0.0718 - val_loss: 0.0047 - val_acc: 0.0370\n",
"Epoch 29/40\n",
"432/432 [==============================] - 0s - loss: 0.0076 - acc: 0.0718 - val_loss: 0.0047 - val_acc: 0.0370\n",
"Epoch 30/40\n",
"432/432 [==============================] - 0s - loss: 0.0073 - acc: 0.0718 - val_loss: 0.0047 - val_acc: 0.0370\n",
"Epoch 31/40\n",
"432/432 [==============================] - 0s - loss: 0.0073 - acc: 0.0718 - val_loss: 0.0047 - val_acc: 0.0370\n",
"Epoch 32/40\n",
"432/432 [==============================] - 0s - loss: 0.0075 - acc: 0.0718 - val_loss: 0.0048 - val_acc: 0.0370\n",
"Epoch 33/40\n",
"432/432 [==============================] - 0s - loss: 0.0076 - acc: 0.0718 - val_loss: 0.0048 - val_acc: 0.0370\n",
"Epoch 34/40\n",
"432/432 [==============================] - 0s - loss: 0.0075 - acc: 0.0718 - val_loss: 0.0048 - val_acc: 0.0370\n",
"Epoch 35/40\n",
"432/432 [==============================] - 0s - loss: 0.0074 - acc: 0.0718 - val_loss: 0.0049 - val_acc: 0.0370\n",
"Epoch 36/40\n",
"432/432 [==============================] - 0s - loss: 0.0077 - acc: 0.0718 - val_loss: 0.0048 - val_acc: 0.0370\n",
"Epoch 37/40\n",
"432/432 [==============================] - 0s - loss: 0.0076 - acc: 0.0718 - val_loss: 0.0048 - val_acc: 0.0370\n",
"Epoch 38/40\n",
"432/432 [==============================] - 0s - loss: 0.0074 - acc: 0.0718 - val_loss: 0.0048 - val_acc: 0.0370\n",
"Epoch 39/40\n",
"432/432 [==============================] - 0s - loss: 0.0078 - acc: 0.0718 - val_loss: 0.0048 - val_acc: 0.0370\n",
"Epoch 40/40\n",
"432/432 [==============================] - 0s - loss: 0.0076 - acc: 0.0718 - val_loss: 0.0048 - val_acc: 0.0370\n",
"case completed: 2\n",
"Train on 30 samples, validate on 8 samples\n",
"Epoch 1/40\n",
"30/30 [==============================] - 9s - loss: 0.0715 - acc: 0.0000e+00 - val_loss: 0.0015 - val_acc: 0.0000e+00\n",
"Epoch 2/40\n",
"30/30 [==============================] - 0s - loss: 0.0587 - acc: 0.0000e+00 - val_loss: 3.8799e-04 - val_acc: 0.0000e+00\n",
"Epoch 3/40\n",
"30/30 [==============================] - 0s - loss: 0.0546 - acc: 0.0000e+00 - val_loss: 2.4943e-05 - val_acc: 0.0000e+00\n",
"Epoch 4/40\n",
"30/30 [==============================] - 0s - loss: 0.0476 - acc: 0.0000e+00 - val_loss: 6.1844e-04 - val_acc: 0.0000e+00\n",
"Epoch 5/40\n",
"30/30 [==============================] - 0s - loss: 0.0416 - acc: 0.0000e+00 - val_loss: 0.0022 - val_acc: 0.0000e+00\n",
"Epoch 6/40\n",
"30/30 [==============================] - 0s - loss: 0.0348 - acc: 0.0000e+00 - val_loss: 0.0046 - val_acc: 0.0000e+00\n",
"Epoch 7/40\n",
"30/30 [==============================] - 0s - loss: 0.0404 - acc: 0.0000e+00 - val_loss: 0.0069 - val_acc: 0.0000e+00\n",
"Epoch 8/40\n",
"30/30 [==============================] - 0s - loss: 0.0366 - acc: 0.0000e+00 - val_loss: 0.0086 - val_acc: 0.0000e+00\n",
"Epoch 9/40\n",
"30/30 [==============================] - 0s - loss: 0.0342 - acc: 0.0000e+00 - val_loss: 0.0097 - val_acc: 0.0000e+00\n",
"Epoch 10/40\n",
"30/30 [==============================] - 0s - loss: 0.0367 - acc: 0.0000e+00 - val_loss: 0.0099 - val_acc: 0.0000e+00\n",
"Epoch 11/40\n",
"30/30 [==============================] - 0s - loss: 0.0325 - acc: 0.0000e+00 - val_loss: 0.0095 - val_acc: 0.0000e+00\n",
"Epoch 12/40\n",
"30/30 [==============================] - 0s - loss: 0.0351 - acc: 0.0000e+00 - val_loss: 0.0085 - val_acc: 0.0000e+00\n",
"Epoch 13/40\n",
"30/30 [==============================] - 0s - loss: 0.0332 - acc: 0.0000e+00 - val_loss: 0.0071 - val_acc: 0.0000e+00\n",
"Epoch 14/40\n",
"30/30 [==============================] - 0s - loss: 0.0318 - acc: 0.0000e+00 - val_loss: 0.0056 - val_acc: 0.0000e+00\n",
"Epoch 15/40\n",
"30/30 [==============================] - 0s - loss: 0.0302 - acc: 0.0000e+00 - val_loss: 0.0042 - val_acc: 0.0000e+00\n",
"Epoch 16/40\n",
"30/30 [==============================] - 0s - loss: 0.0299 - acc: 0.0000e+00 - val_loss: 0.0030 - val_acc: 0.0000e+00\n",
"Epoch 17/40\n",
"30/30 [==============================] - 0s - loss: 0.0262 - acc: 0.0000e+00 - val_loss: 0.0021 - val_acc: 0.0000e+00\n",
"Epoch 18/40\n",
"30/30 [==============================] - 0s - loss: 0.0273 - acc: 0.0000e+00 - val_loss: 0.0016 - val_acc: 0.0000e+00\n",
"Epoch 19/40\n",
"30/30 [==============================] - 0s - loss: 0.0348 - acc: 0.0000e+00 - val_loss: 0.0015 - val_acc: 0.0000e+00\n",
"Epoch 20/40\n",
"30/30 [==============================] - 0s - loss: 0.0267 - acc: 0.0000e+00 - val_loss: 0.0017 - val_acc: 0.0000e+00\n",
"Epoch 21/40\n",
"30/30 [==============================] - 0s - loss: 0.0268 - acc: 0.0000e+00 - val_loss: 0.0023 - val_acc: 0.0000e+00\n",
"Epoch 22/40\n",
"30/30 [==============================] - 0s - loss: 0.0277 - acc: 0.0000e+00 - val_loss: 0.0033 - val_acc: 0.0000e+00\n",
"Epoch 23/40\n",
"30/30 [==============================] - 0s - loss: 0.0248 - acc: 0.0000e+00 - val_loss: 0.0045 - val_acc: 0.0000e+00\n",
"Epoch 24/40\n",
"30/30 [==============================] - 0s - loss: 0.0292 - acc: 0.0000e+00 - val_loss: 0.0062 - val_acc: 0.0000e+00\n",
"Epoch 25/40\n",
"30/30 [==============================] - 0s - loss: 0.0254 - acc: 0.0000e+00 - val_loss: 0.0081 - val_acc: 0.0000e+00\n",
"Epoch 26/40\n",
"30/30 [==============================] - 0s - loss: 0.0248 - acc: 0.0000e+00 - val_loss: 0.0097 - val_acc: 0.0000e+00\n",
"Epoch 27/40\n",
"30/30 [==============================] - 0s - loss: 0.0238 - acc: 0.0000e+00 - val_loss: 0.0110 - val_acc: 0.0000e+00\n",
"Epoch 28/40\n",
"30/30 [==============================] - 0s - loss: 0.0262 - acc: 0.0000e+00 - val_loss: 0.0114 - val_acc: 0.0000e+00\n",
"Epoch 29/40\n",
"30/30 [==============================] - 0s - loss: 0.0232 - acc: 0.0000e+00 - val_loss: 0.0115 - val_acc: 0.0000e+00\n",
"Epoch 30/40\n",
"30/30 [==============================] - 0s - loss: 0.0252 - acc: 0.0000e+00 - val_loss: 0.0110 - val_acc: 0.0000e+00\n",
"Epoch 31/40\n",
"30/30 [==============================] - 0s - loss: 0.0237 - acc: 0.0000e+00 - val_loss: 0.0103 - val_acc: 0.0000e+00\n",
"Epoch 32/40\n",
"30/30 [==============================] - 0s - loss: 0.0252 - acc: 0.0000e+00 - val_loss: 0.0090 - val_acc: 0.0000e+00\n",
"Epoch 33/40\n",
"30/30 [==============================] - 0s - loss: 0.0238 - acc: 0.0000e+00 - val_loss: 0.0075 - val_acc: 0.0000e+00\n",
"Epoch 34/40\n",
"30/30 [==============================] - 0s - loss: 0.0254 - acc: 0.0000e+00 - val_loss: 0.0063 - val_acc: 0.0000e+00\n",
"Epoch 35/40\n",
"30/30 [==============================] - 0s - loss: 0.0262 - acc: 0.0000e+00 - val_loss: 0.0055 - val_acc: 0.0000e+00\n",
"Epoch 36/40\n",
"30/30 [==============================] - 0s - loss: 0.0267 - acc: 0.0000e+00 - val_loss: 0.0050 - val_acc: 0.0000e+00\n",
"Epoch 37/40\n",
"30/30 [==============================] - 0s - loss: 0.0248 - acc: 0.0000e+00 - val_loss: 0.0051 - val_acc: 0.0000e+00\n",
"Epoch 38/40\n",
"30/30 [==============================] - 0s - loss: 0.0251 - acc: 0.0000e+00 - val_loss: 0.0056 - val_acc: 0.0000e+00\n",
"Epoch 39/40\n",
"30/30 [==============================] - 0s - loss: 0.0200 - acc: 0.0000e+00 - val_loss: 0.0064 - val_acc: 0.0000e+00\n",
"Epoch 40/40\n",
"30/30 [==============================] - 0s - loss: 0.0258 - acc: 0.0000e+00 - val_loss: 0.0074 - val_acc: 0.0000e+00\n",
"case completed: 3\n",
"Train on 30 samples, validate on 8 samples\n",
"Epoch 1/40\n",
"30/30 [==============================] - 7s - loss: 0.0609 - acc: 0.0000e+00 - val_loss: 0.0011 - val_acc: 0.0000e+00\n",
"Epoch 2/40\n",
"30/30 [==============================] - 0s - loss: 0.0546 - acc: 0.0000e+00 - val_loss: 4.3755e-04 - val_acc: 0.0000e+00\n",
"Epoch 3/40\n",
"30/30 [==============================] - 0s - loss: 0.0441 - acc: 0.0000e+00 - val_loss: 4.8964e-04 - val_acc: 0.0000e+00\n",
"Epoch 4/40\n",
"30/30 [==============================] - 0s - loss: 0.0394 - acc: 0.0000e+00 - val_loss: 0.0014 - val_acc: 0.0000e+00\n",
"Epoch 5/40\n",
"30/30 [==============================] - 0s - loss: 0.0349 - acc: 0.0000e+00 - val_loss: 0.0030 - val_acc: 0.0000e+00\n",
"Epoch 6/40\n",
"30/30 [==============================] - 0s - loss: 0.0361 - acc: 0.0000e+00 - val_loss: 0.0051 - val_acc: 0.0000e+00\n",
"Epoch 7/40\n",
"30/30 [==============================] - 0s - loss: 0.0350 - acc: 0.0000e+00 - val_loss: 0.0073 - val_acc: 0.0000e+00\n",
"Epoch 8/40\n",
"30/30 [==============================] - 0s - loss: 0.0376 - acc: 0.0000e+00 - val_loss: 0.0090 - val_acc: 0.0000e+00\n",
"Epoch 9/40\n",
"30/30 [==============================] - 0s - loss: 0.0365 - acc: 0.0000e+00 - val_loss: 0.0099 - val_acc: 0.0000e+00\n",
"Epoch 10/40\n",
"30/30 [==============================] - 0s - loss: 0.0379 - acc: 0.0000e+00 - val_loss: 0.0099 - val_acc: 0.0000e+00\n",
"Epoch 11/40\n",
"30/30 [==============================] - 0s - loss: 0.0369 - acc: 0.0000e+00 - val_loss: 0.0092 - val_acc: 0.0000e+00\n",
"Epoch 12/40\n",
"30/30 [==============================] - 0s - loss: 0.0380 - acc: 0.0000e+00 - val_loss: 0.0079 - val_acc: 0.0000e+00\n",
"Epoch 13/40\n",
"30/30 [==============================] - 0s - loss: 0.0330 - acc: 0.0000e+00 - val_loss: 0.0066 - val_acc: 0.0000e+00\n",
"Epoch 14/40\n",
"30/30 [==============================] - 0s - loss: 0.0345 - acc: 0.0000e+00 - val_loss: 0.0054 - val_acc: 0.0000e+00\n",
"Epoch 15/40\n",
"30/30 [==============================] - 0s - loss: 0.0303 - acc: 0.0000e+00 - val_loss: 0.0043 - val_acc: 0.0000e+00\n",
"Epoch 16/40\n",
"30/30 [==============================] - 0s - loss: 0.0301 - acc: 0.0000e+00 - val_loss: 0.0035 - val_acc: 0.0000e+00\n",
"Epoch 17/40\n",
"30/30 [==============================] - 0s - loss: 0.0320 - acc: 0.0000e+00 - val_loss: 0.0030 - val_acc: 0.0000e+00\n",
"Epoch 18/40\n",
"30/30 [==============================] - 0s - loss: 0.0322 - acc: 0.0000e+00 - val_loss: 0.0028 - val_acc: 0.0000e+00\n",
"Epoch 19/40\n",
"30/30 [==============================] - 0s - loss: 0.0340 - acc: 0.0000e+00 - val_loss: 0.0028 - val_acc: 0.0000e+00\n",
"Epoch 20/40\n",
"30/30 [==============================] - 0s - loss: 0.0270 - acc: 0.0000e+00 - val_loss: 0.0030 - val_acc: 0.0000e+00\n",
"Epoch 21/40\n",
"30/30 [==============================] - 0s - loss: 0.0289 - acc: 0.0000e+00 - val_loss: 0.0034 - val_acc: 0.0000e+00\n",
"Epoch 22/40\n",
"30/30 [==============================] - 0s - loss: 0.0300 - acc: 0.0000e+00 - val_loss: 0.0041 - val_acc: 0.0000e+00\n",
"Epoch 23/40\n",
"30/30 [==============================] - 0s - loss: 0.0284 - acc: 0.0000e+00 - val_loss: 0.0049 - val_acc: 0.0000e+00\n",
"Epoch 24/40\n",
"30/30 [==============================] - 0s - loss: 0.0293 - acc: 0.0000e+00 - val_loss: 0.0060 - val_acc: 0.0000e+00\n",
"Epoch 25/40\n",
"30/30 [==============================] - 0s - loss: 0.0276 - acc: 0.0000e+00 - val_loss: 0.0070 - val_acc: 0.0000e+00\n",
"Epoch 26/40\n",
"30/30 [==============================] - 0s - loss: 0.0290 - acc: 0.0000e+00 - val_loss: 0.0080 - val_acc: 0.0000e+00\n",
"Epoch 27/40\n",
"30/30 [==============================] - 0s - loss: 0.0257 - acc: 0.0000e+00 - val_loss: 0.0089 - val_acc: 0.0000e+00\n",
"Epoch 28/40\n",
"30/30 [==============================] - 0s - loss: 0.0257 - acc: 0.0000e+00 - val_loss: 0.0097 - val_acc: 0.0000e+00\n",
"Epoch 29/40\n",
"30/30 [==============================] - 0s - loss: 0.0302 - acc: 0.0000e+00 - val_loss: 0.0100 - val_acc: 0.0000e+00\n",
"Epoch 30/40\n",
"30/30 [==============================] - 0s - loss: 0.0283 - acc: 0.0000e+00 - val_loss: 0.0100 - val_acc: 0.0000e+00\n",
"Epoch 31/40\n",
"30/30 [==============================] - 0s - loss: 0.0261 - acc: 0.0000e+00 - val_loss: 0.0097 - val_acc: 0.0000e+00\n",
"Epoch 32/40\n",
"30/30 [==============================] - 0s - loss: 0.0245 - acc: 0.0000e+00 - val_loss: 0.0094 - val_acc: 0.0000e+00\n",
"Epoch 33/40\n",
"30/30 [==============================] - 0s - loss: 0.0279 - acc: 0.0000e+00 - val_loss: 0.0088 - val_acc: 0.0000e+00\n",
"Epoch 34/40\n",
"30/30 [==============================] - 0s - loss: 0.0274 - acc: 0.0000e+00 - val_loss: 0.0081 - val_acc: 0.0000e+00\n",
"Epoch 35/40\n",
"30/30 [==============================] - 0s - loss: 0.0219 - acc: 0.0000e+00 - val_loss: 0.0075 - val_acc: 0.0000e+00\n",
"Epoch 36/40\n",
"30/30 [==============================] - 0s - loss: 0.0269 - acc: 0.0000e+00 - val_loss: 0.0071 - val_acc: 0.0000e+00\n",
"Epoch 37/40\n",
"30/30 [==============================] - 0s - loss: 0.0242 - acc: 0.0000e+00 - val_loss: 0.0069 - val_acc: 0.0000e+00\n",
"Epoch 38/40\n",
"30/30 [==============================] - 0s - loss: 0.0235 - acc: 0.0000e+00 - val_loss: 0.0070 - val_acc: 0.0000e+00\n",
"Epoch 39/40\n",
"30/30 [==============================] - 0s - loss: 0.0281 - acc: 0.0000e+00 - val_loss: 0.0072 - val_acc: 0.0000e+00\n",
"Epoch 40/40\n",
"30/30 [==============================] - 0s - loss: 0.0280 - acc: 0.0000e+00 - val_loss: 0.0074 - val_acc: 0.0000e+00\n",
"case completed: 4\n",
"All cases completed!!\n"
]
}
],
"source": [
"from keras.models import Sequential\n",
"from keras.layers import Dense\n",
"from keras.layers import Dropout\n",
"import os\n",
"\n",
"\n",
"total_ans = []\n",
"\n",
"for page in range(1,5):\n",
" \n",
" #提取資料\n",
" page_data = list_train_order_value[page]\n",
" \n",
" page_data = np.trim_zeros(page_data, trim='f') #拿掉前面有太多0的，會有bias\n",
" \n",
" page_data_label = page_data[10:] #每10組給出一次答案，所以答案不包含前10組\n",
" \n",
" train_feature = []\n",
" for i in range(len(page_data_label)):\n",
" train_feature.append(page_data[i:i+10])\n",
" train_feature_array = np.array(train_feature)\n",
" \n",
" \n",
" #標準化\n",
" train_feature_array = train_feature_array/max(page_data)\n",
" page_data_label = page_data_label/max(page_data)\n",
"\n",
" #print(train_feature_array) #測試用\n",
" #print(page_data_label) #測試用\n",
" \n",
" \n",
" #訓練模型\n",
" model = Sequential() \n",
" \n",
" model.add(Dense(units=100, \n",
" input_dim=10, \n",
" kernel_initializer='uniform'))\n",
" model.add(Dropout(0.5))\n",
"\n",
" model.add(Dense(units=100, \n",
" kernel_initializer='uniform'))\n",
" model.add(Dropout(0.5))\n",
"\n",
" model.add(Dense(units=1))\n",
"\n",
" #print(model.summary()) #測試用\n",
"\n",
" '''\n",
" weights_path = 'Savemodels/WIKI(Kaggles)_MLP.h5'\n",
"\n",
" if os.path.isfile(weights_path): \n",
" model.load_weights(weights_path) #只要跑過一次就取出來直接使用\n",
" else:\n",
" pass\n",
" '''\n",
" \n",
" model.compile(loss='mse', \n",
" optimizer='adam', metrics=['accuracy'])\n",
"\n",
" model.fit(x=train_feature_array, y=page_data_label, \n",
" validation_split=0.2, epochs=20, batch_size=200, verbose=1)\n",
" \n",
" #model.save_weights(\"Savemodels/WIKI(Kaggles)_MLP.h5\") #方便下一次跑?\n",
" \n",
" \n",
" \n",
" #訓練後跑答案\n",
" ans_list = train_feature_array[-1].reshape(-1,10)\n",
" rand_list = np.random.randn(60) #常態分佈的雜訊\n",
" \n",
" for i in range(60): #做好做滿60次預測\n",
" ans = model.predict(ans_list[i:i+10].reshape(-1,10))\n",
" ans_add_noise = ans + 0.2*np.std(train_feature_array)*rand_list[i]\n",
" #避免ans太快收斂，加入rand條件讓他上下波動\n",
" \n",
" if ans_add_noise < 0:\n",
" ans_add_noise = 0 #瀏覽人次沒有在<0的啦～\n",
" else:\n",
" ans_add_noise = ans_add_noise\n",
" \n",
" ans_list = np.append(ans_list, ans_add_noise) \n",
" \n",
" \n",
" ans_list_round = np.round(ans_list*np.max(page_data)) #四捨五入成整數的瀏覽人次\n",
" real_ans = ans_list_round[11:] #最終每組60個數字的答案\n",
" \n",
" \n",
" total_ans.append(real_ans) #把每一組的60個數字通通加總在一張表上，最後再貼回去答案卷\n",
" print('case completed: ', page)\n",
" \n",
" \n",
"print('All cases completed!!')"
]
},
{
"cell_type": "code",
"execution_count": 275,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"[array([ 2., 3., 2., 2., 1., 2., 2., 2., 2., 2., 2., 2., 2.,\n",
" 2., 2., 1., 3., 2., 2., 1., 2., 2., 2., 2., 2., 2.,\n",
" 2., 2., 2., 2., 2., 2., 2., 2., 3., 3., 2., 2., 2.,\n",
" 2., 2., 2., 2., 3., 2., 2., 1., 2., 2., 1., 2., 2.,\n",
" 2., 2., 2., 2., 2., 2., 3.]),\n",
" array([ 3., 4., 3., 3., 3., 4., 4., 2., 4., 4., 3., 4., 3.,\n",
" 3., 3., 3., 3., 4., 4., 3., 3., 4., 4., 4., 3., 3.,\n",
" 3., 3., 4., 4., 3., 3., 2., 3., 3., 3., 3., 3., 4.,\n",
" 4., 3., 3., 3., 3., 3., 3., 4., 2., 3., 3., 4., 3.,\n",
" 4., 3., 3., 3., 4., 4., 4.]),\n",
" array([ 11744., 13556., 18877., 14524., 14094., 12429., 13344.,\n",
" 8226., 6892., 5692., 7635., 11605., 10471., 13326.,\n",
" 11992., 13345., 14287., 11901., 15411., 13130., 11847.,\n",
" 12417., 11660., 5249., 5998., 7023., 7795., 9281.,\n",
" 10505., 14180., 15272., 12683., 16766., 14914., 12857.,\n",
" 11917., 12323., 5879., 8393., 8208., 7761., 9543.,\n",
" 8144., 9954., 10435., 11769., 13544., 17232., 13318.,\n",
" 15024., 11791., 11072., 11672., 9204., 7347., 9714.,\n",
" 10393., 12922., 10333.]),\n",
" array([ 100., 171., 135., 167., 203., 206., 165., 173., 209.,\n",
" 208., 137., 95., 102., 108., 122., 158., 141., 125.,\n",
" 78., 150., 154., 104., 122., 123., 99., 145., 187.,\n",
" 158., 121., 138., 88., 89., 120., 130., 138., 104.,\n",
" 86., 85., 167., 214., 149., 119., 155., 131., 93.,\n",
" 152., 193., 138., 152., 121., 155., 136., 153., 84.,\n",
" 147., 107., 123., 127., 143.])]"
]
},
"execution_count": 275,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"total_ans"
]
},
{
"cell_type": "code",
"execution_count": 250,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"total_ans = np.array(total_ans).reshape(1,-1)"
]
},
{
"cell_type": "code",
"execution_count": 247,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"array([[ 2.00000000e+00, 4.00000000e+00, 4.00000000e+00,\n",
" 4.00000000e+00, 3.00000000e+00, 4.00000000e+00,\n",
" 3.00000000e+00, 3.00000000e+00, 5.00000000e+00,\n",
" 5.00000000e+00, 3.00000000e+00, 5.00000000e+00,\n",
" 3.00000000e+00, 3.00000000e+00, 4.00000000e+00,\n",
" 4.00000000e+00, 5.00000000e+00, 4.00000000e+00,\n",
" 3.00000000e+00, 4.00000000e+00, 4.00000000e+00,\n",
" 4.00000000e+00, 4.00000000e+00, 4.00000000e+00,\n",
" 4.00000000e+00, 5.00000000e+00, 3.00000000e+00,\n",
" 4.00000000e+00, 3.00000000e+00, 3.00000000e+00,\n",
" 4.00000000e+00, 3.00000000e+00, 5.00000000e+00,\n",
" 4.00000000e+00, 4.00000000e+00, 3.00000000e+00,\n",
" 4.00000000e+00, 4.00000000e+00, 3.00000000e+00,\n",
" 5.00000000e+00, 3.00000000e+00, 3.00000000e+00,\n",
" 5.00000000e+00, 4.00000000e+00, 4.00000000e+00,\n",
" 4.00000000e+00, 3.00000000e+00, 3.00000000e+00,\n",
" 3.00000000e+00, 5.00000000e+00, 4.00000000e+00,\n",
" 4.00000000e+00, 5.00000000e+00, 4.00000000e+00,\n",
" 3.00000000e+00, 4.00000000e+00, 4.00000000e+00,\n",
" 3.00000000e+00, 3.00000000e+00, 1.00000000e+00,\n",
" 1.00000000e+00, 2.00000000e+00, 2.00000000e+00,\n",
" 2.00000000e+00, 2.00000000e+00, 2.00000000e+00,\n",
" 2.00000000e+00, 2.00000000e+00, 1.00000000e+00,\n",
" 1.00000000e+00, 2.00000000e+00, 1.00000000e+00,\n",
" 2.00000000e+00, 1.00000000e+00, 2.00000000e+00,\n",
" 2.00000000e+00, 2.00000000e+00, 1.00000000e+00,\n",
" 2.00000000e+00, 1.00000000e+00, 2.00000000e+00,\n",
" 2.00000000e+00, 2.00000000e+00, 1.00000000e+00,\n",
" 1.00000000e+00, 1.00000000e+00, 1.00000000e+00,\n",
" 2.00000000e+00, 2.00000000e+00, 1.00000000e+00,\n",
" 2.00000000e+00, 1.00000000e+00, 2.00000000e+00,\n",
" 2.00000000e+00, 2.00000000e+00, 1.00000000e+00,\n",
" 2.00000000e+00, 2.00000000e+00, 1.00000000e+00,\n",
" 2.00000000e+00, 2.00000000e+00, 1.00000000e+00,\n",
" 1.00000000e+00, 2.00000000e+00, 2.00000000e+00,\n",
" 2.00000000e+00, 3.00000000e+00, 2.00000000e+00,\n",
" 1.00000000e+00, 2.00000000e+00, 2.00000000e+00,\n",
" 2.00000000e+00, 2.00000000e+00, 2.00000000e+00,\n",
" 2.00000000e+00, 2.00000000e+00, 2.00000000e+00,\n",
" 2.00000000e+00, 4.00000000e+00, 3.00000000e+00,\n",
" 2.00000000e+00, 4.00000000e+00, 4.00000000e+00,\n",
" 2.00000000e+00, 4.00000000e+00, 4.00000000e+00,\n",
" 2.00000000e+00, 3.00000000e+00, 4.00000000e+00,\n",
" 4.00000000e+00, 3.00000000e+00, 3.00000000e+00,\n",
" 3.00000000e+00, 4.00000000e+00, 3.00000000e+00,\n",
" 3.00000000e+00, 3.00000000e+00, 4.00000000e+00,\n",
" 3.00000000e+00, 3.00000000e+00, 3.00000000e+00,\n",
" 3.00000000e+00, 3.00000000e+00, 3.00000000e+00,\n",
" 3.00000000e+00, 4.00000000e+00, 3.00000000e+00,\n",
" 4.00000000e+00, 3.00000000e+00, 3.00000000e+00,\n",
" 3.00000000e+00, 3.00000000e+00, 4.00000000e+00,\n",
" 3.00000000e+00, 4.00000000e+00, 4.00000000e+00,\n",
" 4.00000000e+00, 3.00000000e+00, 4.00000000e+00,\n",
" 3.00000000e+00, 4.00000000e+00, 3.00000000e+00,\n",
" 3.00000000e+00, 4.00000000e+00, 4.00000000e+00,\n",
" 4.00000000e+00, 3.00000000e+00, 3.00000000e+00,\n",
" 3.00000000e+00, 2.00000000e+00, 3.00000000e+00,\n",
" 4.00000000e+00, 3.00000000e+00, 3.00000000e+00,\n",
" 4.00000000e+00, 3.00000000e+00, 3.00000000e+00,\n",
" 1.43700000e+04, 1.20400000e+04, 7.76600000e+03,\n",
" 1.50500000e+04, 1.35890000e+04, 1.45690000e+04,\n",
" 1.29000000e+04, 9.97100000e+03, 9.32700000e+03,\n",
" 8.48300000e+03, 9.00600000e+03, 7.98600000e+03,\n",
" 9.01500000e+03, 8.23900000e+03, 9.00100000e+03,\n",
" 7.90600000e+03, 1.16470000e+04, 8.99400000e+03,\n",
" 1.08550000e+04, 1.51180000e+04, 1.52050000e+04,\n",
" 1.39440000e+04, 1.46860000e+04, 1.19160000e+04,\n",
" 8.37500000e+03, 7.31000000e+03, 1.08790000e+04,\n",
" 3.44400000e+03, 5.44400000e+03, 3.40600000e+03,\n",
" 8.93100000e+03, 1.43530000e+04, 9.32600000e+03,\n",
" 4.46900000e+03, 7.68100000e+03, 1.00510000e+04,\n",
" 9.31900000e+03, 8.01900000e+03, 1.25840000e+04,\n",
" 7.60500000e+03, 1.01790000e+04, 1.24920000e+04,\n",
" 1.20980000e+04, 1.38890000e+04, 7.16100000e+03,\n",
" 7.70800000e+03, 1.32620000e+04, 1.15010000e+04,\n",
" 1.34130000e+04, 1.14510000e+04, 1.31420000e+04,\n",
" 1.48930000e+04, 1.12720000e+04, 1.17310000e+04,\n",
" 1.33480000e+04, 1.42830000e+04, 1.09480000e+04,\n",
" 1.26090000e+04, 1.10810000e+04, 1.06000000e+02,\n",
" 1.67000000e+02, 1.64000000e+02, 1.91000000e+02,\n",
" 1.11000000e+02, 1.81000000e+02, 1.68000000e+02,\n",
" 1.34000000e+02, 5.30000000e+01, 7.50000000e+01,\n",
" 1.29000000e+02, 1.35000000e+02, 1.25000000e+02,\n",
" 1.41000000e+02, 1.80000000e+02, 9.20000000e+01,\n",
" 1.23000000e+02, 1.60000000e+02, 1.79000000e+02,\n",
" 1.60000000e+02, 1.12000000e+02, 1.64000000e+02,\n",
" 1.68000000e+02, 1.32000000e+02, 9.00000000e+01,\n",
" 1.24000000e+02, 1.31000000e+02, 1.43000000e+02,\n",
" 1.80000000e+02, 1.59000000e+02, 1.53000000e+02,\n",
" 1.30000000e+02, 1.29000000e+02, 1.31000000e+02,\n",
" 1.53000000e+02, 1.27000000e+02, 1.61000000e+02,\n",
" 1.75000000e+02, 1.65000000e+02, 1.23000000e+02,\n",
" 9.20000000e+01, 1.22000000e+02, 9.40000000e+01,\n",
" 5.10000000e+01, 9.20000000e+01, 1.31000000e+02,\n",
" 2.10000000e+02, 1.35000000e+02, 1.42000000e+02,\n",
" 1.20000000e+02, 1.73000000e+02, 2.28000000e+02,\n",
" 2.17000000e+02, 1.82000000e+02, 2.07000000e+02,\n",
" 1.59000000e+02, 1.54000000e+02, 1.58000000e+02,\n",
" 1.77000000e+02]])"
]
},
"execution_count": 247,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"total_ans"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"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": "3.5.4"
}
},
"nbformat": 4,
"nbformat_minor": 1
}
| mit |
MLinTT/ML2016 | torch/ex2/example-logistic-regression.ipynb | 2 | 13508 | {
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"require 'nn'\n",
"require 'optim'\n",
"require 'csvigo'"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"loaded = csvigo.load('example-logistic-regression.csv')"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"brands = torch.Tensor(loaded.brand)\n",
"females = torch.Tensor(loaded.female)\n",
"ages = torch.Tensor(loaded.age)\n",
"dataset_inputs = torch.Tensor( (#brands)[1],2 )\n",
"dataset_inputs[{ {},1 }] = females\n",
"dataset_inputs[{ {},2 }] = ages\n",
"dataset_outputs = brands\n",
"numberOfBrands = torch.max(dataset_outputs) - torch.min(dataset_outputs) + 1"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"linLayer = nn.Linear(2,3)\n",
"softMaxLayer = nn.LogSoftMax()\n",
"model = nn.Sequential()\n",
"model:add(linLayer)\n",
"model:add(softMaxLayer)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"criterion = nn.ClassNLLCriterion()"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"x, dl_dx = model:getParameters()\n",
"feval = function(x_new)\n",
" if x ~= x_new then\n",
" x:copy(x_new)\n",
" end\n",
"\n",
" _nidx_ = (_nidx_ or 0) + 1\n",
" if _nidx_ > (#dataset_inputs)[1] then _nidx_ = 1 end\n",
"\n",
" local inputs = dataset_inputs[_nidx_]\n",
" local target = dataset_outputs[_nidx_]\n",
"\n",
" dl_dx:zero()\n",
"\n",
" local loss_x = criterion:forward(model:forward(inputs), target)\n",
" model:backward(inputs, criterion:backward(model.output, target))\n",
"\n",
" return loss_x, dl_dx\n",
"end\n",
"sgd_params = {\n",
" learningRate = 1e-3,\n",
" learningRateDecay = 1e-4,\n",
" weightDecay = 0,\n",
" momentum = 0\n",
"}"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"epochs = 1e2 -- number of times to cycle over our training data\n",
"\n",
"print('')\n",
"print('============================================================')\n",
"print('Training with SGD')\n",
"print('')\n",
"\n",
"for i = 1,epochs do\n",
"\n",
" current_loss = 0\n",
"\n",
" for i = 1,(#dataset_inputs)[1] do\n",
"\n",
" _,fs = optim.sgd(feval,x,sgd_params)\n",
"\n",
" current_loss = current_loss + fs[1]\n",
" end\n",
"\n",
" current_loss = current_loss / (#dataset_inputs)[1]\n",
" print('epoch = ' .. i .. \n",
"\t ' of ' .. epochs .. \n",
"\t ' current loss = ' .. current_loss)\n",
"\n",
"end"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"model:reset()\n",
"\n",
"-- next we re-define the closure that evaluates f and df/dx, so that\n",
"-- it estimates the true f, and true (exact) df/dx, over the entire\n",
"-- dataset. This is a full batch approach.\n",
"\n",
"feval = function(x_new)\n",
" -- set x to x_new, if differnt\n",
" -- (in this simple example, x_new will typically always point to x,\n",
" -- so the copy is really useless)\n",
" if x ~= x_new then\n",
" x:copy(x_new)\n",
" end\n",
"\n",
" -- reset gradients (gradients are always accumulated, to accomodate \n",
" -- batch methods)\n",
" dl_dx:zero()\n",
"\n",
" -- and batch over the whole training dataset:\n",
" local loss_x = 0\n",
" for i = 1,(#dataset_inputs)[1] do\n",
" -- select a new training sample\n",
" _nidx_ = (_nidx_ or 0) + 1\n",
" if _nidx_ > (#dataset_inputs)[1] then _nidx_ = 1 end\n",
"\n",
" local inputs = dataset_inputs[_nidx_]\n",
" local target = dataset_outputs[_nidx_]\n",
"\n",
" -- evaluate the loss function and its derivative wrt x, for that sample\n",
" loss_x = loss_x + criterion:forward(model:forward(inputs), target)\n",
" model:backward(inputs, criterion:backward(model.output, target))\n",
" end\n",
"\n",
" -- normalize with batch size\n",
" loss_x = loss_x / (#dataset_inputs)[1]\n",
" dl_dx = dl_dx:div( (#dataset_inputs)[1] )\n",
"\n",
" -- return loss(x) and dloss/dx\n",
" return loss_x, dl_dx\n",
"end"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"lbfgs_params = {\n",
" lineSearch = optim.lswolfe,\n",
" maxIter = epochs,\n",
" verbose = true\n",
"}\n",
"\n",
"print('')\n",
"print('============================================================')\n",
"print('Training with L-BFGS')\n",
"print('')\n",
"\n",
"_,fs = optim.lbfgs(feval,x,lbfgs_params)\n",
"\n",
"-- fs contains all the evaluations of f, during optimization\n",
"\n",
"print('history of L-BFGS evaluations:')\n",
"print(fs)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"\n",
"print('')\n",
"print('============================================================')\n",
"print('Testing the model')\n",
"print('')\n",
"\n",
"-- Now that the model is trained, one can test it by evaluating it\n",
"-- on new samples.\n",
"\n",
"-- The model constructed and trained above computes the probabilities\n",
"-- of each class given the input values.\n",
"\n",
"-- We want to compare our model's results with those from the text.\n",
"-- The input variables have narrow ranges, so we just compare all possible\n",
"-- input variables in the training data.\n",
"\n",
"-- Determine actual frequency of the each female-age pair in the \n",
"-- training data\n",
"\n",
"-- return index of largest value\n",
"function maxIndex(a,b,c)\n",
" if a >=b and a >= c then return 1 \n",
" elseif b >= a and b >= c then return 2\n",
" else return 3 end\n",
"end\n",
"\n",
"-- return predicted brand and probabilities of each brand\n",
"-- for the model in the text\n",
"\n",
"-- The R code in the text computes the probabilities of choosing\n",
"-- brands 2 and 3 relative to the probability of choosing brand 1:\n",
"-- Prob(brand=2)/prob(brand=1) = exp(-11.77 + 0.52*female + 0.37*age)\n",
"-- Prob(brand=3)/prob(brand=1) = exp(-22.72 + 0.47*female + 0.69*age)\n",
"function predictText(age, female)\n",
" -- 1: calculate the \"logit's\"\n",
" -- The coefficients come from the text.\n",
" -- If you download the R script and run it, you may see slightly\n",
" -- different results.\n",
" local logit1 = 0\n",
" local logit2 = -11.774655 + 0.523814 * female + 0.368206 * age\n",
" local logit3 = -22.721396 + 0.465941 * female + 0.685908 * age\n",
"\n",
" -- 2: calculate the unnormalized probabilities\n",
" local uprob1 = math.exp(logit1)\n",
" local uprob2 = math.exp(logit2)\n",
" local uprob3 = math.exp(logit3)\n",
"\n",
" -- 3: normalize the probabilities\n",
" local z = uprob1 + uprob2 + uprob3\n",
" local prob1 = (1/z) * uprob1\n",
" local prob2 = (1/z) * uprob2\n",
" local prob3 = (1/z) * uprob3\n",
"\n",
" return maxIndex(prob1, prob2, prob3), prob1, prob2, prob3\n",
"end\n",
"\n",
"-- return predicted brand and the probabilities of each brand\n",
"-- for our model\n",
"function predictOur(age, female)\n",
" local input = torch.Tensor(2)\n",
" input[1] = female -- must be in same order as when the model was trained!\n",
" input[2] = age\n",
" local logProbs = model:forward(input) \n",
" --print('predictOur', age, female, input)\n",
" local probs = torch.exp(logProbs)\n",
" --print('logProbs', logProbs)\n",
" --print('probs', probs[1], probs[2], probs[3] )\n",
" local prob1, prob2, prob3 = probs[1], probs[2], probs[3]\n",
" return maxIndex(prob1, prob2, prob3), prob1, prob2, prob3\n",
"end\n",
" \n",
"counts = {}\n",
"\n",
"function makeKey(age, brand, female)\n",
" -- return a string containing the values\n",
"\n",
" -- Note that returning a table will not work, because each\n",
" -- table is unique.\n",
"\n",
" -- Because Lua interns the strings, a string with a given sequence\n",
" -- of characters is stored only once.\n",
" return string.format('%2d%1d%1f', age, brand, female)\n",
"end\n",
"\n",
"for i = 1,(#brands)[1] do\n",
" local brand = brands[i]\n",
" local female = females[i]\n",
" local age = ages[i]\n",
" local key = makeKey (age, brand, female)\n",
" counts[key] = (counts[key] or 0) + 1\n",
"end\n",
"\n",
"-- return probability of each brand conditioned on age and female\n",
"function actualProbabilities(age, female)\n",
" function countOf(age, brand, female)\n",
" return counts[makeKey(age, brand, female)] or 0\n",
" end\n",
" local count1 = countOf(age, 1, female)\n",
" local count2 = countOf(age, 2, female)\n",
" local count3 = countOf(age, 3, female)\n",
" local sumCounts = count1 + count2 + count3\n",
" if sumCounts == 0 then\n",
" return 0, 0, 0\n",
" else\n",
" return count1/sumCounts, count2/sumCounts, count3/sumCounts\n",
" end\n",
"end\n",
"\n",
"\n",
"print(' ')\n",
"print('summary of data')\n",
"summarizeData()\n",
"\n",
"print(' ')\n",
"print('training variables')\n",
"for k,v in pairs(sgd_params) do\n",
" print(string.format('%20s %f', k, v))\n",
"end\n",
"print(string.format('%20s %f', 'epochs', epochs))\n",
"\n",
"print(' ')\n",
"print('current loss', current_loss)\n",
"\n",
"-- print the headers \n",
"print(' ')\n",
"lineFormat = '%-6s %-3s| %-17s | %-17s | %-17s | %-1s %-1s %-1s'\n",
"print(\n",
" string.format(lineFormat,\n",
"\t\t '', '', \n",
"\t\t 'actual probs', 'text probs', 'our probs', \n",
"\t\t 'best', '', ''))\n",
"choices = 'brnd1 brnd2 brnd3'\n",
"print(string.format(lineFormat,\n",
"\t\t 'female', 'age', \n",
"\t\t choices, choices, choices, \n",
"\t\t 'a', 't', 'o'))\n",
"\n",
"-- print each row in the table\n",
"\n",
"function formatFemale(female)\n",
" return string.format('%1d', female)\n",
"end\n",
"\n",
"function formatAge(age)\n",
" return string.format('%2d', age)\n",
"end\n",
"\n",
"function formatProbs(p1, p2, p3)\n",
" return string.format('%5.3f %5.3f %5.3f', p1, p2, p3)\n",
"end\n",
"\n",
"function indexString(p1, p2, p3)\n",
" -- return index of highest probability or '-' if nearly all zeroes\n",
" if p1 < 0.001 and p2 < 0.001 and p3 < 0.001 then\n",
" return '-'\n",
" else \n",
" return string.format('%1d', maxIndex(p1, p2, p3))\n",
" end\n",
"end\n",
"\n",
"-- print table rows and accumulate accuracy\n",
"for female = 0,1 do\n",
" for age = torch.min(ages),torch.max(ages) do\n",
" -- calculate the actual probabilities in the training data\n",
" local actual1, actual2, actual3 = actualProbabilities(age, female)\n",
" -- calculate the prediction and probabilities using the model in the text\n",
" local textBrand, textProb1, textProb2, textProb3 = \n",
"\t predictText(age, female)\n",
" -- calculate the probabilities using the model we just trained\n",
" --print(\"main\", age, female)\n",
" local ourBrand, ourProb1, ourProb2, ourProb3 = \n",
"\t predictOur(age, female)\n",
" print(\n",
"\t string.format(lineFormat,\n",
"\t\t formatFemale(female), \n",
"\t\t formatAge(age),\n",
"\t\t formatProbs(actual1, actual2, actual3),\n",
"\t\t formatProbs(textProb1, textProb2, textProb3),\n",
"\t\t formatProbs(ourProb1, ourProb2, ourProb3),\n",
"\t\t indexString(actual1,actual2,actual3),\n",
"\t\t indexString(textProb1,textProb2,textProb3),\n",
"\t\t indexString(ourProb1,ourProb2,ourProb3))\n",
"\t )\n",
" end\n",
"end"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": []
}
],
"metadata": {
"kernelspec": {
"display_name": "iTorch",
"language": "lua",
"name": "itorch"
},
"language_info": {
"name": "lua",
"version": "20100"
}
},
"nbformat": 4,
"nbformat_minor": 0
}
| mit |
SJSlavin/phys202-2015-work | assignments/assignment05/InteractEx04.ipynb | 1 | 21106 | {
"cells": [
{
"cell_type": "markdown",
"metadata": {
"nbgrader": {}
},
"source": [
"# Interact Exercise 4"
]
},
{
"cell_type": "markdown",
"metadata": {
"nbgrader": {}
},
"source": [
"## Imports"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {
"collapsed": false,
"nbgrader": {}
},
"outputs": [],
"source": [
"%matplotlib inline\n",
"import matplotlib.pyplot as plt\n",
"import numpy as np"
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {
"collapsed": false,
"nbgrader": {}
},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
":0: FutureWarning: IPython widgets are experimental and may change in the future.\n"
]
}
],
"source": [
"from IPython.html.widgets import interact, interactive, fixed\n",
"from IPython.display import display"
]
},
{
"cell_type": "markdown",
"metadata": {
"nbgrader": {}
},
"source": [
"## Line with Gaussian noise"
]
},
{
"cell_type": "markdown",
"metadata": {
"nbgrader": {}
},
"source": [
"Write a function named `random_line` that creates `x` and `y` data for a line with y direction random noise that has a normal distribution $N(0,\\sigma^2)$:\n",
"\n",
"$$\n",
"y = m x + b + N(0,\\sigma^2)\n",
"$$\n",
"\n",
"Be careful about the `sigma=0.0` case."
]
},
{
"cell_type": "code",
"execution_count": 41,
"metadata": {
"collapsed": false,
"nbgrader": {
"checksum": "f1fccd14526477d1457886a737404055",
"solution": true
}
},
"outputs": [],
"source": [
"def random_line(m, b, sigma, size=10):\n",
" \"\"\"Create a line y = m*x + b + N(0,sigma**2) between x=[-1.0,1.0]\n",
" \n",
" Parameters\n",
" ----------\n",
" m : float\n",
" The slope of the line.\n",
" b : float\n",
" The y-intercept of the line.\n",
" sigma : float\n",
" The standard deviation of the y direction normal distribution noise.\n",
" size : int\n",
" The number of points to create for the line.\n",
" \n",
" Returns\n",
" -------\n",
" x : array of floats\n",
" The array of x values for the line with `size` points.\n",
" y : array of floats\n",
" The array of y values for the lines with `size` points.\n",
" \"\"\"\n",
" # YOUR CODE HERE\n",
" \n",
" #http://docs.scipy.org/doc/numpy/reference/generated/numpy.random.randn.html#numpy.random.randn\n",
" x = np.linspace(-1.0, 1.0, num=size)\n",
" y = (m * x) + b + (sigma * np.random.randn(size))\n",
" return x, y\n"
]
},
{
"cell_type": "code",
"execution_count": 42,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"(array([-1. , -0.89473684, -0.78947368, -0.68421053, -0.57894737,\n",
" -0.47368421, -0.36842105, -0.26315789, -0.15789474, -0.05263158,\n",
" 0.05263158, 0.15789474, 0.26315789, 0.36842105, 0.47368421,\n",
" 0.57894737, 0.68421053, 0.78947368, 0.89473684, 1. ]), array([ 2.73002584, 0.99870474, 1.79778587, 5.12282987, 2.92003379,\n",
" 2.31966011, 9.01716784, 1.34611584, 4.81963413, 3.04496532,\n",
" -1.12553022, 0.97791011, 1.33357251, 3.44947373, 3.6699668 ,\n",
" 2.49400079, 6.99536361, 2.84516679, 4.31651931, 6.75180823]))\n"
]
}
],
"source": [
"print(random_line(2, 3, 2, 20))"
]
},
{
"cell_type": "code",
"execution_count": 43,
"metadata": {
"collapsed": false,
"deletable": false,
"nbgrader": {
"checksum": "085b717fea11f553f5549a88b1090e24",
"grade": true,
"grade_id": "interactex04a",
"points": 2
}
},
"outputs": [],
"source": [
"m = 0.0; b = 1.0; sigma=0.0; size=3\n",
"x, y = random_line(m, b, sigma, size)\n",
"assert len(x)==len(y)==size\n",
"assert list(x)==[-1.0,0.0,1.0]\n",
"assert list(y)==[1.0,1.0,1.0]\n",
"sigma = 1.0\n",
"m = 0.0; b = 0.0\n",
"size = 500\n",
"x, y = random_line(m, b, sigma, size)\n",
"assert np.allclose(np.mean(y-m*x-b), 0.0, rtol=0.1, atol=0.1)\n",
"assert np.allclose(np.std(y-m*x-b), sigma, rtol=0.1, atol=0.1)"
]
},
{
"cell_type": "markdown",
"metadata": {
"nbgrader": {}
},
"source": [
"Write a function named `plot_random_line` that takes the same arguments as `random_line` and creates a random line using `random_line` and then plots the `x` and `y` points using Matplotlib's `scatter` function:\n",
"\n",
"* Make the marker color settable through a `color` keyword argument with a default of `red`.\n",
"* Display the range $x=[-1.1,1.1]$ and $y=[-10.0,10.0]$.\n",
"* Customize your plot to make it effective and beautiful."
]
},
{
"cell_type": "code",
"execution_count": 63,
"metadata": {
"collapsed": true,
"nbgrader": {}
},
"outputs": [],
"source": [
"def ticks_out(ax):\n",
" \"\"\"Move the ticks to the outside of the box.\"\"\"\n",
" ax.get_xaxis().set_tick_params(direction='out', width=1, which='both')\n",
" ax.get_yaxis().set_tick_params(direction='out', width=1, which='both')"
]
},
{
"cell_type": "code",
"execution_count": 77,
"metadata": {
"collapsed": false,
"nbgrader": {
"checksum": "701a9529400e32449715b0090b912d11",
"solution": true
}
},
"outputs": [],
"source": [
"def plot_random_line(m, b, sigma, size=10, color='red'):\n",
" \"\"\"Plot a random line with slope m, intercept b and size points.\"\"\"\n",
" x, y = random_line(m, b, sigma, size)\n",
" ax = plt.subplot(111)\n",
" \n",
" plt.scatter(x, y , color=color)\n",
" ticks_out(ax)\n",
" plt.xlim((-1.1, 1.1))\n",
" plt.ylim((-10.0, 10.0))"
]
},
{
"cell_type": "code",
"execution_count": 78,
"metadata": {
"collapsed": false,
"nbgrader": {
"solution": false
}
},
"outputs": [
{
"data": {
"image/png": "iVBORw0KGgoAAAANSUhEUgAAAXgAAAEECAYAAAAifS8cAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nLQAAAABJRU5ErkJggg==\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x7f7d104ac358>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"plot_random_line(5.0, -1.0, 2.0, 50)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true,
"deletable": false,
"nbgrader": {
"checksum": "b079fa9a413c8bc761692d3bfd9eb813",
"grade": true,
"grade_id": "interactex04b",
"points": 4
}
},
"outputs": [],
"source": [
"assert True # use this cell to grade the plot_random_line function"
]
},
{
"cell_type": "markdown",
"metadata": {
"nbgrader": {}
},
"source": [
"Use `interact` to explore the `plot_random_line` function using:\n",
"\n",
"* `m`: a float valued slider from `-10.0` to `10.0` with steps of `0.1`.\n",
"* `b`: a float valued slider from `-5.0` to `5.0` with steps of `0.1`.\n",
"* `sigma`: a float valued slider from `0.0` to `5.0` with steps of `0.01`.\n",
"* `size`: an int valued slider from `10` to `100` with steps of `10`.\n",
"* `color`: a dropdown with options for `red`, `green` and `blue`."
]
},
{
"cell_type": "code",
"execution_count": 79,
"metadata": {
"collapsed": false,
"deletable": false,
"nbgrader": {
"checksum": "6cff4e8e53b15273846c3aecaea84a3d",
"solution": true
}
},
"outputs": [
{
"data": {
"image/png": "iVBORw0KGgoAAAANSUhEUgAAAXgAAAEECAYAAAAifS8cAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nAAAASUVORK5CYII=\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x7f7d106a5780>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"# YOUR CODE HERE\n",
"interact(plot_random_line, m=(-10.0, 10.0, 0.1), b=(-5.0, 5.0, 0.1), sigma = (0.0, 5.0, 0.01), size = (10, 100, 10), color = [\"green\", \"red\", \"blue\"])"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true,
"deletable": false,
"nbgrader": {
"checksum": "49bbb321697a88612357059cba486cd3",
"grade": true,
"grade_id": "interactex04c",
"points": 4
}
},
"outputs": [],
"source": [
"#### assert True # use this cell to grade the plot_random_line interact"
]
}
],
"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": "3.4.0"
}
},
"nbformat": 4,
"nbformat_minor": 0
}
| mit |
ajhalthor/statistical-learning-with-R | Chapter 4/Question 11.ipynb | 2 | 318145 | {
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"_11 In this problem, you will develop a model to predict whether a given car gets high or low gas mileage based on the Auto data set._\n",
"\n",
"_(a) Create a binary variable, mpg01, that contains a 1 if mpg contains a value above its median, and a 0 if mpg contains a value below its median. You can compute the median using the median() function. Note you may find it helpful to use the data.frame() function to create a single data set containing both mpg01 and the other Auto variables._"
]
},
{
"cell_type": "code",
"execution_count": 84,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"<ol class=list-inline>\n",
"\t<li>'/Library/Frameworks/R.framework/Versions/3.4/Resources/library'</li>\n",
"\t<li>'/Users/Ajay/anaconda/lib/R/library'</li>\n",
"</ol>\n"
],
"text/latex": [
"\\begin{enumerate*}\n",
"\\item '/Library/Frameworks/R.framework/Versions/3.4/Resources/library'\n",
"\\item '/Users/Ajay/anaconda/lib/R/library'\n",
"\\end{enumerate*}\n"
],
"text/markdown": [
"1. '/Library/Frameworks/R.framework/Versions/3.4/Resources/library'\n",
"2. '/Users/Ajay/anaconda/lib/R/library'\n",
"\n",
"\n"
],
"text/plain": [
"[1] \"/Library/Frameworks/R.framework/Versions/3.4/Resources/library\"\n",
"[2] \"/Users/Ajay/anaconda/lib/R/library\" "
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
".libPaths(\"/Library/Frameworks/R.framework/Versions/3.4/Resources/library\")\n",
".libPaths() #Added R Studio's Package Path to Anaconda's Path\n",
"library(ISLR)\n",
"library(MASS)"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"<table>\n",
"<thead><tr><th scope=col>mpg</th><th scope=col>cylinders</th><th scope=col>displacement</th><th scope=col>horsepower</th><th scope=col>weight</th><th scope=col>acceleration</th><th scope=col>year</th><th scope=col>origin</th><th scope=col>name</th></tr></thead>\n",
"<tbody>\n",
"\t<tr><td>18 </td><td>8 </td><td>307 </td><td>130 </td><td>3504 </td><td>12.0 </td><td>70 </td><td>1 </td><td>chevrolet chevelle malibu</td></tr>\n",
"\t<tr><td>15 </td><td>8 </td><td>350 </td><td>165 </td><td>3693 </td><td>11.5 </td><td>70 </td><td>1 </td><td>buick skylark 320 </td></tr>\n",
"\t<tr><td>18 </td><td>8 </td><td>318 </td><td>150 </td><td>3436 </td><td>11.0 </td><td>70 </td><td>1 </td><td>plymouth satellite </td></tr>\n",
"\t<tr><td>16 </td><td>8 </td><td>304 </td><td>150 </td><td>3433 </td><td>12.0 </td><td>70 </td><td>1 </td><td>amc rebel sst </td></tr>\n",
"\t<tr><td>17 </td><td>8 </td><td>302 </td><td>140 </td><td>3449 </td><td>10.5 </td><td>70 </td><td>1 </td><td>ford torino </td></tr>\n",
"\t<tr><td>15 </td><td>8 </td><td>429 </td><td>198 </td><td>4341 </td><td>10.0 </td><td>70 </td><td>1 </td><td>ford galaxie 500 </td></tr>\n",
"</tbody>\n",
"</table>\n"
],
"text/latex": [
"\\begin{tabular}{r|lllllllll}\n",
" mpg & cylinders & displacement & horsepower & weight & acceleration & year & origin & name\\\\\n",
"\\hline\n",
"\t 18 & 8 & 307 & 130 & 3504 & 12.0 & 70 & 1 & chevrolet chevelle malibu\\\\\n",
"\t 15 & 8 & 350 & 165 & 3693 & 11.5 & 70 & 1 & buick skylark 320 \\\\\n",
"\t 18 & 8 & 318 & 150 & 3436 & 11.0 & 70 & 1 & plymouth satellite \\\\\n",
"\t 16 & 8 & 304 & 150 & 3433 & 12.0 & 70 & 1 & amc rebel sst \\\\\n",
"\t 17 & 8 & 302 & 140 & 3449 & 10.5 & 70 & 1 & ford torino \\\\\n",
"\t 15 & 8 & 429 & 198 & 4341 & 10.0 & 70 & 1 & ford galaxie 500 \\\\\n",
"\\end{tabular}\n"
],
"text/markdown": [
"\n",
"mpg | cylinders | displacement | horsepower | weight | acceleration | year | origin | name | \n",
"|---|---|---|---|---|---|\n",
"| 18 | 8 | 307 | 130 | 3504 | 12.0 | 70 | 1 | chevrolet chevelle malibu | \n",
"| 15 | 8 | 350 | 165 | 3693 | 11.5 | 70 | 1 | buick skylark 320 | \n",
"| 18 | 8 | 318 | 150 | 3436 | 11.0 | 70 | 1 | plymouth satellite | \n",
"| 16 | 8 | 304 | 150 | 3433 | 12.0 | 70 | 1 | amc rebel sst | \n",
"| 17 | 8 | 302 | 140 | 3449 | 10.5 | 70 | 1 | ford torino | \n",
"| 15 | 8 | 429 | 198 | 4341 | 10.0 | 70 | 1 | ford galaxie 500 | \n",
"\n",
"\n"
],
"text/plain": [
" mpg cylinders displacement horsepower weight acceleration year origin\n",
"1 18 8 307 130 3504 12.0 70 1 \n",
"2 15 8 350 165 3693 11.5 70 1 \n",
"3 18 8 318 150 3436 11.0 70 1 \n",
"4 16 8 304 150 3433 12.0 70 1 \n",
"5 17 8 302 140 3449 10.5 70 1 \n",
"6 15 8 429 198 4341 10.0 70 1 \n",
" name \n",
"1 chevrolet chevelle malibu\n",
"2 buick skylark 320 \n",
"3 plymouth satellite \n",
"4 amc rebel sst \n",
"5 ford torino \n",
"6 ford galaxie 500 "
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"head(Auto)"
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"<table>\n",
"<thead><tr><th scope=col>mpg01</th><th scope=col>mpg</th><th scope=col>cylinders</th><th scope=col>displacement</th><th scope=col>horsepower</th><th scope=col>weight</th><th scope=col>acceleration</th><th scope=col>year</th><th scope=col>origin</th></tr></thead>\n",
"<tbody>\n",
"\t<tr><td>0 </td><td>18 </td><td>8 </td><td>307 </td><td>130 </td><td>3504</td><td>12.0</td><td>70 </td><td>1 </td></tr>\n",
"\t<tr><td>0 </td><td>15 </td><td>8 </td><td>350 </td><td>165 </td><td>3693</td><td>11.5</td><td>70 </td><td>1 </td></tr>\n",
"\t<tr><td>0 </td><td>18 </td><td>8 </td><td>318 </td><td>150 </td><td>3436</td><td>11.0</td><td>70 </td><td>1 </td></tr>\n",
"\t<tr><td>0 </td><td>16 </td><td>8 </td><td>304 </td><td>150 </td><td>3433</td><td>12.0</td><td>70 </td><td>1 </td></tr>\n",
"\t<tr><td>0 </td><td>17 </td><td>8 </td><td>302 </td><td>140 </td><td>3449</td><td>10.5</td><td>70 </td><td>1 </td></tr>\n",
"\t<tr><td>0 </td><td>15 </td><td>8 </td><td>429 </td><td>198 </td><td>4341</td><td>10.0</td><td>70 </td><td>1 </td></tr>\n",
"</tbody>\n",
"</table>\n"
],
"text/latex": [
"\\begin{tabular}{r|lllllllll}\n",
" mpg01 & mpg & cylinders & displacement & horsepower & weight & acceleration & year & origin\\\\\n",
"\\hline\n",
"\t 0 & 18 & 8 & 307 & 130 & 3504 & 12.0 & 70 & 1 \\\\\n",
"\t 0 & 15 & 8 & 350 & 165 & 3693 & 11.5 & 70 & 1 \\\\\n",
"\t 0 & 18 & 8 & 318 & 150 & 3436 & 11.0 & 70 & 1 \\\\\n",
"\t 0 & 16 & 8 & 304 & 150 & 3433 & 12.0 & 70 & 1 \\\\\n",
"\t 0 & 17 & 8 & 302 & 140 & 3449 & 10.5 & 70 & 1 \\\\\n",
"\t 0 & 15 & 8 & 429 & 198 & 4341 & 10.0 & 70 & 1 \\\\\n",
"\\end{tabular}\n"
],
"text/markdown": [
"\n",
"mpg01 | mpg | cylinders | displacement | horsepower | weight | acceleration | year | origin | \n",
"|---|---|---|---|---|---|\n",
"| 0 | 18 | 8 | 307 | 130 | 3504 | 12.0 | 70 | 1 | \n",
"| 0 | 15 | 8 | 350 | 165 | 3693 | 11.5 | 70 | 1 | \n",
"| 0 | 18 | 8 | 318 | 150 | 3436 | 11.0 | 70 | 1 | \n",
"| 0 | 16 | 8 | 304 | 150 | 3433 | 12.0 | 70 | 1 | \n",
"| 0 | 17 | 8 | 302 | 140 | 3449 | 10.5 | 70 | 1 | \n",
"| 0 | 15 | 8 | 429 | 198 | 4341 | 10.0 | 70 | 1 | \n",
"\n",
"\n"
],
"text/plain": [
" mpg01 mpg cylinders displacement horsepower weight acceleration year origin\n",
"1 0 18 8 307 130 3504 12.0 70 1 \n",
"2 0 15 8 350 165 3693 11.5 70 1 \n",
"3 0 18 8 318 150 3436 11.0 70 1 \n",
"4 0 16 8 304 150 3433 12.0 70 1 \n",
"5 0 17 8 302 140 3449 10.5 70 1 \n",
"6 0 15 8 429 198 4341 10.0 70 1 "
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"mpg_med = median(Auto$mpg) #Find median\n",
"mpg01 = rep(1, length(Auto$mpg)) #Assign vector of 1s\n",
"mpg01[Auto$mpg < mpg_med] = 0 #Change to 0 if condition fails\n",
"new_Auto = data.frame(mpg01,Auto[-c(length(names(Auto)))]) #Create new dataframe, eliminating name\n",
"head(new_Auto)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"_11 (b) Explore the data graphically in order to investigate the association between mpg01 and the other features. Which of the other features seem most likely to be useful in predicting mpg01? Scatterplots and boxplots may be useful tools to answer this question. Describe your findings._\n",
"\n",
"Let us start with scatterplots of our new _mpg01_ with respect to the other columns."
]
},
{
"cell_type": "code",
"execution_count": 147,
"metadata": {},
"outputs": [
{
"data": {
"image/png": "iVBORw0KGgoAAAANSUhEUgAAA0gAAANICAYAAAD958/bAAAEDWlDQ1BJQ0MgUHJvZmlsZQAA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\nKKCAAgoooIACCiigQHkC/wXhmdbRoDJ4JAAAAABJRU5ErkJggg==",
"text/plain": [
"Plot with title “Main”"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"image/png": "iVBORw0KGgoAAAANSUhEUgAAA0gAAANICAYAAAD958/bAAAEDWlDQ1BJQ0MgUHJvZmlsZQAA\nOI2NVV1oHFUUPrtzZyMkzlNsNIV0qD8NJQ2TVjShtLp/3d02bpZJNtoi6GT27s6Yyc44M7v9\noU9FUHwx6psUxL+3gCAo9Q/bPrQvlQol2tQgKD60+INQ6Ium65k7M5lpurHeZe58853vnnvu\nuWfvBei5qliWkRQBFpquLRcy4nOHj4g9K5CEh6AXBqFXUR0rXalMAjZPC3e1W99Dwntf2dXd\n/p+tt0YdFSBxH2Kz5qgLiI8B8KdVy3YBevqRHz/qWh72Yui3MUDEL3q44WPXw3M+fo1pZuQs\n4tOIBVVTaoiXEI/MxfhGDPsxsNZfoE1q66ro5aJim3XdoLFw72H+n23BaIXzbcOnz5mfPoTv\nYVz7KzUl5+FRxEuqkp9G/Ajia219thzg25abkRE/BpDc3pqvphHvRFys2weqvp+krbWKIX7n\nhDbzLOItiM8358pTwdirqpPFnMF2xLc1WvLyOwTAibpbmvHHcvttU57y5+XqNZrLe3lE/Pq8\neUj2fXKfOe3pfOjzhJYtB/yll5SDFcSDiH+hRkH25+L+sdxKEAMZahrlSX8ukqMOWy/jXW2m\n6M9LDBc31B9LFuv6gVKg/0Szi3KAr1kGq1GMjU/aLbnq6/lRxc4XfJ98hTargX++DbMJBSiY\nMIe9Ck1YAxFkKEAG3xbYaKmDDgYyFK0UGYpfoWYXG+fAPPI6tJnNwb7ClP7IyF+D+bjOtCpk\nhz6CFrIa/I6sFtNl8auFXGMTP34sNwI/JhkgEtmDz14ySfaRcTIBInmKPE32kxyyE2Tv+thK\nbEVePDfW/byMM1Kmm0XdObS7oGD/MypMXFPXrCwOtoYjyyn7BV29/MZfsVzpLDdRtuIZnbpX\nzvlf+ev8MvYr/Gqk4H/kV/G3csdazLuyTMPsbFhzd1UabQbjFvDRmcWJxR3zcfHkVw9GfpbJ\nmeev9F08WW8uDkaslwX6avlWGU6NRKz0g/SHtCy9J30o/ca9zX3Kfc19zn3BXQKRO8ud477h\nLnAfc1/G9mrzGlrfexZ5GLdn6ZZrrEohI2wVHhZywjbhUWEy8icMCGNCUdiBlq3r+xafL549\nHQ5jH+an+1y+LlYBifuxAvRN/lVVVOlwlCkdVm9NOL5BE4wkQ2SMlDZU97hX86EilU/lUmkQ\nUztTE6mx1EEPh7OmdqBtAvv8HdWpbrJS6tJj3n0CWdM6busNzRV3S9KTYhqvNiqWmuroiKgY\nhshMjmhTh9ptWhsF7970j/SbMrsPE1suR5z7DMC+P/Hs+y7ijrQAlhyAgccjbhjPygfeBTjz\nhNqy28EdkUh8C+DU9+z2v/oyeH791OncxHOs5y2AtTc7nb/f73TWPkD/qwBnjX8BoJ98VVBg\n/m8AAEAASURBVHgB7N0HuDRlfTdgkQhIF6UqUkRRELEjxhLEihqJKIpYUCK2RKOJGo3iJ/AJ\nsQcUCKJ+aiQCFsRKEMUuIKggqNhoIr0q0vl+f9gly3lP2XPO7p6d2fu5rt87s7OzM89zz3nP\nnGen3elOCgECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAEC\nBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAEC\nBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAEC\nBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAEC\nBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAEC\nBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAEC\nBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAEC\nBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAEC\nBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAEC\nBAhMhMByE9FKjSQwPIHNs+h79iz+ioyf0vO6RldJtpky7cS8/tOUaXO9/KvM8LjOTBdmePpc\nH/A+AQIECEyMgP3RxGxqDSVAgMB4CxyY6t3Sk4syPvWLh+f2vN+d90GZNt9yt3yg+/nD5/th\n8xMgQIBAqwXsj1q9eTVulAJ3HuXKrIvABAisnTZO7fw8cQLarYkECBAgMF4C9kfjtT3UpkEC\ndcqOQoDAYATq6E4dPaoO0c96Frl9Z7z7fs9b8xq9OnM/ufOJC+b1STMTIECAwCQJdPc39keT\ntNW1dWACOkgDo7QgAnc6LQZ19OhJyfs6HhtleJ/O+KkZbt0ZnzrYIBP+NqnrmZZP6lS9Y5Pe\n64yq87VGUuWq2wa3/vuQ/FvruDE5KqllPCHZLKnPfzm5JlEIECBAYDIE7I8mYztrJQECBMZS\noPec7w+mhvWt3Z+TFTq13b0z7eYM/6MzXvP0noa3Q17f0PNevV+5KfmHpFtmugapW4fqND0j\nqZs/dJdRw+okVQdMIUCAAIH2CnT3BfV73/6ovdtZy0Yg4BqkESBbxcQIHN9p6coZProzvn1n\n+PMML+mM9w5Wy4uDkzqaW6fNHZocndQRn/r/eUDSb+em7pZXR5DOTD6SnJ9U2SJ5w61j/iFA\ngACBSRA4vtNI+6NJ2NraOHABHaSBk1rgBAuclLZXx6ZKnfddpU51q/Kt2wbL/Lthpnw2OS6p\noz8vT56VfCjplvt3R+YY1v/nLyUPTfZI6shUtzy4O2JIgAABAq0XsD9q/SbWwGEK6CANU9ey\nJ03g+jT4+51GVwdpq2TdzuvjO8OpgzMyoY7u1Pw/SerUu1ckj026ZdXuSB/DOuLULXWjiDrd\nr8rdbxv4lwABAgQmQMD+aAI2siYOT0AHaXi2ljyZAt0jRQ9P83fqENT54N+ehaOO+HwqqYe/\nVqemTrnbNumW+ny/5aIpM3aPaNUpfAoBAgQITI6A/dHkbGstHbCAP5oGDGpxEy9wfEdg+Qxf\n3xk/NcPLOuNTB9URqrvV1fVDdY1SXYP0tWST5L1JlbrBQ7/luikzzuezUz7qJQECBAg0WOD4\nTt3tjxq8EVV9aQR0kJbG3VrbK/DjNK1Oa6sOz+qdZna/xeu8vMPgZXlV89ZRou2SuplDlTrt\nrlvmcwRpPvN2l29IgAABAu0TsD9q3zbVohEJOMVuRNBWMzECdbvu7nVI3UYf3x2ZZlg3aahS\nzziqjlKVeySvvHXstn/qTncKAQIECBCYj4D90Xy0zEugR0AHqQfDKIEBCfQeMapT3L4zy3KP\n6Xnvuxn/UVK35960Z3r3Rg89k4wSIECAAIE5BeyP5iQyA4FlBXSQljUxhcBiBY7vWUDddOHy\nntdTR/fPhAOT6kjdJdkmqU7S1skVSZVn3DbwLwECBAgQmJfA8T1z2x/1YBglQIAAgfEXWCNV\nrGcVrTn+VVVDAgQIEGixgP1RizeuphEgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAA\nAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAA\nAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAA\nAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAA\nAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAA\nAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAA\nAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAA\nAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAA\nAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQKTKrD8pDZcuwkQIECAwIAE7pzl3DJlWSvn\n9S7JzskDk8uTSxOFAAECBAgQIECAAAECrRVYLS2rztHzelq4ecZ/15le71VuSN6SKAQIECAw\n5gJ/Neb1a0r1fpaK3qcplVVPAgQmTuCraXEdyVBGI/DJrKaOIL0uOSKp/cOLknclZyRfTIZV\n7I+GJWu5BAgMQqAR+yMdpEFs6tt2fu/Lok4azOIshQABAgMTeHaWtNXAlmZBcwmslxkemfxb\nsn9n5gsy/H5S22HXZJgdpOqM2R8FQSFAYOwEGrM/0kEazM9OnT5RnaMvD2ZxlkKAAIGBCTwg\nS9JBGhjnnAuq/cHNyeenmfPwTHv5NNMHOcn+aJCalkWAwCAFGrM/qgtLFQIECBAgQGBxApvk\n46skFybfSx6UTC1PyYRzp070mgABAgTGS0AHaby2h9oQIECAQLMEujdg2DfVvio5PVk/OSBZ\nN6nysOSYZIfk44lCgAABAmMs4BS7Md44qkaAAAECYy/wp9Rw1WTL5MHJQzrD6hytlFTZMdk+\n2TM5MlEIEGimwIap9n2Ts5PfNrMJat2PgA5SP0rmIUCAAAECMwtcn7d+0kn3CNFyeV1Hl6oc\nkrw/qWchLbR8Ih/coo8P12l+D01cE9sHllkI9ClQX3bU/+O6G+VNyfLJV5IXJlckSssEdJBa\ntkE1hwABAgTGQqDbOarKDOK6o6OznNP6aFmdzle3GFcIEBicwAezqCck2yXHJ/UlxKeTTyXP\nTJSWCeggtWyDDqg5a2c5GwxoWXMtpr5lXSu5dK4ZB/R+3V2qrhGooUKAAIFBCdw1C6rOSV3b\ne3Ly52RqqdPsrku+N/WNPl5/ro95apZ3J7UOhQCBwQjUKbQvS+oOlH+T7JGcnbw5qVv2b5r8\nLlFaJKCD1KKNOcCmfCzLesYAlzdui6o7Sf3PuFVKfQgQaKzAVqn5UUn9oVTlkuQ1yRH1oqe8\nJeOXJQvpIPUsxigBAiMUWD/rukvygaSeafbd5EnJa5P6svXeiQ5SENpUdJDatDUH15ZnZ1F1\nHvsoSl28XN943m8UK8s66pfZVSNal9UQINB+gToKXqfZ3JDs1hnWN8z1zKNNkn9PFAIEmitw\nbqpefzvUTRkendT/9fp/X1+KPDPROQpC24oOUtu26GDaU//5rxjMouZcyjWZo87VH9X65qyQ\nGQgQIDAPgY0z79bJk5NjkyqHJfsk+yWXJocmCgECzRS4e6p956S+yN0l+WbyiOSRSXWU1kvO\nSZQWCeggtWhjagoBAgQIjFyg/jiqb5d/OGXNb8vr1ZKDkvoG+phEIUCgeQLdv5XrSHH9f66b\noFyf/L+krkvqvp9RpS0C1SNWCBAgQIAAgYUJnJWP1b70WdN8/PWZVqfh1LVIdQMHhQCB5gmc\nnSqfmdSNWOpo0mZJ3VzqwqSuKTwlUVomoNfbsg2qOQQIECAwUoE/Zm31zKEDkm2TfZM/JFXq\nyNKuydHJt5Ork+8mCgECzRKo6wq/llTn6FtJnWJXp9W+ILk2UVom4AhSyzao5hAgQIDAyAVe\nmjV+J3lVUn9A9ZY6FadufHNkUqfjKQQINE+gvuCoo8C/TB6fXJw8Oqn/10oLBRxBauFGbViT\nfp76fqZhdVZdAgQI9ArUbb13TNZMrut9ozNeN6OpTlRdv7B2Z5oBAQLNEvhFqvuKZlVZbRcq\noIM0u1wdOt1k9llufXfF/LtOH/OZZVmBMzLpdctONoUAAQKNE5jrbpwnNq5FKkyAAIEJFNBB\nmn2jb5e3HzD7LLe+W44P7mM+sxAgQIAAAQIECBAgMMYCOkizb5y6fWM/pS7EvbyfGc1DgAAB\nAgQIECBAgMD4CrhJw/huGzUjQIAAAQIECBAYD4GnpRp1M5YLkhOSemis0lIBHaSWbljNIkCA\nAAECBAgQGIhAdYbqdv51F7u6brpu1/+J5J8TpYUCOkgt3KgNa9LDU9/PNazOqkuAAAECBAhM\nhkD9rfz+ZO/kvcmfk+ocvTqpaasnSssEdJBatkEb2JxNU+fHNLDeqkyAAAECBAi0X6DuZlzP\nMKuHw/4q+e/k1KTudLxS4iZdQWhb0UFq2xbVHgIECBAgQIAAgUEJXNVZ0EMy3CZZLdkiqU7T\ncsmVidIyAR2klm1QzSFAgAABAgQIEBiYQD38ue5WXB2h33WWem6GF3fG65Q7pWUCbvPdsg2q\nOQQIECBAgAABAgMTWDdLqgMK9Tfzb5I6ve7+SbfjdK/O9AyUtgjoILVlS2oHAQIECBAgQIDA\noAXOyQL/lLwnqaNI90s+2hmvm0zVdUlKywScYteyDao5BAgQIECAAAECAxOoI0XvTuoOdndL\nDk9uSA5JPpL8MVFaJuAIUss2aAObU9/GXNHAeqsyAQIECBAgMBkC+6SZNyb7Jmsmdd3RQclb\nE6WFAjpILdyoDWvSManvAxtWZ9UlQIAAAQIEJkfgljS1Okd1mt3ayaXJ9YnSUoE2dJDqHvRb\nJ+sndSFd/RBfntRFdGd2XmegjLFAHapWCBAgQIAAAQLjLFBHkZxSN85baEB1a3IHqepeTzDe\nI1lrBo+TMn335LQZ3jeZAAECBAgQIECAAAECtws0+SYNdXHcq5NDk8cndcvFdZINkzqitHNS\n96g/OakHeykECBAgQIAAAQIECBCYVaCpR5DWSKtekuyQ1DUsU8t5mVCn2B2ZHJHskpyQKAQI\nECBAgAABAgQIEJhRoKlHkDZJi+pao+NmbNn/vnFsRh/3vy+NjZnAeqlPdXYVAgQIECBAgAAB\nAksu0NQOUh0duiTZcQ7BOkJWp9r9ao75vL10AtV5recLKAQIECBAgAABAgSWXKCpp9jdHLkD\nk8OSXZOjkwuSuu3iCkndtGHz5IXJZsm2iUKAAAECBAgQIECAAIFZBZraQapG7ZWcmByQTHck\nqW7FWNcgvTipI04KAQIECBAgQIAAAQIEZhVocgepGvb15L5J3bluo2T15Ork/E7+kqFCgAAB\nAgQIECBAgACBvgSa3kHqNvLcjFQUAgQIECBAgAABAgQILFigDR2kldL6eu7R+sm6Sd3d7vKk\nTqs7s/M6A4UAAQIECBAgQIAAAQKzCzS5g1R13zvZI6mbMkxXTsrE3ZPTpnvTtLEQqA5tRSFA\ngAABAgQIzFdgzXzgofP90CLm3zifPWsRn5/vR0/PBy6c74fMvziBJneQDknTd0oOTr6S1A/P\nZcmKSXWY6i52uyUnJ49NPCg2CGNYjkmd6m6DCgECBAgQWCqB1bLipyX3SE5JfpQozRDYJdWs\nG3YtN6Lq1iNy6m7Koyr7ZkVvG9XKrKfZAmuk+jclT+mjGUdkng/2Md9iZqn/KO9czAJ8lgAB\nAkMSeGOWW0fTlckQsD+a/3auR4F0v2Q9I+P198Xnk/rCVSHQK1BfwNdZL1v1TjTet0Bj9kdN\nfVDsJtkU9QN6XB+b5NjM87g+5jMLAQIECBAgMFkCq6a5X0jqbIYNki2ShyWPTnzxGQSFwCQK\nNLWDdGo21iXJdM8/6t2OdQrhzsmveicaJ0CAAAECBAhE4KlJHSl6RXJtUuWnyZ7JS+qFQoDA\n5Ak09RqkOoXgwOSwZNfk6OSC5NJkhaR7DVJd27JZUofPFQIECBAgQIBAr0Bdc1Sn1019buJZ\nmXb3RCFAYAIFmtpBqk21V3JiUhfmTXck6cZMPzJ5cVJHnBZS6oLNjfr84F36nM9sdxSoo5jr\nJNXBVQgQIECAwCgF6kZO90semtTNGbrl+Rmp9xQCBCZQoMkdpNpcX0/um2yYVEdm9eTq5PxO\npn4jlMnzKnWL8C37/ETdOEKZv8Az85Hq5N57/h/1CQIECBAgsCiBuoHJ55L6e+Idye+T5yV1\nBsqTE4UAgQkUaHIHqY481Kl2Vc7tZOUMn5PUOcV1yLx+4dXDYhdaaln9lKpHXROlzF+gzv12\np6D5u/kEAQIECAxGoE7V3zP5t2TtpI4cVefoW4lCoFfgyrzYPzm7d6Lx9gk0tYO0WjbFVUkd\nAj+8s1k2z/BrSd3hrlvqNLv6pbdvd4IhAQIECBAgQKBH4PqMv62TnslGCSwjULeAf90yU01o\nnUBT72I33Yb4ZCbWEaT6wV0/eUzy0eRdybMShQABAgQIECBAgAABArMKNPUI0tRGrZcJj0zq\n8Hgd+qxSF/1/P9kqqcPnX0wUAgQIECBAgAABAgQIzCjQliNIt6SFdR1QPfl6aqlT8B4wdaLX\nBAgQIECAAAECBAgQmCrQ9A5SXW+0SlI3ZPhe8qBkanlKJtRNHBQCBAgQIECAAAECBAjMKtDU\nDlIdMbohqZsv1M0aTk/quqO6XfS6SZWHJcckOyQfT5TxFPh5qvWZ8ayaWhEgQIAAAQIEbhdY\nKWNnJBvcPsVIKwWaeg3Sn7I1Vk3qGUUPTh7SGVbnqH54q9TDY7dP6i52RybKeArULxp3hBnP\nbaNWBAgQIECAwP8K1M3A6rKNuyf1zE2lpQJN7SDV5qjbcv6kk+4RouXyuo4uVTkkeX9yeb1Q\nCBAgQIAAgcYJ1APbtxlRreuL1zp1/7QRra+unX538rsRrc9qCBDoU6DJHaTpmtjtHD0qb26a\nHDbdTKYRIECAAAECjRCos0LqWuNRlA2zkjorZVQdluogte3vsFFsJ+sgMHSBtv7HfHrk6tlH\nOkhD/xGyAgIECBAgMDSBD2fJlVGUnbOSupa5Hg2iECAwwQJN7SDVs41m6/zUtUirJd3D5HWz\nhn9JFAIECBAgQIAAAQIECMwo0NQO0sVpUd2Bb4vkh8lvkt5Sh8jvktQ1SlXOuvVf/4yjwMNT\nqbckO41j5dSJAAECBAgQIEBgsgSa2kG6IJup/rCuixtfmnwqOSjplr0zUqfYvbg7wXBsBepa\nsceMbe1UjAABAgQIECBwm8BfMvhjcgWQdgs0tYNUW6V+SP8x+UpSd7F7ZlJ3u6kfXIUAAQIE\nCIxaoG4osHVSz+WrU73rxkGXJ6cmZ3ZeZ6AQINBQgfrb0zOQGrrx5lPtJneQuu38ekbqmqSP\nJHXN0SsThQABAgQIjEqg9qV15sIeyVozrPSkTK8v8brXxs4wm8lLKHBT1l1RCBCYcIE2dJBq\nE16S/F3y98n/S+pBshclCgECBAgQGLZAPXevrqM8OKmzGi5MLktWTKrDtHmyW3Jy8tjkhEQZ\nP4Hadr8fv2qpEQECoxZoSwep63ZoRo5PPphclSgECBAgQGCYAmtk4S9JdkjqjqlTy3mZUKfY\nHZkckeyS6CAFYQzLtanTKWNYL1UiQGDEAm3rIBVf3dHuGSN2tDoCBAgQmEyBTdLsutbouD6a\nf2zmeVUf85mFAAECBJZQoI0dpCXkHNqql8uSt03q1uVtK/dOg65LHt+2hnXac06GTtlo6cbV\nLAIRqKNDdZr3jslnk5lK7W/rQaS/mmkG0wkQGHuBesTMO5K6i/Kfx762KrhgAR2kBdON9IPV\nOfr+SNc4+pUdP/pVjmSNdc1B3ZJeIUCgnQI3p1kHJvXw8l2To5N6FMWlyQrJWkldg/TCZLOk\nfp8rBAg0U2DNVHvPpL4MccOVZm7Dvmqtg9QX05LP1N1O9c1FncqhNEPgtalm3bVKIUCg3QJ7\npXknJgckdSRparkxE+oapHo2Xx1xWkh5dT5UHax+ysr9zGSeZQSqQ3uf5BfLvGMCAQITJdD9\nw3uiGq2xBAgQIEBgwAL1yIn7JhsmGyWrJ1cn53dSz09ZTNk4H64/3vspbTwdu592L3aeun65\nOrn3XOyCfJ4AgWYL6CA1e/upPQECBAiMh8BdU42HJXWkv06tne76hO0zva65/F4y3/KmPj9Q\np/xd2ee8ZrujQP1N5O+iO5p4RWAiBeoXuUKAAAECBAgsXGCrfPTnyXeTbydnJXVDhqnlLZlQ\np94qBAgQIDDGAr4pGeONo2oECBAgMKNAXWezSlJ3+ewtdYRmlEdQav2fSm5IdusM98jw8GST\n5N8ThQABAgQaJKCD1KCNpaoECBAgcKetY/CJpIbTlboZwnRHb6abdxDTNs5Cqi5PTuo5R1UO\nS/ZJ9kvqbnb1EHOFAIHmC9zUaUKdyqq0WEAHqcUbV9MIECDQQoE6WrNeUrfaPS/p/sGS0VvL\n2d2REQ2rLvXH0g+nrO9teb1aclBybnJMohAg0GyBOjr9mOSMZjdD7ecS0EGaS8j7BAgQIDAu\nAvUMkrrep26l/cUxqdRZqUddz/us5NNJb3l9XmyQHJE8ofcN4wQINFag7c+lbOyGGWTFdZBm\n19w2b99r9lluf3f528eMECBAgMAwBK7JQq9N6vbZ41L+mIp8OanbQ9c+Y9/kD0mVOrLUfXjs\ntzNe9f5uooynwI9TrQ+NZ9XUigCBUQroIM2uvXfeftDss9z+7t1vHzNCgAABAsMQuD4LPSp5\nefLNYaxggct8aT5X1xm9KqlroLodpIzeqer87OTDyW6JMr4Cv0vVar+vECAw4QI6SLP/ADxx\n9rdvf7e+Jbzo9ldGCBAgQGBYAt/LguvOcKd0MvWOdT/L9E8moyyXZGV12l+dAlh30Zta6shX\ndaLqeqS1p77pNQECBAiMl4AO0nhtD7UhQGBZgfqjc5PkvOTiZd82ZcIEXpf21qlq6ydPn6bt\ndWOEUXeQutW4ojsyw/DEGaabTIBAcwQemKrWc8+UFgvoILV442oagYYL3CX1f2/y6qR+V9WR\n2rp98iuTPyfKZArcbzKbrdUECIyBwOqpw2nJFskvxqA+qjAkAR2kIcFaLAECixaoZ8i8IHlu\nUtebPCKp6zw+mjw/USZXYLk0/W+ThyT1B8upyclJ/eGiECBAYFgC3b+bu8Nhrcdyl1jABl7i\nDWD1BAhMK7Bypr4meXFyVGeO4zJ8YfK95E3JOYkyeQJ3S5PrrnGP7jT9sgxrWnWa3p7skygE\nFiLwmHzo/yaPX8iHfYYAgfYI3Lk9TdESAgRaJHDPtGXFpDpDveUHeXFLsmnvROMTJfDBtLau\nSXttZ1h3EF01eUPyf5I64qgQWIjABvnQ/RfyQZ8hQKBdAjpI7dqeWkOgLQLnpyF1N7C/ntKg\nes5MHSn4/ZTpXk6GQO2zqgNUt9M+IDkrqVJ3iftAUneJe0aiECBAgACBBQvoIC2YzgcJEBii\nwJ+z7Ppj98NJXWuySvI3yX8lRyZnJ8rkCaydJt81+ekMTa9bfG81w3smEyBAgACBvgT+qq+5\nzESAAIHRC9R1RnUnu88nyyd1at1nkj0SZTIFLkqz/5Q8MambdUwtNf23Uyd6TYAAAQIE5iOg\ngzQfLfMSIDBKgRuysn9I9kw2Tc5NLkyUyRWoTnIdWazrkNZMvpJcmtw7eUXyvORJiUKAAIFh\nCFyZhe6fOIthGLpjtEwdpDHaGKpCgMC0AnWXsopCoATekdQpl+9J6jlZ3XJ1RqpD/Y3uBEMC\nBAgMWOCmLK8eVq20XEAHqeUbWPMIECDQMoG/pD2vSd6X1PVG6ydnJT9OLkkUAgsVqCPUjgws\nVM/nCLRIQAepRRtTUwi0VGDLtOt+Sf3hckpL26hZ8xf4XT5SUQgMSuDbWdAjB7UwyyFAoLkC\nbeggrRT+rZP6FnHdpM5RvzypJ6uf2XmdgUKAQMMEVk19P538bVL/p+uak+8lz0nqYn1lMgQ2\nSjP/KflS8s1k36R+789Ufpo3PjHTm6YTIECAAIG5BPrtIG2fBZ2Y1DneU8sjMqE6JvVk81GW\nqvveSd3Raq0ZVnxSpu+enDbD+yYTIDC+AnWL7zqF6qHJT5LNkiOT/07qd5IyGQL1+33n5NdJ\ndZD+LlktmamsnDd0kGbSMZ0AAQIE5hTot4P0uSxpu6T+SJlaXpsJ9dT7UXeQDsk6d0oOTupO\nRnXucF3IvWJSO9TNk92Sk5PHJickCgECzRBYPdV8QfKMpPt75zcZf1FSX3jcN6k/mJX2C9T2\nr31Mt9y/O2JIgACBEQvU0etTkicm9UBzpaUCs3WQ9k+bH9dpd31b95mkLo7tLdUZqWsD/rN3\n4gjG18g6XpLskBwzzfrOy7RTk/q2+Yhkl0QHKQgKgYYI1FHp+v308yn1PSOvb07qD2YdpCk4\nE/Kyjh6O2xkNE0KvmQQmXqCOUD8guXuig9TiH4fZOkjV6blbp+1bZFjf3tYRmt5yY158Ifn3\n3okjGN8k66hrjY7rY13HZp5X9TGfWQgQGB+Bc1KVq5OnJ3W0uFue1hn5RXeC4cQJjOMZDRO3\nEVra4Lrera5xfF9L2zfKZn0pK7vXKFc4onUt31lPffl+7YjWOcrVXJWVPaWlbZuX42wdpNOz\npDqdpUodKXpL8tt6MQaljg7V7Vx3TD47S32qfXXu+q9mmcdbBAiMn8B1qdK+yfuT+v1T157U\n9Y7vTQ5KLkyUyREY5zMaJmcrtL+l26SJb0p0kBa/reuP7I8mbfz766FpV90Mps5maFNZL415\nc1JnjbWx8zevbTVbB6l3QdXJqFLzr5MsVy96yl8yPvXoUs/bAx+tH8oDk8OSXZOjkwuSS5MV\nkrWSzZMXJpsl2yYKAQLNEtgv1b0h2TOpP5CvSurGDfVamSyB/0xzx/WMhsnaElpLoH+Bz2fW\nOotHaYZAnS1WHSQlAv12kKqTcXiyVXKXZGqpa326naip7w3r9V5Z8InJAUkdSZpa6vS/qteL\nk1Onvuk1AQJjL1Cn0dYRozqKVF96XJ7clCiTJzDOZzRM3tbQYgIECLRcoN8OUp1ruUFSR21+\nnNQRnN5S1wssRfl6Vlp3s9owqXOHV0/quoW6cK5SR7baVO7fpsZMQFvWTRunHm2dgGYPvIn1\n++aSgS/VApsq0P0ybqU0oFLlzkntz+6R3DPxrXUQFAIECBBYmEA/HaTqdGydvDT55MJWM9RP\n1Q6yOm+1Y6w/SOtb5zqPstp2Zud1Bq0oZ7SiFZPViPoZVAgQGJxAnTJd+6LNZlhknTmggzQD\njskECBAgMLdAPx2k67KYOl1t3P7Qq7rvneyR1Ok305WTMnH35LTp3jSNAAECBBonUNcj1Y07\n9kmendQp1L9Onp7UxcUvTxQCBAgQILBggTotYa5SHaS60O7VST/zz7W8Qb1/SKdOh2b4+KRO\nP1snqdPt6ohXnYZxcXJysk2iECDQTIG6OP9hydrNrL5aD1Cgzhh4YPLW5O3Jl5Krkrpxx2OT\nuk6tOkoKgYUI3JQPVRQCBCZcYKYjSPeOy+t6bP6c8ToSUx2PU5LLkt7ys7wY5el3a2R9L0l2\nSI5JppbzMqG+VTwyqeundklOSJpeDmp6Ayas/nVTk7tPWJsH2dy6Icz7klcl9buqTp89LHll\n8qdEmTyBOuW7ruv7Yafpddrxmzrj12T46eR5Sf2cKATmK/CVfOD38/2Q+QkQaJ/ATB2kup7n\nBVOae0Fer5M8dcr0elmnNYyyg7RJ1ld/LB2XzFXqXPT6A6sN5TVpRLVbaYbAa1PN+mJBWZjA\nv+djz0+ek3wzeXjyseSjSf0RrEyewEVpch0xqi8ffpv8MtkiqaOMdfTo2mSDRCGwEIH6+akv\ngRUCBCZcYKYOUv2CWH+MberoUN3VyoNix3gjqRqBRQisnM++Onlx8sXOcr6V4a7J95M3Juck\nyuQJHJUm/2dSRxi/nFyZ7JfUGQP1JdJXE4UAAQIECCxYYKYO0oIXOKIP3pz1HJjUaRT1B9PR\nSR3hujRZIVkr8aDYICgEGipwz9S7LsT/3pT616lV9f9/00QHaQrOhLz8p7Tzv5Ltk+oU/WtS\npx/vkdR+wOl1QVAIECBAYOEC/XaQNswq7jzDauqPlTrloTLK07/2yvpOTA5IhvWg2Adk2f0e\nSfO8m2ApBAYk8Ics57rkMckRPct8dMbrd9HveqYZnSyB16e570l+0Gl2HU36TrJZ8vXkhkQh\nQIAAAQILFui3g3Ra1lA3Rpit1Pnf9S3ev80204Dfq53hfZPqwG2U1AW8Vyfnd/KXDBdTPpkP\nP7TPBazb53xmI0BgboG64L6OEn8oqY7Scckjko8l1WE6J1EmT6Cud6072NXv9uOTbvlFRioK\ngcUIrJAP3yfxs7QYRZ8l0AKBfjtI/5y2HpzUN3Z1l5dTk+qQ1Pneqyb7JFsnb0rqKNLbkmGX\n+ha5jl5VObeTlTOsC7rrRhIXJtWBOjNZaKk/yPopVY86tUMhQGBwAm/Ooup31Gc7w/rdcljy\nykSZTIE/pdm/SR6U1FH7+plQCAxK4BlZUJ2VUqf4KgQITLBAvx2kOue7jgy9e4rV5/P67KS+\n7X1dUt+6/Gsy7A5SfYtYp/Q9Pzk8qVLXHH0tqTvcdcuNGdkz2bc7wZAAgcYI1KlSr03ekWya\n1BchFyXK5ApUh6i+rNs7+Vnyk2Tql1P1Bd6nE4XAfAXqb6J+/y6a77LNT4BAgwT6+UVwt7Tn\ngckXpmnXxZn2pWS75DPJMclByTrJqP+QqdPh6ghSddSOSOow+YuSdyVnJF9MFAIEmidweap8\ncvOqrcZDEqhO87XJ+p1MXc3RmaCDNFXFawIECBDoW6CfDtIVWVp1dp6X7DNlyXfN6+oc1UWy\nVepaoCuTy+rFCMt6WdcjkzrKtX9nvfWt4veTrZJdEx2kICgEBiRw9yyn/s+NqmyWFdWpVaMq\nP8uKzh/VyqxnXgJ1NFEhQIAAAQJDE+ing1SnNNTRo39JVkm+mtQRmY2TNydrJHWqXd0soU5l\nq4up69S2UZaqY10HVPWYWuoUvJdPneg1AQKLEtgpn/5AstyiltL/h+vLmDpqUP/XR1HenZX8\nn1GsyDoIECBAgACB8RLop4NUNf7H5KbkXzvJ4NZyYf6tozM/Sf5vUp2kNySjKptkRdVpq3p8\nL3lQ8suktzwlL+raBYUAgcEJHJJFVUZR1spKLk3qiNVpo1ihdTRC4K9Tywcn1XH+aPLw5MeJ\nQoAAAQIEFiVQd4Lrp9yQmeqOdfdKnpX8Q/L0pK7z+WxS5X1JnRN+Qr0YcqlvkatOdcTqquT0\npNZ9QLJuUuVhyTHJDsnHE4UAAQIEmi+wWprwtaS+FPtQsktSX5TVvqder5QoBAgQIEBgwQIz\nHUHaKEusO9d9KflmUh2RqTudzTLtSUmVnyafuHVsNP/8KatZNdkyqW8QH9IZVueoW88dM759\nUnexOzJRCBAgQKD5Av+RJjwgqY7RFsmjk2uSOtPhPcnxSfeLu4wqAxaoL1b/JrnLgJc7Dou7\nayrx9aTOPGlj+X0atZhHn8zXpP6f1t9rSjMENm5GNUdTy5k6SHVKy87Jr5PqIP1dUt/azVRW\nzhuj7CBVPa5P6tS+SvcIUV0PcUtSpU7/eX9yeb1QCBAgQKDxAvXH+fOT2ifVGQJvSqrU7/0D\nk+owPSPRQQrCkMq2WW5da3zdkJY/Dot93jhUYsB1WD7LOzWps2tGUervsfoyQyHQSIGZOkjV\n6bhnT4vu3zM+zqO1k1wh2S7ZOPlBooMUBIUAAQItELhH2lDf8p89Q1tqeh1ZUoYnUH9oV6nt\n0P1C8tYJ/hlrgdemdruPdQ1VjsAYCfR7DdIYVfn2qlTd35aclBydPDKpnec5SR0iPzj5WbJf\nohAgQIBA8wUuShMuTV44TVOWy7T6A/BX07xnEgECBAgQ6FtgpiNIMy3gr/NGXfNzbbLUdw36\nQOpQ55wfnzwqqeulfphckLwu+V3y98mbk+pEfS5RCBBonsCVqXJdW/L75lVdjYcgUKdOvyO5\nd1JHMFZJXp7sltSdVH1LHgSFwBIL1P/N/07OWuJ6WH3/AnfPrHv0P7s5S6CuP6q7BtUPfOUb\nSe2U6tbfH0q6N0bI6EhKHdqva5Be0FlbXQP1P0nV7dGdad3BtzJSnadhlpuz8HcOcQWPy7Kr\nbfUNqdIcgTqloY5iKgSWUuCNWXl9SdSWUmcPvCupL+rq92I3l2T8JcmkF/ujSf8JmL79o94f\n1d9o3Rt5TV8jU8dNoK7hrN+naw+xYo3ZH/V7il1daFd3I6lzu/fuwF2TYR3BeWlSF8WOsmyV\nlS2fHNVZadXlsOSK5Aedad3B5zOyafeFIQECBAg0WqA6AG9NNkyekOyaPCbZJPlEohAgQIAA\ngUUJ9HOKXXWinp/8XXJM8qakSvUyD0yW4q5BdRpd1Wu75CtJleos1TcWyyVVt26pbzDO6b4w\nJECAAIFGCdwrtX3VHDXeMu/v0JnntAw/M8f83iZAgAABAjMK9NNBGse7BlWHpzpGtROsa6H+\nJamjR3UUqVsekZF3Jk9Lnt2daEiAAAECjRKo59u9Zh41ri/LdJDmAWZWAgQIELijQD8dpIvy\nkUuTumvQ2+748VuP1tQFsXVb8FGX52SF/5C8IvmnaVb+4kx7YlJHvL4wzfsmESBAgMD4C5yc\nKq45/tVUQwIECBBoi0A/HaRq6zjeNagu0H1vsn9VcJrynkz7t+Sqad4ziQCB5gjUTVl+ntTN\nSv7QnGqr6ZAFxumuqnXKd10b1VtWzov6Im/z5MLk68mZiUKAAAECYy7Qbwdpv7Rj1eQNyYqd\nNj0qwzqy9LLk+51pSzGo646mK647mk7FNALNE6gO0qbJWokOUvO236BrvFoWeETy1M6Cj8uw\nTqk7ITkoqVOu6wu0UZWqT30R9/zk8M5Kq1NUd37dpPO6Bjcmeyb71guFAAECBMZXoL716qfU\nN2PuGtSPlHkIECBAYJgC43ZX1ena+slMrCNI9Uy+9ZPHJHW9bN2e/FmJQoAAAQJjLNDvEaQj\n04b6lu7o5Ftj3B5VI0CAAIH2CtSXenWkZpzuqjpVe71MeGRSp3jv33nzggzrTIutkrot+RcT\nhQABAgTGVKDfDtJNqX9d71O39T4xqbsEVX6ZKMMX6N623Pnrw7ce5BrqwnKnhA1S1LImXeAe\nAahTLs+eAaKm1/P6lrLU7+s666KewTe11Cl4L5860WsCBAgQGC+BfjtI9Y1dXXu0XfKM5JVJ\nnUf9q6Q6SnV77VMTZTgC1Sl9adLv9hpOLSx1IQK+RFiIms8QmF7gokyua1/H7a6qVdtNklWS\nC5PvJQ9Kpv7/f0qmnZsoBAgQINBSgbo7z2+T+rbsiJa2sd9m1beF7+x3ZvMRIDAvgbUyd/2e\nqdOTlPkLvDEfOWn+HxvbT9T1sNcldZ3PJ5IfJnVUpk5hq7Md6u52oyx1A6O6WVD9jNb6T0/q\naH91lOoZTlUelhyT1DzPTYZZhr0/elwqX+1YbpiNsOyBC7w2S/zZwJc68wLr/8STZn7bO2Mo\nsEXqVP+31x5i3RqzP5rPEYmNAvaEnmyQ8UuSOmWgdlQKgYUIbJkP1RHJf1zIh31mIgT+klae\nlVw+Ea3VyLkE9ssM1Sl5Q7JiZ+ZHZVhHll6WVEdplOVPWVnVp36XPTh5SGdYnaOVkio7Jtsn\neyZHJgspj8iH7tXnB5fvcz6zESBAgMA0Av12kOr0ufr29sqkvq37QPKNpL6NqN6mQmChAvVH\nxc6JDtJCBdv/ueog1elLCoESqCMkb01qP/TAZP3k7KT2U1cnS1Hq2/KfdPLxTgXqCEt3/3hI\nxut5govp5L87n9866afcvZ+ZzEOAAAEC0wv020Hqfvr8jJyZ1LVHv0u6v/wzqhAgQIAAgZEJ\nXJw1fauztrtkeMPI1tzfinr3j4O47mi7/lZ7aweyrtVSCBAgQGCBAnfu83N1selmyQHJhsmn\nkjqd4QfJ3slDE4UAAQIECAxboPZbf5/s37OiF2X82GSbnmlGCRAgQIDAggTmcwTpt1nDQZ3U\n+c1PTqpz9LZk86ROk1IIECBAgMAwBfbJwutC3xp2yykZ2SWpU8CfltQNEUZV6vqj+8xjZVdk\n3jolUCHQdoGnp4GbtrCRdaprndLbe5S4Dc2s05WVjsB8OkjVKapv556SVOeoLhitawM+l3w0\naWO5ZxpVz91QCBAgQGA8BHZKNfZIPt5TnZ9mvO6YdVRS1zOOsoNUZ1jM58YQdZOGNnyhuHLa\noTRHYIURV/U7Wd9TOxnxqoe6ujqCfd/k90lde9i2clIatFTXco6VZb8dpE+n1vVNwBrJH5Iv\nJXsl30yuS9pavpaGbdVn4zbocz6zESAwP4H6cub/JnXEoO4YpkyuwJpp+v2S2olPV76dibtN\n98YQp/0gy35ZcnBSfxS+J5mtXDDbmw16z//FBm2sTlXrGvJRlSeOakUjXs9aWV9dYvKs5LQR\nr9vqRijQbweprjuq872PTk5O2nZYMU2attRRsrtO+84dJ16Wl+ffcZJXfQpcm/kqCoGZBOqL\nmTcn9UWNHdJMSpMxvU5Pq9O9d09eP02TX5xpo/wjsFuFOppVd607NHlX8q1EIUCAAIGGCvTb\nQXpcQ9u32GrX0bE2HyFbrM8gPl9HI380iAVZBgECEyHwybTyX5M6glGPm6gvqOoI/ouSujZg\nuo5TJg+9fCxreH6yX1KnoysECBAg0FCBfjtIDW2eajdAoI5GuiVtAzaUKhIYE4E6vfvG5B1J\n3SSoW+r3yEuS47sTlmBY1xZtktS+terY5vKVNjeuhW3bOG2q05WVxQnU4wTq75brF7cYnx53\nAR2kcd9C6keAAAECUwXqNLa61ucBSXVI/pjU6Zd146ClLHUK4E+WsgIjXPczs676Q1FphsBr\nU806NVVZnMDV+fimyVmLW4xPj7vAnce9gupHgAABAgR6BGq/9ffJ+5K61e4XkwcmdY2sU9uC\noBAgMFSBs4a6dAsfCwEdpLHYDCpBgAABAn0K7JP5DkrqTlLdckpnpJ6DVI+iUAgQIECAwIIF\ndJAWTOeDAxKoO5T5g2ZAmC1dzE2ddnWHLW2mZvUpsFPm2yN5Z8/8P834k5I6ivSPPdONEiBA\ngACBeQvoIM2bzAcGLPDkLO8TA16mxbVL4Mo055HJGe1qltYsQKCf5yBtuIDl+ggBAgQIELhd\nQAfpdgojSySwXNZbUQjMJjDTg0Fn+4z32ifQ+xyk6Vq3VM9Bmq4uphEg0E6Bp6VZ/n5u57a9\nvVXuYnc7hRECBAgQaIDAJ1PHcXwOUgPoVJEAgUUK1FHsryZbJT9f5LJ8fIwFdJDGeOOoGgEC\nBAgsI7BXpozrc5CWqawJBAi0SqB75MiZL63arMs2RgdpWRNTCBAgQGC8BaY+B+ncVPcXyVI/\nB2m81dSOAAECBPoS6PaE+5rZTAQIEFgigYct0XqtdnwFlk/Vzkm+nZyVrJZskdTd7BQCBAgQ\nILBggTYcQVoprd86WT9ZN6kne1+e1AMEz+y8zkAhQKChAnUr+B8nWybuZNfQjTjAam+bZX0y\n2WyGZR6Z6cfO8J7JBAgQIEBgToEmd5Cq7nsn9TyMtWZoad35avfktBneN3npBerb339e+mqo\nwRgL1JGCKt3hba/8O6kC/5mGr5jskzw7qS/Dfp08PamjSC9PFAIECBAgsGCBJp9id0ha/erk\n0OTxyf2TdZJ6BkYdUdo5uTg5OdkmUcZT4MJU67/Gs2pqRYDAmAnUGQMPTN6avD35UnJVsmfy\n2KTOHqiOkkKAAAECBBYs0NQjSHXKzUuSHZJjpmn9eZlW3yoemRyR7JKckCgECBAg0FyB1VP1\nunvUDztNqFMu39QZvybDTyfPSw7rTDMgQIDAIAXqS5h/SX4zyIVa1vgJNPUI0iahrGuNjuuD\ntM5Ff1wf85mFAAECBMZb4KJUr44Y1TNIqvwy2SK5W71IuTbZ4NYx/xAgQGDwAvW35/sSd8wc\nvO1YLbGpHaQ6OnRJsuMcmnWErE61+9Uc83mbAAECBJohcFSqWdchPTep60uvTPZLnpi8JvlG\nohAgQIAAgQULNPUUu5vT4gOTOo1i1+To5ILk0mSFZK1k8+SFyWbJtokyvgK1za4f3+qpGQEC\nYyTwT6nLfyXbJ3Ua9b8mByV7JLUfqP2CQoAAAQIEFizQ1A5SNXiv5MTkgGS6I0k3ZnrtPF+c\n1BEnZTwFnppq7Z/cbzyrp1ZjIFBHCN6T/H4M6qIKSy/QvRFDfbFSpY4mfSepL8O+ntyQKAQI\nECBAYMECTe4gVaNrZ3jfpO5ct1FSF/BenZzfiXNEAzHmpbZZ3XRDITCTwE15o3sh/kzzmD55\nAr1HnX+R5leU4QvUGRxV6nowpTkCd01V63b4yuIF6vKU7v+DxS/NEsZSoOkdpEJdKamLcu+R\nrJvUBXTrJdW2MzuvM1AIECBAgACBRQr8KJ//u6QNfz8skqJxH3fntcVvspWziD8mD0rOXvzi\nLGFcBZr8C67qvneyR1LXHE1XTsrE3ZO6kFch0HaBM9LAe7W9kS1s31VpU10z+ecWtk2T2idQ\np6/XjTIUApMoUF/K15kvFaXFAk3uIB2S7bJTcnDylaQeOHpZsmJSHab6g2O3pB4UWw8Q9Byk\nICitFtgsrasvDX7a6la2q3F1evCHk/pWUgepXdtWawgQIECgoQJN7SB5UGxDf+BUe+gCdfpL\nPftLaYbAFs2oploSmAiBv0kr35s8fCJaq5EECMwo0NTnIHlQ7Iyb1BsECBAgQIDAAgTWyWfq\nqK5CgMCECzS1g3RqtpsHxbbjh/e3aca329EUrSBAgAABAgQIEGi6QFNPsfOg2Kb/5P1v/esa\nsZ3/96UxAgQIECBAgAABAksn0NQOUontlZyYDPNBse5UUtIKAQIECBAgQIBA3Uyn/va8GEW7\nBZrcQaotM+wHxf4g69iyzx+BehaTQoAAAQIECBAg0E6B69KsbdrZNK3qFWhyB6n3ScbnplGV\nulXuc5KnJnXb7+pA1cNiF1oelw/erY8P19Opz+9jPrMQIECAAAECBAgQIDDGAk3tIK0W03q4\n4vOTwzu+9dyjryV1h7tuqQfa7Zns250wz2E9V6miECBAgAABAu0W+EOaVw/cVggQmHCBpt7F\nbrrN9slMrCNIr0vWTx6TfDR5V/KsRBlPgTqFsa4jUwgQIECAwFIKfD8r324pK2DdBAiMh0Bb\nOkjrhfORyf6dXJBh/aJ7ZVLXEe2aKOMpUB0kd7Ebz22jVgQIECBAgACBiRNoSwfplmy5uvX3\n56fZgnUK3gOmmW4SAQIECBAgQIAAgX4F6tKUjyVr9vsB8zVToOkdpLreaJWkbsjwveRBydTy\nlEyoGzgoBAgQIECAAAECBBYqsHo++NJkw4UuwOeaIdDUDlIdMbohqZsv1M0aTk/quqO6lmXd\npMrDkmOSHZKPJwoBAgQIECBAgAABAgRmFWhqB+lPadWqyUOTv0+OS+q6o5U6yeBOOybbJ3UX\nuyMThQABAgQIECAwk8B98kb9zaAQIDDhAk29zXdttuuTn3TSPUK0XF7X0aUqhyTvTy6vFwoB\nAgQIECBAYBaBOvPkNcles8zjLQIEJkCgyR2k2jx3TeoXWh0JOzn5c9It3euO6ihSPfm4rlFS\nxk/g2lSpohAgQIAAAQIECBBYcoEmd5C2it5RyaYdxUsyrG9+jui87g7ekpHLEh2krsh4Db+U\n6vxovKqkNgQIECBAgECDBOo5mKMo9cV8lbqkY1Tr/EvW1T07qtatjECgqR2kOpXuU0ndqGG3\nznCPDA9P6s52/54ozRCo//QXNaOqakmAAAECBAiMmcBrU5//GHGdThzh+t6ddb15hOuzqgg0\ntYO0ceq+dfLk5NikymHJPsl+yaXJoYlCgAABAgQIECDQXoGPpmk/SOrL81GUumvyH0exos46\nfj3CdVlVR6CpHaT1Uv96MOwPp2zJt+X1aslBSV2DVLf5VggQIECAAAECBNopUNef/7idTdOq\npRJoagfprIDVjRmelXw66S2vz4sNkiOSJ/S+YZzABAjsljY+fgLa2ZYm3qMtDdEOAi0QuDFt\nqCgECEy4QFM7SHVo88vJAcm2ST0w9g9JlTqytGtydPLt5Orku4kyngJrpFqPShztW/z2qdML\nXrD4xVgCAQIEJlKgbhp0xkS2XKMJELiDQB2FaWp5aSr+neRVyWZTGlHPSHp2Ug+IrdPxlPEV\nqOvIPjG+1VMzAgQIEJgQgbrx0y8npK2aSYDALAJNPYJUTarbeu+YrJlcl0wt12RCdaLqeqS1\np77p9dgI1FGPikKAAAECBAgQIEBgyQWa3EHq4l3RHZlhOMpbMc5QBZMJjESgbple1+CdNJK1\nWckgBDbOQv5rEAuyDAIECBAgQGAwAm3oIA1GwlIItEPg9DTj++1oykS04vKJaKVGEiBAgACB\nBgnoIDVoY6kqAQIECIy1wEqpXT2jb/1k3aSO6lYn+NTkzM7rDJQxFbhr6rVFcvKY1k+1CBAY\nkYAO0oigrYYAAQIEWitQ+9K9kz2StWZoZZ36unty2gzvm7z0Ak9PFfZP6lEhCgECEyyggzTB\nG3+Wpr8j743qWTp1A4260cY3Z6nPIN+q28D/c/KzQS7UsggQmGiBQ9L6nZKDk68kFyaXJSsm\n1WHaPNktqSMTj01OSJTxE6g7+y4/ftVSIwIERi2ggzRq8Was76ep5qhuAb9K1vXrZFTfqlYH\n6eJEIUCAwCAE6lluL0l2SKZ7ntt5mV6n2B2Z1APMd0l0kIKgECBAYFwFdJDGdcssbb2+mNVX\nFAIECBCYXWCTvF3XGh03+2y3vnts/n1VH/OZhQABAgSWUGBURwmWsIlWTYAAAQIEhiZQR4e6\nz+WbbSX1heTOya9mm8l7BAgQILD0Ao4gLf02UAMCBAgQaK5AnbZ7YHJYsmtydHJBcmmyQtK9\nBumFGd8s2TZRCBAgQGCMBXSQxnjjqBqBeQrUaT4fSf48z881YfaNUsnzkpuaUNl51LEu4q9S\n205prsBeqXo9lPyAZMdpmnFjptU1SC9O6ojTQspd86G6ZlMhQIAAgSEL6CANGdjiCYxQoC7+\nXm+E6xvVqpbLiuoC+Lp+4/pRrXSE67kq66pTtJRmC3w91b9vsmFSHfrVk6uT8zv5S4aLKdUB\ne2CfC6i7gyrzF/hRPrLf/D/mEwQItE1AB2n2LVq3+6xTJBQCTRD4fBMqucA6fniBn/MxAqMW\nODcrrAy6PCkLvHsfC62O1Ff7mM8sywqck0kfWHayKQQITJqADtLsW/yUvP2g2We5/d31bx8z\nQoAAAQIEBitQ1zVV5ip1TZRCgAABAosQ0EGaHe9ZeXud2We59d06LN/PjquPRZmFAAECBBok\nsGrqep951PeKzHv2POY3KwECBAiMWEAHaXbws/J2pZ/nFzBlAABAAElEQVTiIut+lMxDgACB\ndgnUWQbfn0eT6mYNdbtvhQABAgTGVEAHaUw3jGoRIECAQCMEfpBaviw5OPlO8p5ktuJsg9l0\nvEeAAIExENBBGoONoAoECMwqULc3/k3yqGQYF7/PunJvEuhD4OOZp+62eGjyruRbidI8gb9J\nld+bPLx5VVdjAgQGKXDnQS7MsggQIDAEgeogbZCsOYRlWySBQQl8LAv6RrLfoBZoOSMXqGuO\n6zbtCgECEy7gCNKE/wBoPgECBAgMTKCuLdokqX1rPRxWIUCAAIEGCuggNXCjqTIBAgQIjKVA\n3aHuJ2NZM5UiQIAAgb4FnGLXN5UZCRAgQIAAAQIECBBou4AOUtu3sPYRIECAAAECBAgQINC3\ngA5S31RmJECAAAECBAgQIECg7QI6SG3fwtpHoPkC16QJv0wubX5TtIAAgTEW+EPqdsYY10/V\nCBAYkYCbNIwI2moIEFiwwLX55AMW/GkfJECAQH8C389s2/U3q7kIEGizgCNIbd662kaAAAEC\nBAgQIECAwLwE2nAEaaW0eOtk/WTd5Jbk8uTU5MzO6wwUAgQIECBAgAABAgQIzC7Q5A5S1X3v\nZI9krRmaeVKm756cNsP7JhMgQIAAAQIECBAgQOB2gSafYndIWvHq5NDk8cn9k3WSDZM6olRP\nNL84OTnZJlEIECBAgAABAgQIECAwq0BTO0hrpFUvSaoT9ObkO8mvkuoQnZfU6XVHJk9Pjkp2\nSRQCBJopsHyq/cFktWZWX60JEGiIwH1Szz0bUlfVJEBgiAJN7SBtEpO61ui4PmyOzTyP62M+\nsxAgMJ4C9YXI65KNx7N6akWAQEsEHpZ2vKYlbdEMAgQWIdDUDlIdIbok2XGOttd1SnWUqY4u\nKQQIECBAgAABAgQIEJhVoKk3abg5rTowOSzZNTk6uSCpB0mukNRNGzZPXphslmybKAQIECBA\ngAABAgQIEJhVoKkdpGrUXsmJyQHJdEeSbsz0ug7pxUkdcVIIECBAgAABAgQIECAwq0CTO0jV\nsK8n903qznUbJasnVyfnd/KXDBUCBAgQIECAAAECBAj0JdD0DlI1sh4Uu0Fyj6T7oNj1Ml5t\n86DYICgECBAgQIAAAQIECPQn0OQOUtXdg2L7287mIjBogVdlgfsnyw16wbMs7yezvDfot/bN\nAt8+6IVaHgEC8xb4bD4x3Wn0815Qnx+om1fVKfqjKHU99VOTb45iZdZBgED/Ak3uINWDYndK\nDk6+klyYXJasmHRv0rBbxutBsY9NTkgUAgQGI/DfWUwdoR1VqVv7/35UK8t6fj7CdVkVAQIz\nC7wpbx0089sDfaeeuVZnpJw70KXOvLB6XMn3Z37bOwQILJVAUztI3QfF7hC4Y6bBOy/T6sYM\ndZOGI5J6UKwOUhAUAgMSuCLL6ec5ZANancUQIDChAr9LuysKAQIERibQ1OcgeVDsyH5ErIgA\nAQIECBAgQIDA5Ag0tYNUR4c8KHZyfk61lAABAgQIECBAgMBIBJp6il1d2OhBsSP5EbESAgQI\nECBAgAABApMj0NQOUm0hD4qdnJ9TLSVAgAABAgQIECAwEoEmd5AKaNgPiv1B1vHgPrfEOn3O\nZzYCBAgQIECAAAECBMZUoMkdpLp+qk61q1K35KysnDwnqecK1G2/qwO1mFsRvyqfr1t+zlW+\nkBnOSraZa0bvLyNQt1V9VFJ3RVMIzCRQd66sLyyU+Qvce/4f8YkGC9SzyTZPLm5wG5aq6vZH\nSyXfrPXaHy18ezVmfzTKhzwunHPZT66WSVclz08O77xdO4SvJXWHu26ph73tmezbnTCk4dlZ\nbmM2+pAMLJYAgfEVqMch1BdHSvsF7I/av421kECTBRqxP2ryEaSpPxyfzIQ6gvS6pJ59dJ/k\nRcm7kjOSLybDKptmwXcZ1sJbvtx62O/7k/u1vJ2at3CBu+Wj9ZDYhyenL3wxE/3J6ye69ZPV\nePujhW9v+6OF203KJ+2PFr+lG7E/aksHab1sr0cm/5bs39l2F2RYT6jeKtk1GWYH6aYsv6LM\nX+CGzkeunP9HfWJCBOq0lyr1S/XaW8f8Q4DATAL2RzPJzD3d/mhuo0mfw/5oQn4C6jqeNpRb\n0oibk89P05g6Be8B00w3iQABAgQIECBAgAABAncQaHoHqa43WiW5MPle8qBkanlKJpw7daLX\nBAgQIECAAAECBAgQmCrQ1A5SHTGqQ+F184W6WUNdl7B+ckCyblLlYUldCLZD8vFEIUCAAAEC\nBAgQIECAwKwCTb0G6U9p1arJlkk9p+ghnWF1jlZKquyYbJ/UXeyOTBQCBAgQIECAAAECBAhM\nlMByPa3dMON1txFlvAV2TvXqFEmFwEwCa+WNOmpcN1xRCBAgMCwB+6NhybZnufZH7dmWs7ak\nqUeQZmpU/RHVLa476koYEiBAgAABAgQIECDQl0BTr0Hqq3FmIkCAAAECBAgQIECAwHwEdJDm\no2XeYQhclIX+YRgLtszWCPwlLannml3RmhZpCAEC4yhgfzSOW2W86mR/NF7bY2i16b1mZ2gr\nGcKC6wYN95nHcusPq7PnMb9ZCRAgQIAAAQIECBAg0BiBR6emdb1RvzmiMS1TUQIECBAgQIAA\nAQIElkygqUeQCuylycHJd5L3JLOVOj3n1Nlm8B4BAgQIECBAgAABAgSaLvCyNODmZLumN0T9\nCRAgQIAAAQIECBAgMAiB/8lCThjEgiyDAAECBAgQIECAAAECTRdYMw14SNK2Zzo1fbuoPwEC\nBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAEC\nBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAEC\nBAgQIECAAAECBAgQIECAAAECBAgMWWC5LP9FyReTM5NPJRsmCoHpBF6XiSdO94ZpBAgQWKSA\n/dEiASfs4/ZHE7bBNZfAKAX2yMquTd6avDD5cXJ2sk6iEOgV2DEvbkx+1TvROAECBAYkYH80\nIMgJWIz90QRsZE0ksFQC62XFVybVOeqWNTJydfL27gTDiRdYMwIfTW5Jrkh0kIKgECAwUAH7\no4FytnZh9ket3bQaRmB8BHZLVeqP3o2mVOmwvD59yjQvJ1fgXWn6RckLko8kOkhBUAgQGKjA\nblma/dFASVu5MPujVm7WmRt155nf8g6BoQlsmSXfkNQpdb3lN3nhOqRekckerw7zxkkNFQIE\nCAxDwP5oGKrtW6b9Ufu26awt0kGalcebQxKoQ9WXTrPsyzJttWSVad4zafIEfp4mXzN5zdZi\nAgRGKGB/NELsBq/K/qjBG28hVddBWoiazwxC4PppFlJHlaqsfNvAvwQIECBAYOgC9kdDJ7YC\nAs0S0EFq1vZqS23PT0PWmqYx3WlXTfOeSQQIECBAYNAC9keDFrU8Ai0Q0EFqwUZsYBNqh7Rq\nJ73VXz8v6r3reicaJ0CAAAECQxKwPxoSrMUSaLKADlKTt15z635cqn5z8rc9TagH9T0zObZn\nmlECBAgQIDBMAfujYepaNgECBAjMS+Dzmfuc5FHJPZIDksuTOoqkEJgq4DbfU0W8JkBgUAL2\nR4OSnIzl2B9NxnbWSgJLIlDXG301qSNJ9QyKE5M6gqQQmE7ADmk6FdMIEBiEgP3RIBQnZxn2\nR5OzrbWUwJIJrJE1b7Bka7diAgQIECBwm4D9kZ8EAgQIECBAgAABAgQIECBAgAABAgQIECBA\ngAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBA\ngAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBA\ngAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBA\ngAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBA\ngAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBA\ngAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBA\ngAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECCw\nNALLZbUvSr6YnJl8Ktkwmak8IG/8IdlyphlMJ0CAAAECCxCwP1oAmo8QIECAwOAF9sgir03e\nmrww+XFydrJOMrXcKxN+ndySbD31Ta8JECBAgMAiBOyPFoHnowQIECAwGIH1spgrk+ocdcsa\nGbk6eXt3Qob1rV7tuGreKxIdpCAoBAgQIDAwAfujgVFaEAECBJotsEuq/4Zkq+Sg5NjknclK\nSZ3m9sHk68lrkrsl3VKfe2PyyOQTyVeTf0runvSWTfOiOjq13EOSzZJ3J09OquyWVGdno6S3\nHJYXp/dMeHDGb0w+lDwr0UEKgkKAAIEWCdgftWhjagoBAgSaLLB/Kv/75Nzk0OTw5Ibks8lv\nky8nNc9fkgOTbqlpf0wuSfZKXpvUaXHfTe6SVFk3OS+5NNkv+VhS8/85+ZekynuS628du+M/\ntcyreibdI+P36byuzpUOUg+OUQIECLRAwP6oBRtREwgQINAGgdohVWfjuT2N+Vxn2r/2TNsn\n43VjhDrVrUr3c6+47eWt/26Sf6uz0+38HJnxOh1utaRbqkNU6+vO85GMV0draqmjUTXfKlPf\nyGsdpGlQTCJAgEDDBbr7Ffujhm9I1R8vgTuPV3XUhkBjBKojcnRPbU/ujH+hZ9pvMr5BsnbP\ntBr9Us/r32f8+OSvO9Mem2GdWlfXE3VL7zK706Y7glRHsaqsfNvAvwQIECAwAQL2RxOwkTVx\ntAI6SKP1trb2CFyYplzX05xu5+RX00zrHkGqt05Pzu+Zp0brKNMWSZ0SV6fYfTvpLT/Ki2t6\nJtTn1+p53R3tTruqO8GQAAECBFovYH/U+k2sgaMW0EEatbj1tUWg2yGarT29HaPufNP9n6uO\nzdlJ3W3upmT1pLcsnxd1A4huqQ7Sqp10p9Vw/aTe6+241XSFAAECBNorYH/U3m2rZUskMN0f\na0tUFaslMBECddOEOu2uW+p0uEcldYpe7eROSP426S3PzIve/6vH5fXNSe981Rmr+Y5NFAIE\nCBAgMJeA/dFcQt6fWIHeP7omFkHDCYxQYIWs66jkyck9k48ndSvuDydV3p48PPlB8vzkg0nN\n01vq2qYvJnWXu+pc1al5+yd1VOktiUKAAAECBOYSsD+aS8j7EyuggzSxm17Dl0jggqz3p0k9\nA+ncZNOkjgTVrb2rfDN5WlJHhA5J6qYNuyZVeq9D+vu8/nlSHamLk22SFyfT3d0ukxUCBAgQ\nIHAHAfujO3B4QYAAAQJLIVBHec7prHi1DOuGDFPLgzKhrknqLRvlRd2lqPc2rt3318hI7yl7\n3emGBAgQIEBgJgH7o5lkTCdAgACBkQr07pBmWnHdAvzXSfdW3XWDhk8ldfRovUQhQIAAAQKL\nFbA/WqygzxMgQIDAQAT62SE9LGuq0x7+kvwoqTvbXZvUNUsKAQIECBAYhID90SAULaO1AtPd\nhri1jdUwAksssGXWX0eB6i50s5X6f/mQ5K+TOppUd7a7PFEIECBAgMAgBOyPBqFoGQQIECBA\ngAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBA\ngAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBA\ngAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBA\ngAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBA\ngAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBA\ngAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBA\ngAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBA\ngAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBA\ngAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBA\ngAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBA\ngAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBA\ngAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBA\ngAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBA\ngAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBA\ngAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgMWWG7Ay7M4ApMmsHkafM+eRl+R8VN6\nXtfoKsk2U6admNd/mjJtrpd/lRke15npwgxPn+sD3idAgACBiRGwP5qYTa2hBAgQGG+BA1O9\nW3pyUcanfvHw3J73u/M+KNPmW+6WD3Q/f/h8P2x+AgQIEGi1gP1Rqzevxo1S4M6jXJl1EZgA\ngbXTxqmdnydOQLs1kQABAgTGS8D+aLy2h9o0SKBO2VEIEBiMQB3dqaNH1SH6Wc8it++Md9/v\neWteo1dn7id3PnHBvD5pZgIECBCYJIHu/sb+aJK2urYOTEAHaWCUFkTgTqfFoI4ePSl5X8dj\nowzv0xk/NcOtO+NTBxtkwt8mdT3T8kmdqnds0nudUXW+1kiqXHXb4NZ/H5J/ax03JkcltYwn\nJJsl9fkvJ9ckCgECBAhMhoD90WRsZ60kQIDAWAr0nvP9wdSwvrX7c7JCp7a7d6bdnOF/dMZr\nnt7T8HbI6xt63qv3Kzcl/5B0y0zXIHXrUJ2mZyR184fuMmpYnaTqgCkECBAg0F6B7r6gfu/b\nH7V3O2vZCARcgzQCZKuYGIHjOy1dOcNHd8a37wx/nuElnfHewWp5cXBSR3PrtLlDk6OTOuJT\n/z8PSPrt3NTd8uoI0pnJR5LzkypbJG+4dcw/BAgQIDAJAsd3Gml/NAlbWxsHLqCDNHBSC5xg\ngZPS9urYVKnzvqvUqW5VvnXbYJl/N8yUzybHJXX05+XJs5IPJd1y/+7IHMP6//yl5KHJHkkd\nmeqWB3dHDAkQIECg9QL2R63fxBo4TAEdpGHqWvakCVyfBn+/0+jqIG2VrNt5fXxnOHVwRibU\n0Z2a/ydJnXr3iuSxSbes2h3pY1hHnLqlbhRRp/tVufttA/8SIECAwAQI2B9NwEbWxOEJ6CAN\nz9aSJ1Oge6To4Wn+Th2COh/827Nw1BGfTyX18Nfq1NQpd9sm3VKf77dcNGXG7hGtOoVPIUCA\nAIHJEbA/mpxtraUDFvBH04BBLW7iBY7vCCyf4es746dmeFlnfOqgOkJ1t7q6fqiuUaprkL6W\nbJK8N6lSN3jot1w3Zcb5fHbKR70kQIAAgQYLHN+pu/1Rgzeiqi+NgA7S0rhba3sFfpym1Wlt\n1eFZvdPM7rd4nZd3GLwsr2reOkq0XVI3c6hSp911y3yOIM1n3u7yDQkQIECgfQL2R+3bplo0\nIgGn2I0I2momRqBu1929Dqnb6OO7I9MM6yYNVeoZR9VRqnKP5JW3jt32T93pTiFAgAABAvMR\nsD+aj5Z5CfQI6CD1YBglMCCB3iNGdYrbd2ZZ7jE973034z9K6vbcm/ZM797ooWeSUQIECBAg\nMKeA/dGcRGYgsKyADtKyJqYQWKzA8T0LqJsuXN7zeuro/plwYFIdqbsk2yTVSdo6uSKp8ozb\nBv4lQIAAAQLzEji+Z277ox4MowQIECAw/gJrpIr1rKI1x7+qakiAAAECLRawP2rxxtU0AgQI\nECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQI\nECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQI\nECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQI\nECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQI\nECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQI\nECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQI\nECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQI\nECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQI\nECBAgAABAgQIECBAgAABAgQITLjAci1o/0ppw9bJ+sm6yS3J5cmpyZmd1xkoBAgQIECAAAEC\nBAgQmF2gyR2kv0rT9k72SNaaoZknZfruyWkzvD+oyT/Lgu4zqIVZDgECBAYs8NUsb+cBL9Pi\nxlPA/mg8t4taESBwm0Aj9kfVyWhqOSQV3yk5OPlKcmFyWbJiUh2mzZPdkpOTxyYnJMMq1Tl6\nX1IdMoUAAQLjJPDsVGarcapQS+ty17TrYcmdk9rv/DmZWrbPhOuS7019Y4Cv7Y8GiGlRBAgM\nVMD+aKCcyy5sjUy6KXnKsm8tM+WITPngMlMHO+HqLO4Zg12kpREgQGAgAm/MUnx5MxDKGRdS\nHdDfJnWKd+XiZLojdt/I9NonDbPYHw1T17IJEFiMQGP2R/VNVxPLJql07YSO66Pyx2aex/Ux\nn1kIECBAgMB8BepU9U8lNyS7JbsmpyeHJ29OFAIECBBomEBTT7E7Nc6XJDsmn53FvNpX3+L9\napZ5vEWAAAECBBYqsHE+WDcKenJSX8hVOSzZJ9kvuTQ5NFEIECBAoCECTe0g3RzfA5PaCdW3\ndUcnFyS1I1ohWSupa5BemGyWbJso4ynw01TrXkltzz3Hs4pqRYAAgRkF1ss7tU/64ZQ53pbX\nqyUHJecmxyQKAQIECBAYusBTs4ZfJ93zvnuHN2R6daDqm71hF+d8z1/4q/lI/VFR26yG3TT5\nzopphkJg7AQac8732Mn1V6H1M1v9Hqsv66aWOo39yOTKpG7g8I3ENUhBUAgQmEiBxuyPmnoE\nqftT9fWM3DfZMNkoWT2pzsr5nfwlQ2X8BGqbVee2ylFJ/WFRR/9WTKpj2/SfyzRBIUBgQgT+\nmHZ+OTkgqbMV9k3+kFSpL37q91ud5fDtpPZP300UAgQIEBhjgbb8IVqnL1SmK1tmYn17d950\nb84x7Z55/x5zzFNvL9/HPGb5X4FTOqP7ZLhnZ7xukVt3JmzqjUM6zVjSwb2z9ruNqAbVma0v\nJdpa6vfFNSNq3J+ynroDmtJcgZem6ocmr0rqiFG3g5TRO12fPDv5cLJbstAyqfujtQO2wULR\n5vm5OoOhTtGvL+xGUaoDfXpSQ2XhAvX3w/0W/vF5f3KdfOKieX9q4R/4XT5aX64oBAYqUL94\nFnpKw6n5bJ060U/qGhqlP4EbM9t0O4TaKU03vb+lmqt+gfbzs2qe8XKqn/k1h/jj25hTGoZo\nMKpF13asP9ZmKo/MG0+f6c05pk/q/uhLcWnz76wnz7HdvT23wOta/jPy7rkJGjNHY/ZHbTmC\nNNtPxjvy5i9mm2GW9x6R92bb2XU/Wg+ovbj7wnBOgTqiV0c6Ppfs1DP3qI5+9KyyVaN1VKe+\nqf7WiFpVp7S2tXQ7m8Nu3/2zgh8mdxn2iix/JAJXTFnLCnn9+KTOZDg5+W6y0DKp+6P6nbbK\nQtHm+bkdM3/9MTqqoxH15chV86yj2ZcV2D+TPrHs5KFMqS9Bfp/8dXLGUNaw7EL9jCxrMvQp\nk9BB2nsRitflsxVlsAJ12mKdTvd3yYVJnZvf7SjdkHFl4QJ1utbUP9IWvrTZPzmq9cxei2a/\na8fX7O3Xrf1aGTkgqQeGn5W8NflO8tNk06RbPp2RFyW3dCfMYzip+6PaJ4zqd801nW0zqvXN\nY/ObdRaB+v80qm3WvQygvkQb1Tpnabq3hiXQ3dDDWv5SLLe+sXtK8opkq6WogHXOKVC/zP5P\nZ661M3xuUud+17dpKyYKAQIEmiTwqVS2vvA5Krky+a/kk8nlybOSeyVvSXZOXp4oBAgQIEBg\nKALVuXtbclJydFLndteRiXoeUv0BXqk/uPdLhl1qPe8c9kpauvx/TLu+mtQfEMriBOpi8Cct\nbhE+PWKBLbK++l1VXxQMq7wxC67fk8pwBDbMYmsbPrNn8V/oTNu2Z1qNfi6pu68Os9gfLVy3\nOrB1VoNCYCaBOlpc/999AT+T0OzTG7M/avIRpA9kG+yV1GHORyVfSg5NqoP0/KQ6TB9J3pzs\nlCjjKXBAqrVDct54Vk+tCBAgMKtAfblTnZJv9sx1fMZr33RCz7QaPTa555RpXhIgQIDAmAk0\ntYNUN054VfLC5AnJxsnPkjqV4dXJ4Ul9Y1qn2R2f7JYoBAgQIEBg0AK/ywJrX1pfzFWpG27U\nacOrJU9Mesvf5IVbuveKGCdAgMAYCjS1g1SHNpdP6nzvKtckhyV1wdwPkt7y+bzYtHeCcQIE\nCBAgMCCBC7OcTyZ1BkPtf/6QrJ+8L+mexbBdxg9KnteZloEyhgI/T50+M4b1UqXxEagjw59O\nnPUyPttkKDX5q6EsdfgLrdPoqnNXO52vdFZXnaW6BmO5pM4P7Za6JuOc7gtDAgQIECAwYIGX\nZ3m/T+oGQf+TvCepx0s8POleB3tDxuuU7+4+K6PKmAmckfq8bszqpDrjJVD/j+vsJYXA2Ap8\nOTWrnvwHk+k6eo/I9K8m1VmquwsNs7godpi6lt2vgJs09Cs1PvO5ScP4bIth1aSuUdo+qZsI\njaLYH41C2ToIEFiIwBvzoUbcNKiOwjS1PCcVf2fy9OTGaRrx4kyr87/flNQdhRQCBAgQIDBq\ngToV57jkklGv2PoIECBAYGECTe4gXZsmvzfZcoam1ykO9Y1dDRUCBAgQIECAAAECBAjMKTDd\nqWlzfmjMZqjTiqYrrjuaTsU0AgQIECBAgAABAgRmFGjyEaQZG+UNAgQIECBAgMA8BR6e+T83\nz8+YfbIEVkxzv5usO1nNnrzW6iBN3jbXYgIECBAgQGBZgU0z6THLTjaFwO0Cq2SsfkbWuX2K\nkVYK6CC1crNqFAECBAgQIECAAAECCxHQQVqIms8QIECAAAECBAgQINBKAR2kVm5WjSJAgAAB\nAgQIECBAYCECbbiL3ULa7TMECBAgQIAAAQLDETgli91wOIte0qUu11n7tzO8aUlrMpyVX5XF\nbpVcM5zFN2epOkjN2VZqSoAAAQIECBBogsADU8l/T37WhMrOs44PyPy/mOdnmjB7dWjfn9SN\nKHSQmrDF1JEAAQIECBAgMGSBK7P8K4a8jkla/HfS2GMnqcENb+sWDa//QKvvGqSBcloYAQIE\nCBAg0FCBY1LvOvKhECAw4QI6SBP+A6D5BAgQIECAwO0CN9w+ZoQAgYkV0EGa2E2v4QQIECBA\ngAABAgQITBXQQZoq4jUBAgQIECBAgAABAhMroIM0sZtewwkQIECAAAECBAgQmCqggzRVxGsC\nBAgQIEBgEgXWS6NfMokN12YCBO4ooIN0Rw+vCBAgQIAAgckUeFya/e7JbLpWEyDQK+BBsb0a\nxgkQIECAwMIFVspHt07WT9ZNbkkuT05Nzuy8zkAhQIAAgXEW0EEa562jbgQIECDQBIHal+6d\n7JGsNUOFT8r03ZPTZnjfZAIECBAYEwGn2I3JhlANAgQIEGiswCGp+auTQ5PHJ/dP1kk2TOqI\n0s7JxcnJyTaJQoAAAQJjLOAI0hhvHFUjQIAAgbEXWCM1rAv7d0iOmaa252VanWJ3ZHJEskty\nQqIQIECAwJgK/P/27gNOurK8G7CEIEgR7CAiRSxUNaKoiYmxRmygsQU12ONnFI2xkYA0lUhQ\nIwkQwBhFSQSjUlQQUSxoRBELRRAUBBUUpApI/f43zphhnX13dnZm9pyZ6/79/u/MnDnlOdfZ\nd88+c8o4gtTQDaNZBAgQINAKgY3TyrrW6MQBWntCxqkbASgCBAgQaLCADlKDN46mESBAgEDj\nBero0KXJ9gu0tM7YqFPtzl5gPG8vn0B1dCuKAIEZF3CK3Yz/AFh9AgQIEFiSwC2Z+oDk8GTH\n5Ojk4uSy5I5J3bThgckLk02TRyWqmQJ1imRtJ0WAwIwL6CDN+A+A1SdAgACBJQvsmTmckuyf\n9DuSdFOG1zVIL07qiJNqpsBVadbnm9k0rSJAYJICOkiT1LYsAgQIEJhWgeOyYvdP6s51GyZ3\nTq5OftbJdXlcSu2eiR8w4Axq2YoAAQIEhhTQQRoSzmQECBAgQKCPwIUZVhl1XZMZVodrkKrT\n/hQBAgQIDCmggzQknMkIECBAgMACAnUN0p8lWyT1HUhfSYatfx5wwldkvOpMKQIECBAYUsBd\n7IaEMxkBAgQIEOgI1I0YPppcmXw3eWqyVnJW8rnkvcmXk48kKyWqmQL1N9G6zWyaVhEgMEkB\nHaRJalsWAQIECEyjwGFZqR2STyXVSaqO0IeTy5NnJvdJ3pbUbb7rCI9qpsDT06y62YYiQGDG\nBZxiN+M/AFafAAECBJYkUDdl2C55RnJMZ06fzGPdze7Rydc7w/bJ48OTZyUHd4Z5aJbAqmlO\nRREgMOMCjiDN+A+A1SdAgACBJQnU0aG6KcIXeuZyUp7XDRW+0TOsnp6QrD9nmJcECBAg0DAB\nHaSGbRDNIUCAAIFWCfwora196fM7rV4lj89J6hqkJ3SGdR8emyfndV94JECAAIFmCrT5FLva\nIc29lenqGfaXSX1r+SVJfS/FOYkiQIAAAQLjEKh9zYeTQ5OXJZsmdfRov+SQ5ICkrmup64+e\nlzwtUQQIECDQYIG2dpDqk7n6xuv6xO5jHd/qFH022bjzuh5uSnZL3lUvFAECBAgQGINA3Xjh\nx8mTk7pr3b5J3cFum6SuPaq6MXlL8ul6oQgQIECguQJt7SD1E61P8OoI0s7JEcn9khcl70zO\nTI5KFAECBAgQGLXADZnh7p30zvuxeVHXKNUHeHX770sTRYAAAQINF5iWDlJ9b8Ejkn9I3t8x\nvziPJydbJTsmOkhBUAQIECAwUYGLsrSKar7A6Wnifze/mVpIgMC4Beo6nmmoW7MSdT3SJ/qs\nTJ2Ct1mf4QYRIECAAAECBLoCdbZJnYWiCBCYcYG2d5DqeqM1krpI9qvJ1sncqnPCL5w70GsC\nBAgQIECAAAECBAjMFWhrB6mOGNUFr3XzhbpZwxnJesn+yb2SqoclxyfbJR9MFAECBAgQIECA\nAAECBFYo0NZrkK7JWq2ZbJE8JHlo57E6R6slVfUt5o9P6i52RyaKAAECBAgQIECAAAECKxRo\nawepVqruGnRaJ90jRCvldR1dqjo4eU9yeb1QBAgQIECAAAECBAgQWEigrafYzbde3c5RvV/X\nHekczSdlOAECBAgQINArUN9b9T+9AzwnQGA2Bdp8BGnQLVan4V2ZXDToBD3jPSHP79vzekVP\nV1nRm94jQIAAAQIEGi2wSVr3J41uocYRIDARgVnoIH0/kh9PnjuE6GszzZYDTrfOgOMZjQAB\nAgQIECBAgACBhgrMQgfp7bE/a0j/Zw44XX0H0y8HHNdoBAgQIECAAAECBAg0VGAWOkh7NdRe\nswgQIECAAAECBAgQaJhA2ztId4pnfd9R3Wzi1OTXydyqW33/Jvnq3De8JkCAAAECBAgQIECA\nQK9Am+9it1VW5PTkK8mXkvOTftcZvS3DX5coAgQIECBAgAABAgQIrFCgrR2k+r6jw5Ibk52S\nHZMzko8lb0kUAQIECBAgQGAxAnXH2ysWM4FxCRCYToG2nmK3UTbHg5MnJSckVYcneyf7JJcl\nhyaKAAECBAgQIDCIwPEZadA71w4yP+MQINBSgbZ2kNaNd9057utz3P8xr9dKDkwuTOqXnSJA\ngAABAgQIDCJQZ6YoAgRmXKCtp9idn+1Wbe93G+43ZPinkiOSuoGDIkCAAAECBAgQIECAwEAC\nbe0g/Txrd2yyf/KvyfpJt+rIUl2TVEeX6uYNWySKAAECBAgQIECAAAECCwq0tYNUK/aS5MvJ\nq5NNk966IS+elRyZ1Ol4igABAgQITFrgjlngE5PXJ4+Z9MItjwABAgSGE2hzB+nSrPL2yd2S\nU/qs/rUZVp2obZMP9XnfIAIECBAgMAqBu2YmH03qLmjfTZ6a1PWwZyWfS96b1Ad6H0lWSlQz\nBeoD1b9uZtO0igCBSQq0uYPUdapbcl7XfdHnsTpPn+4z3CACBAgQIDAKgfraiR2Suv61OknV\nEfpwcnlS18reJ6nv5Kvv6ntFopop8Kdp1rub2TStIkBgkgJtvYvdJI0siwABAgQIzCewQd7Y\nLnlGckxnpE/msc5weHTSvdtqfQXFw5M6/fvgRBEgQIBAQwWm4QhSQ2k1iwABAgRmQKCODtXN\ngb7Qs64n5fnVyTd6htXT+t6+3psKzXnbSwIECBBogoAOUhO2gjYQIECAQFsFfpSG1770+Z0V\nWCWPz0nqGqQndIZ1Hx6bJ+d1X3gkQIAAgWYKOMWumdtFqwgQIECgHQKXpJl1vdGhycuSTZM6\nerRfckhyQFLXwtb1R89LnpYoArMgUB8QrDcLKzol63jvKVmPkayGDtJIGM2EAAECBGZYoG68\n8OPkyUndtW7fpO5gt01S1x5V3Zi8JRn2pkFrZ9qKItAGgbpb45vb0FBtJNBPQAepn4phBAgQ\nIEBgcIEbMurunfRO9di8qGuUHpjU7b/r6ymGrZMz4aBffO6T4OGUb81kFUWAwIwL6CDN+A+A\n1SdAgACBJQmsmqnfmByXfLvPnC7KsMpSq75odp0BZlLXOP1sgPGM8vsCx2fQC39/sCEECMya\ngA7SrG1x60uAAAECoxRYLTN7R7J7J3VKXd3VbtR1eWZYUeMTuCqz/vz4Zj9Tc64jcf+cnDlT\na93ula0jz3u3exVG13odpNFZmhMBAgQIzK5AnQK3R/KU5OXJ2YkiMMsCdT1e3dpetUNg8zRT\nB6mzrdzmux0/tFpJgAABAs0WOCjNe0RSN1KoGzTUKXdPT+xng6AIECDQJgG/uNu0tbSVAAEC\nBJoscFoaV3eu2zXZMjk6qWuC9kx2SO6f2O8GQREgQKDJAn5RN3nraBsBAgQItE2g7mhX1yRt\nmNQRpO8luySfSM5JDk9UMwXqb6J1m9k0rSJAYJICrkGapLZlESBAgMCsCNycFT22k9XzuFkn\n1+dRNVOgOrT7J/dtZvO0igCBSQnoIE1K2nIIECBAYFYFrs2Kn9rJrBq0Yb3rlu0VRYDAjAvo\nIM34D4DVJ0CAAIElCVyTqbdNzl3SXExMgAABAo0R0EFqzKbQEAIECBBooUCdSndKC9utyQQI\nECAwj4CbNMwDYzABAgQIECBAgAABArMnoIM0e9vcGhMgQIAAAQIECBAgMI+ADtI8MAYTIECA\nAAECBAgQIDB7AjpIs7fNrTEBAgQIECDw+wKnZ9B///5gQwgQmDUBHaRZ2+LWlwABAgQIEOgn\ncGYG7tzvDcMIEJgtAR2k2dre1pYAAQIECBAgQIAAgRUILPY23/Vt4GskK82Z52/y+so5w7wk\nQIAAAQIECBAgQIBAqwQG7SA9OGv1oaQe+9WRGfjcfm8YRoAAAQIECEyNwCZZk2OSVaZmjWZn\nRU7Kqr5ydlbXmhIYXmDQDtJhWcS6yW7JRUl9MV5vXdD7wnMCBAgQIEBgKgXuk7XaPHnVVK7d\n9K7U47Jq207v6lkzAqMVGKSDtE4WuVWyfXLUaBdvbgQIECBAYFECe2bsLyQnLWoqI49a4JDM\n8NZRz9T8xiawWua82djmbsYEpkxgkJs0XJt1vj65esrW3eoQIECAQLsE1kpzd0ke1a5may0B\nAgQItElgkA7SDVmhTyWvaNOKaSsBAgQITJ3ANVmjc5Otk7k3C5q6lbVCBAgQILA8AoOcYlct\n+2ryT8m3O5l7x7rvZviHE0WAAAECBMYlUKd0HZTsldR+57Tk4qS3vpcXH+0d4DkBAgQIEFiM\nwKAdpPritDrFbr3kqX0WUKc96CD1gTGIAAECBEYq8LrMrU77rv1RZW4dnQE6SHNVvCZAgACB\ngQUG7SA9YOA5GpEAAQIECIxPYJPxzdqcCRAgQIDAHe4waAepyVZ1Z5b6fqb6JPFeSZ2CcXlS\np1mc03mdB0WAAAECUyRQv/srVXU9be3P7p6sn5yQKAIECBAgMJTAfB2kDTO31yf1ZXB1O9V3\nJd0dUZ7+Xn0nQ+qLZCdZ1fY6D72+9Oyu8yz4mxn+suT787xvMAECBAi0S+BRaW6d0r3pPM2u\nLy7XQZoHx2ACBAgQWFhgvg5SdTiem/wwqQ7SDkldZzRfrZ43Jt1BOjjLfHZSF+x+Orkk+VWy\nalLtf2CyU3Jq8pjkG4kiQIAAgXYL/HuaX7/n906elXwvqX1VXR9b+yl3XA2CIkCAAIHhBebr\nINWdgdbvme2Dep434enaacRfJ9slx/dp0EUZVjvN+iTxiOQFiQ5SEBQBAgRaLFBnMmyZvDj5\nSLJKcpdkt2Sf5ItJdZQOT5aj6sO5+qCuW3+SJ1slP0tOTi5NFAECBAg0XGC+DtLcZm+QAXWO\nd7+6JQOv6qSu/5lEbZyF1LJOHGBhdarFqwcYzygECBAg0GyBO6d59f1HX+8088w8vrnzvL7U\nvO5e97xk0h2kB2SZ70jqGqg/T6qdn0gen3Sr9llvSP6lO8AjAQIECDRTYL5Oz9zW1jU858+T\nn2T4FcllSe0gJlF1dKg+idt+gYVVB7BOFTx7gfG8TYAAAQLNF/hFmlgfyNVRmaofJJsndRSp\nqm7/fe/bnk32n/dlcZsl+3UW+948Pjp5e1LDH5nUsMpfJYoAAQIEGiww6BGkN2Yd6lqfryV1\nvU91UDZMXpOsmeydPDipT/LqU7J/TMZZddTqgKQ+JdwxOTq5OKlO2h2TuyZ1DdILk02TRyWK\nAAECBNov8Kmswr8nqyTHJlcmdXpdnVJd+6TPJJOsu2VhT0zqyNFXOwveIY8fSfbsvK6HOs27\n9kf1Xu27FAECBAi0XKCOIFXnZ27dIwOuTZ7feeNv8nh+5/kkHv4iC6mLc6tTNjc3ZljthKrj\nNu6qDtse417IFM7/6qxT2VVuTt6VqOEFbsik9Yeaao9AHf2o3131u3Rc9abM+JvjmvkyzLeO\nFtUHdfWhXdWrkvodUo4/T7pHl/J0IrV1llK/v+q0uqo6BbDOcHhRvZhTr83rU+cMG/XLce+P\n/jQNLutaT9Uegdelqd+dYHPtjyaIPaJF2R/1QA5yil3tjLZMPtkzXffpL/PkmKQ+Oas6Ptkw\nuWe9mEAdl2XcP7lv8pjkqUn98q5P6WpnVacyTPIXQhanBhSoPyjW6Bm3drZvSbqfwPa85SkB\nAgR+J3B5ntXv+vqDr6qOJm2RPCOpfUF9oDfJOjMLuy6pjmj9HqvOw2eTnZLeulNevCD5Tu9A\nzwkQIECgeQJ/OECTrsg4dd53Xfi695zx6xd+dY5qB1W1QXJl8qt6McG6MMuqqHYIfD3N7P4h\nsXKnybvncbfk0Z3XHhYvUKaHJL9e/KSmWCaBOiVYDSfw8Ez2kOT65ANJfeBSZw5Mum7KAv82\n+WDyR0ntD9+X/FdySvKRpLbzjsnGyYsSRYAAAQINFhikg1SfhtXRo79Pagf0maQ+MdsoqU/8\n104+kdSRnHclJya1w2hK1SeL1Wm7aIgGvSHTbDrgdGWjBhN4RGe0bueoXu6e1LVrgxzVzGhq\nHoE6gqsITLPAWlm5I5I6xbqq9jn/ndQ1Pgcmta+qTtMk6z+zsEuStyVHJb1VHbmqzyWvSM6r\nF0OU/dEQaCYhQIDAMAKDdJBqvnXe9M3JWzvJw21VO4T6VOy05B1JdZL+LmlS1ekWH0+eO0Sj\n7pZp7jHgdINaDjg7oxEgQIBAH4F/ybDNkjpdrc6Zr6PO1ya1n9o3OSmp3/mTrjqtrrJucp9k\n/aSOHF2UXJD8LFlK2R8tRc+0BAgQWITAoH/U12kLr0nemTws2SD5cfKlpHs6z355XqdIVUeq\nSfX2NOasIRtURzQGqVsy0pWDjGic2wRqe9QfNvWz0j2K9Lg8d/QoCEusOiW2/r+qdgjUz3/d\ndVMNJlC/I56f7JAcn7w5qaozHQ5I6vfK05JJdpBWzfLemByXfDu5uJNv5XGUZX80Sk3zIkCA\nwAoEBu0gdWfx0zyp9KtJX3fUrw39hu3Vb6BhyyqwZZZenaOVkupc9tYPe194viiB+iOxjpSe\nsKipjLycAvUH/RnL2YCWLbu+iPVOSR2R6Vc1vI4sTbJWy8LqDIrdO9knj3N/r2WQIkCAAIG2\nCMzXQdowK/D65JjkC0ldW1Q7gfnqO3njQ/O9OcbhtaOsI1r1qeKpSfdoVp7+rh6fZ79Jvvq7\nIZ40QaA+Oa8jHfXYrRPz5IndFx4JECAwR+AXeX1Z8sJk7hGV+sDlZUmd8r0cdXIWukfylOTl\nydmJIkCAAIEWCszXQapTPuqT6Po0vzpIdTrDWsl8tXremHQHaass81PJJp1GXZrHOg3wiM7r\n7sPb8qSObukgdUWa87hKc5qiJQQItETgPWlnnTp936SOmtYNcl6R7JTcP6lO0nLUQVloXYNb\n+8I6jbhuyvBvyacTR5SCoAgQINAWgfk6SPUJ3Po9K/GgnudNeFqfFB6W1BGInTqPr8zjx5KN\nk39KFAECBAhMn0CdwrZmUp2Ruv6n6pFJHVl6aVJHcparat+5TfKm5NXJ0cn5Se2v6r3Tk/MS\nHaYgKAIECDRVYL4O0tz27pkBdSTppLlvLNPrjbLcBydPSk5Iqg5P9k5q51k7ykMTRYAAAQLT\nJVCdi12S9yZ1PeN6yQXJ95Krk+WuG9KAdyS1L6rT7eroVrV35aSqPsirG00oAgQIEGiowCAd\npDq1rn65X5eclDSh1k0jaif59TmNqXPSq70HJhcmxyeKAAECBNorUB+GPT6pDlGdUtetX+bJ\nF7svGvhYN6I5tpM6DX2zTq7PoyJAgACBBgvUzQ0WqmsywrnJ1kmd2taEOj+NqLY/s09j3pBh\ndW1SXYtUN3BQBAgQINBegVXS9P2SOovhvi1djWvT7lOTjyQfb+k6aDYBAgRmRmCQI0j1id1B\nyV7Jd5PTkouT3qpTGz7aO2DMz3+e+dcnc/snj0relfw0qaojSzsmde73l5I65eIriSJAgACB\n9gl8K01+UfIvyfeT1yYfTppS9SHitkl9kKgIECBAYAoEBjmCVKv5uqROC6hzvbdLXjonj8vr\nSddLssAvJ3Uh7KZzFl7ngD8rOTKp0/EUAQIECLRXoI68bJ58PvlQ8j/J3ZMmVJ1Kd0pSd0tV\nBAgQIDAFAoMcQarV3KSB63pp2rR9sk7ymz7tq1MaqhNV1yPdo8/7BhEgQIBAewQuSVOf3Un3\naNLOeV13heuty/PiR70DPCdAgAABAosRGLSDVPNcKXlG8tDkzkmdVlfnVNcpD8tZVyyw8Ppk\nTxEgQIDAdAjU0aNvdlJ3hJtbdeZAfY+fIkCAAAECQwkM2kG6S+Ze1/w8urOUOpWghlWnaddk\n70QRIECAAIFxC9SZAf+UrJXsmcw9WnR+hikCBAgQIDC0wKAdpPdlCRsnr0uOSc5PVk9eleyb\nnJ3Up3aKAAECBAiMQ2CjzPSQ5AnJN5LqKJ2VKAIECBAgMFKBQTpIf5AlPid5QXJUz9LrGp/3\nJnV90tMSHaQgKAIECBAYqUDtg16T1N1Ka5/11uSfk7o5giJAgAABAiMXqB3PQlU3OLhT8p15\nRvxuhm81z3sGEyBAgACBpQhsk4nfn5yZ/FFSp9fpHAVBESBAgMB4BAY5gvSLLLq+56FOa/hA\nn2bU8PP6DDeIAAECBAgsVaC+tmGX5N2JjtFSNU1PYHICf5xFrTm5xVnSEgU2WOL0UzX5IB2k\n+qLYulV2XYe0TvLp5LKkvtG8rkF6XvLERBEgQIAAgVEL1NkL853BMOplmR8BAqMR+EFmU9et\nq3YJXJDm/rpdTR5PawfpINWS356skeyb1Lnf3bo6T/42qS/vUwQIECBAgAABAgS2RkCgzQKD\ndpCuy0rWRbL7JXW90XrJ+cm3kksTRYAAAQIECBAgQIAAgdYLDNpB6q5ofd/E3O+c6L7nkQAB\nAgQIECBAgAABAq0WGOQudq1eQY0nQIAAAQIECBAgMAKBlTOPuiZ/rRHMyywaLKCD1OCNo2kE\nCBAgQIAAAQKNEVg7Ldk52agxLdKQsQjoII2F1UwJECBAgAABAgQIEGijgA5SG7eaNhMgQIAA\nAQIECBAgMBYBHaSxsJopAQIECBAgQIAAAQJtFNBBauNW02YCBAgQIECAAAECBMYioIM0FlYz\nJUCAAAECtwk8Mv/+FQsCBAgQaI+ADlJ7tpWWEiBAgED7BJ6aJr+1fc3WYgIE+gjc1BnWfewz\nikHTILDYL4qdhnW2DgQIECBAYFQCW2VGh69gZvfKe/WdKd/vjHN8Hv++89wDAQLtErgqzd06\nOatdzdbaxQroIC1WzPgECBAgQOD/BH6Zp3U2xubJ15Nzk956SF6skpzWGXh+59EDAQLtFOh+\n2NHO1mv1QAI6SAMxGYkAAQIECPQVuDhDt0nenbwkOSw5MOnWXnnyzOTF3QEeCRAgQKDZAq5B\navb20ToCBAgQaL7AdWnia5O/THZLPpOslygCBAgQaKGAI0gt3GiaTIAAAQKNFDguraprkg5J\n6jScv0mmtZ42rSs2peu1RdbLh+JTunGt1ugFdJBGb2qOBAgQIDC7Apdm1XdIXp78Z3JN8otk\nqVXXMa221JmMcPqjRzgvs5qMwDmTWczUL+XRWcO63vDWqV/TGV5BnybM8Ma36gQIECAwNoFD\nM+e6QcO3ktNHsJRTM4+6g9ZCWSnjOL1vBOBmQaCPwNoZdnJSN2VRUyzgCNIUb1yrRoAAAQJj\nF1g1S3hjUqfXfXvO0uqOdqM6Fa2+T+kec+bf72V1yH7e7w3DCBBYssDKnTk4wLBkymbPQAep\n2dtH6wgQIECg2QJ12ts7kt072SePtySjrgszw0pTqjqFqj0Cf5KmPqA9zdVSAssroIO0vP6W\nToAAAQLTIVCn3eyRPCV5eXJ2Ms313qycazDas4VvSlPv157maimB5RVwiHB5/S2dAAECBKZD\n4KCsxiOSukbhrKROuXt6Yj8bBEWAAIE2CfjF3aatpa0ECBAg0GSB09K4+tLYXZMtk7rT23nJ\nnknd2e7+if1uEBQBAgSaLOAXdZO3jrYRIECAQNsEbkiD65qkDZM6gvS9ZJfkE0ndZvnwRBEg\nQIBAgwVcg9TgjaNpoaro8wAAGddJREFUBAgQINBagZvT8mM7WT2Pm3VyfR4VAQLtFLgizd4r\n+VE7m6/Vgwq0uYNUR7/m3imodkJ/mTwwuSSpc8DrEztFgAABAgSWS+DaLLi+x6iiCBBor0D9\n3blbe5uv5YMKtLWDtFZWsL4s7/nJxzorW52izyYbd17XQ921pX6Q31UvFAECBAgQGLHANZnf\ntkl955EiQIAAgSkQmKZrkD6c7VFHkHZO6lvE657/H0jemTwzUQQIECBAYNQCdSrdKcmvRj1j\n8yNAgACB5RFo6xGkuVrrZkDdXvUfkvd33rw4j/W9FFslOyZHJYoAAQIECBAgQIAAAQLzCkzL\nEaT6sro6L7TuEjS36hS8ujhWESBAgAABAgQIECBAYIUCbe8g1fVGayR1Q4avJlsnc+vJGXDh\n3IFeEyBAgAABAgQIEFiEwJ0y7k+TDRYxjVFbKNDWDlIdMboxqZsv1M0azkjquqP9k3slVQ9L\njk+2Sz6YKAIECBAgQIAAAQLDClQH6d7JOsPOwHTtEGjrNUh116A1ky2ShyQP7TxW52i1pGr7\n5PFJ3cXuyEQRIECAAAECBAgQIEBghQJt7SDVSt2QnNZJ9wjRSnldR5eqDk7ek1xeLxQBAgQI\nECBAgAABAgQWEmhzB6nfunU7R/We6476CRlGgAABAgQIECBAgMC8AtPWQeq3onUa3pXJRf3e\nXGBYHZka9A54d11gXt4mQIAAAQIECBAgQKDhArPQQfp+tsHHk+cOsS0+m2l+MMB09R1Mvx5g\nPKMQIECAAAECBAgQINBggVnoIL09/mcNuQ2OGHC6upvebwYc12gECBAgQIAAAQLtE7g2Ta4P\nzi9rX9O1eDECs9BB2msxIMYlQIAAAQIECBAg0Efg+gwb9NKLPpMb1BaBtn4PUlt8tZMAAQIE\nCBAgQIAAgRYJ6CC1aGNpKgECBAgQIECAAAEC4xVo6yl29SWx91sEzRUZ94JFjG9UAgQIECBA\ngAABAgRmUKCtHaSts61OXsT2OjLjDnMXu0UswqgECBAgQIAAAQIECLRdoK0dpK8F/qXJQcmX\nk32TFdXFK3rTewQIECBAgAABAgQWEFg57++X7JpcvcC43m6xQFs7SEVeX+K6UnJo8s7ki4ki\nQIAAAQIECBAgMA6BtTPTnZMPJPU9m2pKBdp+k4b/yHb5fLLPlG4fq0WAAAECBAgQIECAwAQF\n2nwEqctU1xZtnNS63NQd6JEAAQIECExQ4E5Z1sOS+uDx1OTXydx6fAbUl4p/de4bXhMgQIBA\ncwTafgSpJOsOdaclOkeloQgQIEBg0gJbZYGnJ19JvpScn/S7MdDbMvx1iSJAgACBBgtMQwep\nwbyaRoAAAQJTLlDXwh6W3JjslOyYnJF8LHlLoggQIECgZQLTcIpdy8g1lwABAgSmSGCjrMuD\nkyclJyRVhyd7J3V97GVJ3UxIESBAgEBLBHSQWrKhNJMAAQIEGimwblp1S/L1Oa37x7xeKzkw\nuTA5PlEECLRboHs5R/ex3Wuj9fMKOMVuXhpvECBAgACBBQXOzxi1L31mnzHfkGGfSo5I6gYO\nigCBdgtcleZvnZzV7tXQ+oUEdJAWEvI+AQIECBCYX+DneevYZP/kX5P1k27VkaW6JqmOLtXN\nG7ZIFAEC7Rbw/Uft3n4Dtd4pdgMxGYkAAQIECMwr8JK8U9cZvTo5Mvlp0q0b8uRZyb8lOyXD\n1ksz4SYDTly3HB931TVWt457IeY/MoGHj2xOZkRgBgR0kGZgI1tFAgQIEBirwKWZ+/bJOkl9\nz9HcujYDqhNV1yPdY+6bA75+aMZ70IDjrjbgeMOMdl4m+mzyiGEmNs2yCtTpnooAgQEEdJAG\nQDIKAQIECBCYR2DVDH9jclzy7XnG6Q4+pftkiMfXDjhNndZ3+YDjDjNaHR3bbpgJTUOAAIG2\nCLgGqS1bSjsJECBAoIkCdbTmHcn/Jrsk9qtBaGnVqYluptHSjTfBZj86y6rvP1NTLOAX+RRv\nXKtGgAABAhMTODlL2iP5UvLAiS3VgkYp8NTM7JhRztC8pk5g7axR/V/ffOrWzArdTkAH6XYc\nXhAgQIAAgaEEDspUdV1O/QFVtwCuU+6entjPBqElVdtq5Za0VTOXR6D78+H/9fL4T2ypNvDE\nqC2IAAECBKZc4LSs3zbJrsmWydFJ3dRgz2SH5P6J/W4QFAECBJos4Bd1k7eOthEgQIBA2wTq\ntt51TdKGSR1B+l5S1yZ9IjknOTxRBAgQINBgAXexa/DG0TQCBAgQaK3AzWl5fYFsZfVks06u\nz6MiQIAAgQYL6CA1eONoGgECBAhMhUB9D9KpnUzFClkJAgQITLOADtI0b13rRoAAAQLjFrgm\nC9g2OXfcCzJ/AgQIEJiMgA7SZJwthQABAgSmU6BOpVvKF8BOp0o716q+y2qfdjZdqyckcEWW\ns1fyowktz2KWSUAHaZngLZYAAQIECBBolMBP0pr3NqpFGtM0gVvSoN2a1ijtGb2Au9iN3tQc\nCRAgQIAAAQIECBBoqYAOUks3nGYTIECAAAECBAgQIDB6AR2k0ZuaIwECBAgQIECAAAECLRXQ\nQWrphtNsAgQIECBAgAABAgRGL6CDNHpTcyRAgAABAgTaJ/DYNPlb7Wu2Fk9Q4E5Z1k+TDSa4\nTItaBgEdpGVAt0gCBAgQIECgcQL3TIv84du4zdKoBlUH6d7JOo1qlcaMXEAHaeSkZkiAAAEC\nBAgQIECAQFsFdJDauuW0mwABAgQIECBAgACBkQvoII2c1AwJECBAgAABAgQIEGirgA5SW7ec\ndhMgQIAAAQIECBAgMHIBHaSRk5ohAQIECBAgQIAAAQJtFdBBauuW024CBAgQIEBglAJ1++Yz\nRzlD85o6gWuzRj9ILpu6NbNCtxP4w9u98oIAAQIECBAgMJsCJ2e1/3w2V91aDyhwfcbbbMBx\njdZiAUeQWrzxNJ0AAQIECBAgQIAAgdEKTMMRpNVC8uBkveReya3J5cn3knM6r/OgCBAgQIAA\nAQIECBAgsGKBNneQqu17Ja9M7jrPan4zw1+WfH+e9w0mQIAAAQIECBAgQIDA7wTa3EE6OGvx\n7OSg5NPJJcmvklWT6jA9MNkpOTV5TPKNRBEgQIAAgXEJOKNhXLLmS4AAAQILCqydMW5Onrzg\nmHe4wxEZ530DjLeUUW7JxHssZQamJTACgRsyjyeOYD5mMTmBzbOoOi34HmNc5Jsy7zqarsYn\nUB82viupO1vV9uyXUzJ8q2TcZX80vPD9Muluw09uyhkQWDnrWH9TrjUD6zqOVWzN/qitN2nY\nOFutdkAnDrD1Tsg4fzrAeEYhQIAAAQLDCNQZDf8vOTT5s+RByT2TDZK6Rva5yS+TOqNh20Q1\nU+BhadZrmtk0rWqIQH1Av3OyUUPaoxljEmjrKXZ1A4ZLk+2Tj6/Aptavdkxnr2AcbxEgQIAA\ngWEF6g+mv062S47vM5OLMqz2WUcmRyQvSJzyHQRFgACBpgq0tYNUpxAckBye7JgcnVyc1OkN\nd0y61yC9MM83TR6VKAIECBAgMGqBxZ7R8OpRN8D8CBAgQGC0Am3tIJXCnkmd071/UkeS5tZN\nGVCf2L04qU/vFAECBAgQGLVA7V+c0TBqVfMjQIDAMgq0uYNUbMcl90/qPO8NkzsnVyc/6+S6\nPE5L1XrW9zypdgmclub+ul1N1loCBBYh4IyGRWAZlQABAm0QaHsHqWt8YZ5U+tUWGXhlclG/\nN1s07G1p60ta1F5N/a3AH+WhOkmKAIHpFXBGw/RuW2tGgMAMCkxLB2lFm+77ebNu5FA3a1hs\n1Sl89QfuIHXPQUYacpz6TqcXDTmtyZZXYL8s/nETakKdVlpHVesOj+OulbKAyrRWGU7CsetX\n2061W2DcZzQ0ZX806a1U++9+p9GPqx11d99J/X+so49/kXxhXCszI/Ot6/ren0xynzTJDz7r\nKwR2nZFt2ZjVnIUO0tujfdaQ4jtluvUGmPbYjHP8AOMNO8oPM+Hnku2GnYHplk2gvi9hUlU3\nI7n7hBa2RpbzgAktazkW8+Ms9IoJLbhOC758QsuymNELrJpZvjGpDtK3kxWd0ZC3h66dMmUT\n9kdDr8CQE7450x045LSLnay+4+beyXxnpCx2fguNXx/CnLzQSN5fUOC/MsY5C441uhE2zqxq\nHzGpOn1SC7Kc/xOYhQ7SXv+3uot+dmamqCxU9WnTOD9xqk+ZDk1q56vaJfDNCTb3uxNclkUR\nIPBbgdXy8I5k9072yWP9zh51NWV/NOr1Wmh+P8oIFUVgPoH6MOvE+d40nMAwArPQQRrGpYnT\nfDKNqigCBAgQaJ5AHQnYI3lK8vLk7EQRIECAQAsF6lxbRYAAAQIECCxN4KBM/ohk7aRO665T\n7p6e2M8GQREgQKBNAn5xt2lraSsBAgQINFngtDRum6QuqN4yOTo5L9kz2SGpr2uw3w2CIkCA\nQJMF2nqK3ZpBvd8iYOv81AsWMb5RCRAgQIDAMAI3ZKK6JqmuRarT7V6R7JLUDQCqPpY8/7Zn\n/iFAgACBRgq0tYO0dTTrfO9B68iM+NxBRzYeAQIECBBYosDNmb7ucFpZPdmsk+vzqAgQIECg\nwQJt7SB9LaYvTeqc7y8n+yYrqotX9OYI3qt779d3Ff1yBPOatVnUp6qPTCZ1S+VZ852W9a3r\nOur/vVq8wH0XP4kpRixwbeZ3aicjnvXvzc7+6PdIBh5gfzQw1UyPaH80/OZvzf6ofpG2uaqT\nVLe/fnzyxWVckTp9rzUbfRmdLJoAgeURqO9p+4vlWfTUL7X+qH5Ycm7yqwasrf1RAzaCJhAg\nMK9AK/ZHbe8glf7nkurNb1svlqlqB7nKMi277Yt9dlbgPck0f+lo27fRcrf/LmlAfSlfXfx+\nxnI3pqXLr+tibmlp2zV7cQL2R4vz6h3b/qhXw/N+AvZH/VQWN6wV+6O2nmLXuynq2qKNk1qX\ncX5Za+8y5z6vc80ravECN3YmuXLxk5piRgTqD76q+qXq+o3bKPxDYF4B+6N5aRZ8w/5oQaKZ\nH8H+aEZ+BKahg1TXrtStVRUBAgQIECBAgAABAgSWJOD7GJbEZ2ICBAgQIECAAAECBKZJQAdp\nmramdSFAgAABAgQIECBAYEkCOkhL4jMxAQIECBAgQIAAAQLTJKCDNE1b07oQIECAAAECBAgQ\nILAkAR2kJfGZmAABAgQIECBAgACBaRLQQZqmrWldCBAgQIAAAQIECBBYkoAO0pL4TDwCgV9k\nHj8dwXzMYnoFrsuqXZzULf0VAQIExiVgfzQu2emZr/3R9GxLa0KAAAECBAgQIECAAAECBAgQ\nIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQ\nIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQ\nIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQ\nIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQmFGBlbLeL0qOSs5JDks2SBSBfgI7Z+Ap/d4w\njAABAksUsD9aIuCMTW5/NGMb3OoSmKTAK7Ow65Ndkhcm30ouSO6ZKAK9AtvnxU3J2b0DPSdA\ngMCIBOyPRgQ5A7OxP5qBjWwVCSyXwLpZ8JVJdY66tXaeXJ3s2h3gceYF1onAB5JbkysSHaQg\nKAIERipgfzRSzqmdmf3R1G5aK0agOQI7pSn1R++Gc5p0eF6fMWeYl7Mr8M6s+i+Sv0oOSXSQ\ngqAIEBipwE6Zm/3RSEmncmb2R1O5WedfqT+Y/y3vEBibwBaZ841JnVLXW+fmheuQekVm+3l1\nmDdK6lERIEBgHAL2R+NQnb552h9N3zZd4RrpIK2Qx5tjEqhD1Zf1mfevMmytZI0+7xk0ewKn\nZ5Wvnb3VtsYECExQwP5ogtgtXpT9UYs33jBN10EaRs00oxC4oc9M6qhS1eq/ffAvAQIECBAY\nu4D90diJLYBAuwR0kNq1vaaltT/Lity1z8p0h13V5z2DCBAgQIDAqAXsj0Ytan4EpkBAB2kK\nNmILV6F2SGt20tv89fKi3vtN70DPCRAgQIDAmATsj8YEa7YE2iygg9Tmrdfetp+Ypt+SPKNn\nFeqL+p6enNAzzFMCBAgQIDBOAfujceqaNwECBAgsSuATGfsnySOTuyf7J5cndRRJEZgr4Dbf\nc0W8JkBgVAL2R6OSnI352B/Nxna2lgSWRaCuN/pMUkeS6jsoTknqCJIi0E/ADqmfimEECIxC\nwP5oFIqzMw/7o9nZ1taUwLIJrJ0l33vZlm7BBAgQIEDgtwL2R34SCBAgQIAAAQIECBAgQIAA\nAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAA\nAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAA\nAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAA\nAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAA\nAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAA\nAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAA\nAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAA\nAQIECBAgQIDA8gislMW+KDkqOSc5LNkgma82yxs/TbaYbwTDCRAgQIDAEAL2R0OgmYQAAQIE\nRi/wyszy+mSX5IXJt5ILknsmc+s+GfDD5NbkwXPf9JoAAQIECCxBwP5oCXgmJUCAAIHRCKyb\n2VyZVOeoW2vnydXJrt0BeaxP9WrHVeNekeggBUERIECAwMgE7I9GRmlGBAgQaLfAC9L8v0u2\nSg5MTkj2SFZL6jS39yXHJa9J7pJ0q6Z7U/KI5EPJZ5LXJ3dLemuTvKiOTs334GTT5N3Jk5Kq\nnZLq7GyY9NbheXFGz4CH5PlNyb8mz0x0kIKgCBAgMEUC9kdTtDGtCgECBNos8P40/sfJhcmh\nyceSG5OPJ+clxyY1znXJAUm3atjPk0uTPZPXJXVa3FeSVZKqeyUXJZcl+yT/kdT4v07+Pqna\nN7nhtme3/6fmeVXPoLvn+f06r6tzpYPUg+MpAQIEpkDA/mgKNqJVIECAwDQI1A6pOhvP6VmZ\n/+kMe2vPsL3zvG6MUKe6VXWne9VvX97278b5tzo73c7PkXlep8OtlXSrOkS1vO44h+R5dbTm\nVh2NqvHWmPtGXusg9UExiAABAi0X6O5X7I9aviE1v1kCf9Cs5mgNgdYIVEfk6J7Wntp5/sme\nYefm+b2Te/QMq6fH9Lz+cZ6flPxxZ9hj8lin1tX1RN3qnWd3WL8jSHUUq2r13z74lwABAgRm\nQMD+aAY2slWcrIAO0mS9LW16BC7JqvymZ3W6nZOz+wzrHkGqt85IftYzTj2to0ybJ3VKXJ1i\n96Wkt/43L67tGVDT37Xndfdpd9hV3QEeCRAgQGDqBeyPpn4TW8FJC+ggTVrc8qZFoNshWtH6\n9HaMuuP1+z9XHZsLkrrb3M3JnZPeWjkv6gYQ3aoO0pqddIfV43pJvdfbcavhigABAgSmV8D+\naHq3rTVbJoF+f6wtU1MslsBMCNRNE+q0u27V6XCPTOoUvdrJfSN5RtJbT8+L3v+rJ+b1LUnv\neNUZq/FOSBQBAgQIEFhIwP5oISHvz6xA7x9dM4tgxQlMUOCOWdankicl6ycfTOpW3P+WVO2a\nbJN8LXl+8r6kxumturbpqKTucledqzo17/1JHVV6W6IIECBAgMBCAvZHCwl5f2YFdJBmdtNb\n8WUSuDjL/U5S34F0YbJJUkeC6tbeVV9InpLUEaGDk7ppw45JVe91SC/P69OT6kj9Mtk2eXHS\n7+52GawIECBAgMDtBOyPbsfhBQECBAgsh0Ad5flJZ8Fr5bFuyDC3ts6AuiaptzbMi7pLUe9t\nXLvvr50nvafsdYd7JECAAAEC8wnYH80nYzgBAgQITFSgd4c034LrFuA/TLq36q4bNByW1NGj\ndRNFgAABAgSWKmB/tFRB0xMgQIDASAQG2SE9LEuq0x6uS/43qTvbXZ/UNUuKAAECBAiMQsD+\naBSK5jG1Av1uQzy1K2vFCCyzwBZZfh0FqrvQrajq/+VDkz9O6mhS3dnu8kQRIECAAIFRCNgf\njULRPAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECA\nAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECA\nAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECA\nAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECA\nAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECA\nAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQmJzA/wcaBvUnr2RV\n3wAAAABJRU5ErkJggg==",
"text/plain": [
"Plot with title “Main”"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"par(mfrow=c(2,2))\n",
"boxplot(mpg~mpg01, data=new_Auto, xlab=\"mpg01\", ylab=\"mpg\", main=\"Main\")\n",
"boxplot(cylinders~mpg01, data=new_Auto, xlab=\"mpg01\", ylab=\"cylinders\", main=\"Main\")\n",
"boxplot(displacement~mpg01, data=new_Auto, xlab=\"mpg01\", ylab=\"displacement\", main=\"Main\")\n",
"boxplot(horsepower~mpg01, data=new_Auto, xlab=\"mpg01\", ylab=\"horsepower\", main=\"Main\")\n",
"boxplot(weight~mpg01, data=new_Auto, xlab=\"mpg01\", ylab=\"weight\", main=\"Main\")\n",
"boxplot(acceleration~mpg01, data=new_Auto, xlab=\"mpg01\", ylab=\"acceleration\", main=\"Main\")\n",
"boxplot(origin~mpg01, data=new_Auto, xlab=\"mpg01\", ylab=\"origin\", main=\"Main\")\n",
"boxplot(year~mpg01, data=new_Auto, xlab=\"mpg01\", ylab=\"Year\", main=\"Main\")"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"- From the 1st plot of _mpg01_ vs _mpg_, we see a perfect separation between the categories 0 and 1. This makes sense as we created _mpg01_ using median miles per gallon as a threshold. \n",
"- The next plot against number of cylinders also sees a clear distinction. Nearly all sample Automobiles with 4 cylinders have been classified _mpg01_ as 1. Let us verify this by determining the number of sample automobiles _mpg01_ = 1 but **not** having 4 cylinders. "
]
},
{
"cell_type": "code",
"execution_count": 103,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"<ol class=list-inline>\n",
"\t<li>17</li>\n",
"\t<li>9</li>\n",
"</ol>\n"
],
"text/latex": [
"\\begin{enumerate*}\n",
"\\item 17\n",
"\\item 9\n",
"\\end{enumerate*}\n"
],
"text/markdown": [
"1. 17\n",
"2. 9\n",
"\n",
"\n"
],
"text/plain": [
"[1] 17 9"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"dim(new_Auto[new_Auto$cylinders !=4 & new_Auto$mpg01 == 1,])"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"There are only 17 such samples in this dataset.\n",
"\n",
"- Engine Displacement is also well segregated by _mpg01_. Automobiles with Smaller Engine Displacement have _mpg01_=1. Once again, let us verify this by determining the number of Automobiles with Engine displacement >= 200."
]
},
{
"cell_type": "code",
"execution_count": 115,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"<ol class=list-inline>\n",
"\t<li>165</li>\n",
"\t<li>9</li>\n",
"</ol>\n"
],
"text/latex": [
"\\begin{enumerate*}\n",
"\\item 165\n",
"\\item 9\n",
"\\end{enumerate*}\n"
],
"text/markdown": [
"1. 165\n",
"2. 9\n",
"\n",
"\n"
],
"text/plain": [
"[1] 165 9"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"text/html": [
"<ol class=list-inline>\n",
"\t<li>5</li>\n",
"\t<li>9</li>\n",
"</ol>\n"
],
"text/latex": [
"\\begin{enumerate*}\n",
"\\item 5\n",
"\\item 9\n",
"\\end{enumerate*}\n"
],
"text/markdown": [
"1. 5\n",
"2. 9\n",
"\n",
"\n"
],
"text/plain": [
"[1] 5 9"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"dim(new_Auto[new_Auto$displacement >=200,]) #Num Automobiles with displacement >= 200\n",
"dim(new_Auto[new_Auto$displacement >=200 & new_Auto$mpg01 == 1,]) #Of these, how many have _mpg01=1?"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Of the 165 automobiles that have Engine Displacements greater than 200, 5 of these have _mpg01_ = 1. The rest have _mpg01_=0."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"- From the _horsepower_ and _mpg01_ boxplot, we observe a similar trend, although not as strong. Automobiles with horsepowers greater than 120 tend to have _mpg01_ = 0. I chose 120 because it is the approximate value of the upper fence."
]
},
{
"cell_type": "code",
"execution_count": 116,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"<ol class=list-inline>\n",
"\t<li>106</li>\n",
"\t<li>9</li>\n",
"</ol>\n"
],
"text/latex": [
"\\begin{enumerate*}\n",
"\\item 106\n",
"\\item 9\n",
"\\end{enumerate*}\n"
],
"text/markdown": [
"1. 106\n",
"2. 9\n",
"\n",
"\n"
],
"text/plain": [
"[1] 106 9"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"text/html": [
"<ol class=list-inline>\n",
"\t<li>3</li>\n",
"\t<li>9</li>\n",
"</ol>\n"
],
"text/latex": [
"\\begin{enumerate*}\n",
"\\item 3\n",
"\\item 9\n",
"\\end{enumerate*}\n"
],
"text/markdown": [
"1. 3\n",
"2. 9\n",
"\n",
"\n"
],
"text/plain": [
"[1] 3 9"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"dim(new_Auto[new_Auto$horsepower >=120,]) #Num Automobiles with horsepower >= 120\n",
"dim(new_Auto[new_Auto$horsepower >=120 & new_Auto$mpg01 == 1,]) #Of these, how many have mpg01=1?"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Of the 106 cars with horsepower >= 120, only 3 of them have _mpg01_= 1."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"- Heavier automobiles tend to have an _mpg01_= 0. "
]
},
{
"cell_type": "code",
"execution_count": 120,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"<ol class=list-inline>\n",
"\t<li>144</li>\n",
"\t<li>9</li>\n",
"</ol>\n"
],
"text/latex": [
"\\begin{enumerate*}\n",
"\\item 144\n",
"\\item 9\n",
"\\end{enumerate*}\n"
],
"text/markdown": [
"1. 144\n",
"2. 9\n",
"\n",
"\n"
],
"text/plain": [
"[1] 144 9"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"text/html": [
"<ol class=list-inline>\n",
"\t<li>11</li>\n",
"\t<li>9</li>\n",
"</ol>\n"
],
"text/latex": [
"\\begin{enumerate*}\n",
"\\item 11\n",
"\\item 9\n",
"\\end{enumerate*}\n"
],
"text/markdown": [
"1. 11\n",
"2. 9\n",
"\n",
"\n"
],
"text/plain": [
"[1] 11 9"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"dim(new_Auto[new_Auto$weight <=2500,]) #Num Automobiles with weight <= 2500 lbs.\n",
"dim(new_Auto[new_Auto$weight <=2500 & new_Auto$mpg01 == 0,]) #Of these, how many have mpg01=0?"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Of the 144 Automobiles that weight less than 2500 pounds, only 11 of them have _mpg01_ = 0"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"- We don't observe such a well defined separation between the 2 classes of _mpg01_ with respect to acceleration.\n",
"- In _origin_, we observe that nearly all Automobiles with _mpg01_ = 0 are American made (1)."
]
},
{
"cell_type": "code",
"execution_count": 126,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"<ol class=list-inline>\n",
"\t<li>196</li>\n",
"\t<li>9</li>\n",
"</ol>\n"
],
"text/latex": [
"\\begin{enumerate*}\n",
"\\item 196\n",
"\\item 9\n",
"\\end{enumerate*}\n"
],
"text/markdown": [
"1. 196\n",
"2. 9\n",
"\n",
"\n"
],
"text/plain": [
"[1] 196 9"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"text/html": [
"<ol class=list-inline>\n",
"\t<li>173</li>\n",
"\t<li>9</li>\n",
"</ol>\n"
],
"text/latex": [
"\\begin{enumerate*}\n",
"\\item 173\n",
"\\item 9\n",
"\\end{enumerate*}\n"
],
"text/markdown": [
"1. 173\n",
"2. 9\n",
"\n",
"\n"
],
"text/plain": [
"[1] 173 9"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"dim(new_Auto[new_Auto$mpg01 == 0,]) #Num Automobiles with mpg01 = 0\n",
"dim(new_Auto[new_Auto$mpg01 ==0 & new_Auto$origin == 1,]) #Of these, how many were made in America?"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Of the 196 Automobiles with _mpg01_ = 0, 173 of them are American made. Now let us plot some scatterplots."
]
},
{
"cell_type": "code",
"execution_count": 123,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"?Auto"
]
},
{
"cell_type": "code",
"execution_count": 128,
"metadata": {},
"outputs": [
{
"data": {
"image/png": "iVBORw0KGgoAAAANSUhEUgAAA0gAAANICAYAAAD958/bAAAEDWlDQ1BJQ0MgUHJvZmlsZQAA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\nAQlIQAISkMCkEfj/kBkkaCafUzIAAAAASUVORK5CYII=",
"text/plain": [
"plot without title"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"par(mfrow=c(2,4))\n",
"fields = names(new_Auto)[-1]\n",
"for (i in 1:length(fields)){\n",
" plot(new_Auto$mpg0, new_Auto[,i], xlab=\"mpg01\", ylab=fields[i])\n",
"}"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Since _mpg01_ is a categorical field, it's effects are not so easily observed on a scatter plot. "
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"_11 (c) Split the data into a training set and a test set._"
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"<ol class=list-inline>\n",
"\t<li>274</li>\n",
"\t<li>9</li>\n",
"</ol>\n"
],
"text/latex": [
"\\begin{enumerate*}\n",
"\\item 274\n",
"\\item 9\n",
"\\end{enumerate*}\n"
],
"text/markdown": [
"1. 274\n",
"2. 9\n",
"\n",
"\n"
],
"text/plain": [
"[1] 274 9"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"text/html": [
"<ol class=list-inline>\n",
"\t<li>117</li>\n",
"\t<li>9</li>\n",
"</ol>\n"
],
"text/latex": [
"\\begin{enumerate*}\n",
"\\item 117\n",
"\\item 9\n",
"\\end{enumerate*}\n"
],
"text/markdown": [
"1. 117\n",
"2. 9\n",
"\n",
"\n"
],
"text/plain": [
"[1] 117 9"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"train = new_Auto[1:(0.7*nrow(new_Auto)), ] #Note Parenthesis around arithmatic operation. \n",
"test = new_Auto[(0.7*nrow(new_Auto)+1): nrow(new_Auto), ]\n",
"dim(train)\n",
"dim(test)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"We split the dataset in a 70:30 ratio."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"_11 (d). Perform LDA on the training data in order to predict mpg01 using the variables that seemed most associated with mpg01 in (b). What is the test error of the model obtained?_"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Let us consider how all variables except for _acceleration_, _year_, and _origin_. In other words, we will include the following fields:\n",
"- miles per gallon _mpg_\n",
"- number of _cyclinders_\n",
"- _displacement_\n",
"- _horsepower_\n",
"- _weight_ in pounds"
]
},
{
"cell_type": "code",
"execution_count": 9,
"metadata": {
"scrolled": false
},
"outputs": [
{
"data": {
"text/plain": [
" \n",
" 1 0\n",
" 1 94 2\n",
" 0 3 18"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"cols = names(new_Auto) #[-c(1,7,8,9)]\n",
"train_part = train[,cols[-c(7,8,9)]]\n",
"test_part = test[,cols[-c(7,8,9)]]\n",
"\n",
"cols = paste(cols[-c(1,7,8,9)], collapse=\"+\")\n",
"model = lda(mpg01~mpg+cylinders+displacement+horsepower+weight, train_part)\n",
"\n",
"#The following lines would work with glm. Not sure why LDA is acting up\n",
"#response = \"mpg01\"\n",
"#model = lda(paste(response,\"~\",cols), train_part)\n",
"\n",
"predicitons = predict(model, test_part)\n",
"# See columns with names(predicitons)\n",
"\n",
"#Switch 1 and 0 for notation\n",
"table(predicitons$class, test_part[,1])[2:1, 2:1]"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"From the confusion matrix above, we have:\n",
"- 94 True Positives\n",
"- 18 True Negatives\n",
"- 2 False Positives \n",
"- 3 False Negatives\n",
"\n",
"$$\n",
"\\textit{Simple Accuracy} = \\frac{TP + TN}{TP + TN + FP + FN} = \\frac{94+18}{94+18+2+3} = 0.5576 = \\textbf{95.72%}\n",
"$$\n",
"\n",
"This gives us a testing error rate of $100-95.72 = \\textbf{4.28%}$. However, let us see the performance of LDA with just the 1 predictor _mpg_."
]
},
{
"cell_type": "code",
"execution_count": 10,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
" \n",
" 1 0\n",
" 1 97 0\n",
" 0 0 20"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"train_part = train[,1:2]\n",
"test_part = test[,1:2]\n",
"\n",
"model = lda(mpg01~mpg, train_part)\n",
"predicitons = predict(model, test_part)\n",
"\n",
"#Switch 1 and 0 for notation\n",
"table(predicitons$class, test_part[,1])[2:1, 2:1]"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Wow! With just one predictor miles per gallon _mpg_, we were able to predict _mpg01_ with a test error of $\\textbf{0%}$. As I mentioned before, this is because _mpg01_ was created based on a threshold value median of _mpg_. Perfect Separation is expected."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"_11 (e) Perform QDA on the training data in order to predict mpg01 using the variables that seemed most associated with mpg01 in (b). What is the test error of the model obtained?_"
]
},
{
"cell_type": "code",
"execution_count": 11,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
" \n",
" 1 0\n",
" 1 83 2\n",
" 0 14 18"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"cols = names(new_Auto)\n",
"train_part = train[,cols[-c(7,8,9)]]\n",
"test_part = test[,cols[-c(7,8,9)]]\n",
"\n",
"cols = paste(cols[-c(1,7,8,9)], collapse=\"+\")\n",
"model = qda(mpg01~mpg+cylinders+displacement+horsepower+weight, train_part)\n",
"\n",
"predicitons = predict(model, test_part)\n",
"\n",
"#Switch 1 and 0 for notation\n",
"table(predicitons$class, test_part[,1])[2:1, 2:1]"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"From the confusion matrix above, we have:\n",
"- 83 True Positives\n",
"- 18 True Negatives\n",
"- 2 False Positives\n",
"- 14 False Negatives\n",
"\n",
"QDA's test accuracy (for this particular set of features) isn't as good as that of LDA. \n",
"\n",
"$$\n",
"\\textit{Simple Accuracy} = \\frac{TP + TN}{TP + TN + FP + FN} = \\frac{83+18}{83+18+2+14} = 0.8632 = \\textbf{86.32%}\n",
"$$\n",
"\n",
"This gives us a testing error rate of $100-86.32 = \\textbf{13.68%}$. Let us see the performance of QDA with just the 1 predictor _mpg_."
]
},
{
"cell_type": "code",
"execution_count": 12,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
" \n",
" 1 0\n",
" 1 97 0\n",
" 0 0 20"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"train_part = train[,1:2]\n",
"test_part = test[,1:2]\n",
"\n",
"model = qda(mpg01~mpg, train_part)\n",
"predicitons = predict(model, test_part)\n",
"\n",
"#Switch 1 and 0 for notation\n",
"table(predicitons$class, test_part[,1])[2:1, 2:1]"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Like LDA, we get a test error of $\\textbf{0%}$"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"_11 (f) Perform logistic regression on the training data in order to predict mpg01 using the variables that seemed most associated with mpg01 in (b). What is the test error of the model obtained?_"
]
},
{
"cell_type": "code",
"execution_count": 33,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
" \n",
"preds 1 0\n",
" 1 76 0\n",
" 0 21 20"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"cols = names(new_Auto)\n",
"train_part = train[,cols[-c(7,8,9)]]\n",
"test_part = test[,cols[-c(7,8,9)]]\n",
"\n",
"model = glm(mpg01~cylinders+displacement+horsepower+weight, train_part, family=binomial)\n",
"\n",
"predictions = predict(model, test_part, type=\"response\")\n",
"preds = rep(0, length(test_part[,1]))\n",
"\n",
"preds[predictions >= 0.5] = 1\n",
"table(preds, test_part[,1])[2:1, 2:1]"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"From the confusion matrix above, we have:\n",
"- 76 True Positives\n",
"- 20 True Negatives\n",
"- 0 False Positives\n",
"- 21 False Negatives\n",
"\n",
"Logistic Regression has the following simple Accuracy:\n",
"$$\n",
"\\textit{Simple Accuracy} = \\frac{TP + TN}{TP + TN + FP + FN} = \\frac{76+20}{76+20+0+21} = 0.8205 = \\textbf{82.05%}\n",
"$$\n",
"\n",
"This gives us a test error rate of $100-82.05=\\textbf{17.95%}$.\n",
"\n",
"**NOTE**: I removed the _mpg_ covariate from the train set to prevent the **Hauck-Donner effect**, also known as perfect separation."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"_11 (g) Perform KNN on the training data, with several values of K, in order to predict mpg01. Use only the variables that seemed most associated with mpg01 in (b). What test errors do you obtain? Which value of K seems to perform the best on this data set?_"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"We'll plot a graph of performance with different values of K."
]
},
{
"cell_type": "markdown",
"metadata": {
"collapsed": true
},
"source": [
"## K=1 with _mpg_"
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
" y_test\n",
"predictions 0 1\n",
" 0 20 0\n",
" 1 0 97"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"library(class)\n",
"\n",
"X_train = data.frame(mpg=train[,2])\n",
"X_test = data.frame(mpg=test[,2])\n",
"y_train = train[,1]\n",
"y_test = test[,1]\n",
"\n",
"predictions = knn(X_train, X_test, y_train, k=1)\n",
"table(predictions, y_test)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"The problem of perfect separation in Logistic Regression is not a problem for KNN with K=1."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## K=1 without _mpg_ "
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Will will replace miles per gallon with the following covariates:\n",
"- number of _cyclinders_\n",
"- _displacement_\n",
"- _horsepower_\n",
"- _weight_ in pounds"
]
},
{
"cell_type": "code",
"execution_count": 14,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
" y_test\n",
"predictions 1 0\n",
" 1 73 2\n",
" 0 24 18"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"cols = names(new_Auto)\n",
"X_train = train[,cols[-c(1,2,7,8,9)]]\n",
"X_test = test[,cols[-c(1,2,7,8,9)]]\n",
"y_train = train[,1]\n",
"y_test = test[,1]\n",
"\n",
"predictions = knn(X_train, X_test, y_train, k=1)\n",
"confusion=table(predictions, y_test)[2:1, 2:1]"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"From the confusion matrix above, we have:\n",
"- 73 True Positives\n",
"- 18 True Negatives\n",
"- 2 False Positives \n",
"- 24 False Negatives\n",
"\n",
"$$\n",
"\\textit{Simple Accuracy} = \\frac{TP + TN}{TP + TN + FP + FN} = \\frac{73+18}{73+18+2+24} = 0.7777 = \\textbf{77.77%}\n",
"$$\n",
"\n",
"This gives us a testing error rate of $100-77.77 = \\textbf{22.23%}$. However, let us see the performance of LDA with just the 1 predictor _mpg_."
]
},
{
"cell_type": "code",
"execution_count": 34,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"<ol class=list-inline>\n",
"\t<li>0.777777777777778</li>\n",
"\t<li>0.973333333333333</li>\n",
"\t<li>0.752577319587629</li>\n",
"\t<li>0.848837209302326</li>\n",
"</ol>\n"
],
"text/latex": [
"\\begin{enumerate*}\n",
"\\item 0.777777777777778\n",
"\\item 0.973333333333333\n",
"\\item 0.752577319587629\n",
"\\item 0.848837209302326\n",
"\\end{enumerate*}\n"
],
"text/markdown": [
"1. 0.777777777777778\n",
"2. 0.973333333333333\n",
"3. 0.752577319587629\n",
"4. 0.848837209302326\n",
"\n",
"\n"
],
"text/plain": [
"[1] 0.7777778 0.9733333 0.7525773 0.8488372"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"f_beta_measure = function(precision, recall, beta){\n",
" return ( 1/((beta*(1/precision))+ ((1-beta)*(1/recall))))\n",
"}\n",
"\n",
"get_accuracy_scores = function(conf){\n",
" TP = conf[1,1]\n",
" FP = conf[1,2]\n",
" FN = conf[2,1]\n",
" TN = conf[2,2]\n",
" \n",
" simple_accuracy = (TP+TN)/(TP+FP+TN+FN)\n",
" precision = TP/(TP+FP)\n",
" recall = TP/(TP+FN)\n",
" f = f_beta_measure(precision, recall, beta=0.5)\n",
" return (c(simple_accuracy, precision, recall, f))\n",
"}\n",
"\n",
"confusion=table(predictions, y_test)[2:1, 2:1]\n",
"get_accuracy_scores(confusion)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"I created a function to determine the Simple Accuracy, Precision, Recall, and the $ F_\\beta$ measure for any $\\beta$. The simple accuracy of $77.77%$ returned is the same as computed by hand above. I will now create a script that varies the value of _K_ from 1 to 100 and observe it's effects."
]
},
{
"cell_type": "code",
"execution_count": 112,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"<table>\n",
"<thead><tr><th></th><th scope=col>Accuracy</th><th scope=col>Precision</th><th scope=col>Recall</th><th scope=col>F</th></tr></thead>\n",
"<tbody>\n",
"\t<tr><th scope=row> 1</th><td>0.7777778</td><td>0.9733333</td><td>0.7525773</td><td>0.8488372</td></tr>\n",
"\t<tr><th scope=row> 2</th><td>0.7692308</td><td>0.9861111</td><td>0.7319588</td><td>0.8402367</td></tr>\n",
"\t<tr><th scope=row> 3</th><td>0.7777778</td><td>1.0000000</td><td>0.7319588</td><td>0.8452381</td></tr>\n",
"\t<tr><th scope=row> 4</th><td>0.7863248</td><td>1.0000000</td><td>0.7422680</td><td>0.8520710</td></tr>\n",
"\t<tr><th scope=row> 5</th><td>0.8034188</td><td>1.0000000</td><td>0.7628866</td><td>0.8654971</td></tr>\n",
"\t<tr><th scope=row> 6</th><td>0.8034188</td><td>1.0000000</td><td>0.7628866</td><td>0.8654971</td></tr>\n",
"</tbody>\n",
"</table>\n"
],
"text/latex": [
"\\begin{tabular}{r|llll}\n",
" & Accuracy & Precision & Recall & F\\\\\n",
"\\hline\n",
"\t 1 & 0.7777778 & 0.9733333 & 0.7525773 & 0.8488372\\\\\n",
"\t 2 & 0.7692308 & 0.9861111 & 0.7319588 & 0.8402367\\\\\n",
"\t 3 & 0.7777778 & 1.0000000 & 0.7319588 & 0.8452381\\\\\n",
"\t 4 & 0.7863248 & 1.0000000 & 0.7422680 & 0.8520710\\\\\n",
"\t 5 & 0.8034188 & 1.0000000 & 0.7628866 & 0.8654971\\\\\n",
"\t 6 & 0.8034188 & 1.0000000 & 0.7628866 & 0.8654971\\\\\n",
"\\end{tabular}\n"
],
"text/markdown": [
"\n",
"| <!--/--> | Accuracy | Precision | Recall | F | \n",
"|---|---|---|---|---|---|\n",
"| 1 | 0.7777778 | 0.9733333 | 0.7525773 | 0.8488372 | \n",
"| 2 | 0.7692308 | 0.9861111 | 0.7319588 | 0.8402367 | \n",
"| 3 | 0.7777778 | 1.0000000 | 0.7319588 | 0.8452381 | \n",
"| 4 | 0.7863248 | 1.0000000 | 0.7422680 | 0.8520710 | \n",
"| 5 | 0.8034188 | 1.0000000 | 0.7628866 | 0.8654971 | \n",
"| 6 | 0.8034188 | 1.0000000 | 0.7628866 | 0.8654971 | \n",
"\n",
"\n"
],
"text/plain": [
" Accuracy Precision Recall F \n",
" 1 0.7777778 0.9733333 0.7525773 0.8488372\n",
" 2 0.7692308 0.9861111 0.7319588 0.8402367\n",
" 3 0.7777778 1.0000000 0.7319588 0.8452381\n",
" 4 0.7863248 1.0000000 0.7422680 0.8520710\n",
" 5 0.8034188 1.0000000 0.7628866 0.8654971\n",
" 6 0.8034188 1.0000000 0.7628866 0.8654971"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"tabl = data.frame(matrix(ncol=4, nrow=0))\n",
"\n",
"for (k in 1:100){\n",
" predictions = knn(X_train, X_test, y_train, k=k)\n",
" confusion=table(predictions, y_test)[2:1, 2:1]\n",
" scores = get_accuracy_scores(confusion)\n",
" tabl = rbind(tabl, scores)\n",
"}\n",
"colnames(tabl) = c(\"Accuracy\",\"Precision\", \"Recall\", \"F\")\n",
"rownames(tabl) = paste(\"\", 1:nrow(tabl)) # A little work around to visually display row numbers\n",
"\n",
"head(tabl)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Let us see a graph of K"
]
},
{
"cell_type": "code",
"execution_count": 110,
"metadata": {},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"Warning message in plot.xy(xy, type, ...):\n",
"“plot type 'line' will be truncated to first character”"
]
},
{
"data": {
"image/png": "iVBORw0KGgoAAAANSUhEUgAAA0gAAANICAYAAAD958/bAAAEDWlDQ1BJQ0MgUHJvZmlsZQAA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",
"text/plain": [
"Plot with title “K Vs. Accuracy”"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"x = as.numeric(rownames(tabl))\n",
"y = tabl$Accuracy\n",
"\n",
"plot(x, y, type=\"line\", xlab=\"K\", ylab=\"Simple Accuracy\", main=\"K Vs. Accuracy\")"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Of these, we wee the maximum is at a K = 23, as shown below"
]
},
{
"cell_type": "code",
"execution_count": 111,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"<table>\n",
"<thead><tr><th></th><th scope=col>Accuracy</th><th scope=col>Precision</th><th scope=col>Recall</th><th scope=col>F</th></tr></thead>\n",
"<tbody>\n",
"\t<tr><th scope=row> 23</th><td>0.8376068</td><td>1 </td><td>0.8041237</td><td>0.8914286</td></tr>\n",
"</tbody>\n",
"</table>\n"
],
"text/latex": [
"\\begin{tabular}{r|llll}\n",
" & Accuracy & Precision & Recall & F\\\\\n",
"\\hline\n",
"\t 23 & 0.8376068 & 1 & 0.8041237 & 0.8914286\\\\\n",
"\\end{tabular}\n"
],
"text/markdown": [
"\n",
"| <!--/--> | Accuracy | Precision | Recall | F | \n",
"|---|\n",
"| 23 | 0.8376068 | 1 | 0.8041237 | 0.8914286 | \n",
"\n",
"\n"
],
"text/plain": [
" Accuracy Precision Recall F \n",
" 23 0.8376068 1 0.8041237 0.8914286"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"tabl[tabl$Accuracy == max(tabl$Accuracy),]"
]
}
],
"metadata": {
"kernelspec": {
"display_name": "R",
"language": "R",
"name": "ir"
},
"language_info": {
"codemirror_mode": "r",
"file_extension": ".r",
"mimetype": "text/x-r-source",
"name": "R",
"pygments_lexer": "r",
"version": "3.4.1"
}
},
"nbformat": 4,
"nbformat_minor": 2
}
| mit |
linsalrob/Genotype-Phenotype-Modeling | iPythonNotebooks/Gapfilling.ipynb | 2 | 32339 | {
"metadata": {
"name": ""
},
"nbformat": 3,
"nbformat_minor": 0,
"worksheets": [
{
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# How to gapfill a genome scale metabolic model\n",
"\n",
"## Getting started\n",
"\n",
"### Installing libraries\n",
"\n",
"Before you start, you will need to install a couple of libraries:\n",
" \n",
"The [ModelSeedDatabase](https://github.com/ModelSEED/ModelSEEDDatabase) has all the biochemistry we'll need. You can install that with `git clone`.\n",
" \n",
"The [PyFBA](http://linsalrob.github.io/PyFBA) library has detailed [installation instructions](http://linsalrob.github.io/PyFBA/installation.html). Don't be scared, its mostly just `pip install`.\n",
"\n",
"(Optional) Also, get the [SEED Servers](https://github.com/linsalrob/SEED_Servers_Python) as you can get a lot of information from them. You can install the git python repo from github. Make sure that the SEED_Servers_Python is in your PYTHONPATH.\n",
"\n",
"We start with importing some modules that we are going to use. \n",
"\n",
"We *import sys* so that we can use standard out and standard error if we have some error messages. We import copy so that we can make a deep copy of data structures for later comparisons.\n",
"\n",
"Then we import the PyFBA module to get started."
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"import sys\n",
"import copy\n",
"\n",
"import PyFBA"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 23
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Running an SBML model\n",
"\n",
"If you have run your genome through RAST, you can download the [SBML](http://www.sbml.org/) model and use that directly.\n",
"\n",
"We have provided an [SBML model of *Citrobacter sedlakii*](https://raw.githubusercontent.com/linsalrob/PyFBA/master/example_data/Citrobacter/Citrobacter_sedlakii.sbml) that you can download and use. You can right/ctrl click on this link and save the SBML file in the same location you are running this iPython notebook.\n",
"\n",
"We use this SBML model to demonstrate the key points of the FBA approach: defining the reactions, including the boundary, or drainflux, reactions; the compounds, including the drain compounds; the media; and the reaction bounds. \n",
"\n",
"We'll take it step by step!\n",
"\n",
"We start by parsing the model:\n",
"\n"
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"sbml = PyFBA.parse.parse_sbml_file('Citrobacter_sedlakii.sbml')"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 2
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Find all the reactions and identify those that are boundary reactions\n",
"\n",
"We need a set of reactions to run in the model. In this case, we are going to run all the reactions in our SBML file. However, you can change this set if you want to knock out reactions, add reactions, or generally modify the model. We store those in the `reactions_to_run` set.\n",
"\n",
"The boundary reactions are compounds that are secreted but then need to be removed from the model. We usually include a consumption of those compounds that is open ended, as if they are draining away. We store those reactions in the `uptake_secretion_reactions` dictionary.\n"
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"# get a dict of reactions. The key is the reaction ID, and the value is a metabolism.reaction.Reaction object\n",
"reactions = sbml.reactions\n",
"reactions_to_run = set()\n",
"uptake_secretion_reactions = {}\n",
"biomass_equation = None\n",
"for r in reactions:\n",
" if 'biomass_equation' in reactions[r].name.lower():\n",
" biomass_equation = reactions[r]\n",
" continue\n",
" is_boundary = False\n",
" for c in reactions[r].all_compounds():\n",
" if c.uptake_secretion:\n",
" is_boundary = True\n",
" if is_boundary:\n",
" reactions[r].is_uptake_secretion = True\n",
" uptake_secretion_reactions[r] = reactions[r]\n",
" else:\n",
" reactions_to_run.add(r)\n"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 3
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"At this point, we can take a look at how many reactions are in the model:"
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"print(\"There are {} reactions in the model\".format(len(reactions)))\n",
"print(\"There are {} uptake/secretion reactions in the model\".format(len(uptake_secretion_reactions)))\n",
"print(\"There are {} reactions to be run in the model\".format(len(reactions_to_run)))"
],
"language": "python",
"metadata": {},
"outputs": [
{
"output_type": "stream",
"stream": "stdout",
"text": [
"There are 1574 reactions in the model\n",
"There are 174 uptake/secretion reactions in the model\n",
"There are 1399 reactions to be run in the model\n"
]
}
],
"prompt_number": 4
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Find all the compounds in the model, and filter out those that are secreted\n",
"\n",
"We need to filter out uptake and secretion compounds from our list of all compounds before we can make a stoichiometric matrix"
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"# Get a dict of compounds. \n",
"# The key is the string representation of the compound and the value is a metabolite.compound.Compound object\n",
"all_compounds = sbml.compounds\n",
"# filter for compounds that are boundary compounds\n",
"filtered_compounds = {}\n",
"for c in all_compounds:\n",
" if not all_compounds[c].uptake_secretion:\n",
" filtered_compounds[c] = all_compounds[c]"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 5
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Again, we can see how many compounds there are in the model"
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"print(\"There are {} total compounds in the model\".format(len(all_compounds)))\n",
"print(\"There are {} compounds that are not involved in uptake and secretion\".format(len(filtered_compounds)))"
],
"language": "python",
"metadata": {},
"outputs": [
{
"output_type": "stream",
"stream": "stdout",
"text": [
"There are 1475 total compounds in the model\n",
"There are 1301 compounds that are not involved in uptake and secretion\n"
]
}
],
"prompt_number": 6
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"And now we have the size of our stoichiometric matrix! Notice that the stoichiometric matrix is composed of the reactions that we are going to run and the compounds that are in those reactions (but not the uptake/secretion reactions and compounds)."
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"print(\"The stoichiometric matrix will be {} reactions by {} compounds\".format(len(reactions_to_run), len(filtered_compounds)))"
],
"language": "python",
"metadata": {},
"outputs": [
{
"output_type": "stream",
"stream": "stdout",
"text": [
"The stoichiometric matrix will be 1399 reactions by 1301 compounds\n"
]
}
],
"prompt_number": 7
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Read the media file, and correct the media names\n",
"\n",
"In our [media](https://github.com/linsalrob/PyFBA/tree/master/media) directory, we have a lot of different media formulations, most of which we use with the Genotype-Phenotype project. For this example, we are going to use Lysogeny Broth (LB). There are many different formulations of LB, but we have included the recipe created by the folks at Argonne so that it is comparable with their analysis. You can download [ArgonneLB.txt](https://raw.githubusercontent.com/linsalrob/PyFBA/master/media/ArgonneLB.txt) and put it in the same directory as this iPython notebook to run it.\n",
"\n",
"Once we have read the file we need to correct the names in the compounds. Sometimes when compound names are exported to the SBML file they are modified slightly. This just corrects those names."
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"# read the media file\n",
"media = PyFBA.parse.read_media_file('ArgonneLB.txt')\n",
"# correct the names\n",
"media = PyFBA.parse.correct_media_names(media, all_compounds)"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 8
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Set the reaction bounds for uptake/secretion compounds\n",
"\n",
"The uptake and secretion compounds typically have reaction bounds that allow them to be consumed (i.e. diffuse away from the cell) but not produced. However, our media components can also increase in concentration (i.e. diffuse to the cell) and thus the bounds are set higher. Whenever you change the growth media, you also need to adjust the reaction bounds to ensure that the media can be consumed!\n"
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"# adjust the lower bounds of uptake secretion reactions for things that are not in the media\n",
"for u in uptake_secretion_reactions:\n",
" is_media_component = False\n",
" for c in uptake_secretion_reactions[u].all_compounds():\n",
" if c in media:\n",
" is_media_component = True\n",
" if not is_media_component:\n",
" reactions[u].lower_bound = 0.0\n",
" uptake_secretion_reactions[u].lower_bound = 0.0\n"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 9
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Run the FBA\n",
"\n",
"Now that we have constructed our model, we can run the FBA!"
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"status, value, growth = PyFBA.fba.run_fba(filtered_compounds, reactions, reactions_to_run, media, biomass_equation,\n",
" uptake_secretion_reactions)\n",
"print(\"The FBA completed with value; {} and growth: {}\".format(value, growth))\n"
],
"language": "python",
"metadata": {},
"outputs": [
{
"output_type": "stream",
"stream": "stdout",
"text": [
"The FBA completed with value; 281.841757437 and growth: True\n"
]
}
],
"prompt_number": 10
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Running a basic model\n",
"\n",
"The SBML model is great if you have built a model elsewhere, what about if you want to build a model from a genome.\n",
"\n",
"We typically start with an assigned_functions file from RAST. The easiest way to find that is in the RAST directory by choosing `Genome Directory` from the Downloads menu on the job details page.\n",
"\n",
"For this example, [here is an assigned_functions file](https://raw.githubusercontent.com/linsalrob/PyFBA/master/example_data/Citrobacter/ungapfilled_model/citrobacter.assigned_functions) from our *Citrobacter* model that you can download to the same directory as this iPython notebook. Notice that it has two columns, the first column is the protein ID (using SEED standard IDs that start with fig|, and then have the taxonomy ID and version number of the genome, and then peg to indicate *protein encoding gene*, rna to indicate *RNA*, crispr_spacer to indicate *crispr spacers* or other acronym, followed by the feature number. After the tab is the *functional role* of that feature. Download that file to use in this test. \n",
"\n",
"We start by converting this assigned_functions file to a list of reactions."
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"# assigned functions is a dict of peg id and functional role\n",
"assigned_functions = PyFBA.parse.read_assigned_functions('citrobacter.assigned_functions')\n",
"# get a list of unique functional roles\n",
"roles = set(assigned_functions.values())\n",
"print(\"There are {} unique roles in this genome\".format(len(roles)))"
],
"language": "python",
"metadata": {},
"outputs": [
{
"output_type": "stream",
"stream": "stdout",
"text": [
"There are 3606 unique roles in this genome\n"
]
}
],
"prompt_number": 11
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Convert those roles to reactions. We start with a dict of roles and reactions, but we only need a list of unique reactions, so we convert the keys to a set."
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"roles_to_reactions = PyFBA.filters.roles_to_reactions(roles)\n",
"reactions_to_run = set()\n",
"for role in roles_to_reactions:\n",
" reactions_to_run.update(roles_to_reactions[role])\n",
"print(\"There are {} unique reactions associated with this genome\".format(len(reactions_to_run)))"
],
"language": "python",
"metadata": {},
"outputs": [
{
"output_type": "stream",
"stream": "stdout",
"text": [
"There are 1305 unique reactions associated with this genome\n"
]
}
],
"prompt_number": 13
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Read all the reactions and compounds in our database\n",
"\n",
"We read all the reactions, compounds, and enzymes in the [ModelSEEDDatabase]() into three data structures. Each one is a dictionary with a string representation of the object as the key and the object as the value.\n",
"\n",
"We modify the reactions specifically for Gram negative models (there are also options for Gram positive models, Mycobacterial models, general microbial models, and plant models)."
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"compounds, reactions, enzymes = PyFBA.parse.model_seed.compounds_reactions_enzymes('gramnegative')"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 14
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"#### Update reactions to run, making sure that all reactions are in the list!\n",
"\n",
"There are a very reactions that come from functional roles that do not appear in the reactions list. We're working on tracking these down, but for now we just check that all reaction IDs in *reactions_to_run* are in *reactions*, too."
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"tempset = set()\n",
"for r in reactions_to_run:\n",
" if r in reactions:\n",
" tempset.add(r)\n",
" else:\n",
" sys.stderr.write(\"Reaction ID {} is not in our reactions list. Skipped\\n\".format(r))\n",
"reactions_to_run = tempset"
],
"language": "python",
"metadata": {},
"outputs": [
{
"output_type": "stream",
"stream": "stderr",
"text": [
"Reaction ID rxn37218 is not in our reactions list. Skipped\n"
]
}
],
"prompt_number": 16
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Test whether these reactions grow on ArgonneLB\n",
"\n",
"We can test whether this set of reactions grows on ArgonneLB media. The media is the same one we used above, and you can download the [ArgonneLB.txt](https://raw.githubusercontent.com/linsalrob/PyFBA/master/media/ArgonneLB.txt) and text file and put it in the same directory as this iPython notebook to run it.\n",
"\n",
"(Note: unlike above, we don't need to convert the media components, because the media and compounds come from the same source.)"
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"media = PyFBA.parse.read_media_file('ArgonneLB.txt')\n",
"print(\"Our media has {} components.\".format(len(media)))"
],
"language": "python",
"metadata": {},
"outputs": [
{
"output_type": "stream",
"stream": "stdout",
"text": [
"Our media has 65 components.\n"
]
}
],
"prompt_number": 17
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Define a biomass equation\n",
"\n",
"The biomass equation is the part that says whether the model will grow! This is a [metabolism.reaction.Reaction](https://github.com/linsalrob/PyFBA/blob/master/PyFBA/metabolism/reaction.py) object."
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"biomass_equation = PyFBA.metabolism.biomass_equation('gramnegative')"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 18
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Run the FBA\n",
"\n",
"With the reactions, compounds, reactions_to_run, media, and biomass model, we can test whether the model grows on this media"
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"status, value, growth = PyFBA.fba.run_fba(compounds, reactions, reactions_to_run, media, biomass_equation, verbose=True)\n",
"print(\"Initial run has \" + str(value) + \" --> Growth: \" + str(growth))"
],
"language": "python",
"metadata": {},
"outputs": [
{
"output_type": "stream",
"stream": "stdout",
"text": [
"Initial run has 0.0 --> Growth: False\n"
]
},
{
"output_type": "stream",
"stream": "stderr",
"text": [
"In parsing the bounds we found 65 media uptake and secretion reactions and 110 other u/s reactions\n",
"Length of the media: 65\n",
"Number of reactions to run: 1304\n",
"Number of compounds in SM: 1276\n",
"Number of reactions in SM: 1480\n",
"Revised number of total reactions: 34871\n",
"Number of total compounds: 45616\n",
"SMat dimensions: 1276 x 1480\n"
]
}
],
"prompt_number": 19
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Gap fill the model\n",
"\n",
"Since the model does not grow on ArgonneLB we need to gap fill it to ensure growth. There are several ways that we can gap fill, and we will work through them until we get growth.\n",
"\n",
"As you will see, we update the reactions_to_run list each time, and keep the media and everything else consistent. Then we just need to run the FBA like we have done above and see if we get growth.\n",
"\n",
"We also keep a copy of the original reactions_to_run, and a list with all the reactions that we are adding, so once we are done we can go back and bisect the reactions that are added."
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"added_reactions=[]\n",
"original_reactions_to_run = copy.copy(reactions_to_run)"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 24
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Essential reactions\n",
"\n",
"There are ~100 reactions that are in every model we have tested, and we construe these to be essential for all models, so we typically add these first!"
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"essential_reactions = PyFBA.gapfill.suggest_essential_reactions()\n",
"added_reactions.append(essential_reactions)\n",
"reactions_to_run.update(essential_reactions)"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 25
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"status, value, growth = PyFBA.fba.run_fba(compounds, reactions, reactions_to_run, media, biomass_equation)\n",
"print(\"FBA has \" + str(value) + \" --> Growth: \" + str(growth))"
],
"language": "python",
"metadata": {},
"outputs": [
{
"output_type": "stream",
"stream": "stdout",
"text": [
"FBA has 0.0 --> Growth: False\n"
]
}
],
"prompt_number": 28
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Media import reactions\n",
"\n",
"We need to make sure that the cell can import everything that is in the media ... otherwise it won't be able to grow. Obviously only do this step if you are sure that the cell can grow on the media you are testing."
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"media_reactions = PyFBA.gapfill.suggest_from_media(compounds, reactions, reactions_to_run, media)\n",
"added_reactions.append(media_reactions)\n",
"reactions_to_run.update(media_reactions)"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 32
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"status, value, growth = PyFBA.fba.run_fba(compounds, reactions, reactions_to_run, media, biomass_equation)\n",
"print(\"FBA has \" + str(value) + \" --> Growth: \" + str(growth))"
],
"language": "python",
"metadata": {},
"outputs": [
{
"output_type": "stream",
"stream": "stdout",
"text": [
"FBA has 1.11493060109e-12 --> Growth: False\n"
]
}
],
"prompt_number": 33
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Subsystems\n",
"\n",
"The reactions connect us to subsystems, and this test ensures that all the subsystem are complete. We add reactions required to complete the subsystem."
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"subsystem_reactions = PyFBA.gapfill.suggest_reactions_from_subsystems(reactions, reactions_to_run, threshold=0.5)\n",
"added_reactions.append(subsystem_reactions)\n",
"reactions_to_run.update(subsystem_reactions)"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 34
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"status, value, growth = PyFBA.fba.run_fba(compounds, reactions, reactions_to_run, media, biomass_equation)\n",
"print(\"FBA has \" + str(value) + \" --> Growth: \" + str(growth))"
],
"language": "python",
"metadata": {},
"outputs": [
{
"output_type": "stream",
"stream": "stdout",
"text": [
"FBA has 4.51193262361e-28 --> Growth: False\n"
]
}
],
"prompt_number": 35
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Orphan compounds\n",
"\n",
"Orphan compounds are those compounds which are only in one reaction. They are either produced, or trying to be consumed. We need to add reaction(s) that complete the network of those compounds.\n",
"\n",
"You can change the maximum number of reactions that a compound is in to be considered an orphan (try increasing it to 2 or 3)."
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"orphan_reactions = PyFBA.gapfill.suggest_by_compound(compounds, reactions, reactions_to_run, max_reactions=1)\n",
"added_reactions.append(orphan_reactions)\n",
"reactions_to_run.update(orphan_reactions)"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 37
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"status, value, growth = PyFBA.fba.run_fba(compounds, reactions, reactions_to_run, media, biomass_equation)\n",
"print(\"FBA has \" + str(value) + \" --> Growth: \" + str(growth))"
],
"language": "python",
"metadata": {},
"outputs": [
{
"ename": "MemoryError",
"evalue": "",
"output_type": "pyerr",
"traceback": [
"\u001b[0;31m---------------------------------------------------------------------------\u001b[0m\n\u001b[0;31mMemoryError\u001b[0m Traceback (most recent call last)",
"\u001b[0;32m<ipython-input-38-eeee196c27f7>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mstatus\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mvalue\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mgrowth\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mPyFBA\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfba\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mrun_fba\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mcompounds\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mreactions\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mreactions_to_run\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mmedia\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mbiomass_equation\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 2\u001b[0m \u001b[0;32mprint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"FBA has \"\u001b[0m \u001b[0;34m+\u001b[0m \u001b[0mstr\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mvalue\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m+\u001b[0m \u001b[0;34m\" --> Growth: \"\u001b[0m \u001b[0;34m+\u001b[0m \u001b[0mstr\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mgrowth\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;32m/data/PyFBA/PyFBA/fba/run_fba.pyc\u001b[0m in \u001b[0;36mrun_fba\u001b[0;34m(compounds, reactions, reactions_to_run, media, biomass_equation, uptake_secretion, verbose)\u001b[0m\n\u001b[1;32m 31\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 32\u001b[0m cp, rc, reactions = PyFBA.fba.create_stoichiometric_matrix(reactions_to_run, reactions, compounds, media, biomass_equation,\n\u001b[0;32m---> 33\u001b[0;31m uptake_secretion, verbose=False)\n\u001b[0m\u001b[1;32m 34\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 35\u001b[0m \u001b[0mrbvals\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mPyFBA\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfba\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mreaction_bounds\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mreactions\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mrc\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mmedia\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mverbose\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mverbose\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;32m/data/PyFBA/PyFBA/fba/create_stoichiometric_matrix.pyc\u001b[0m in \u001b[0;36mcreate_stoichiometric_matrix\u001b[0;34m(reactions_to_run, reactions, compounds, media, biomass_equation, uptake_secretion, verbose)\u001b[0m\n\u001b[1;32m 119\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 120\u001b[0m \u001b[0;31m# load the data into the model\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 121\u001b[0;31m \u001b[0mPyFBA\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mlp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mload\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdata\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcp\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mrc\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 122\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 123\u001b[0m \u001b[0;31m# now set the objective function.It is the biomass_equation\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;32m/data/PyFBA/PyFBA/lp/glpk_solver.pyc\u001b[0m in \u001b[0;36mload\u001b[0;34m(matrix, rowheaders, colheaders, verbose)\u001b[0m\n\u001b[1;32m 63\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mverbose\u001b[0m \u001b[0;34m>\u001b[0m \u001b[0;36m4\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 64\u001b[0m \u001b[0msys\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mstderr\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mwrite\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"Matrix: \"\u001b[0m \u001b[0;34m+\u001b[0m \u001b[0mstr\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mtemp\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m+\u001b[0m \u001b[0;34m\"\\n\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 65\u001b[0;31m \u001b[0msolver\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmatrix\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mtemp\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 66\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 67\u001b[0m \u001b[0;31m# name the rows and columns\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;31mMemoryError\u001b[0m: "
]
}
],
"prompt_number": 38
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"reactions_to_run = original_reactions_to_run\n",
"reactions_to_run.update(essential_reactions)\n",
"reactions_to_run.update(media_reactions)\n",
"reactions_to_run.update(subsystem_reactions)\n",
"print(\"There are {} reactions to run\".format(len(reactions_to_run)))\n",
"print(\"Orphans is {}\".format(len(orphan_reactions)))"
],
"language": "python",
"metadata": {},
"outputs": [
{
"output_type": "stream",
"stream": "stdout",
"text": [
"There are 1780 reactions to run\n",
"Orphans is 3290\n"
]
}
],
"prompt_number": 41
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Reactions from closely related organisms\n",
"\n",
"We also gap fill on closely related organisms. We assume that an organism is most likely to have reactions in its genome that are similar to those in closely related organisms. \n",
"\n",
"\n",
"\n"
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"with open('our_reactions.txt', 'w') as out:\n",
" out.write(\"\\n\".join(reactions_to_run))"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 42
},
{
"cell_type": "code",
"collapsed": false,
"input": [],
"language": "python",
"metadata": {},
"outputs": []
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"reactions_from_other_orgs = PyFBA.gapfill.suggest_from_roles(args.c, reactions, True)"
],
"language": "python",
"metadata": {},
"outputs": []
}
],
"metadata": {}
}
]
} | mit |
bflaven/BlogArticlesExamples | extending_streamlit_usage/003_99_ambitieuses_derwenai_spacy_tutorials/Extract_Text_from_PDF_4.ipynb | 1 | 9585 | {
"metadata": {
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.8.3"
},
"orig_nbformat": 2,
"kernelspec": {
"name": "python3",
"display_name": "Python 3.8.3 64-bit ('base': conda)"
},
"metadata": {
"interpreter": {
"hash": "15ce07131e6ac9ef2286e9dc443e97a8b7a1385e6fe0cfbd91ad33021bc29612"
}
},
"interpreter": {
"hash": "15ce07131e6ac9ef2286e9dc443e97a8b7a1385e6fe0cfbd91ad33021bc29612"
}
},
"nbformat": 4,
"nbformat_minor": 2,
"cells": [
{
"cell_type": "code",
"execution_count": 1,
"metadata": {},
"outputs": [
{
"output_type": "stream",
"name": "stdout",
"text": [
"\n--- step_1 pdf loaded\n"
]
}
],
"source": [
"## for plotting\n",
"import matplotlib.pyplot as plt\n",
"import seaborn as sns\n",
"import wordcloud\n",
"\n",
"# pip install pdfx\n",
"import pdfx\n",
"# pdf = pdfx.PDFx(\"http://europepmc.org/articles/PMC3001474?pdf=render\")\n",
"pdf = pdfx.PDFx(\"/Users/brunoflaven/Documents/01_work/blog_articles/extending_streamlit_usage/003_99_ambitieuses_derwenai_spacy_tutorials/article_bf_2.pdf\")\n",
"print(\"\\n--- step_1 pdf loaded\")\n",
"# pdf"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {},
"outputs": [
{
"output_type": "stream",
"name": "stdout",
"text": [
"\n--- step_2 text loaded\n"
]
}
],
"source": [
"text = pdf.get_text()\n",
"# text\n",
"print(\"\\n--- step_2 text loaded\")\n",
"# text"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {
"tags": []
},
"outputs": [
{
"output_type": "stream",
"name": "stdout",
"text": [
"\n--- step_3 spacy loaded\n"
]
}
],
"source": [
"import spacy\n",
"nlp = spacy.load(\"en_core_web_sm\")\n",
"# nlp = spacy.load('fr_core_news_sm')\n",
"doc = nlp(text)\n",
"print(\"\\n--- step_3 spacy loaded\")\n"
]
},
{
"cell_type": "code",
"execution_count": 15,
"metadata": {
"tags": []
},
"outputs": [
{
"output_type": "stream",
"name": "stdout",
"text": [
"\n--- step_4 spacy in treatment\n"
]
}
],
"source": [
"\n",
"print(\"\\n--- step_4 spacy in treatment\")\n",
"# 1. Creating and updating our list of tokens using list comprehension \n",
"tokens = [token.text for token in doc]\n",
"# print(tokens)\n",
"\n",
"# 2. Creating and updating our list of filtered tokens using list comprehension \n",
"# filtered = [token.text for token in doc if token.is_stop == False]\n",
"# print(filtered)\n",
"\n",
"# 3. Remove punctuation from our text as well using \"isalpha\" method of string objects and using list comprehensions.\n",
"filtered = [token.text for token in doc if token.is_stop == False and token.text.isalpha() == True]\n",
"# print(filtered)\n",
"\n",
"# Source : https://iq.opengenus.org/text-preprocessing-in-spacy/"
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {
"tags": []
},
"outputs": [
{
"output_type": "stream",
"name": "stdout",
"text": [
"\n--- step_5 spacy pos\n"
]
}
],
"source": [
"print(\"\\n--- step_5 spacy pos\")\n",
"#Extracting POS\n",
"# pos = [[token.text,token.pos_] for token in doc]\n",
"# print (pos)\n",
"\n",
"pos = [[token.text,token.pos_] for token in doc if token.is_stop == False and token.text.isalpha() == True]\n",
"# print (pos)\n"
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {
"tags": []
},
"outputs": [
{
"output_type": "stream",
"name": "stdout",
"text": [
"\n--- step_6 spacy extracting entities \n"
]
}
],
"source": [
"\n",
"print(\"\\n--- step_6 spacy extracting entities \")\n",
"# extracting entities \n",
"entities=[(i, i.label_, i.label) for i in doc.ents]\n",
"# print(entities)\n"
]
},
{
"cell_type": "code",
"execution_count": 8,
"metadata": {
"tags": []
},
"outputs": [
{
"output_type": "stream",
"name": "stdout",
"text": [
"\n--- step_7 spacy doc object into pandas dataframe\n"
]
},
{
"output_type": "execute_result",
"data": {
"text/plain": [
" text lemma POS explain stopword\n",
"0 Python Python PROPN proper noun False\n",
"1 Randomization Randomization PROPN proper noun False\n",
"2 Random Random PROPN proper noun False\n",
"3 good good ADJ adjective False\n",
"4 reasons reason NOUN noun False\n",
".. ... ... ... ... ...\n",
"295 mayo mayo NOUN noun False\n",
"296 sandwich sandwich NOUN noun False\n",
"297 concept concept NOUN noun False\n",
"298 let let VERB verb False\n",
"299 code code NOUN noun False\n",
"\n",
"[300 rows x 5 columns]"
],
"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>text</th>\n <th>lemma</th>\n <th>POS</th>\n <th>explain</th>\n <th>stopword</th>\n </tr>\n </thead>\n <tbody>\n <tr>\n <th>0</th>\n <td>Python</td>\n <td>Python</td>\n <td>PROPN</td>\n <td>proper noun</td>\n <td>False</td>\n </tr>\n <tr>\n <th>1</th>\n <td>Randomization</td>\n <td>Randomization</td>\n <td>PROPN</td>\n <td>proper noun</td>\n <td>False</td>\n </tr>\n <tr>\n <th>2</th>\n <td>Random</td>\n <td>Random</td>\n <td>PROPN</td>\n <td>proper noun</td>\n <td>False</td>\n </tr>\n <tr>\n <th>3</th>\n <td>good</td>\n <td>good</td>\n <td>ADJ</td>\n <td>adjective</td>\n <td>False</td>\n </tr>\n <tr>\n <th>4</th>\n <td>reasons</td>\n <td>reason</td>\n <td>NOUN</td>\n <td>noun</td>\n <td>False</td>\n </tr>\n <tr>\n <th>...</th>\n <td>...</td>\n <td>...</td>\n <td>...</td>\n <td>...</td>\n <td>...</td>\n </tr>\n <tr>\n <th>295</th>\n <td>mayo</td>\n <td>mayo</td>\n <td>NOUN</td>\n <td>noun</td>\n <td>False</td>\n </tr>\n <tr>\n <th>296</th>\n <td>sandwich</td>\n <td>sandwich</td>\n <td>NOUN</td>\n <td>noun</td>\n <td>False</td>\n </tr>\n <tr>\n <th>297</th>\n <td>concept</td>\n <td>concept</td>\n <td>NOUN</td>\n <td>noun</td>\n <td>False</td>\n </tr>\n <tr>\n <th>298</th>\n <td>let</td>\n <td>let</td>\n <td>VERB</td>\n <td>verb</td>\n <td>False</td>\n </tr>\n <tr>\n <th>299</th>\n <td>code</td>\n <td>code</td>\n <td>NOUN</td>\n <td>noun</td>\n <td>False</td>\n </tr>\n </tbody>\n</table>\n<p>300 rows × 5 columns</p>\n</div>"
},
"metadata": {},
"execution_count": 8
}
],
"source": [
"\n",
"print(\"\\n--- step_7 spacy doc object into pandas dataframe\")\n",
"# GREAT load the spacy doc object into into a dataframe of the parsed tokens\n",
"import pandas as pd\n",
"\n",
"cols = (\"text\", \"lemma\", \"POS\", \"explain\", \"stopword\")\n",
"rows = []\n",
"\n",
"for t in doc:\n",
" if t.is_stop == False and t.text.isalpha() == True:\n",
" # print(t.text)\n",
" row = [t.text, t.lemma_, t.pos_, spacy.explain(t.pos_), t.is_stop]\n",
" rows.append(row)\n",
"\n",
"# We can either keep it in dictionary format or put it into a pandas dataframe\n",
"pd.set_option('max_colwidth',150)\n",
"data_df = pd.DataFrame(rows, columns=cols)\n",
"data_df = data_df.sort_index()\n",
"\n",
"# OUTPUT\n",
"# data_df"
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {},
"outputs": [
{
"output_type": "stream",
"name": "stdout",
"text": [
"<class 'pandas.core.frame.DataFrame'>\nRangeIndex: 300 entries, 0 to 299\nData columns (total 5 columns):\n # Column Non-Null Count Dtype \n--- ------ -------------- ----- \n 0 text 300 non-null object\n 1 lemma 300 non-null object\n 2 POS 300 non-null object\n 3 explain 300 non-null object\n 4 stopword 300 non-null bool \ndtypes: bool(1), object(4)\nmemory usage: 9.8+ KB\n"
]
}
],
"source": [
"\n",
"# data_df.sample(5)\n",
"data_df.info()"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": []
}
]
} | mit |
mne-tools/mne-tools.github.io | 0.13/_downloads/plot_compute_source_psd_epochs.ipynb | 1 | 3783 | {
"nbformat_minor": 0,
"nbformat": 4,
"cells": [
{
"execution_count": null,
"cell_type": "code",
"source": [
"%matplotlib inline"
],
"outputs": [],
"metadata": {
"collapsed": false
}
},
{
"source": [
"\n# Compute Power Spectral Density of inverse solution from single epochs\n\n\nCompute PSD of dSPM inverse solution on single trial epochs restricted\nto a brain label. The PSD is computed using a multi-taper method with\nDiscrete Prolate Spheroidal Sequence (DPSS) windows.\n\n\n"
],
"cell_type": "markdown",
"metadata": {}
},
{
"execution_count": null,
"cell_type": "code",
"source": [
"# Author: Martin Luessi <[email protected]>\n#\n# License: BSD (3-clause)\n\nimport numpy as np\nimport matplotlib.pyplot as plt\n\nimport mne\nfrom mne.datasets import sample\nfrom mne.minimum_norm import read_inverse_operator, compute_source_psd_epochs\n\nprint(__doc__)\n\ndata_path = sample.data_path()\nfname_inv = data_path + '/MEG/sample/sample_audvis-meg-oct-6-meg-inv.fif'\nfname_raw = data_path + '/MEG/sample/sample_audvis_raw.fif'\nfname_event = data_path + '/MEG/sample/sample_audvis_raw-eve.fif'\nlabel_name = 'Aud-lh'\nfname_label = data_path + '/MEG/sample/labels/%s.label' % label_name\n\nevent_id, tmin, tmax = 1, -0.2, 0.5\nsnr = 1.0 # use smaller SNR for raw data\nlambda2 = 1.0 / snr ** 2\nmethod = \"dSPM\" # use dSPM method (could also be MNE or sLORETA)\n\n# Load data\ninverse_operator = read_inverse_operator(fname_inv)\nlabel = mne.read_label(fname_label)\nraw = mne.io.read_raw_fif(fname_raw)\nevents = mne.read_events(fname_event)\n\n# Set up pick list\ninclude = []\nraw.info['bads'] += ['EEG 053'] # bads + 1 more\n\n# pick MEG channels\npicks = mne.pick_types(raw.info, meg=True, eeg=False, stim=False, eog=True,\n include=include, exclude='bads')\n# Read epochs\nepochs = mne.Epochs(raw, events, event_id, tmin, tmax, picks=picks,\n baseline=(None, 0), reject=dict(mag=4e-12, grad=4000e-13,\n eog=150e-6))\n\n# define frequencies of interest\nfmin, fmax = 0., 70.\nbandwidth = 4. # bandwidth of the windows in Hz\n\n# compute source space psd in label\n\n# Note: By using \"return_generator=True\" stcs will be a generator object\n# instead of a list. This allows us so to iterate without having to\n# keep everything in memory.\n\nstcs = compute_source_psd_epochs(epochs, inverse_operator, lambda2=lambda2,\n method=method, fmin=fmin, fmax=fmax,\n bandwidth=bandwidth, label=label,\n return_generator=True)\n\n# compute average PSD over the first 10 epochs\nn_epochs = 10\nfor i, stc in enumerate(stcs):\n if i >= n_epochs:\n break\n\n if i == 0:\n psd_avg = np.mean(stc.data, axis=0)\n else:\n psd_avg += np.mean(stc.data, axis=0)\n\npsd_avg /= n_epochs\nfreqs = stc.times # the frequencies are stored here\n\nplt.figure()\nplt.plot(freqs, psd_avg)\nplt.xlabel('Freq (Hz)')\nplt.ylabel('Power Spectral Density')\nplt.show()"
],
"outputs": [],
"metadata": {
"collapsed": false
}
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 2",
"name": "python2",
"language": "python"
},
"language_info": {
"mimetype": "text/x-python",
"nbconvert_exporter": "python",
"name": "python",
"file_extension": ".py",
"version": "2.7.12",
"pygments_lexer": "ipython2",
"codemirror_mode": {
"version": 2,
"name": "ipython"
}
}
}
} | bsd-3-clause |
hrichstein/Stellar_mass_env_Density | Codes/iPython_Notebooks/eco_mass_complete_ratios.ipynb | 1 | 13023 | {
"cells": [
{
"cell_type": "code",
"execution_count": 1,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"%matplotlib inline\n",
"import astropy.stats\n",
"import matplotlib.pyplot as plt \n",
"from matplotlib import ticker\n",
"from matplotlib.ticker import FormatStrFormatter\n",
"import numpy as np \n",
"import os\n",
"import pandas as pd\n",
"from scipy import spatial"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"#! /usr/bin/env python\n",
"\n",
"# Victor Calderon\n",
"# June 29, 2016\n",
"# Vanderbilt University\n",
"\n",
"from __future__ import print_function, division, absolute_import\n",
"__author__ =['Victor Calderon']\n",
"__copyright__ =[\"Copyright 2016 Victor Calderon, Index function\"]\n",
"__email__ =['[email protected]']\n",
"__maintainer__ =['Victor Calderon']\n",
"import glob\n",
"\"\"\"\n",
"\"\"\"\n",
"def Index(directory, datatype):\n",
" \"\"\"\n",
" Indexes the files in a directory `directory' with a\n",
" specific data type.\n",
" \n",
" Parameters\n",
" ----------\n",
" directory: str\n",
" Absolute path to the folder that is indexed.\n",
"\n",
" datatype: str\n",
" Data type of the files to be indexed in the folder.\n",
"\n",
" Returns\n",
" -------\n",
" file_array: array_like \n",
" num.array of indexed files in the folder 'directory' \n",
" with specific datatype.\n",
"\n",
" Examples\n",
" --------\n",
" >>> Index('~/data', '.txt')\n",
" >>> array(['A.txt', 'Z'.txt', ...])\n",
" \"\"\"\n",
" assert(os.path.exists(directory))\n",
" files = np.array(glob.glob('{0}/*{1}'.format(directory, datatype)))\n",
" \n",
" return files"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"def sph_to_cart(ra,dec,cz):\n",
" \"\"\"\n",
" Converts spherical coordinates to Cartesian coordinates.\n",
" \n",
" Parameters\n",
" ----------\n",
" ra: array-like\n",
" right-ascension of galaxies in degrees\n",
" dec: array-like\n",
" declination of galaxies in degrees\n",
" cz: array-like\n",
" velocity of galaxies in km/s\n",
" \n",
" Returns\n",
" -------\n",
" coords: array-like, shape = N by 3\n",
" x, y, and z coordinates\n",
" \"\"\"\n",
" cz_dist = cz/70. #converts velocity into distance\n",
" x_arr = cz_dist*np.cos(np.radians(ra))*np.cos(np.radians(dec))\n",
" y_arr = cz_dist*np.sin(np.radians(ra))*np.cos(np.radians(dec))\n",
" z_arr = cz_dist*np.sin(np.radians(dec))\n",
" coords = np.column_stack((x_arr,y_arr,z_arr))\n",
" \n",
" return coords"
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"def calc_dens(n_val,r_val):\n",
" \"\"\"\n",
" Returns densities of spheres with radius being the distance to the \n",
" nth nearest neighbor.\n",
" \n",
" Parameters\n",
" ----------\n",
" n_val = integer\n",
" The 'N' from Nth nearest neighbor\n",
" r_val = array-like\n",
" An array with the distances to the Nth nearest neighbor for\n",
" each galaxy\n",
" \n",
" Returns\n",
" -------\n",
" dens: array-like\n",
" An array with the densities of the spheres created with radii\n",
" to the Nth nearest neighbor.\n",
" \"\"\"\n",
" dens = np.array([(3.*(n_val+1)/(4.*np.pi*r_val[hh]**3)) \\\n",
" for hh in range(len(r_val))])\n",
" \n",
" return dens"
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"def plot_calcs(mass,mass_err=False,ratio_err=False):\n",
" \"\"\"\n",
" Returns values for plotting the stellar mass function and \n",
" mass ratios\n",
" \n",
" Parameters\n",
" ----------\n",
" mass: array-like\n",
" A 1D array with mass values\n",
" \n",
" Returns\n",
" -------\n",
" bin_centers: array-like\n",
" An array with the medians of the mass bins\n",
" \n",
" \"\"\"\n",
" bins = np.linspace(9.2,11.8,14)\n",
" dlogM = 0.2\n",
"\n",
" mass_counts, edges = np.histogram(mass,bins)\n",
" bin_centers = 0.5*(edges[:-1]+edges[1:])\n",
"\n",
" mass_freq = mass_counts/float(len(mass))/dlogM\n",
" \n",
" ratio_dict = {}\n",
" frac_val = [2,4,10]\n",
" \n",
" for ii in frac_val:\n",
" ratio_dict[ii] = {}\n",
" \n",
" # Calculations for the lower density cut\n",
" frac_data = int(len(mass)/ii)\n",
" frac_mass = mass[0:frac_data]\n",
" counts, edges = np.histogram(frac_mass,bins)\n",
" \n",
" # Calculations for the higher density cut\n",
" frac_mass_2 = mass[-frac_data:]\n",
" counts_2, edges_2 = np.histogram(frac_mass_2,bins)\n",
" \n",
" if ratio_err == True:\n",
" yerr = []\n",
" yerr.append(((counts_2*1.)/(counts*1.)*np.sqrt(1./counts + 1./counts_2)))\n",
" \n",
" # Ratio determination\n",
" ratio_counts = (1.*counts_2)/(1.*counts)\n",
" ratio_dict[ii] = ratio_counts\n",
" \n",
" if ratio_err == True:\n",
" \n",
" ratio_dict_list = [[] for xx in xrange(2)]\n",
" ratio_dict_list[0] = ratio_dict\n",
" ratio_dict_list[1] = yerr\n",
" ratio_dict = ratio_dict_list\n",
" \n",
" if mass_err == True:\n",
" mass_freq_list = [[] for xx in xrange(2)]\n",
" mass_freq_list[0] = mass_freq\n",
" mass_freq_list[1] = np.sqrt(mass_counts)/float(len(mass))/dlogM\n",
" \n",
" mass_freq = np.array(mass_freq_list)\n",
" \n",
"\n",
" return bin_centers, mass_freq, ratio_dict"
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"def plot_ratios(data,nn_val,ax,ii,zz):\n",
" if zz==16 :\n",
" ax.set_xlabel('$\\log\\ M_{*}$',fontsize=18)\n",
" if zz%3 != 0:\n",
"# ax.get_yaxis().set_visible(False)\n",
" ax.yaxis.set_ticklabels([])\n",
"# ax.get_yaxis().set_ticks([])\n",
" bins = np.linspace(9.2,11.8,14)\n",
" if ii==0:\n",
" title_label = 'Mass Ratio 50/50, {0} NN'.format(nn_val)\n",
" frac_val = 2\n",
" ax.text(0.05, 0.95, title_label,horizontalalignment='left',verticalalignment='top',transform=ax.transAxes,fontsize=12)\n",
" elif ii==1:\n",
" title_label = 'Mass Ratio 25/75, {0} NN'.format(nn_val)\n",
" frac_val = 4\n",
" ax.text(0.05, 0.95, title_label,horizontalalignment='left',verticalalignment='top',transform=ax.transAxes,fontsize=12)\n",
" elif ii==2:\n",
" title_label = 'Mass Ratio 10/90, {0} NN'.format(nn_val)\n",
" frac_val = 10\n",
" ax.text(0.05, 0.95, title_label,horizontalalignment='left',verticalalignment='top',transform=ax.transAxes,fontsize=12)\n",
" # Calculations\n",
" frac_data = (len(data)/frac_val)\n",
" frac_mass = data[0:frac_data]\n",
" counts, edges = np.histogram(frac_mass,bins)\n",
" bins_cens = 0.5*(edges[:-1]+edges[1:])\n",
" #\n",
" frac_mass_2 = data[-frac_data:]\n",
" counts_2, edges_2 = np.histogram(frac_mass_2,bins)\n",
" \n",
" ratio_counts = (1.*counts_2)/(1.*counts)\n",
" x_plots = bins_cens\n",
" \n",
" yerr = ((counts_2*1.)/(counts*1.)*np.sqrt(1./counts + 1./counts_2))\n",
" \n",
"# return yerr\n",
" \n",
" ax.axhline(y=1,c=\"blue\",linewidth=0.5,zorder=0)\n",
" ax.set_ylim([0,5])\n",
" ax.set_xlim([9.2,11.8])\n",
" ax.tick_params(axis='both', labelsize=12)\n",
" ax.set_xticks(np.arange(9., 12., 0.5))\n",
" ax.set_yticks([1.,3.])\n",
" ax.plot(x_plots,ratio_counts)\n",
" ax.errorbar(x_plots,ratio_counts,yerr = yerr)"
]
},
{
"cell_type": "code",
"execution_count": 8,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"neigh_vals = [1,2,3,5,10,20]\n",
"\n",
"eco_path = r\"C:\\Users\\Hannah\\Desktop\\Vanderbilt_REU\\Stellar_mass_env_density\\Catalogs\\ECO_true\"\n",
"ECO_true = (Index(eco_path,'.txt'))\n",
"names = ['ra','dec','cz','logMstar']\n",
"PD_eco = pd.read_csv(ECO_true[0],sep=\"\\s+\", usecols=(0,1,2,4),header=None,\\\n",
" skiprows=1,names=names)\n",
"eco_comp = PD_eco[PD_eco.logMstar >= 9.3]\n",
"\n",
"ra_eco = (np.array(eco_comp)).T[0]\n",
"dec_eco = (np.array(eco_comp)).T[1] \n",
"cz_eco = (np.array(eco_comp)).T[2] \n",
"mass_eco = (np.array(eco_comp)).T[3]\n",
"\n",
"coords_eco = sph_to_cart(ra_eco,dec_eco,cz_eco)\n",
"eco_neighbor_tree = spatial.cKDTree(coords_eco)\n",
"eco_tree_dist = np.array(eco_neighbor_tree.query(coords_eco,21)[0])\n",
"\n",
"eco_mass_dist = np.column_stack((mass_eco,eco_tree_dist.T[neigh_vals].T))\n",
"\n",
"eco_dens = ([calc_dens(neigh_vals[jj],\\\n",
" (eco_mass_dist.T[range(1,7)[jj]])) for jj in range(len(neigh_vals))])\n",
"\n",
"eco_mass_dens = [(np.column_stack((mass_eco,eco_dens[ii]))) for ii in range(len(neigh_vals))]\n",
"\n",
"eco_idx = [(eco_mass_dens[jj].T[1].argsort()) for jj in range(len(neigh_vals))]\n",
"\n",
"eco_mass_dat = [(eco_mass_dens[jj][eco_idx[jj]].T[0]) for jj in range(len(neigh_vals))]\n",
"\n",
"# eco_ratio_info = [[] for xx in xrange(len(eco_mass_dat))]\n",
"\n",
"# for ii in range(len(eco_mass_dat)):\n",
"# bin_centers, eco_freq, eco_ratio_info[ii] = plot_calcs(eco_mass_dat[ii],mass_err=True,ratio_err=True)\n",
" \n",
"# for jj in (range(len(eco_mass_dat))):\n",
"# eco_medians = np.array(bin_func(eco_mass_dist,(jj+1),bootstrap=True))"
]
},
{
"cell_type": "code",
"execution_count": 9,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"C:\\Users\\Hannah\\Anaconda2\\lib\\site-packages\\ipykernel\\__main__.py:23: DeprecationWarning: using a non-integer number instead of an integer will result in an error in the future\n",
"C:\\Users\\Hannah\\Anaconda2\\lib\\site-packages\\ipykernel\\__main__.py:27: DeprecationWarning: using a non-integer number instead of an integer will result in an error in the future\n"
]
}
],
"source": [
"np.seterr(divide='ignore',invalid='ignore')\n",
"nn_val_arr = [1,2,3,5,10,20]\n",
"nrow_num = int(6)\n",
"ncol_num = int(3)\n",
"%matplotlib qt\n",
"fig, axes = plt.subplots(nrows=nrow_num, ncols=ncol_num, figsize=(100,200), sharex= True)\n",
"plt.subplots_adjust(left=0.02, bottom=0.09, right=1.00, top=1.00,hspace=0,wspace=0)\n",
"# figtitle = fig.suptitle(\"Mass Distributions by NN Density\", fontsize=18)\n",
"# figtitle.set_y(1.04)\n",
"axes_flat = axes.flatten()\n",
"\n",
"zz = int(0)\n",
"yerr = [[[] for ii in range(0,3)] for yy in range(len(eco_mass_dat))]\n",
"for kk in range(len(eco_mass_dat)):\n",
" for ii in range(0,3):\n",
" yerr[kk][ii] = plot_ratios(eco_mass_dat[kk], nn_val_arr[kk], axes_flat[zz],ii,zz)\n",
" zz += 1"
]
},
{
"cell_type": "code",
"execution_count": 10,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"# print (yerr)\n",
"plt.show()"
]
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 2",
"language": "python",
"name": "python2"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 2
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython2",
"version": "2.7.11"
}
},
"nbformat": 4,
"nbformat_minor": 0
}
| mit |
ronnydw/data-science-projects | class-central-survey-2016-17/Charts for Class Central Survey article.ipynb | 1 | 136288 | {
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Class Central Survey Dec 2016 - Jan 2017"
]
},
{
"cell_type": "code",
"execution_count": 1,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"import pandas as pd\n",
"import seaborn as sns\n",
"import matplotlib.pyplot as plt\n",
"sns.set(style=\"white\")\n",
"sns.set_context(\"talk\")"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Read the data file"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"df = pd.read_csv('raw/2016-17-ClassCentral-Survey-data-noUserText.csv', decimal=',', encoding = \"ISO-8859-1\")"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Experience with MOOCs"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Decision criteria to take MOOCs"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"def multi_select_categorical_barh(df, question, selectors):\n",
" \"\"\"draw a barchart for Survey results on a question that allows to select multiple categories\n",
" df: dataframe to use\n",
" question: the question you want to analyse\n",
" selectors: list of df column containing the selectors (values 0/1)\"\"\"\n",
" size_df = len(df)\n",
" \n",
" graph_data = df.loc[:, selectors]\n",
" graph_data['target'] = 'target_name'\n",
" \n",
" melted = pd.melt(graph_data, id_vars='target', var_name='select', value_name='percentage')\n",
" grouped = melted.groupby(['select'], as_index=False).sum()\n",
"\n",
" grouped.percentage = grouped.percentage/size_df*100 # make it percentage of total\n",
" grouped['select'] = grouped['select'].apply(lambda x: x.split(\": \")[1]) # remove prefix from string\n",
" print(grouped) \n",
" \n",
" sns.barplot(x='percentage', y='select', data=grouped.sort_values('percentage', ascending=False), orient='h')\n",
" sns.plt.title(question)\n",
" sns.plt.xlabel('Percentage')\n",
" sns.plt.ylabel('')\n",
" \n",
" sns.despine(left=True, bottom=True)\n",
" sns.plt.show()\n"
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
" select percentage\n",
"0 Institution/university 41.268567\n",
"1 Instructor 16.057808\n",
"2 Others recommendations 25.250903\n",
"3 Platform 27.177840\n",
"4 Ratings 32.838218\n",
"5 Topic/Subject 91.087916\n"
]
},
{
"data": {
"image/png": "iVBORw0KGgoAAAANSUhEUgAAAu4AAAHMCAYAAACKkS1qAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x110de53c8>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"decisions = ['Decide: Topic/Subject', 'Decide: Instructor', 'Decide: Institution/university', \n",
" 'Decide: Platform', 'Decide: Ratings', 'Decide: Others recommendations']\n",
"multi_select_categorical_barh(df,\n",
" question='Which are the most important factors in deciding which MOOC to take?', \n",
" selectors=decisions)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Benefits of MOOCs"
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"# new chart here on benefits"
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
" select percentage\n",
"0 Helped me get a new job in a different field 4.255319\n",
"1 Helped me get a new job in the same field 4.897631\n",
"2 Higher performance evaluation at current job 11.401044\n",
"3 Not Really 34.524287\n",
"4 Promotion at current organization 2.930550\n",
"5 School credit towards a degree 3.091128\n"
]
},
{
"data": {
"image/png": "iVBORw0KGgoAAAANSUhEUgAAA10AAAHMCAYAAAAu3sKQAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz\nAAALEgAACxIB0t1+/AAAIABJREFUeJzs3Xl4Tdfi//F3ZEAQMTVFjRepKTJJhJhiDjH7VmlMlWue\nhyZFK1VzKyQUQQ01jx0MpUITJYm5Olz0qrYIIQg1Zvz9kV/248hYdar33s/refI8cvbea6+99j6x\nP2etvY5Fenp6OiIiIiIiImIWBV50BURERERERP6bKXSJiIiIiIiYkUKXiIiIiIiIGSl0iYiIiIiI\nmJFCl4iIiIiIiBkpdImIiAiazPjv6b/xvPw3HpNIXhS6REReAH9/f8aMGZPtsg0bNuDo6PgX10gC\nAwP5v//7P7PuY/v27Tg6OvL48WOz7uePOn/+PP/85z//8v3Gxsbi6OjIhQsXgNzfFwCXL1/G0dGR\nqKioZ95nWFgYjRo1eubtn6f09HTeeecdXFxcqF+/Pp999plJezyP83L69Gnatm1L3bp1mT59+vOo\n9jNLSkoiODiYI0eO5Hubp6+Rp/1V7ylHR0ccHR3Zvn17tsuXLl2Ko6Njlus3NTWVjRs30r17d1xc\nXPD09KRv377s378/x33t3r2bN954A1dXV9zc3HjttdfYvn17jmE1MjKSgQMH0qBBA9zc3OjRowef\nf/75Hz7Gzz77DD8/P1xcXPD19WXNmjWkpaX94XIke1YvugIiIiJ/B0OHDv3bhaG/yt69e/nxxx//\n8v3Wrl2bTZs28corr/zl+/47OHPmDJs2bWLEiBF4eHhQs2ZNk/Z4Hudl4cKFpKenEx4eTvny5Z9H\ntZ/Z9evXWb9+Pc2bN39uZTZr1oxNmzZhY2Pz3MrMiYWFBfv376dr165Zlu3duzfLaykpKYwYMYLo\n6GjeeOMNxo4dy+PHjzlw4AAjRoygT58+BAUFmWwTHBzM5s2b6dGjBwEBARQoUIAjR44wdepUvv76\na0JCQrC0tDTWnz9/PsuWLaN79+706tULa2trIiMjmThxIv/+978ZO3Zsvo5t586dTJw4EX9/f4KC\ngjhx4gSzZ8/m7t27DB8+/A+2lGRHoUtERASoWLHii67C/5yiRYvi7Oz8oqvxwty9exeATp06UaFC\nBYDn3h6///47zs7OeHl5Pddy/y5KlixJyZIl/5J9OTs7c/jwYR4+fEjhwoWN169cucLZs2epVq2a\nyfrh4eEcPnyYtWvX4uTkZLzevHlz3NzceOutt3BxcaFt27ZARk/Thg0bWLRoES1atDDWb9y4MU2b\nNmXAgAGsWrWKN998E4BDhw6xePFiZs+eTefOnU3Wt7e3JywsjPbt2+dr5MTSpUtp3rw5kydPBqBh\nw4Y8ePCAFStWMHToUAoU0OC4P0stKCLyN5eens7y5cvx9fWlTp06uLu7M3z4cOLj44GMYXEdO3Y0\n2SYlJQVPT09WrFgBZAzrmTNnDt7e3jg5OeHv78/Zs2dz3Of7779P48aNswwt8fHxYeHChQDcu3eP\n6dOn07x5c5ycnOjVqxenTp0y1s1u2M+FCxdwdHQkNjY22/1mDiXauHEjXl5eeHt7c+/ePeOTeh8f\nH+rWrUvXrl2zlHHr1i0mTJiAp6cn9evXZ+TIkUYbAcTHxzN69Gjc3Nxwc3NjwoQJ3L5921j+5PBC\nf39/hgwZkqX8WrVq8eWXXwIZN7NTpkzB09MTZ2dnBg8ezJUrV0y2iYiIwM/PDycnJ958802T/eXk\n4MGD9OzZE2dnZ5ycnOjZsycnTpwwlvv7+/PBBx8we/ZsGjRogIuLC+PGjePevXtA/s7dk8LCwli4\ncCEJCQkm5+bkyZP0798fV1dX6tatS8eOHYmIiDBpr/HjxxMeHk7jxo1xcnJi0KBBJm2emppKSEgI\n3t7euLi4EBQUxIcffoi/vz+Q/dCxlJQU3nvvPVxdXWnUqBHz58/PdYjTqVOneP3113FycqJx48aE\nhYXla0jUxo0bady4sdF+t27dMlm+bds22rZtS506dfD19WXPnj3GssxhjpGRkfTt2xcnJyeaN2/O\nxo0bTcrI7ZoLCwtj4MCBALRs2ZLAwECT9sjpvISHh9OiRQvq1KlDmzZtWLt2bY7H6OjoyOnTp/n0\n009xdHTk8uXL+Pv7ExwcjL+/P05OTixduhSAH374gf79++Pu7k6DBg2YMmUKv//+u1GWv78/c+bM\nYfr06dSvXx8PDw/mzZvH3bt3GTt2LM7OzrRs2TLHYXOXL182gkRAQACBgYFARvAMDg6mSZMm1KlT\nB29vb2bMmEFycnK25Vy6dIlGjRoxaNAgUlJSsvyd8fHxYdWqVQQFBeHm5oaHhwfTpk0zKe/WrVvG\nefH29mbZsmX069ePsLCwHNsSMsJSSkoKhw8fNnn9q6++ws3NzST8JScns3r1al577TWTwJWpc+fO\neHh4sGzZMuO15cuX4+PjYxK4MjVo0IBOnTrx8ccfG9f3qlWrqF27tkngytS/f3/eeOMNUlNTAbh/\n/z5BQUE0atTI+LsSHR0NZLxPnZ2d6dmzp0kZdevW5cGDB/n6uyV5U+gSEXlB0tPTSUlJyfLz9A3j\nsmXLWLhwIb179+bjjz9m7NixxMTEMGfOHADatWvHuXPn+O2334xtYmJiuHPnDr6+vgAEBQWxefNm\nhgwZQmhoKDY2NvTp04dr165lW7eOHTty/fp1jh8/brx26tQprly5gp+fH6mpqfTv3589e/YwbNgw\nQkNDsbW1pU+fPvzwww9/um1WrVrFrFmzmDRpEkWLFiUkJISwsDBee+01Fi1aRJUqVRg4cKCxr5SU\nFPr168fJkyeZNGkSc+bM4eLFi0ZwevDgAX369OH8+fNMnz6dadOmcerUKQYNGmTclDypXbt2HD58\nmPv37xuvffXVVxQsWJBmzZqRlpbGoEGDiIyMJCgoiA8++IAbN27Qp08fY5uTJ08yYsQI6tSpY9Q5\nJCQk1+M+deoUQ4cOxdnZmSVLljB79mx+//13xo8fb1LPDRs2cPHiRebMmcPEiRPZt28fixcvBvI+\nd0/r0aMH3bt3x97enk2bNlG7dm0uX75Mv379KF26NIsWLWL+/PkUKVKE8ePHG70zkBEQDxw4QHBw\nMNOnT+fUqVPMnDnTWD5v3jxWrVrFwIEDCQkJ4fLly6xatSrXNoiIiOCnn37iww8/pF+/fixfvpwF\nCxZku+7Zs2fp27ev8al+QEAAK1asYO7cubnu49atWyxZsoS33nqL9957j9jYWJMhVJs2bWLy5Mn4\n+PiwePFiGjVqxJgxYzhw4IBJOUFBQXh5ebF06VJcXV159913+fe//w3kfc316NHDGFq2cOFChg4d\nmud5+fTTT42w9vHHH9O2bVumTZtGZGRktse5adMmqlevTtOmTdm0aRMvvfQSAJs3b6ZevXqEhYXR\nokULvv/+e3r27Im1tTUffPAB48aNIyIigoCAAJPrbuPGjcTHxxMaGkqnTp1YunQpPXr0oFy5cixe\nvBgHBwcCAwN58OBBlrq89NJLRugPCgoyjnfs2LEcOXKEoKAgli1bRteuXVm9ejU7duzI9rwNHDiQ\nSpUqsWDBAqyssh+wFRYWhoWFBWFhYbz55pusXbuWLVu2ABl/dwcPHsyZM2eYNm0agYGBrFu3zuSD\njZzY2dnh4eGRJVju27ePNm3amLz2r3/9i8TERBo3bpxjea1bt+b777/n1q1bJCQkcP78+VzXb9Wq\nFQkJCXz//fekp6cTGxub4/pFihRhypQp1KpVC4Dp06dz7Ngx3nnnHZYuXUqJEiUYMmQIt27dwtLS\nkmnTptGsWTOTMvbv34+Dg8Nf1pP4307DC0VEXpA9e/aYfHqek+vXrzNy5Eh69+4NgIeHBz///LPR\n69CoUSPs7e3Zu3cvAQEBQMbzBc7OzpQtW5affvqJnTt3Mm/ePNq3bw+At7c3bdq0YcWKFUyaNCnL\nPp2cnKhSpQq7du3Cw8MDgF27dlGvXj0qVarE/v37OXPmDOvXr8fNzQ3IGNLi6+vLokWL+Oijj/5U\n2wwYMICmTZsCkJiYyMqVKxkxYoQxqUCTJk3o3bu3sa+vv/6ac+fO8dlnn/Hqq68C4ODgwIgRI7h0\n6RJRUVFcuXKFvXv3Gs+11KpVi3bt2nHw4EFatmxpsv+2bdvy/vvvExkZaQTXvXv34uPjQ6FChYiM\njOTEiRNs2rTJGA7m4eFBs2bN2LJlC/369ePjjz+mZs2aRghp3Lgxly9f5uDBgzke94ULF+jQoYPR\nCwBgZWXF8OHDiYuLM4agFSlShIULFxo3ncePHycqKooJEybkee6e9vLLL/Pyyy9jZWVlHMuJEyeo\nX78+s2fPNoYVlS1bli5duvDjjz/SoEEDIKMHNTw8HDs7OwB++uknPvnkEyCjJ3TNmjWMHTuWfv36\nAeDp6Zntp/hPKlasGEuWLKFIkSI0b96cu3fvsnbtWoYNG5Zl3cWLF1OhQgUWLlxoPOdSuHBhgoOD\nGThwIKVKlcp2H2lpacyfP984Xnt7ewYOHMipU6eoV68eoaGh9OjRg4kTJwIZ5y4xMZEFCxbg4+Nj\nlNO1a1cGDx4MQL169fjyyy+JioqiWrVq7NixI89rrmrVqgDUrFmTV155hatXr+Z6Xk6ePEn58uXp\n2bMnFhYWeHh4YG1tbTLU7UnOzs7Y2tpSsmRJk2GLpUqVYvz48cbvw4YNo3z58ixevNhoxypVqtC7\nd2+T94etrS0ffvgh1tbWuLu7s2HDBsqWLWuUZWNjQ69evfjll1+Mm/1MNjY21KxZE4CqVatSsWJF\nHj16RGpqKsHBwcY15eXlxddff82JEydMJrZ5/PgxQ4YMoXDhwixdupRChQple8wA//jHP5gxYwaQ\nMUzu4MGDREVF0atXL44cOcK3337L9u3bqV27NgCVK1emW7duOZb3pFatWrFgwQJSU1OxtLTkxo0b\nnD59mpCQEJPnujJ7vXN7ji7z2b2rV68a4bZcuXJ5rn/t2jVeeeUVkpOTKVu2bL7qffLkSRo2bGiE\nw1q1arFkyRIePnyY7fohISHs3r3bCLDy56mnS0TkBfH29mbr1q1ZfgYNGmSy3uTJkxkwYAAJCQnE\nxsaybt06Tp48aQyXsbKyonXr1nz11VdAxlCR/fv3G2Hh2LFjxv4ye9Mg42Ykp2F+kNFjsm/fPlJS\nUkhNTWXPnj106NAByLgpL1OmjBG4ACwtLWnTpk2+PjHOS5UqVYx/f/vttyQlJdG4cWOTHkFvb2+j\n/qdOneLll182Ahdk3FRERERQoUIFjh07RvXq1XFwcDC2f+WVV6hYsSIxMTFZ9l+yZEkaNGhgtGli\nYiKxsbFGmx49ehR7e3vq1KljlGdra4uzs7PJ8LwmTZqYlPt0uHta9+7dmTt3Lvfu3ePMmTN8+umn\nxixkTw6Pql27tsmn/A4ODia9C7mdu/xo2rQpK1asICkpiR9//JFdu3axfv36LPWoUqWKEbgy65F5\nE5d53p4MWYULFzbCdE4aNWpEkSJFjN+bNWvGvXv3OHfuXJZ1jx07RqNGjUx6jRs3bkxycjInT57M\ncR9lypQxCSHe3t5YW1tz8uRJLl68SEJCAk2aNDG53ho3bszZs2dJTEw0tqtXr57xb1tbW+zs7Izz\n8Eevufxwd3fn4sWLdO/eneXLl3Px4kWGDx9uhOv8qly5ssnvJ06coHXr1iYTNLi7u1OmTBmT93Ot\nWrWwtrYGwNramuLFi5uEK3t7ewCTYYm5KVSoECtXrsTT05PffvuNyMhIwsPDuXnzZpbhhW+//Tan\nT59m3LhxFCtWLNdynx7O9+T74+jRo7z88stG4AKoU6dOvidzadGiBXfu3DF6kvfv30/dunVxcHAw\nWS9zpsEn2/RpmcuenJUwp967p9fP/DAku5767Li7u7N582aGDh3K1q1bSU1N5a233so2FH7yyScs\nWbKEKVOm0KpVq3yVL3lTT5eIyAtiZ2dH3bp1s7z+/fffm/z+73//m0mTJnH69GmKFClC7dq1KViw\noMl/1L6+vmzZsoVr165x8eJFEhMTjYezM28Ss7sxy23YiJ+fH6GhocTExFCgQAFu375thI67d+9S\nunTpbMt7ckjes3qyXpn1z+65BYCHDx9y586dXI8lMTGRH3/80eRGK9OTQe1Jvr6+TJ8+ncePH7N/\n/34KFy5sDOVJTEwkMTEx2/Iyz+ndu3cpUaKEybLs2uxJ9+/fZ/LkyXz55ZdYWlpSrVo142bwyfP9\n9Kf8BQoUMFme27nLj5SUFGbMmMHmzZtJS0ujSpUqRjvltx6Zz4E8fV5Kly7N5cuXc9z30+tntuG9\ne/eytGdiYiKrV69m9erVWcq5fv16vvdhYWGBvb09v//+u1Hv7HrWABISEozjLliwoMmyJ4//Wa65\nvHTs2JHk5GTWrVvH3LlzmTt3Li4uLsyaNStLkMrN0z2Ad+/ezbZXsFSpUsazgoBJGM6UUy9bfu3f\nv5/p06cTFxdH6dKlcXZ2zvL3DeDmzZvUqFGDsLAwvL29c+19yeu6zO5vRV7vzUwODg7Uq1eP/fv3\n4+npyb59+2jdunWW9TLDzNWrV3M8N5m9YWXLljXqFxcXl+O+n1zf3t4eW1vbHIeIQ8YzhZlhcPLk\nyZQuXZrPPvuMiIgIrKysaNeuHdOmTTM5h0lJScyfP5/+/fsboyvk+VDoEhH5G0tLS2PIkCG8/PLL\n7NmzhypVqmBhYcHcuXNNnuHy8PCgVKlS7N+/nwsXLuDu7m48v2FnZ4eNjY3RU/Gk3D5VrVChAq6u\nrnz11VdYWFjg5eVl3JjY2dmRkJCQZZubN29SvHhxAOOm6MlPYrN71iMvmT0pK1asMMp+ko2NDcWK\nFcv2Ye/IyEjq1q2LnZ0dLi4u2Q6lzK5MyBhGNHXqVI4cOcK+ffto2bKlMS21nZ0d5cuXz/ZZo8wb\nmOLFi3Pz5k2TZU/2kmTn/fff58SJE6xZswZnZ2dj+ufMHrf8yu3c5ceSJUv47LPPCA0NpWHDhhQq\nVIgLFy6wc+fOfJeRef3dvHmTokWLGq8/PWHF057uJclc/+nABRlDETt06JBtIM9tmNbT+0hLSyMx\nMZGSJUsa19vMmTOpXr16lm1feeWVbK/9pz3LNZcf3bp1o1u3bsTHxxMREUFoaCjTpk0zJs15FnZ2\ndlmuVTB9P5vDL7/8wujRo+nVqxcBAQGUKVMGyHie7Wnz5s2jQIEC9OrVi61bt2a7Tn689NJL2R5r\nXtflk1q1asX69esZPnw4x44d47333suyTp06dShVqhQRERE5zhx54MABateubQTe2rVrExERkeOx\nHThwgFKlShlB3svLi8OHDzNu3Lgs6z569Ii2bdvSs2dP3nrrLQoVKsTo0aMZPXo058+fZ9euXYSH\nh1O9enWT0RW//vor9+7dy/FDLnl2Gl4oIvI3duvWLX777Tdef/11qlatioWFBWlpaURHR5tMuGFp\naUnbtm2JiooiIiKCdu3aGctcXFxISkoiNTWVunXrGj+fffaZMRNfTjp27EhkZCSRkZEmkzC4ublx\n48YNk6FHqamp7Nu3zxi2ZWtrC2DySeyzDD10cnLCysqKO3fumNT/8OHDbNiwAUtLS+rVq8fVq1dN\nhqBlfrHszz//jIuLC7/++itVqlQxtq9evTphYWF899132e63ePHieHt7s3fvXqKjo7O06fXr1ylV\nqpRRXp06dVi5cqUxs1n9+vWJiIgw+cT+0KFDuR7r6dOn8fHxoX79+sYwrswvks3pi1FzktO5y87T\n00GfPn0aV1dX4xk2wDiu/NajZs2a2Nramkw+kZycnGXmt6cdPXrUZGjZvn37sLe3zzIdN2Sch19+\n+cXkurCysmL+/PnZ3lhniouL4+effzZ+P3DgAMnJybi7u1O1alXs7e1JSEgwKffcuXOEh4fne+rs\nZ7nmnvb0vqZOncrIkSOBjB6XXr160aZNm1x7O/LD1dWVffv2mXxAcvz4cW7cuPFcp7B/eqjdjz/+\nSHJyMoMGDTICV+aEEk9PKFSyZEnc3Nzo0KEDH374YZ4fYOTEzc2N+Ph4kwl/Lly4wKVLl/JdRqtW\nrbhy5YoRWjKftXySpaUlAwcOZOPGjdkOdd29ezdRUVHG9O8AgwYN4uDBg9k+63vixAm2bt1Kv379\njHZ84403+OGHH7L9IuRly5bx4MED2rVrR3p6Op07dzYmsalRowZjxoyhcuXKWa6dIkWKMGrUqFw/\ntJBno54uEZG/sVKlSlG2bFlWrFhBkSJFSEtLY8OGDfz4449ZhtC0a9cOf39/LCwsTGbSql27Ns2b\nN2fkyJGMGDGCChUqsG/fPtatW8cHH3yQ6/7btWvH+++/j6WlpcnzSM2aNaNOnTqMGjWKMWPGULp0\nadatW8eVK1eMmeMyH/J///33CQgI4Oeff2bNmjXP1AY9e/Zk6tSpxvTZR48eZfHixYwaNQrImCa6\nRo0ajBgxgtGjR1OwYEHmz5+Pq6srrq6u1KhRg9WrVzNw4EDefPNNbGxs+Pjjjzlz5ozJpBXZHX9g\nYCB2dnY0bNjQeL158+ZUr16dgQMHMnToUEqWLMnmzZv56quveP311wEYPHgw3bt3Z/To0XTv3p2j\nR4+aTLmenTp16vDll1/i5uZG6dKlOXDgAOvWrQP+eC9hTucuO3Z2dty5c4evv/4aFxcX6tSpw8cf\nf8ymTZuoXLkyx44dIzw8/A/Vo1ixYvj7+7NgwQIsLCyoUqUK69evJz4+PtfvRLt58yZjx46lV69e\nnDx5krVr1zJx4sRse2UHDx5M7969CQoKwtfXlzt37hASEkLhwoVNngt8WsGCBY1r986dO8yePZsW\nLVoYzycNGjSI+fPnk5ycjJubG2fPniUkJISOHTvm+0t4u3Xr9kzX3JOePi/169dn7NixzJ8/Hy8v\nL3777Td27dr1p4eBDR48mF69ejFkyBB69+7NjRs3mD9/PnXr1s0yo92fkfks1qFDh3jllVd49dVX\nsbS0ZNasWXTr1o3r16+zZMkSHj9+nOMEDxMnTqRt27bMmzcv2x6mvHh5eeHi4sLIkSMZN24cBQoU\nICQkBAsLi3xPGFGpUiXjb0pOw1AB+vbty3fffUf//v3p06cPDRs2JCUlhQMHDrBp0yb8/f2NyY0A\n2rRpw4ABAxg3bhxHjx7Fx8cHKysroqOjWb16Nc2aNTMJaQ0bNqRXr14EBgZy+vRpmjZtSmpqKl99\n9RU7duxgyJAhxvNtTk5OfPTRR9ja2lKpUiWio6O5ePEiU6ZMMalz5t+6zGd/5flR6BIR+RuzsLAw\nhg+NHDkSOzs76tevz7x58xgzZgznzp0zvvjSzc0NBwcHqlSpkuX5jJCQEEJCQliwYAF3796lcuXK\nzJ07N88ekOLFi+Pk5ISDg4PJEDErKytWrFjBnDlzmDNnDo8fP6Zu3bqsWbPG+E++RIkSzJ8/nw8/\n/JCAgABq167NggUL6N69+x9uh7fffpsSJUqwevVqbty4Qbly5XjrrbeMWfEyb2hnzpzJlClTsLKy\nokmTJgQFBVGgQAHs7Oz45JNPmDNnjnHDW6dOHVavXm3MIJedFi1aYGNjQ6tWrYyeJ8iYRCBzavJp\n06bx+PFjatSoweLFi6lfvz6Q8dzOsmXLmDt3LsOGDaNWrVpMmDAh1xvFwMBAHj58yNSpU7GysqJG\njRqsWrWKf/7zn3z77bfZft9PTnI6d9nx9fVl27ZtDB8+nNmzZ/PPf/6T+Ph45s2bR2pqKlWrVmXB\nggXMmDGDb7/91qTXLzcjR44kOTnZuJFu3749bdq04dGjRzlu06VLF5KTkxkyZAh2dnZMmDDBOM9P\nc3Z2ZsWKFcyfP59hw4ZRpEgRvL29mTBhgsn5elqVKlXo0qULkyZNIikpifbt2xvTt0PG7JmFChVi\n9erVLF68mJdeeon+/fvneoP9tGe95p709Hlp3749t27dYu3ataxYsYISJUrQq1cvRowYke96ZcfJ\nyYmVK1fy4YcfMnz4cIoVK0arVq0YP358rkOQ/6iiRYsyYMAA1q1bx2+//cbSpUuZMWMGixYtYu/e\nvTg4ONC2bVtat27Npk2bsp0kwsHBgX/+85/GDJPPIiwsjPfee49JkyZha2tLQEAAa9asMXrn86NV\nq1YsWrQo2+e5MllaWjJv3jw+/fRTNm7cyPr167G0tKRWrVqEhoZm+2HIW2+9hZubG5988gm7d+8m\nJSWF6tWr884779C1a9cswfDdd9+lbt26bNq0iZ07d5Kamkq1atWYN2+eyXOcQUFBWFtbs3DhQm7d\nukWlSpWYNWuWyYdJkPF9bX369GHmzJl07do13+0hebNI/6PjFURE5H/GnTt3jC+czWvWOfl7edHn\nLikpid27d9OkSROTiQt69uyJk5MTb7/99l9eJ5FLly7x3Xff0bZtW2P45r179/Dy8iIkJCTPXmGR\nZ6XQJSIiWdy8eZMNGzYQFRXFnTt32LNnT76fZZEX6+907nx8fKhYsSL9+/fHxsaGffv2sWXLFrZt\n22b00Ir8lX799Vd8fX3p2bMnrVq14uHDh6xatYrLly+za9euXL//S+TPUOgSEZEs7t69S+vWrSla\ntCjz5s37Q8Pa5MX6O527n376iblz53L69GmSkpKoWbMmo0ePxtPT84XVSSQiIoKPPvqICxcuYGNj\nQ4MGDXL8ziqR50WhS0RERERExIw0VkRERERERMSMFLpERERERETMSKFLRERERETEjBS6RERERERE\nzEihS0RERERExIwUukRERERERMxIoUtERERERMSMFLpERERERETMSKFLRERERETEjKxedAVE5D9f\nr4nrXnQVRERERLJYP6f3i64CoJ4uERERERERs1LoEhERERERMSOFLhERERERETNS6BIRERERETEj\nhS4REREREREzUugSERERERExI4UuERERERERM1LoEhERERERMSOFLhERERERETNS6BIRERERETEj\nhS4REREREREzUugSERERERExI4UuERERERERM1LoEhERERERMSOFLhERERERETNS6BIRERERETEj\nhS4RERHcjbjKAAAgAElEQVQREREzUugSERERERExI4UuERERERERM1LoEnlBLl269KKrICIiIiJ/\nAYUukWfg6OjIO++8k+V1Hx8fDh48mOf2ERERjBkzJttlly9fxtHRERcXF+PH2dkZPz8/Dhw48Kfr\nHhYWxsiRI7P8W0RERETMQ6FL5Blt3ryZqKioZ9r2zp07pKWl5brON998w6lTpzh16hTHjh3Dz8+P\nMWPGkJiY+Ez7FBEREZEXQ6FL5Bn16NGDt99+O8cQlJCQwLhx4/D09KRp06bMmTOHpKQkzpw5w7vv\nvsu//vUvGjVqlK99WVtb88Ybb/Do0SNjWGJcXByDBw/G09OT1q1bs23bNmP9S5cuMXjwYJo2bYqT\nkxM9e/bkwoULOZb/8OFDXFxcOHHihPHagQMH8PX1zVf9RERERCRnCl0iz+iNN96gWrVqTJ06Ndvl\nw4cPBzKGEm7evJmjR48SGhqKk5MTwcHB1KxZk8OHD+drXw8fPmThwoWUKVOGf/zjH6SmpjJ48GCq\nV6/OoUOHCA0NJSQkhJiYGAAmT55M1apViYiIICYmhhIlSrBkyZIcyy9cuDAtW7Zkz549xms7d+7E\nz88vn60hIiIiIjlR6BJ5RhYWFsycOZNvvvmGL774wmTZb7/9xqlTp5g0aRJFixbFwcGBUaNGsWPH\njnyX37RpU9zc3Khbty4NGzYkPj6eNWvWYGtry3fffcfVq1cZM2YMNjY2vPrqq/Ts2ZMtW7YAMGvW\nLEaOHElqaipxcXHY29sTHx+f6/78/Pz48ssvSUtL48GDBxw8eJAOHTr88YYRERERERNWL7oCIv/J\nypYty+TJk3nvvfeoX7++8frNmzextbWlZMmSxmvlypUjISGB5OTkfJUdGRlJkSJFOHv2LEOHDqVK\nlSpUrVoVyBhaeO/ePTw8PIz1U1NTqV27NgA///wzc+fOJT4+nmrVqmFhYUF6enqu+2vUqBHp6ekc\nP36c+Ph4HB0dqVChQr7bQkRERESyp9Al8id17tyZiIgI3n77bSPYlCtXjgcPHnD79m1KlCgBZMxK\naG9vj7W19R8q/9VXX2XBggW8/vrrVKxYkY4dO/LSSy/h4ODA119/bayXkJBAeno6SUlJDB8+nJkz\nZ9K2bVsAFi5cSGxsbK77sbS0pF27duzbt4/4+HgNLRQRERF5TjS8UOQ5CA4O5ty5c8TFxQHg4OCA\nl5cXM2bM4P79+8THxxMaGmoEGRsbG+7fv59n71OmunXrMmjQIKZNm8b169epV68ehQoVYvny5SQn\nJ3Pt2jX69+/PunXrSE5O5vHjxxQuXBiA06dPs2nTpnz1sHXs2JEDBw5w9OhR2rVr94ytISIiIiJP\nUugSeQ5KlizJtGnTTF774IMPSElJoUWLFnTq1Ak3NzcmTJgAYAxFrF+/Po8fP87XPgYNGoSDgwNT\np07F2tqa8PBwjh49ire3N127dsXT05Nhw4ZRpEgRgoODmTx5Mm5ubgQHB/Paa6/x66+/kpKSkus+\nnJycsLa2xtnZ2WRopIiIiIg8O4v0/H7ULiL/E/r370/37t1p3759vrfpNXGdGWskIiIi8mzWz+n9\noqsA6JkuEfn/4uLi+O677zh37hwtW7Z80dURERER+a+h0CUiAKxevZrt27czbdo0ChYs+KKrIyIi\nIvJfQ8MLReRP0/BCERER+Tv6uwwv1EQaIiIiIiIiZqTQJSIiIiIiYkYKXSIiIiIiImak0CUiIiIi\nImJGCl0iIiIiIiJmpNAlIiIiIiJiRgpdIiIiIiIiZqTQJSIiIiIiYkYKXSIiIiIiImak0CUiIiIi\nImJGCl0iIiIiIiJmpNAlIiIiIiJiRgpdIiIiIiIiZqTQJSIiIiIiYkYKXSIiIiIiImak0CUiIiIi\nImJGCl0iIiIiIiJmZJGenp7+oishIiIiIiLy30o9XSIiIiIiImak0CUiIiIiImJGCl0iIiIiIiJm\npNAlIiIiIiJiRgpdIiIiIiIiZqTQJSIiIiIiYkYKXSIiIiIiImak0CUiIiIiImJGCl0iIiIiIiJm\npNAlIiIiIiJiRgpdIiIiIiIiZqTQJSIiIiIiYkZWL7oCIvKfr9/KUS+6CiJmsar/ghddBRER+S+g\nni4REREREREzUugSERERERExI4UuERERERERM1LoEhERERERMSOFLhERERERETNS6BIRERERETEj\nhS4REREREREzUugSERERERExI4UuERERERERM1LoEhERERERMSOFLhERERERETNS6BIRERERETEj\nhS4REREREREzUugSERERERExI4UuERERERERM1LoEhERERERMSOFLhERERERETNS6BIRERERETEj\nhS4REREREREzUugSERERERExI4Uu+dtJSUnh2rVrL2z/Dx8+JCEh4YXt/1lcunTpRVdBRERERHKQ\nZ+hydHTk/PnzWV739PQkNjYWgPbt2xMVFZXnzvz9/Vm7du0zVPOvk5qaypAhQ3B2dmbIkCEvujr/\nUcLCwhg5cuSfLmfs2LHs378fgOPHj+Pj4/Ony/wjevfuzXffffeX7vPPWLt2LXPnzs122eeff07v\n3r3zLCM2NhZPT8/nXTURERERAayeRyG7du16HsX8LVy/fp0DBw6wf/9+KlSo8KKr8z/p9u3bxr/d\n3d05cODAX7r/xMTEv3R/f9aT7fW0jh070rFjx7+wNiIiIiLytOcyvNDHx4eDBw8CGT0THTt2xN3d\nnWHDhjFs2DDCwsKMdc+ePctrr72Gi4sLr7/+OleuXDGWrV+/ntatW+Pp6cmwYcO4ceMGkPEpfLt2\n7QgICMDDw8PoYcsUGxtL+/btmTp1Ki4uLrRo0cIkCMbFxTF48GA8PT1p3bo127ZtM5b5+/sTGBhI\no0aNGDhwIO3atQMyblZ3795NQkIC48aNw9PTk6ZNmzJnzhySkpIACAwMZMyYMTRv3hw/Pz+io6Pp\n0qULc+bMoX79+jRp0oQDBw4wffp03N3d8fHxITo6GoC0tDTmz59P27ZtcXFxoWnTpmzcuBGAy5cv\n4+7uTnh4OI0aNcLLy4sZM2YYdb569SqDBw/G1dWVxo0bs3LlSmPZvn376NChA+7u7vTt25eLFy/m\neN5yau+ePXuybt06Y71Lly7h5OTE3bt3uXTpEoMHD6Zp06Y4OTnRs2dPLly4kKXsp3u9zp8/j6Oj\no/H7mjVr8PPzw83NjYYNGxrXyPTp0zl+/DizZs1i1qxZWXpgVq9eTYsWLahfvz4DBgzg559/Nq4B\nPz8/Zs6ciYeHB02aNGHZsmXZHndubT9s2DDi4uIYNWoUa9asybLt3bt3mTBhAu7u7nh5eTFnzhzS\n09Oz7Sl6sjfY0dGR4OBg6tevz9KlS7NcO2lpaRw7doxu3brh7u5Ojx49OHPmjFGWo6Mja9asoXnz\n5nh4eDB+/HiSkpLYu3cvS5cuZf/+/XTv3j1Lfbdv307Xrl3zbD+A9PR0Zs+eTYMGDfDx8eHLL7/M\ntv1ERERE5I/JV+jq2bMn7u7uJj937tzJsl5iYiJDhgzB39+fmJgYWrVqZQwTyxQbG8sHH3zAkSNH\nsLKyYvHixQDs2bOH8PBwFi1aRFRUFBUqVGDMmDHGdj///DNt27YlMjISNze3LPv+97//jbW1NbGx\nsQQHBxMYGMhPP/1EamoqgwcPpnr16hw6dIjQ0FBCQkKIiYkxtv3hhx/48ssvCQkJYefOnQB88803\n+Pr6Mnz4cAAiIiLYvHkzR48eJTQ01Nj22LFjbNy4kfXr11OgQAF+/PFHSpUqRWxsLF26dGH48OGU\nL1+e6Oho2rdvzwcffABkDPvat28fn3zyCSdPnmTcuHHMmDGD+/fvA/D7779z+fJlDh48yOLFi1m/\nfj2nTp0CYNSoUZQpU4bDhw+zdu1ali9fzjfffMOZM2d4++23CQ4OJjo6mubNmzNo0CCSk5OztFdu\n7d2pUyeT0PrFF1/QrFkz7OzsmDx5MlWrViUiIoKYmBhKlCjBkiVLcrx2snP8+HGWLFlCWFgYJ06c\nIDQ0lEWLFvHrr78yadIk3N3dCQwMJDAw0GS7TZs2sWLFChYtWsThw4dxdXUlICCAR48eARnBrnjx\n4hw5coQpU6Ywb968bJ8Ny63tFy1aRLly5ViwYAF9+vTJsu27777L77//TkREBDt37iQyMpLNmzfn\n67gfP37M4cOHjeF+T147165dY9CgQQwZMoSYmBgGDBhAQECASa9bdHQ0X3zxBZs2beKbb75h3759\ntGnThkGDBtGyZUu2bt2a6/7zar/M93RUVBTTpk1jwoQJ2QZqEREREflj8hW6Nm7cyPHjx01+ihcv\nnmW9r7/+mnLlytGjRw+srKzo3Lkzzs7OJut07tyZChUqULhwYXx8fLh8+TIAW7dupV+/flSvXp2C\nBQsyduxYvv32W6OnpkCBAvj5+VG4cGGsrLKOirS1tWX8+PHY2Njg7e1N48aN2bNnD9999x1Xr15l\nzJgx2NjY8Oqrr9KzZ0+2bNlibOvj40OxYsUoVqyYSZm//fYbp06dYtKkSRQtWhQHBwdGjRrFjh07\njHU8PT1xcHAwtrW2tqZv374UKFCABg0aUKBAAfr06YO1tTUNGzYkLi4OgJYtW7J69WpKly5NfHw8\nBQsW5PHjxyZhNiAgABsbG5ydnalatSq//vorly5d4ttvv2XixIkULlyYSpUqsXr1amrVqsXWrVvp\n3Lkzbm5uWFtb069fP1JSUrL0DObV3r6+vnz//fdGYNm1axedOnUCYNasWYwcOZLU1FTi4uKwt7cn\nPj4+p0snW7Vr12b79u1UrlyZhIQEkpOTKVSoENevX891u88++4y+ffvy6quvYmNjw9ChQ0lKSuLo\n0aMAWFpaEhAQgJWVFa1atcLW1jbbCSby0/bZSUpK4quvvmL06NEUL16cUqVKsXjxYpo2bZqv427f\nvj02NjYULVoUML12du7ciaenJy1btsTKyop27dpRo0YN9u7da2zft29fihYtSpUqVXBxceGXX37J\n134z5dV+tra2xvukUaNGNG7cWL1dIiIiIs/Bc3mmK9P169cpW7asyWvlypUz+f3JsGZtbU1qaiqQ\nMWRu/vz5LFy40FhuYWFBXFwcVlZW2NnZYWNjk+O+X375ZQoWLGjye0JCAnFxcdy7dw8PDw9jWWpq\nKrVr1zZ+L126dLZl3rx5E1tbW0qWLGlyPJlBAaBMmTIm2xQpUsQIhQUKFKBIkSIUKFDA+D0tLQ2A\n5ORk3n//faKjoylbtiw1a9YEMJYDJvu1srIiLS3NqNOTAbFatWpGG8bGxvLpp58ay5KTk7l69WqW\nY8utvatUqUKzZs3YvXs3Xl5eJCQk0KRJEyCjx3Hu3LnEx8dTrVo1LCwsSE9Pz7b9clKgQAE++ugj\n9u7dS6lSpahTp06WY8/OzZs3KV++vEk5ZcuWJT4+nooVK1KsWDGsra2N5Zlt9rT8tH127ty5Q3Jy\nMg4ODsZrFStWBODXX3/N46izXmdPXjtxcXEcOnQId3d347WUlBSTXt0nrwdra+s/3O55td9LL71k\n8h57+eWXjSGnIiIiIvLsnmvoevnll42enEzXrl2jatWqeW5bpkwZBgwYYPJcyoULF6hQoYIxrC43\nN2/eJDU1FUtLSyDjJtbJyYmXXnoJBwcHvv76a2PdhIQEkxtWCwuLbMssV64cDx484Pbt25QoUQLI\neN7K3t7euLnPadu8zJs3j/T0dA4dOkTBggWJi4sz6UHLiYODAw8ePOD33383gtfOnTuxs7OjTJky\nvPnmm4waNcpY/5dffjEJCZlya2/IeKYtPDycxMRE2rdvj7W1NUlJSQwfPpyZM2fStm1bABYuXJht\nT1qBAgWMZ9/AdHKKlStXcv78efbv30+xYsVITk5m9+7deR57uXLlTK6vtLQ04uLiKFWqVJ7bPulZ\n275kyZJYW1sTHx9vXA+HDh0iMTGRsmXLmgzjTE5ONoaKZnr6Wnny9zJlyuDr68ucOXOM1y5dumTs\n53nIq/1u3bqV5T3k6ur63PYvIiIi8r/quX5Pl4+PD/Hx8Wzbto2UlBS+/PJLTp48ma9tu3TpwsqV\nK/n1119JS0vjk08+4f/+7/94+PBhvra/c+cO4eHhJCcnExkZSUxMDO3bt6devXoUKlSI5cuXk5yc\nzLVr1+jfv7/JRBE5cXBwMCaxuH//PvHx8YSGhuLn55evOuXm3r172NjYYGlpye3bt5k9ezaQ0buR\nm7Jly+Lu7s6HH37I48eP+eWXX5g1a5YxnHPLli388MMPpKen89VXX9GhQ4dse7ryau+mTZty6dIl\nPv30U2NoYXJyMo8fP6Zw4cIAnD59mk2bNmX7zFiVKlX47rvviI+P5969e6xatcrk2K2trbG2tub+\n/fvMnj2b5ORk49htbGy4d+9eljI7d+7MmjVrOH/+PElJSXz00UcANGjQIK/mNpFX21tbW2e7f0tL\nS3x9fQkNDeXevXvcuHGDOXPm8OjRIypUqMDDhw+Jjo4mNTWVZcuW5Xkun9S+fXsOHjxIdHQ06enp\nnDhxgo4dO+Zr6vqc2utpebXf3bt3Wbx4MUlJSRw8eJDY2Fg6dOiQ72MQERERkew9156uokWLsmDB\nAoKDg5k+fTre3t7UrVvXZMhXTjp16kRiYiIBAQEkJCRQtWpVli5dmu2zY9mxs7Pj2rVreHt7U6pU\nKRYsWEClSpUACA8P5/3332fZsmXGjfOwYcPyVe4HH3zA9OnTadGiBZDRAzRu3Lh8bZubkSNH8tZb\nb1G/fn3s7Ozo1KkTFStW5MKFCyaz/GVn3rx5vPfeezRp0oTChQszbNgwGjZsCGTMqDhx4kTi4uIo\nX7488+fPz7anMa/2tra2xtfXl2+++YZ69eoBGUMng4ODmTx5Mg8ePKBixYq89tprrFu3LkvAaNmy\nJVFRUXTs2JEiRYowaNAgIiIiAOjfvz/jx4/Hy8uLIkWK4OPjg6urKxcuXKBRo0Z06NCB9957jytX\nrpjc9Hfq1Inbt28zdOhQbt68Sd26dVm5ciW2trbPre0rV65Mly5dmDJlCpcuXWLo0KEm206ZMoUZ\nM2bQunVrLCwseO211+jRowcAEyZMICgoiIcPH9KtWzdq1aqV7zpVrlyZ+fPnM3fuXH755RdKlixJ\nUFAQXl5eeW7brFkzPvnkE9q0aWPyDNjT8mq/SpUqce3aNTw9PXnllVeMSUVERERE5M+xSP+jD4bk\n4tatW8TFxRnP6AD06NGD7t2789prrz2v3WQRGxvLyJEjsx3mJvK/bNu2bWzcuNFk4hhz6LdyVN4r\nifwHWtV/wYuugoiI/Bd4rsMLk5KS8Pf351//+heQMZvh2bNn8/VpvYg8X48ePeLChQt/+Jk3ERER\nEXm+nvtEGu+99x6jR4/m+vXrlC9fnnnz5hkzvInIX2fs2LGcPn2auXPnvuiqiIiIiPxPe67DC0Xk\nf5OGF8p/Kw0vFBGR5+G5Di8UERERERERUwpdIiIiIiIiZqTQJSIiIiIiYkYKXSIiIiIiImak0CUi\nIiIiImJGCl0iIiIiIiJmpNAlIiIiIiJiRgpdIiIiIiIiZqTQJSIiIiIiYkYKXSIiIiIiImak0CUi\nIiIiImJGCl0iIiIiIiJmpNAlIiIiIiJiRgpdIiIiIiIiZqTQJSIiIiIiYkYKXSIiIiIiImak0CUi\nIiIiImJGFunp6ekvuhIiIiIiIiL/rdTTJSIiIiIiYkYKXSIiIiIiImak0CUiIiIiImJGCl0iIiIi\nIiJmpNAlIiIiIiJiRgpdIiIiIiIiZqTQJSIiIiIiYkYKXSIiIiIiImak0CUiIiIiImJGCl0iIiIi\nIiJmpNAlIiIiIiJiRgpdIiIiIiIiZmT1oisgIv/5dvfp/6Kr8B/Ld83KF10FERERMTP1dImIiIiI\niJiRQpeIiIiIiIgZKXSJiIiIiIiYkUKXiIiIiIiIGSl0iYiIiIiImJFCl4iIiIiIiBkpdImIiIiI\niJiRQpeIiIiIiIgZKXSJiIiIiIiYkUKXiIiIiIiIGSl0iYiIiIiImJFCl4iIiIiIiBkpdImIiIiI\niJiRQpeIiIiIiIgZKXSJiIiIiIiYkUKXiIiIiIiIGSl0iYiIiIiImJFCl4iIiIiIiBkpdImIiIiI\niJiRQpeIiIiIiIgZKXT9zaWnp3P58uUXXY3/aNeuXSMlJeVFV0NERERE/keZNXQ5Ojpy/vz5LK97\nenoSGxub5/aBgYHMnj37udera9eubN++/bmXaw5z5sxh3bp1L7oaf0hcXBwuLi48ePAgz3Vzukb+\nTJlPSkhIoG3btjx+/Bgw3zX1om3btg1PT0/q16/Pjh076N27d57bxMbG4unpmePy/L5PRURERCR3\nVi+6ApK727dvU6JEiRddjT+kXLlynDp16m9R5qNHj3j48OFzrcvf0eeff06vXr0YNWoUAF26dHnB\nNRIRERGRTC98eGFiYiITJkzAy8sLHx8fwsPDSU9Pz7JeYGAg7777Ll27dsXFxYW+ffty5coVY/m+\nffvo0KED7u7u9O3bl4sXLxrLjhw5Qvv27XFxcSEoKIjk5ORs63L58mU8PT1ZuXIlXl5eeHp6smXL\nFpYuXUqDBg1o1KgRX3zxhbH+sWPH6NatG+7u7vTo0YMzZ85kW256ejoLFy7Ey8uLpk2b8vHHH1Or\nVi1j2OC5c+fw9/fH3d0dPz8/IiMjAVi5ciVffPEFn3zyCSNHjsxS7qNHj5g6dSqtWrXC2dmZ1q1b\ns3///mzrEBgYyPvvv0+vXr1wcXGha9eu/PDDD3m2X5cuXYxjfvDgAXXq1GHDhg0AJCUl4erqyqVL\nl7K0o6OjI/fv3wfg8OHDdO3aFVdXVzp16mQcX6adO3fi4+ODh4cHCxYsIDU1Ndtzk1lmbGwsfn5+\nzJw5Ew8PD5o0acKyZcuyPe5u3boB4O3tzY8//gjAlStX6N+/P25ubnTs2NF4Pbd2eFpcXBx9+vTB\n3d2dli1bMmfOHOO6jY6OpmfPnjRo0ABXV1dGjhxpBD9/f3+WLFmCn58fzs7ODBs2jBMnTuDn54eL\niwtjxowxjj+/740BAwZw9OhRli1bxuDBg9m+fTtdu3Y1lq9fv57WrVvj6enJsGHDuHHjRrbH9MUX\nX9CiRQtcXV2ZO3dutuuIiIiIyB9n9tDVs2dP3N3dTX7u3LljLJ84cSIWFhZERESwZs0aPv/88xyH\n/n366ae89dZbxMTEULFiRcaMGQPAmTNnePvttwkODiY6OprmzZszaNAgkpOTSUhIYNiwYQwZMoSj\nR49Sp06dXIezJSYmcuXKFaKiohg3bhzvvvsut27d4tChQwwbNozp06cDGTfdgwYNYsiQIcTExDBg\nwAACAgJITEzMUua2bdvYvn07GzZsYNeuXRw7dsy4sb537x5vvvkmbdu2JSYmhsmTJzNhwgQuXrxI\n//798fPzw9/fn9DQ0CzlrlixggsXLrB9+3ZOnDhB165dmTZtWo7H9tlnn/HOO+8QHR1NpUqVmDdv\nXp7t17RpU44cOQLA8ePHsbS05OjRowCcOHGCcuXKUaFChRz3+dNPPzFkyBAGDx7M0aNHGTt2LKNG\njeLcuXPGOqdOnWL79u1s27aNzz//nG3btuVYXqbz589TvHhxjhw5wpQpU5g3bx7Xrl3Ltu0Bvvnm\nG2rVqgVkDKubOHEisbGx1KhRwwgYubXD00JCQqhRowZHjx5l7dq17Nq1i+joaB48eMDw4cMJCAgg\nJiaG3bt38/3337Nz505j2x07drB8+XIiIiI4duwY7777LsuXL2f37t0cOXKEQ4cOAfl/b3z88ce4\nu7sTGBjIkiVLTJbt2bOH8PBwFi1aRFRUFBUqVDDeN086e/YskydPZsaMGcTExGBhYZHttSwiIiIi\nf5zZQ9fGjRs5fvy4yU/x4sUBuHHjBlFRUQQFBWFra8srr7zCm2++yZYtW7Ity8/PD09PTwoWLMj4\n8eP59ttvuXTpElu3bqVz5864ublhbW1Nv379SElJITY2lq+//prKlSvToUMHrK2t6d27N5UqVcq1\nzv3798fa2poGDRqQmppq/N64cWNu377Nw4cP2blzJ56enrRs2RIrKyvatWtHjRo12Lt3b5byPv/8\nc/r06UPlypUpWrQoEyZMMJZFRkZSsmRJevfujZWVFZ6enrRo0YIdO3bk2ba9e/cmNDQUW1tbrl69\nSpEiRYiPj89xfR8fH1599VUKFSqEr68vv/zyC0Cu7desWTNiYmIAiImJoXv37hw7dgyAqKgomjVr\nlmsdd+3ahZeXF61bt8bKyoqmTZvi4+Nj0mM4duxY7O3tqVChAv7+/uzatSvPY7e0tCQgIAArKyta\ntWqFra1tlh63nLRo0YKaNWtiZWVF69atjR7H3NrhaQULFuTYsWPs3bsXW1tbDh48SMOGDSlYsCA7\nduygRYsW/P7771y/fh17e3uT8+Ln54eDgwOlSpWiWrVq+Pr64uDgQNmyZalWrRpXrlz5w++NnGzd\nupV+/fpRvXp1ChYsyNixY/n222+z9ODt3buXxo0b4+npiY2NDSNHjsTW1vYP7UtEREREsvdCn+m6\nevUq6enptGrVyngtLS0Ne3v7bNevWLGi8e/ixYtja2tLQkICV69eJTY2lk8//dRYnpyczNWrV7l5\n8yYODg4m5ZQvXz7XemWGwgIFMjJpsWLFALCwsDDqGBcXx6FDh3B3dze2S0lJwc3NLUt5169fp+z/\nY+/Oo2s69z+OfxJJRFBTIxJNKW3RKjLIUEM05hBiag3VGprSUi6KoARFi5qCSw3XqqnaoMaiRRsu\niakxVAe3MTTpCRrElUQiw/n90eX8nGakttD7fq3Vdc/Ze59nf/eTJ9b53OfZO66ueZ7fZDIpLi7O\nqp3s7GyrPsnPjRs3NGnSJJ08eVLu7u5yd3fPc/nZbRUrVrS8trOzsxxbUP916dJF6enpOnfunGJi\nYt8x3/EAACAASURBVDRt2jTt2rVLcXFx2rdvnyZPnlxgjVevXs3V325ublazUm5ubpbXVapUyXf5\n253Kli0re3t7q+vJyckp9HPS//98Jcne3t4y61hQP/zZuHHjFBERodmzZ2vEiBFq2rSppkyZoscf\nf1x79+7VJ598IumPB4XcvHnT6udy5/lLlCihxx57zPLe1tZWZrP5rn838pOYmKi5c+dqwYIFlm02\nNjYymUyys/v/X/+kpCSr3xMHBwc5Ozvf1bkAAACQt2INXc7OzrKzs9PBgwfl4OAgSbp+/brlXqA/\nu3z5suX1tWvXlJaWpipVqsjZ2Vn9+/e3PERAks6fPy8XFxft2LHD6t4vSQXOBkn/H64Kqz0oKEgz\nZsywbIuPj8/zoReurq5WX9zvDBzOzs5q0KCB1RMKL168qJIlSxZaQ3h4uGrWrKnFixfLzs5OR44c\n0Y4dOwr9XF7Xkl//2draqmnTptq1a5cuXryoWrVqydfXV5s2bdKVK1fk4eFRYNuurq46ceKE1baE\nhARVqVLF8v7OL/wmk8kqhD1IBfXDn/38888KDQ3V6NGj9euvv1pCWEhIiBYuXKjIyEhVr15dkvTa\na69Zfbao4+tufjcKaqdfv37q2rWrZVtcXJzc3d2tHkxSuXJlq3v8srKydOXKlbs6FwAAAPJWrA/S\ncHV1lZeXl2bOnKn09HQlJydryJAhmjNnTp7Hb9myRT/88IMyMjI0Y8YM+fr6ytXVVSEhIYqMjNTp\n06dlNpv19ddfq3379kpMTFSzZs108eJFRUZGKisrS5GRkYqLi/vLtbdr107ffPONoqOjZTabdezY\nMXXo0EGnTp3KdWynTp20cuVKXbhwQWlpaVbX16xZM509e1bbtm1Tdna24uLi1K1bN8sDMRwcHJSS\nkpJnDSkpKXJ0dFSJEiWUmJioefPmSVK+DwrJT0H9d7vGFStWyNvbWzY2NvLz89OqVavUtGlTy2xg\nfoKCghQTE6Pdu3crOztbUVFR2rt3r4KCgizHzJs3T//973919uxZrVy50vLwi/vhdmDJrw/vVFg/\n3GnRokX66KOPlJGRoUqVKqlEiRKqUKGCUlJSZGtrK0dHR2VnZ2vTpk06evToXf+dsLv93chPp06d\ntGLFCl24cEE5OTlatWqVXn755VxPdAwKClJ0dLSioqKUmZmphQsXFqnPAAAAULhif2T87NmzNW3a\nNAUGBio7O1tNmzZVeHh4nsd6enoqPDxccXFx8vPzszwIwsfHR2FhYRo1apRMJpOqVq2quXPnqkaN\nGpKkxYsXa/LkyZo6dar8/f3zXAJ4t6pXr665c+dq5syZOn/+vCpWrKgxY8bI398/17HBwcH65Zdf\n1K1bN5UqVUodO3aU9MfStvLly2vZsmWaNm2aJk6cKCcnJ/Xo0UPdunWTJLVp00b/+Mc/ZDKZtHz5\ncqt2x4wZowkTJmj16tWqWLGiunfvrtOnTysuLk61a9cu8rUU1n+NGzdWamqqfHx8JEl+fn66efNm\nofdzSVK1atW0cOFCffTRRxo5cqSqVq2qWbNmqV69epZj6tatq1atWqlkyZLq06eP2rZtW+TaC+Ps\n7KyAgAC1bt0610Mm/qywfrjTxIkTNX78eDVu3FiSLA/dcHR0VJs2bRQcHCxbW1vVrVtXnTp1uqeg\nfze/G/np2LGjkpOTFRoaqqSkJNWoUUMff/yx1RJHSapZs6ZmzZqladOm6fLlywoKCir03kcAAAAU\njY25oJuAHiJhYWGqUKGCRo8eXdyl3LWffvpJFStWVOXKlSX9sbyrffv2io2NlaOjYzFXd//Fx8er\nRYsWOnHixN/y+pDbl6/1Le4SHllBK1cUdwkAAMBgxf53uv4X7Nu3T6NGjVJqaqrS09O1dOlSNWzY\n8G8ZSG7duqVffvlFTk5Of8vrAwAAAO5WsS8v/F/Qp08fXbhwQc2bN1dmZqZ8fHz+tn98ds2aNZo/\nf74GDBhQ3KUAAAAAD4VHZnkhgIcXywvvHcsLAQD4+2N5IQAAAAAYiNAFAAAAAAYidAEAAACAgQhd\nAAAAAGAgQhcAAAAAGIjQBQAAAAAGInQBAAAAgIEIXQAAAABgIEIXAAAAABiI0AUAAAAABiJ0AQAA\nAICBCF0AAAAAYCBCFwAAAAAYiNAFAAAAAAYidAEAAACAgQhdAAAAAGAgQhcAAAAAGMjGbDabi7sI\nAAAAAPi7YqYLAAAAAAxE6AIAAAAAAxG6AAAAAMBAhC4AAAAAMBChCwAAAAAMROgCAAAAAAMRugAA\nAADAQIQuAAAAADAQoQsAAAAADEToAgAAAAADEboAAAAAwECELgAAAAAwkF1xFwDg0TdtXGRxl/DQ\nGTu1W3GXAAAAHhLMdAEAAACAgQhdAAAAAGAgQhcAAAAAGIjQBQAAAAAGInQBAAAAgIEIXQAAAABg\nIEIXAAAAABiI0AUAAAAABiJ0AQAAAICBCF0AAAAAYCBCFwAAAAAYiNAFAAAAAAYidAEAAACAgQhd\nAAAAAGAgQhcAAAAAGIjQBQAAAAAGInQBAAAAgIEIXQAAAABgIEIXAAAAABiI0AUAAAAABiJ0PWTM\nZrMSEhKKuww8hBgbAAAAj6b7Grpq1aqlM2fO5Nru6+urQ4cOFfr5sLAwTZ8+/X6WJEnq3LmzNm7c\neN/bNcKMGTO0Zs2a4i7jrphMJnl4eCgtLa3QY/MbI3+lzfvlzJkzqlWrVp7nDw8Pl4eHhzp27CiT\nyaTg4GB5eHho6dKlD6y+gsZGSkqKevbsqQYNGmjy5Mlq166d9u3bV2ibvXv31urVq/Pct3r1avXu\n3fsv1QwAAADJrrgLgLVr166pQoUKxV3GXXFzc1NsbOxD3+ZfOf/nn3+uf/3rX/L399emTZuUmpqq\no0ePqkSJEg+spoLGxk8//aTTp0/r4MGDKl269AOrCQAAAIV74MsLk5OTNXLkSPn7+yswMFBLliyR\n2WzOdVxYWJjCw8PVuXNneXh46PXXX9dvv/1m2f/VV1+pffv28vb21uuvv65z585Z9h08eFDt2rWT\nh4eHxowZo8zMzDxrSUhIkK+vr1asWCF/f3/5+voqMjJSH3/8sfz8/NSoUSNt3brVcvyRI0fUpUsX\neXt7q1u3bjp58mSe7ZrNZi1YsED+/v4KCAjQv/71Lz333HOWpWE///yzevfuLW9vbwUHBysqKkqS\ntGLFCm3dulWrVq3SkCFDcrWbnp6uiRMnqmXLlmrQoIFatWql3bt351lDWFiYpkyZop49e8rDw0Od\nO3fW6dOnC+2/Tp06Wa45LS1NdevW1aeffipJunXrljw9PRUfH5+rH2vVqqXU1FRJ0oEDB9S5c2d5\nenqqY8eOluu7bdu2bQoMDJSPj4/mzZun7OzsPH82t9s8dOiQgoOD9cEHH8jHx0dNmzbNd4YpJydH\nc+fOVZs2beTh4aGAgACtW7cu32Nnz54tX19fNW7cWNu3b891/hs3bsjDw0M5OTkaOHCgFixYoPHj\nxysxMVHe3t66dOmSTCaTBg4cKF9fX7Vq1UobNmywtNO7d2+FhYWpUaNGevPNNwvs+4SEBHl7e2vJ\nkiVq1KiR/P39NW3aNEkFj41Dhw6pX79+Sk9PV+PGjRUbG6vAwEB98803klRgfXdKTk7W4MGD5enp\nqfbt2xdpRhIAAACFu++hq3v37vL29rb67/r165b9o0aNko2Njfbs2aOVK1dqy5Yt+S7927Rpk0aP\nHq2YmBg9+eSTGjZsmCTp5MmTGjt2rCZNmqTo6Gi99NJLGjBggDIzM5WUlKRBgwbprbfe0uHDh1W3\nbt0CvzwmJyfrt99+0759+zRixAiFh4fr6tWr2r9/vwYNGqSpU6dK+uOL64ABA/TWW28pJiZG/fr1\nU2hoqJKTk3O1uWHDBm3cuFGffvqptm/friNHjliCRUpKivr37682bdooJiZG7733nkaOHKlz586p\nb9++Cg4OVu/evRUREZGr3eXLlysuLk4bN27UsWPH1LlzZ73//vv5XtvmzZs1YcIERUdHq1q1apo9\ne3ah/RcQEKCDBw9KkmUm5/Dhw5KkY8eOyc3NTe7u7vme8z//+Y/eeustDRw4UIcPH9bw4cM1dOhQ\n/fzzz5ZjYmNjtXHjRm3YsEFbtmzJNwTc6cyZMypXrpwOHjyo8ePHa/bs2bp48WKu47Zs2aKvvvpK\nq1at0nfffacRI0Zo2rRplkB4p08//VS7du3Shg0btH37dh0/fjzXMba2tpYZr8jISA0ePFiTJk1S\nnTp1FBsbq8cff1wDBw7UM888o/379ysiIkJz5sxRTEyMpY3Tp09r586dmjVrVoF9L0k3btxQQkKC\nvvnmGy1atEhr165VbGxsgWPD19dXS5cuVfny5RUbGysPDw/Lvuzs7ELru23ChAmSpP3792vevHn6\n9ttvC/25AAAAoHD3PXStW7dOR48etfqvXLlykqTff/9d+/bt05gxY+Tk5KQnnnhC/fv3V2RkZJ5t\nBQcHy9fXVyVLltS7776rEydOKD4+XuvXr1dISIi8vLxkb2+vPn36KCsrS4cOHdK3336r6tWrq337\n9rK3t1evXr1UrVq1Amvu27ev7O3t5efnp+zsbMv7Jk2a6Nq1a7p586a2bdsmX19ftWjRQnZ2dmrb\ntq2effZZ7dq1K1d7W7Zs0Wuvvabq1aurTJkyGjlypGVfVFSUKlasqF69esnOzk6+vr5q3ry5vvji\ni0L7tlevXoqIiJCTk5MSExNVunRpXbp0Kd/jAwMDVbt2bTk6OiooKEjnz5+XpAL7r1mzZpYv5DEx\nMeratauOHDkiSdq3b5+aNWtWYI3bt2+Xv7+/WrVqJTs7OwUEBCgwMNBqxnD48OEqX7683N3d1bt3\nb6sZpvyUKFFCoaGhsrOzU8uWLeXk5JRrxk2SWrRooU8++USPP/64Ll26pJIlSyojI8Mq+N/25Zdf\nqlevXnriiSdUrly5PGcXC3Pq1CklJiZq2LBhcnBwUO3atdW9e3erMR0YGKiyZcuqbNmyBfb9baGh\noXJwcFCDBg1Uo0YNXbhw4a7rupv6JCkjI0N79+7V4MGDVbp0adWsWVM9evS45/MCAADg/z3Qe7oS\nExNlNpvVsmVLy7acnByVL18+z+OffPJJy+ty5crJyclJSUlJSkxM1KFDh7Rp0ybL/szMTCUmJurK\nlStycXGxaqdq1aoF1nU7FNra/pFBy5YtK0mysbGx1GgymbR//355e3tbPpeVlSUvL69c7V2+fFmu\nrq55nt9kMikuLs6qnezsbKs+yc+NGzc0adIknTx5Uu7u7nJ3d89zaeZtFStWtLy2s7OzHFtQ/3Xp\n0kXp6ek6d+6cYmJiNG3aNO3atUtxcXHat2+fJk+eXGCNV69ezdXfbm5uVrNSbm5ultdVqlTR77//\nXui1ly1bVvb29lbXk5OTk+u4zMxMTZkyRdHR0XJ1dVWdOnUkKc9jk5KSrMbKE088UWgdf2YymZSS\nkiIfHx/LtuzsbD3//POW948//rjldUF9X716dUm5f2551X4/65P+mPHNzMy06o/Cfm8AAABQNA80\ndDk7O8vOzk4HDx6Ug4ODJOn69et5Lv2S/ggvt127dk1paWmqUqWKnJ2d1b9/fw0dOtSy//z583Jx\ncdGOHTus7v2SVOBskPT/4aqw2oOCgjRjxgzLtvj4+DwfbODq6qrExETL+zsDh7Ozsxo0aGD1FLqL\nFy+qZMmShdYQHh6umjVravHixbKzs9ORI0e0Y8eOQj+X17Xk13+2trZq2rSpdu3apYsXL6pWrVry\n9fXVpk2bdOXKFaula3lxdXXViRMnrLYlJCSoSpUqlvd3hh2TyWQVwv6q2bNny2w2a//+/SpZsqRM\nJlO+s4iVK1eWyWSyvC9snOTXhouLi9VSvKSkJKswfOf4Kqjvr1y5ctfnvx/1SVKFChVkb28vk8lk\nGdP30h8AAADI7YE+SMPV1VVeXl6aOXOm0tPTlZycrCFDhmjOnDl5Hr9lyxb98MMPysjI0IwZM+Tr\n6ytXV1eFhIQoMjJSp0+fltls1tdff6327dsrMTFRzZo108WLFxUZGamsrCxFRkYqLi7uL9ferl07\nffPNN4qOjpbZbNaxY8fUoUMHnTp1KtexnTp10sqVK3XhwgWlpaVZXV+zZs109uxZbdu2TdnZ2YqL\ni1O3bt0sD8RwcHBQSkpKnjWkpKTI0dFRJUqUUGJioubNmydJ+T4oJD8F9d/tGlesWCFvb2/Z2NjI\nz89Pq1atUtOmTS2zgfkJCgpSTEyMdu/erezsbEVFRWnv3r0KCgqyHDNv3jz997//1dmzZ7Vy5Up1\n6dLlruovSEpKihwcHFSiRAldu3bN8icIsrKych3boUMHrVq1SufOnVNKSkqe99EVpn79+nJ0dNSy\nZcuUmZmpixcvqm/fvvk+2r2wvi9IQWPjr9bn4OCgtm3bas6cObpx44bOnz+vtWvX3tW5AAAAkLcH\n/vTC2bNn68qVKwoMDFTr1q1VuXJlhYeH53msp6enwsPD5e/vr+vXr1seBOHj46OwsDCNGjVKnp6e\nmjdvnubOnasaNWqoYsWKWrx4sVavXi1vb2/t3bs3zyWAd6t69eqaO3euZs6cKS8vL40ePVpjxoyR\nv79/rmODg4PVvn17devWTW3btrUsk7S3t1f58uW1bNkyffrpp/L19VXfvn3Vo0cPdevWTZLUpk0b\n7dq1S/3798/V7pgxY/Ttt9/K09NTr776qgICAuTk5HTXobKg/pOkxo0bKzU11bIkzc/PTzdv3iz0\nfi5JqlatmhYuXKiFCxfK29tbM2fO1KxZs1SvXj3LMXXr1lWrVq3Ut29fvf7662rbtu1d1V+QIUOG\n6Ndff1XDhg0VEhKiatWq6cknn8yzj7p27aquXbuqZ8+eatGihV544YW7Pp+9vb2WLFmiw4cPq3Hj\nxurcubN8fX01aNCgPI8vrO8LUtDYuB/1hYeHq1y5cgoICFBoaKgCAwOLfB4AAADkz8Zc0E1BxSgs\nLEwVKlTQ6NGji7uUu/bTTz+pYsWKqly5siQpLi5O7du3V2xsrBwdHYu5uvsvPj5eLVq00IkTJ/6W\n14fCTRuX98Nw/peNndqtuEsAAAAPiQc+0/W/YN++fRo1apRSU1OVnp6upUuXqmHDhn/LQHLr1i39\n8ssvcnJy+lteHwAAAPBXPdAHafyv6NOnjy5cuKDmzZsrMzNTPj4+mjlzZnGXZYg1a9Zo/vz5GjBg\nQHGXAgAAADyUHtrlhQAeHSwvzI3lhQAA4DaWFwIAAACAgQhdAAAAAGAgQhcAAAAAGIjQBQAAAAAG\nInQBAAAAgIEIXQAAAABgIEIXAAAAABiI0AUAAAAABiJ0AQAAAICBCF0AAAAAYCBCFwAAAAAYiNAF\nAAAAAAYidAEAAACAgQhdAAAAAGAgQhcAAAAAGIjQBQAAAAAGInQBAAAAgIFszGazubiLAAAAAIC/\nK2a6AAAAAMBAhC4AAAAAMBChCwAAAAAMROgCAAAAAAMRugAAAADAQIQuAAAAADAQoQsAAAAADETo\nAgAAAAADEboAAAAAwECELgAAAAAwEKELAAAAAAxE6AIAAAAAA9kVdwEAHn37tk0s7hLui6btJxZ3\nCQAA4G+ImS4AAAAAMBChCwAAAAAMROgCAAAAAAMRugAAAADAQIQuAAAAADAQoQsAAAAADEToAgAA\nAAADEboAAAAAwECELgAAAAAwEKELAAAAAAxE6AIAAAAAAxG6AAAAAMBAhC4AAAAAMBChCwAAAAAM\nROgCAAAAAAMRugAAAADAQIQuAAAAADAQoQsAAAAADEToAgAAAAADEbpQLOLj4/Pdd/PmTSUlJT3A\nagAAAADjELoMcu7cOb311ltq2LChPDw81KFDB0VGRhbps7Vq1dKZM2fuaz3z58/XkCFD7mubhVm9\nerV69+4tSdqyZYt69eolSfrhhx/Uo0ePfD/Xq1cvnTp16oHUWBS+vr46dOjQAzvf9OnTFRYW9sDO\nBwAAAGPZFXcBf0c5OTl644031LlzZ82ZM0cODg46evSoBg8erMcee0ytW7cu7hIfuA4dOqhDhw6S\npBs3bigzMzPfY5OTkx9UWQAAAIDhmOkywLVr15SQkKAOHTrI0dFRtra28vHx0ciRIy1hIycnRwsW\nLFCTJk3k7e2tt99+W9euXbO08eWXX6p169by8PDQyJEjdevWLUlSUlKSRowYIV9fXwUEBGjGjBmW\nfampqZo0aZIaNWqkRo0aady4cbpx40ah9SYmJmrgwIHy9PRUkyZNtGLFCknSxo0b1bNnT3Xr1k2+\nvr66cOGCTCaTBg4cKF9fX7Vq1UobNmywtJOcnKzBgwfL09NT7du3t5qt27hxozp37qwrV64oNDRU\nycnJ8vDwsLpmSRo0aJBMJpOGDh2qlStXSpI++eQTNW/eXA0bNlS/fv109uxZ5eTkyM/PT999950k\n6fz586pVq5b27dsnSbp8+bLq16+vmzdv6ocfflCfPn3UuHFj1a9fX/369bMsXwwLC9OwYcP00ksv\nKTg4WDk5Odq6dauaN28uT09PzZw506q+rVu3qlWrVmrYsKG6dOmif//733n2aXx8vAYOHKiAgADV\nq1dP3bt3V1xcXJ7HJiQk6PXXX5eHh4e6d++uxMREq/1r165Vq1at5Ovrq0GDBun333+37Pv0008V\nEBCgF198UTNnzlRgYKBlVq5WrVqaNGmSGjZsqI8//ljZ2dlasGCBAgMD5e/vrzFjxiglJcXS1ldf\nfaX27dvL29tbr7/+us6dO5dnvQAAALg7hC4DVKpUST4+Purbt68iIiIUExOjtLQ0devWTe3bt5ck\nffbZZ9q0aZM++eQTHTx4UKVKldKUKVMsbZw5c0YbNmzQ9u3btX//fu3cuVOSNHjwYEnSnj179Pnn\nn+vw4cOKiIiQJE2YMEFnz57V1q1b9eWXXyopKUkTJkwotN6hQ4fK2dlZBw4c0OrVq7Vs2TJLmPju\nu+80fPhw7d69W0888YQGDhyoZ555Rvv371dERITmzJmjmJgYy/klaf/+/Zo3b56+/fbbPPtm6dKl\nKl++vGJjY1WhQgWr/QsXLpSbm5vmzZun1157TZ999pmWL1+uhQsX6sCBA/L09FRoaKhu3bqlJk2a\n6ODBg5Kk6OhoOTo66vDhw5Kkffv2yc/PT6VKldLQoUPVvHlz7d+/X99++61u3Lih1atXW8555MgR\nrVu3TmvXrtWZM2f03nvvadq0aYqJiZGNjY1l5u3mzZsaM2aMZs+erSNHjqhnz54aP368zGZzrut8\n7733VKNGDe3Zs0cxMTGqUKGCFi9enG//P/300zp06JBGjRqlqKgoy74dO3ZoyZIlWrhwofbt2yd3\nd3cNGzbMcs2zZ8/W/Pnz9c033yglJUW//fabVdsZGRk6cOCAevXqpRUrVujrr7/WmjVr9PXXXys9\nPV3vv/++JOnkyZMaO3asJk2apOjoaL300ksaMGBAgTOSAAAAKBpCl0GWLVumV199VYcOHdIbb7wh\nHx8fDR8+3DKzs337dvXu3Vs1atSQg4ODxo0bp4EDB1o+369fP5UpU0Zubm5q0KCBEhIS9Ouvvyo2\nNlbjxo1TmTJl5OLioqFDh+qLL75Qenq6du3apXfffVcVK1ZUuXLlNHr0aO3YsUPp6en51hkfH68T\nJ05o1KhRKlWqlKpVq6ZPPvlEzz33nCTJ2dlZ/v7+Klu2rE6dOqXExEQNGzZMDg4Oql27trp3767I\nyEhlZGRo7969Gjx4sEqXLq2aNWsWeN9WUW3evFmvv/66ateuLQcHB7399tu6deuWDh8+rGbNmik6\nOlqSFBMTo65du1qFrmbNmkmSli9frl69eunmzZu6dOmSKlSooEuXLlnO4evrKxcXF5UtW1a7du1S\nkyZN5OvrKwcHBw0ZMkROTk6WY0uWLKnPP/9csbGx6tixo/bu3SsbG5tcdX/44YcaMmSIsrOzZTKZ\nVL58eatz3tn/33//vaVPPT091a5dO8v+9evXq0+fPnrmmWdUsmRJDR8+XCdOnNC5c+e0ZcsWhYSE\nqF69eipZsqRGjx4tOzvrFcPt2rWTg4ODypQpo/Xr12vw4MFydXVVmTJl9O6772rLli3KyMjQ+vXr\nFRISIi8vL9nb26tPnz7Kysp6oPeyAQAA/F1xT5dBSpYsqT59+qhPnz7KyMjQsWPH9NFHH2ns2LFa\ntGiRkpKSVKVKFcvxFStWVMWKFS3vH3vsMctrBwcHZWdn68qVK3JycrI6zs3NTUlJSbp27ZoyMzNV\ntWpVy76qVavKbDbn+WX/ttttli1b1rLt6aeftrx2dna2vDaZTEpJSZGPj49lW3Z2tp5//nklJycr\nMzNTLi4uVuf/q65cuWLVjq2trVxdXXXp0iW1bt1aY8eOVUpKio4ePaoNGzaodevWun79ug4ePKix\nY8dK+mMWJzQ0VKmpqapVq5auX79u1Yd3XmNSUpLVNTg4OFj2lypVSitXrtSiRYv0xhtvyM7OTv37\n99ebb76Zq+6zZ89q5syZunTpkp5++mnZ2NjkOSP2+++/y8nJSWXKlLFsq1q1qi5cuCDpj6Wfc+fO\n1YIFCyz7bWxsZDKZdPnyZT3zzDOW7U5OTipfvrxV+48//rjldWJiokaNGqUSJUpYttnZ2clkMikx\nMVGHDh3Spk2bLPsyMzNzLXUEAADA3SN0GeDLL7/UrFmztHv3btnY2KhkyZJ68cUX9c4772jy5MmS\nJBcXF6swFB8fr02bNumdd97Jt103NzelpaXp2rVrlmV5CQkJKl++vFxcXOTg4CCTyWQJFAkJCbK1\ntbUKGH/m4uKitLQ03bhxwxK8tm3bZhX6bqtcubJcXFyslg0mJSXJbDarXLlysre3l8lkstRWUNgr\nKjc3N5lMJsv7nJwcmUwmVapUSY899pheeOEFrV27Vi4uLqpSpYrq1Kmj5cuX64knnlCVKlV0kS3J\nuAAAIABJREFU8eJFjR49WmvXrlX9+vUlSWPGjLEKQHfOVFWuXFmnT5+2vM/KytKVK1ckSSkpKUpN\nTdWCBQuUlZWlgwcPatCgQfLx8VGDBg0sn7l165YGDx6sDz74QG3atJEkLViwIM9Zo8qVKystLU3J\nycmWwHRnvzk7O6tfv37q2rWrZVtcXJzc3d21Y8cOq75JT0/P9RCSO6/N2dlZ77//vvz9/SX9Eari\n4+P15JNPytnZWf3799fQoUMtx58/f94qgAIAAODesLzQAP7+/kpNTdXUqVN15coVmc1mXbhwQatW\nrdJLL70kSQoODtbq1av166+/KiMjQxEREZbZjfy4uLjI399f06ZNU2pqqi5duqSIiAgFBwfL1tZW\nHTp00KxZs3T16lVdv35dM2bMUEBAgNUs1p+5urrK29tbs2bNUkZGhs6fP68PP/ww1zI1Sapfv74c\nHR21bNkyZWZm6uLFi+rbt6/WrFkjBwcHtW3bVnPmzNGNGzd0/vx5rV27Ns9zOjg46NatW5YHgPyZ\nvb295QEPISEhWrlypc6cOaNbt27pn//8pyTJz89PktSsWTMtX75cvr6+lu0rV6609HNqaqrMZrMc\nHR1lNpsVFRWlnTt35nuvUlBQkKKjoxUVFaXMzEwtXLjQUktaWpreeOMN7d+/X3Z2dqpcubJsbGxU\nrlw5qzYyMzOVkZGhUqVKSZKOHz+uzz77LM9zPvHEE/Ly8tL06dOVkZGhkydPauvWrZb9nTp10ooV\nK3ThwgXl5ORo1apVevnll3Xz5k2FhIRo8+bNOnXqlG7duqU5c+YoKysrz+u63ZcLFy7U5cuXlZmZ\nqblz5yo0NFRms1khISGKjIzU6dOnZTab9fXXX6t9+/bMdAEAANwHzHQZoEKFClq7dq3mzp2r9u3b\nKy0tTZUqVVJwcLAGDRokSerSpYuuXLmiPn36KCUlRY0aNdKkSZMKbfujjz7S1KlT1bx5c0l/PIp9\nxIgRkv6YwZk5c6Y6dOigjIwMNW/e3LLEriCzZ8/W5MmT1bRpU5UqVUqDBg3Siy++qI0bN1odZ29v\nryVLlmjKlClaunSpSpQooaCgIMs1hYeHKzw8XAEBAapUqZICAwPz/HtjtWrV0tNPPy1fX19t2rRJ\n1apVs9rfqVMnjR8/XvHx8ZanOr799tu6cuWKXnjhBa1YscJyn1WzZs00c+ZMy5JHPz8/LVq0yBK6\natasqbfffluvv/66cnJyVKNGDXXv3t3y8I8/q1mzpmbNmqVp06bp8uXLCgoKstRXuXJlzZgxQ9Om\nTdPFixdVoUIFTZgwQU899ZRVG6VLl9akSZP03nvvKS0tTU8++aReeeUVrVmzRllZWbkC7dy5czVu\n3Dj5+fnJ3d1dLVu2tOzr2LGjkpOTFRoaqqSkJNWoUUMff/yxypUrJ29vb73zzjsaOHCgzGazunXr\nJjs7O9nb2+d5bbcfjPHKK6/ov//9r5577jl9/PHHsrOzk4+Pj8LCwjRq1CiZTCZVrVpVc+fOVY0a\nNfJsCwAAAEVnY87rRhMAD72zZ8/K3t5e7u7ukv54umKDBg20c+fOXEHQaPu2TXyg5zNK0/YTi7sE\nAADwN8TyQuAR9eOPP+qtt97S1atXlZmZqcWLF8vd3V3Vq1cv7tIAAABwB5YXAo+ooKAg/fjjj+rQ\noYPS0tL0/PPPa9GiRXk+wh4AAADFh+WFAP4ylhcCAADkj+WFAAAAAGAgQhcAAAAAGIjQBQAAAAAG\nInQBAAAAgIEIXQAAAABgIEIXAAAAABiI0AUAAAAABiJ0AQAAAICBCF0AAAAAYCBCFwAAAAAYiNAF\nAAAAAAYidAEAAACAgQhdAAAAAGAgQhcAAAAAGIjQBQAAAAAGInQBAAAAgIEIXQAAAABgIBuz2Wwu\n7iIAAAAA4O+KmS4AAAAAMBChCwAAAAAMROgCAAAAAAMRugAAAADAQIQuAAAAADAQoQsAAAAADETo\nAgAAAAADEboAAAAAwECELgAAAAAwEKELAAAAAAxE6AIAAAAAAxG6AAAAAMBAdsVdAIBH3/Avooq7\nhHs2u1NAcZcAAAD+5pjpAgAAAAADEboAAAAAwECELgAAAAAwEKELAAAAAAxE6AIAAAAAAxG6AAAA\nAMBAhC4AAAAAMBChCwAAAAAMROgCAAAAAAMRugAAAADAQIQuAAAAADAQoQsAAAAADEToAgAAAAAD\nEboAAAAAwECELgAAAAAwEKELAAAAAAxE6AIAAAAAAxG6AAAAAMBAhC4AAAAAMBChCwAAAAAMROh6\nxJnNZiUkJBTb+bOysnTx4sViO/+9iI+PL+4SDHfx4kVlZWUVdxkAAAAQoavIatWqpfr168vDw0Me\nHh7y9PRU//79debMmWKta8aMGVqzZo0kyWQyycPDQ2lpaQ/s/MOHD9fu3bsf2Pn+qj179mjYsGHF\nXUaBPDw8FBcXd8+fT0pKUps2bZSRkSFJmjBhgubMmXO/ygMAAMBdsivuAh4lkZGRevbZZyVJmZmZ\nmj17tkJDQ7V3716VKFGiWGq6du2aKlSoIElyc3NTbGzsAz//o+T69evKyckp7jIK9Fd/hunp6bp5\n86bl/eTJk/9qSQAAAPgLmOm6R/b29urcubMuXryo69eva+PGjerZs6e6desmX19fXbhwQd9//71e\nffVVeXl5qU2bNtq4caPl84GBgfrkk0/UqlUrNWjQQBMmTFBUVJRatmwpLy8vTZs2zXJsfu2sWLFC\nW7du1apVqzRkyBAlJCSoVq1aSk1NlSRt27ZNQUFB8vLyUvfu3XXixAlJUkJCgry9vbVkyRI1atRI\n/v7+Vuf7s5UrVyo4OFheXl568cUXNX/+fEnS1KlTdfToUX344Yf68MMPc30uIyNDU6ZMkZ+fn3x9\nfTVmzBhlZGTkqlOSOnfubLmuwMBAjR8/Xr6+vgoPD9f8+fM1YMAABQUFqWnTpkpJSdHPP/+s3r17\ny9vbW8HBwYqKirLq2yVLlqh169by8vLSgAEDdP36dZ08eVLh4eH68ccf1ahRozyvtaA+8/LyUlhY\nmLy9vbV582ZdunRJ/fv3l6enp7p06aLp06erd+/ekv4IPhMnTlTLli3VoEEDtWrVyjIjeOjQIQUH\nB+uDDz6Qj4+PmjZtqqVLl1pqqFWrls6cOaPFixdbZlY9PDxUt25d1alTR+np6bp27ZpGjBihwMBA\n1a9fX8HBwTp27JgkqUuXLpKkxo0b64cfflBYWJimT58u6Y9ZsBEjRsjX11cBAQGaMWOGbt26JUkK\nCwvTlClT1LNnT3l4eKhz5846ffp0vuMCAAAARUPoukfXr1/XqlWr9Oyzz6pixYqSpO+++86y3K5s\n2bLq06ePWrdurZiYGE2fPl3Tp0/Xvn37LG3s3LlTkZGR2rBhg9avX69ly5Zpw4YNWrNmjdasWaP/\n/Oc/unr1ar7t9O3bV8HBwerdu7ciIiKs6tu/f78mTJigSZMm6dChQ+ratav69++v33//XZJ048YN\nJSQk6JtvvtGiRYu0du3aPGdYjh49qsWLF2v+/Pk6duyYIiIitHDhQl24cEHjxo2Tt7e3wsLCFBYW\nluuz8+fP1/Hjx7V582bt2bNHv/32mxYuXFik/jWZTIqKitLIkSMlSTExMZo7d662b98uSerfv7/a\ntGmjmJgYvffeexo5cqTOnTtn+fzu3bu1du1a7dy5U+fPn9e6detUr149TZo0SXXq1NGBAwdynbOw\nPktJSVHVqlV18OBBtWrVSsOHD1eVKlUUHR2tSZMmWYXq5cuXKy4uThs3btSxY8fUuXNnvf/++5b9\nZ86cUbly5XTw4EGNHz9es2fPznVv3MCBAxUbG6vY2Fjt27dPTz75pIYNGyZHR0fNnDlTkvTll1/q\nyJEj8vLy0qxZsyRJGzZskCT9+9//1nPPPWfV5uDBgyX9sczy888/1+HDh63GzubNmzVhwgRFR0er\nWrVqmj17dpF+XgAAAMgfoesudO/eXd7e3vL29lbbtm11+fJlqy+szs7O8vf3V9myZbVnzx5VqVJF\nvXv3lr29verXr6+XX35ZX3zxheX4l19+WeXKlVPNmjXl7Oysrl276rHHHlPt2rXl7Owsk8lUpHby\nsmXLFoWEhKhhw4ays7NT165dVbNmTav7r0JDQ+Xg4KAGDRqoRo0aunDhQq52nn/+eW3cuFHVq1dX\nUlKSMjMz5ejoqMuXLxfaX9u3b9fAgQPl4uKiMmXKaMaMGeratWtRulqtW7eWo6OjypQpI0mqU6eO\nnn32WZUtW1ZRUVGqWLGievXqJTs7O/n6+qp58+ZWffLKK6+oUqVKcnZ2VpMmTXT+/PlCz1mUPgsO\nDpaDg4OuXbumo0ePatSoUSpZsqTq1q2rl19+2XJcr169FBERIScnJyUmJqp06dK6dOmSZX+JEiUU\nGhoqOzs7tWzZUk5OTvk+4CM7O1v/+Mc/VKdOHb355puSpGHDhmnSpEkqUaKETCaTHnvsMav28/Lr\nr78qNjZW48aNU5kyZeTi4qKhQ4da9VtgYKBq164tR0dHBQUFFanfAAAAUDDu6boL69ats9zTlRdn\nZ2fL66tXr6pq1apW+93c3HT06FHL+3LlyllelyhRQo899pjlva2trXJycorUTl6uXr2q2rVr5/rc\nnbMpt2foJMnOzi7Pe51sbW31z3/+U7t27VKlSpVUt25dSSrSfVFJSUmqUqWK5f3t10V52uLjjz9u\n9f7OvjWZTIqLi5O3t7dlW3Z2tlq2bGl5f+e12dvby2w2F3rOovTZ7bouX74sJycnq5+hm5ubjh8/\nLumPmcRJkybp5MmTcnd3l7u7u1UNZcuWlb29veV9fv0vSR988IGSk5OtZgkvX76sqVOnKi4uTk89\n9ZTKly9f6DVeuXJFTk5OVn3j5uZmCdNS7jFRlH4DAABAwQhdBnF1dZXJZLLalpCQYBUmbGxs7ks7\nd/M5T0/PQs95pxUrVujMmTOWJZOZmZn68ssvi/RZFxcXXbp0yRLUTp06pePHj6tFixaSZPmiL0nJ\nyclWn/1z39z53tnZWQ0aNLA8tVH64xHpJUuWvKtr+7Oi9NntOlxdXZWWlqbr169bgted4Sw8PFw1\na9bU4sWLZWdnpyNHjmjHjh13XdO6deu0c+dObdiwQY6Ojpbtw4cP1yuvvKI1a9bIxsZGmzZtKvRJ\nmm5ubkpLS7N6+EpCQoLKly9vFQABAABwf7G80CABAQH6/ffftWbNGmVlZenEiROKjIxUcHDwfW3H\nwcFBKSkpuT4XEhKiTZs26ejRo8rKytL69ev1yy+/WAJPUaWkpMje3l729vZKTU3V9OnTlZmZafkb\nUPmdX/pjKd6SJUuUlJSkGzduaNasWUpKSlKlSpVUtmxZ7d69W2azWV988UWusFOQZs2a6ezZs9q2\nbZuys7MVFxenbt26FenR9Q4ODkpNTc1zBudu+szFxUUvvviiZs6cqYyMDJ05c0br16+37E9JSZGj\no6NKlCihxMREzZs3T5J10CxMdHS0ZsyYoQULFsjFxcVqX0pKikqVKiUbGxvFxcVp2bJllrYdHBws\nx/y55tsPTUlNTdWlS5cUERFx12MSAAAAd4fQZZBy5cpp2bJl2r59u3x8fDRixAiNGDFCrVq1uq/t\ntGnTRrt27VL//v2tPuft7a2JEydqwoQJatiwodatW6elS5fK1dX1rs7ft29f2dnZyd/fX61bt9at\nW7fk6elp+TtS7du318cff6zx48fn+uxbb70lDw8PhYSEqGXLlqpevboGDRokBwcHhYeHa8mSJfL2\n9tbhw4fVrFmzItdUvnx5LVu2TJ9++ql8fX3Vt29f9ejRQ926dSv0sw0bNrT87+2/Y3Xb3fbZ1KlT\nFR8fLz8/P40dO1Z+fn6WGaMxY8bo22+/laenp1599VUFBATIycnprv7+1qJFi5SZmakBAwZYPcXw\n6NGjmjx5spYvXy5PT08NHjxYISEhunbtmq5duyZnZ2cFBARYHr5yp48++khZWVlq3ry5OnbsKC8v\nL8vDSgAAAGAMGzM3bQD3JDo62vLQDUmaOXOmLl68aHmK4P+S4V9EFX7QQ2p2p4DiLgEAAPzNMdMF\n3KNJkyZp/fr1MpvNOn/+vLZu3aomTZoUd1kAAAB4yBC6gHs0a9YsffHFF/Ly8tJrr72mV155RR07\ndizusgAAAPCQ4emFwD16/vnn9dlnnxV3GQAAAHjIMdMFAAAAAAYidAEAAACAgQhdAAAAAGAgQhcA\nAAAAGIjQBQAAAAAGInQBAAAAgIEIXQAAAABgIEIXAAAAABiI0AUAAAAABiJ0AQAAAICBCF0AAAAA\nYCBCFwAAAAAYiNAFAAAAAAYidAEAAACAgQhdAAAAAGAgQhcAAAAAGIjQBQAAAAAGsjGbzebiLgIA\nAAAA/q6Y6QIAAAAAAxG6AAAAAMBAhC4AAAAAMBChCwAAAAAMROgCAAAAAAMRugAAAADAQIQuAAAA\nADAQoQsAAAAADEToAgAAAAADEboAAAAAwECELgAAAAAwEKELAAAAAAxE6AIAAAAAAxG6AAAAAMBA\nhC4AAAAAMBChC8A9++GHH9S1a1c1aNBAHTt21PHjx4u7pEfK8uXLVbduXXl4eFj+O3r0aHGX9Ug4\nefKkGjdubHl//fp1DRo0SF5eXmrWrJkiIyOLsbqH35/779SpU6pTp47VWFy8eHExVvhwOnr0qLp1\n6yYvLy+1aNFC69atk8T4K6r8+o/xVzRffvml2rZtKw8PD7Vr1067d++WxPgrqvz674GNPzMA3IP0\n9HRzkyZNzGvWrDHfunXLHBkZafbz8zOnpKQUd2mPjOHDh5uXLVtW3GU8UnJycsyRkZFmLy8vs4+P\nj2X7O++8Y3733XfN6enp5hMnTph9fHzMsbGxxVjpwym//vvss8/Mb775ZjFW9vBLTk42N2zY0Lxl\nyxZzdna2+fvvvzc3bNjQfODAAcZfERTUf4y/wp09e9Zcv35987Fjx8xms9l84MAB8/PPP2++cuUK\n468ICuq/BzX+mOkCcE9iYmJka2urnj17yt7eXl27dtXjjz+uqKio4i7tkfHjjz+qTp06xV3GI2Xx\n4sVauXKlBg4caNmWmpqq3bt3a8iQISpZsqTq1aun9u3ba9OmTcVY6cMpr/6T/pi1rl27djFV9Wgw\nmUwKCAhQcHCwbG1t9fzzz8vX11ffffcd468ICuo/xl/hnnrqKR04cECenp7KyspSUlKSSpcuLQcH\nB8ZfERTUfw9q/BG6ANyTc+fOqWbNmlbbnnrqKZ09e7aYKnq03Lx5U+fOndPKlSvVqFEjtW3bVuvX\nry/ush56Xbp00ebNm/XCCy9Ytl24cEF2dnZyd3e3bGMs5i2v/pP++D8AvvvuOwUGBqpZs2aaPn26\nbt26VUxVPpzq1KmjmTNnWt5fv37dshyY8Ve4/Pqvdu3ajL8iKl26tOLj41WvXj2NGjVKw4YN06+/\n/sr4K6K8+q9MmTIPbPwRugDck7S0NJUqVcpqm6Ojo9LT04upokdLUlKSvLy81KNHD33zzTd6//33\n9eGHHzJTWIjKlSvLxsbGaltaWpocHR2ttjEW85ZX/0lShQoVFBgYqG3btmnVqlU6dOiQIiIiiqHC\nR8ONGzc0cOBAy2wN4+/u3Nl/gYGBjL+74OrqqhMnTmjFihWaPn269u7dy/i7C3/uv+jo6Ac2/ghd\nAO5JqVKlcv2jnp6eLicnp2Kq6NHi7u6u1atXKyAgQA4ODvL29lbHjh21Z8+e4i7tkVOqVCllZGRY\nbWMs3p3Fixerb9++cnJykru7uwYMGKCvv/66uMt6KMXHx6t79+4qV66cFixYICcnJ8bfXfhz/9na\n2jL+7oKdnZ3s7e3l7++vVq1a6fvvv2f83YU/99+ePXse2PgjdAG4JzVq1NC5c+estp07d05PP/10\nMVX0aDl9+rSWLFlitS0jI0MODg7FVNGjq1q1asrMzJTJZLJsYywW3fXr1zV9+nSlpKRYtmVkZKhk\nyZLFWNXD6fTp03r55ZfVuHFj/fOf/5SjoyPj7y7k1X+Mv6KJiopSnz59rLZlZmbqySefZPwVQX79\nZzabH9j4I3QBuCf+/v66deuWVq1apczMTK1fv15JSUlWj6FG/pycnLRgwQLt3LlTOTk5io6O1vbt\n29WpU6fiLu2RU6ZMGTVv3lyzZs3SzZs3dfLkSW3btk3BwcHFXdojoWzZsvr666+1YMECZWZm6sKF\nC1q8eLE6d+5c3KU9VJKSkvTGG2+ob9++GjNmjGxt//gKxfgrmvz6j/FXNM8995y+//57bdq0STk5\nOYqKilJUVJReeeUVxl8R5Nd/PXr0eGDjz8ZsNpvve6sA/if89NNPmjhxon7++WdVq1ZNEydOVIMG\nDYq7rEfG3r17NWfOHMXHx8vFxUXDhg1TmzZtirusR8KhQ4c0ZMgQHTp0SJKUnJys8PBwRUdHy8nJ\nSYMHD1bXrl2LucqH15/775dfftGUKVN06tQpOTo66pVXXtE777yT5/1f/6sWL16sOXPm5Fq29dpr\nr6lv376Mv0IU1H/BwcGMvyI4evSopk2bpvPnz6t69eoaNWqU/Pz8+PeviPLrvwf17x+hCwAAAAAM\nxPJCAAAAADAQoQsAAAAADEToAgAAAAADEboAAAAAwECELgAAAAAwEKELAAAAAAxkV9wFAACAR1Ng\nYKB+++03y3s7Ozs5OzurXbt2+sc//iF7e/tirC63+Ph4/ec//1FgYGBxlwLgfwyhCwAA3LN3331X\nISEhkqTs7Gx9//33GjlypJycnDRo0KBirs7a2LFjVb9+fUIXgAeO5YUAAOCelSlTRs7OznJ2dlaV\nKlXUokULBQcH66uvviru0gDgoUHoAgAA95WdnZ1laeGGDRvUqlUr1a9fX126dNHBgwctx/Xu3VuT\nJ09W69at1ahRIyUkJCg5OVmjR49Ww4YN5evrq7Fjx+rmzZuSpMzMTE2fPl0vvviivL29NWDAAMXH\nx1vaCwwM1KpVq/Tqq6/qhRdeUOvWrRUVFSVJCgsL0+HDh7V06VL17t1bknTixAn17t1bDRo0UL16\n9dSjRw/9/PPPlvZ++ukn9ejRQ/Xq1VPHjh21YsUKq1myuLg49evXzzJ7NnfuXGVmZhrXsQAeWYQu\nAABwX2RnZys6OlqbN29W8+bNFRUVpRkzZmjEiBHasmWLQkJCNGDAAKtgExkZqYkTJ2rRokV64okn\n9M477yguLk7Lli3T8uXLdfz4cc2YMUOSNGfOHB06dEjz58/XZ599JmdnZ7322mtKT0+3tBcREaGe\nPXtq+/btqlWrlsaOHavMzEyNGzdOHh4eevXVVzV//nylpKQoNDRUDRo00NatW7V27Vrl5ORo+vTp\nkqQbN26oX79+ql69ur744gv17dtXERERlvNkZGTojTfe0LPPPqtNmzZp2rRp2rlzp+bMmfOAehvA\no4R7ugAAwD2bNm2aJRRlZGSoRIkSCg4OVv/+/dW3b1+FhoaqdevWkv6Y2Tp+/LhWrlypqVOnSpIa\nNWokf39/SdJ//vMfHT58WNu3b9fTTz8tSZo8ebJOnTql9PR0rVq1SmvWrFG9evUs+5o1a6Zdu3ap\nY8eOkqTg4GAFBQVJkt5++2117NhRFy/+Xzv3EhL1FsBx/Ouj0TFNkXwkTiaDLlopKSI+VrZQRAX/\nIBpEaUht3IQmjBvNlehSwSeihiS4yZyNi6BcCO2UfNTYyOioIdIipJjUuYu4f5q08HYZLl1/Hxg4\nc87/f+bM2f04j11sNhsXLlzAarUSFxfH3t4ejY2N1NfXExoais1mwzAMM1g5nU5CQ0Npb2/HYrFg\nt9txuVw4nU4AZmZmiIqKorW1FYD09HTa2tp48OABDx8+JCwsLOhzLyJ/DoUuERER+W3379+nvLwc\nAIvFwuXLl82thS6Xi8XFRXp7e83nv379aoYmgNTUVLPscrmwWq1m4ALIyckhJyeHt2/f4vP5uH37\nNiEhIWb7ly9fcLvd5vdr166Z5ejoaPM3f5SQkIBhGIyPj7O6uorb7ebNmzdcunQJgLW1Na5fv47F\nYjHfycrKMkPX+vo6breb7Oxss93v9+Pz+fB6vVy9evUs0yci54RCl4iIiPy2+Ph40tLSTm07Ojqi\npaWF4uLigPrvg0xkZKRZ/tUV80dHRwCMjY0RGxsb0BYTE/PLPvx+/4m6Dx8+UF1dTWZmJkVFRVRU\nVPD+/Xv6+vqAb+fSjo+Pfzqew8NDbty4QWdn54m25OTkn74nIueTznSJiIhIUNjtdra3t0lLSzM/\n09PTzM3Nnfp8eno6nz9/Dli5evHiBaWlpdhsNsLDw9nf3zf7SklJoaenJ+CM2FnNzs4SGRnJyMgI\nd+/eJT8/H6/Xawa0jIwM1tbWAlbJlpaWAv7bxsYGV65cMcezs7NDT0/PqSFPRM43hS4REREJinv3\n7vHkyROmpqbweDyMjo4yODgYsAXwe3a7ncLCQhwOB8vLyywuLtLd3U1BQQHR0dHU1tby+PFjXr16\nxcbGBm1tbSwsLGC32880nosXL+LxeNjf3ycpKYm9vT1evnzJ1tYWk5OTTExM4PP5ACgvL+f4+JiO\njg7W19dxOp2Mj4+bfVVUVADfbkV89+4dr1+/xuFwEB4eTkRExL+bOBH531HoEhERkaC4efMmDoeD\noaEhysrKePr0KV1dXSe2G36vq6uLxMREbt26RWNjI3l5eTQ3NwPQ0tJCSUkJjx49oqqqiq2tLYaH\nh0lMTDzTeGpqalhYWKC+vp7S0lIMw6C5uZmqqiqeP39OR0cHnz59wuPxYLVa6e/vZ2VlhcrKSgYH\nBzEMw9y+GBUVxfDwMB8/fsQwDJqamigoKDh1u6GISIhfa+AiIiIiATY3N9nd3SU3N9esGxgYYH5+\nnrGxsf9wZCLyJ9JKl4iIiMgPDg4OuHPnDs+ePcPr9Zph6+/r6EVE/gmtdImIiIicYnq7W/flAAAA\nYUlEQVR6moGBAba3t0lISKCuro6GhoaAK+tFRM5CoUtERERERCSItL1QREREREQkiBS6RERERERE\ngkihS0REREREJIgUukRERERERIJIoUtERERERCSIFLpERERERESC6C9IMWSsmqWVTQAAAABJRU5E\nrkJggg==\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x1115912e8>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"benefits = [#'Benefit: Have not taken MOOCs', \n",
" 'Benefit: Not Really',\n",
" 'Benefit: School credit towards a degree',\n",
" 'Benefit: Promotion at current organization',\n",
" 'Benefit: Higher performance evaluation at current job',\n",
" 'Benefit: Helped me get a new job in the same field',\n",
" 'Benefit: Helped me get a new job in a different field']\n",
"multi_select_categorical_barh(df,\n",
" question='Have you received any tangible benefits from taking MOOCs?', \n",
" selectors=benefits)"
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"# new charts on aspects"
]
},
{
"cell_type": "code",
"execution_count": 8,
"metadata": {
"scrolled": true
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
" select percentage\n",
"0 Actively contributing to discussion forums 30.429546\n",
"1 Browsing discussion forums 53.873946\n",
"2 Connecting with learners outside the course en... 15.335207\n",
"3 Connecting with other learners in the course e... 38.297872\n",
"4 Taking the course with other people you know (... 15.174629\n"
]
},
{
"data": {
"image/png": "iVBORw0KGgoAAAANSUhEUgAAA/kAAAHMCAYAAACObWg7AAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nrkJggg==\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x11157e5c0>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"aspects = ['Aspects: Browsing discussion forums', \n",
" 'Aspects: Actively contributing to discussion forums',\n",
" 'Aspects: Connecting with other learners in the course environment',\n",
" 'Aspects: Connecting with learners outside the course environment',\n",
" 'Aspects: Taking the course with other people you know (friends, colleagues, etc.)']\n",
"multi_select_categorical_barh(df,\n",
" question='Which of the following are important aspects of the MOOC experience to you?', \n",
" selectors=aspects)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Value of MOOCs"
]
},
{
"cell_type": "code",
"execution_count": 9,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"# MOOCs finished vs importance certificate - heatmap"
]
}
],
"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
}
| mit |
JohannesUIBK/GlaThiDa | DBF_to_GlaThiDa_Farinotti.ipynb | 1 | 58782 | {
"cells": [
{
"cell_type": "code",
"execution_count": 1,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"import pandas as pd\n",
"from glob import glob\n",
"from simpledbf import Dbf5\n",
"import os\n",
"import pyproj\n",
"import numpy as np\n",
"import geopandas as gpd\n",
"import warnings"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"def dist_pyth(lon1, lat1, lon2, lat2):\n",
" \"\"\"Calculate the distance between two points \"\"\"\n",
" return np.sqrt((lat2 - lat1)**2 + (lon2 - lon1)**2)\n",
"\n",
"\n",
"def haversine(lon1, lat1, lon2, lat2):\n",
" \"\"\"\n",
" Calculate the great circle distance between one point \n",
" on the earth and an array of points (specified in decimal degrees)\n",
" \"\"\"\n",
" \n",
" # convert decimal degrees to radians \n",
" lon1, lat1, lon2, lat2 = map(np.radians, [lon1, lat1, lon2, lat2])\n",
"\n",
" # haversine formula \n",
" dlon = lon2 - lon1\n",
" dlat = lat2 - lat1\n",
" a = np.sin(dlat/2)**2 + np.cos(lat1) * np.cos(lat2) * np.sin(dlon/2)**2\n",
" c = 2 * np.arcsin(np.sqrt(a)) \n",
" r = 6371000 # Radius of earth in meters\n",
" return c * r\n",
"\n",
"\n",
"def p_dist(lon1, lat1, lon2, lat2, units=None):\n",
" \"\"\"\n",
" Calculate the distance between two *CLOSE* points in meters using Pythagoras\n",
" \"\"\"\n",
" if units == 'm':\n",
" dist = dist_pyth(lon1, lat1, lon2, lat2)\n",
" elif units == 'deg':\n",
" dist = haversine(lon1, lat1, lon2, lat2)\n",
" else:\n",
" raise ValueError('Units must be meters (m) or degrees (deg).')\n",
" \n",
" return dist\n",
"\n",
"\n",
"def p_thin(df, xcol='x', ycol='y', datacols='data', radius=10, method='nanmean', units=None):\n",
" \"\"\"\n",
" Thin a pandas point series based on distance of the points.\n",
" \n",
" Parameters\n",
" ----------\n",
" pseries: Series of points\n",
" xcol: name of column with x coordinates\n",
" ycol: name of columns with y coordinates\n",
" datacol: name fo columns with data\n",
" radius: search radius for point distance\n",
" method: calculation method (a numpy method). Default: 'nanmean'\n",
" \n",
" Returns\n",
" -------\n",
" A thinned dataframe\n",
" \n",
" \"\"\"\n",
" \n",
" assert units!=None,'Units must be meters (m) or degrees (deg).'\n",
" \n",
" thinned = pd.DataFrame([],columns=df.columns.values)\n",
" \n",
" print(len(df))\n",
" ct=0\n",
" while len(df)>0:\n",
" df['DIST'] = p_dist(df[xcol].iloc[0], df[ycol].iloc[0], df[xcol], df[ycol], units=units)\n",
" df = df.sort_values('DIST')\n",
"\n",
" subset = df[df.DIST <= radius]\n",
" \n",
" if not subset.empty:\n",
" subset = subset.reset_index()\n",
" fill_dict = {}\n",
" fill_dict[xcol] = subset.loc[int(len(subset)/2), xcol]\n",
" fill_dict[ycol] = subset.loc[int(len(subset)/2), ycol]\n",
" for data in datacols:\n",
" try:\n",
" fill_dict[data] = getattr(np,method)(subset[data].values)\n",
" except TypeError:\n",
" fill_dict[data] = np.nan\n",
" thinned.loc[len(thinned)] = pd.Series(fill_dict)\n",
" ct+=1\n",
"\n",
" if ct%100==0:\n",
" print(ct, len(df))\n",
" # delete the used points from the data\n",
" df = df.drop(df[df.DIST <= radius].index.values)\n",
" \n",
" print(len(thinned))\n",
" return thinned"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# GlaThiDa templates"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"templ_t = pd.read_excel('C:\\\\Users\\\\jlandman\\\\Desktop\\\\GlaThiDa_2-0_DataSubmissionForm_for_pandas.xls', sheetname='T - GLACIER THICKNESS OVERVIEW') \n",
"templ_tt = pd.read_excel('C:\\\\Users\\\\jlandman\\\\Desktop\\\\GlaThiDa_2-0_DataSubmissionForm_for_pandas.xls', sheetname='TT - GL. THICKNESS DISTRIBUTION')\n",
"templ_ttt = pd.read_excel('C:\\\\Users\\\\jlandman\\\\Desktop\\\\GlaThiDa_2-0_DataSubmissionForm_for_pandas.xls', sheetname='TTT - GL. THICKNESS POINT DATA')"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# New GlaThiDa IDs for the treated glaciers"
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"gtd_ids = {'brewster': 2077,\n",
" 'north': 2078,\n",
" 'south':2079,\n",
" 'starbuck_gprt':2080,\n",
" 'starbuck_icebr':2081,\n",
" 'tasman': 2082,\n",
" 'urumqui':2083,\n",
" 'west_washmawapta':2084}"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Brewster glacier"
]
},
{
"cell_type": "code",
"execution_count": 52,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"nzgd = pyproj.Proj(init='epsg:2193') # NZGD2000_New_Zealand_Transverse_Mercator_2000\n",
"latlon = pyproj.Proj(init='epsg:4326') #WGS84 lat/lon"
]
},
{
"cell_type": "code",
"execution_count": 53,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"# constants\n",
"brew = {'punit': 'NZ',\n",
"'gname': 'BREWSTER',\n",
"'src_id':'',\n",
"'src_g_id':'',\n",
"'survey_date': '19979999',\n",
"'dem_date': '19860399',\n",
"'gid': gtd_ids['brewster'],\n",
"'lat': -44.07,\n",
"'lon': 169.43,\n",
"'area': 2.62,\n",
"'mean_sl': 15,\n",
"'mean_th': np.nan,\n",
"'mean_th_unc': np.nan,\n",
"'max_th': np.nan,\n",
"'max_th_unc': np.nan,\n",
"'surv_meth': 'GPRt',\n",
"'surv_meth_det': '',\n",
"'no_points': 588,\n",
"'no_prfls': 4,\n",
"'length_prfls': np.nan,\n",
"'interp_meth':'',\n",
"'investig':'Bob JACOBEL and Ian OWENS; compiled by Brian ANDERSON',\n",
"'spons_ag':'',\n",
"'ref':'Willis, I. et al. (2009). Hydrological Processes, 23(3), p.384-396. DOI: 10.1002/hyp.7146',\n",
"'dflag':np.nan,\n",
"'remarks_t':'Source_ID: Outline and DEM from aerial photography for the NZMS260 mapping series flown in 3/1986. Lat/lon from WGMS FoG2015. Data unpublished, but used in given reference. Mean slope calculated from DEM by WGMS.',\n",
"'remarks_ttt':''\n",
" }"
]
},
{
"cell_type": "code",
"execution_count": 55,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"brewster_path = 'C:\\\\Users\\\\jlandman\\\\Desktop\\\\newData\\\\WG_Farinotti_data_package\\\\brewster\\\\brewster_jacobel_owens_bed_xyz.csv'\n",
"\n",
"# read original data\n",
"brew_dat = pd.read_csv(brewster_path)\n",
"\n",
"# convert New Real TM to WGS84 lat/lon\n",
"xs = brew_dat['nztm_easting (m)'].values\n",
"ys = brew_dat[' nztm_northing (m)'].values\n",
"x1,y1 = nzgd(xs, ys)\n",
"lons, lats = pyproj.transform(nzgd,latlon,xs,ys)\n",
"\n",
"# fill T table\n",
"brew_t = templ_t.copy()\n",
"brew_t.loc[0,'GlaThiDa_ID'] = brew['gid']\n",
"brew_t.loc[0,'POLITICAL_UNIT'] = brew['punit']\n",
"brew_t.loc[0,'GLACIER_NAME'] = brew['gname']\n",
"brew_t.loc[0,'SOURCE_ID'] = brew['src_id']\n",
"brew_t.loc[0,'GLACIER_ID'] = brew['src_g_id']\n",
"brew_t.loc[0,'LAT'] = brew['lat']\n",
"brew_t.loc[0,'LON'] = brew['lon']\n",
"brew_t.loc[0,'SURVEY_DATE'] = brew['survey_date']\n",
"brew_t.loc[0,'DEM_DATE'] = brew['dem_date']\n",
"brew_t.loc[0,'AREA'] = brew['area']\n",
"brew_t.loc[0,'MEAN_SLOPE'] = brew['mean_sl']\n",
"brew_t.loc[0,'MEAN_THICKNESS'] = brew['mean_th']\n",
"brew_t.loc[0,'MEAN_THICKNESS_UNCERTAINTY'] = brew['mean_th_unc']\n",
"brew_t.loc[0,'MAXIMUM_THICKNESS'] = brew['max_th']\n",
"brew_t.loc[0,'MAX_THICKNESS_UNCERTAINTY'] = brew['max_th_unc']\n",
"brew_t.loc[0,'SURVEY_METHOD'] = brew['surv_meth']\n",
"brew_t.loc[0,'SURVEY_METHOD_DETAILS'] = brew['surv_meth_det']\n",
"brew_t.loc[0,'NUMBER_OF_SURVEY_POINTS'] = brew['no_points']\n",
"brew_t.loc[0,'NUMBER_OF_SURVEY_PROFILES'] = brew['no_prfls']\n",
"brew_t.loc[0,'TOTAL_LENGTH_OF_SURVEY_PROFILES'] = brew['length_prfls']\n",
"brew_t.loc[0,'INTERPOLATION_METHOD'] = brew['interp_meth']\n",
"brew_t.loc[0,'INVESTIGATOR'] = brew['investig']\n",
"brew_t.loc[0,'SPONSORING_AGENCY'] = brew['spons_ag']\n",
"brew_t.loc[0,'REFERENCES'] = brew['ref']\n",
"brew_t.loc[0,'DATA_FLAG'] = brew['dflag']\n",
"brew_t.loc[0,'REMARKS'] = brew['remarks_t']\n",
"\n",
"\n",
"# fill TTT table\n",
"brew_ttt = templ_ttt.copy()\n",
"brew_ttt['POINT_ID'] = range(1,len(brew_dat)+1)\n",
"brew_ttt['POINT_LAT'] = lats\n",
"brew_ttt['POINT_LON'] = lons\n",
"brew_ttt['ELEVATION'] = brew_dat[' surface_elevation (m asl)'].round()\n",
"brew_ttt['THICKNESS'] = brew_dat[' ice_thickness (m)'].round()\n",
"brew_ttt['THICKNESS_UNCERTAINTY'] = np.nan\n",
"brew_ttt['GlaThiDa_ID'] = brew['gid']\n",
"brew_ttt['POLITICAL_UNIT'] = brew['punit']\n",
"brew_ttt['GLACIER_NAME'] = brew['gname']\n",
"brew_ttt['SURVEY_DATE'] = brew['survey_date']\n",
"brew_ttt['DATA_FLAG'] = brew['dflag']\n",
"brew_ttt['REMARKS'] = brew['remarks_ttt'] \n",
"\n",
"brew_t = brew_t[['GlaThiDa_ID', 'POLITICAL_UNIT', 'GLACIER_NAME', 'SOURCE_ID', 'GLACIER_ID', 'LAT', 'LON', 'SURVEY_DATE', \n",
" 'DEM_DATE', 'AREA', 'MEAN_SLOPE', 'MEAN_THICKNESS', 'MEAN_THICKNESS_UNCERTAINTY', 'MAXIMUM_THICKNESS', \n",
" 'MAX_THICKNESS_UNCERTAINTY', 'SURVEY_METHOD', 'SURVEY_METHOD_DETAILS', 'NUMBER_OF_SURVEY_POINTS',\n",
" 'NUMBER_OF_SURVEY_PROFILES', 'TOTAL_LENGTH_OF_SURVEY_PROFILES', 'INTERPOLATION_METHOD', 'INVESTIGATOR', \n",
" 'SPONSORING_AGENCY', 'REFERENCES', 'DATA_FLAG', 'REMARKS']]\n",
"brew_ttt = brew_ttt[['GlaThiDa_ID','POLITICAL_UNIT','GLACIER_NAME','SURVEY_DATE','POINT_ID','POINT_LAT','POINT_LON',\n",
" 'ELEVATION','THICKNESS','THICKNESS_UNCERTAINTY','DATA_FLAG','REMARKS']]\n",
"\n",
"brew_t.to_excel(os.path.join(os.path.dirname(brewster_path),brewster_path.split('.')[0]+'_Johannes_T.xls'), index=False)\n",
"brew_ttt.to_excel(os.path.join(os.path.dirname(brewster_path),brewster_path.split('.')[0]+'_Johannes_TTT.xls'), index=False)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# North glacier"
]
},
{
"cell_type": "code",
"execution_count": 8,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"utm7n = pyproj.Proj(init='epsg:32607') # UTM 7N (WGS84)\n",
"latlon = pyproj.Proj(init='epsg:4326') #WGS84 lat/lon"
]
},
{
"cell_type": "code",
"execution_count": 30,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"# constants\n",
"north = {'punit': 'CA',\n",
"'gname': 'NORTH',\n",
"'src_id':'',\n",
"'src_g_id':'',\n",
"'survey_date': '20119999',\n",
"'dem_date': '20079999',\n",
"'gid': gtd_ids['north'],\n",
"'lat': 60.911031,\n",
"'lon': -139.150803,\n",
"'area': 6.9,\n",
"'mean_sl': np.nan,\n",
"'mean_th': np.nan,\n",
"'mean_th_unc': np.nan,\n",
"'max_th': np.nan,\n",
"'max_th_unc': np.nan,\n",
"'surv_meth': 'OTH',\n",
"'surv_meth_det': 'Radar sounding data at center frequencies of 10, 35, and 50MHz were collected using the ice-penetrating radar system described by Mingo and Flowers [2010].',\n",
"'no_points': 7277,\n",
"'no_prfls': np.nan,\n",
"'length_prfls': np.nan,\n",
"'interp_meth':'KRG',\n",
"'investig':'Nat WILSON, Glenn FLOWERS, Laurent MINGO',\n",
"'spons_ag':'',\n",
"'ref':'Wilson, N. J., Flowers, G. E., & Mingo, L. (2013). Journal of Geophysical Research: Earth Surface, 118(3), pp.1443-1459. DOI:10.1002/jgrf.20096',\n",
"'dflag':np.nan,\n",
"'remarks_t':'Lat/lon from Google Earth. Survey methods: GPRt and DRIh',\n",
"'remarks_ttt':''\n",
" }\n",
"\n",
"#thin_value_north = 7\n",
"search_dist_north = 10 # "
]
},
{
"cell_type": "code",
"execution_count": 57,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"7278\n",
"100 6980\n",
"200 6740\n",
"300 6508\n",
"400 6299\n",
"500 6073\n",
"600 5815\n",
"700 5576\n",
"800 5173\n",
"900 4839\n",
"1000 4567\n",
"1100 4344\n",
"1200 4090\n",
"1300 3826\n",
"1400 3581\n",
"1500 3296\n",
"1600 2946\n",
"1700 2698\n",
"1800 2452\n",
"1900 2231\n",
"2000 2001\n",
"2100 1773\n",
"2200 1527\n",
"2300 1342\n",
"2400 1059\n",
"2500 777\n",
"2600 530\n",
"2700 318\n",
"2800 34\n",
"2816\n"
]
}
],
"source": [
"north_path = 'C:\\\\Users\\\\jlandman\\\\Desktop\\\\newData\\\\WG_Farinotti_data_package\\\\north_and_south_glacier\\\\depth_GL2_080911.xyz'\n",
"\n",
"# read original data\n",
"north_dat = pd.read_csv(north_path, header=None, sep='\\t') # 0:easting (m), 1:northing (m), 2:depth (m), 3:estimated picking error (m) \n",
"\n",
"# convert New Real TM to WGS84 lat/lon\n",
"xs = north_dat[0].values\n",
"ys = north_dat[1].values\n",
"x1,y1 = utm7n(xs, ys)\n",
"lons, lats = pyproj.transform(utm7n,latlon,xs,ys)\n",
"\n",
"# fill T table\n",
"north_t = templ_t.copy()\n",
"north_t.loc[0,'GlaThiDa_ID'] = north['gid']\n",
"north_t.loc[0,'POLITICAL_UNIT'] = north['punit']\n",
"north_t.loc[0,'GLACIER_NAME'] = north['gname']\n",
"north_t.loc[0,'SOURCE_ID'] = north['src_id']\n",
"north_t.loc[0,'GLACIER_ID'] = north['src_g_id']\n",
"north_t.loc[0,'LAT'] = north['lat']\n",
"north_t.loc[0,'LON'] = north['lon']\n",
"north_t.loc[0,'SURVEY_DATE'] = north['survey_date']\n",
"north_t.loc[0,'DEM_DATE'] = north['dem_date']\n",
"north_t.loc[0,'AREA'] = north['area']\n",
"north_t.loc[0,'MEAN_SLOPE'] = north['mean_sl']\n",
"north_t.loc[0,'MEAN_THICKNESS'] = north['mean_th']\n",
"north_t.loc[0,'MEAN_THICKNESS_UNCERTAINTY'] = north['mean_th_unc']\n",
"north_t.loc[0,'MAXIMUM_THICKNESS'] = north['max_th']\n",
"north_t.loc[0,'MAX_THICKNESS_UNCERTAINTY'] = north['max_th_unc']\n",
"north_t.loc[0,'SURVEY_METHOD'] = north['surv_meth']\n",
"north_t.loc[0,'SURVEY_METHOD_DETAILS'] = north['surv_meth_det']\n",
"north_t.loc[0,'NUMBER_OF_SURVEY_POINTS'] = north['no_points']\n",
"north_t.loc[0,'NUMBER_OF_SURVEY_PROFILES'] = north['no_prfls']\n",
"north_t.loc[0,'TOTAL_LENGTH_OF_SURVEY_PROFILES'] = north['length_prfls']\n",
"north_t.loc[0,'INTERPOLATION_METHOD'] = north['interp_meth']\n",
"north_t.loc[0,'INVESTIGATOR'] = north['investig']\n",
"north_t.loc[0,'SPONSORING_AGENCY'] = north['spons_ag']\n",
"north_t.loc[0,'REFERENCES'] = north['ref']\n",
"north_t.loc[0,'DATA_FLAG'] = north['dflag']\n",
"north_t.loc[0,'REMARKS'] = north['remarks_t']\n",
"\n",
"\n",
"# fill TTT table and thin data\n",
"north_ttt = templ_ttt.copy()\n",
"north_ttt['POINT_LAT'] = lats\n",
"north_ttt['POINT_LON'] = lons\n",
"north_ttt['ELEVATION'] = np.nan\n",
"north_ttt['THICKNESS'] = (north_dat[2]).astype(np.float64)\n",
"north_ttt['THICKNESS_UNCERTAINTY'] = (north_dat[3]).astype(np.float64)\n",
"# old point thinning\n",
"#north_ttt = north_ttt.groupby(north_ttt.index // thin_value_north).mean() # average over every x values as specified above\n",
"# thin the data and filter \"mean on empyt slice\" warnings (in case there is no elevation)\n",
"with warnings.catch_warnings():\n",
" warnings.simplefilter(\"ignore\", category=RuntimeWarning)\n",
" north_ttt = p_thin(north_ttt, xcol='POINT_LON', ycol='POINT_LAT', datacols=['ELEVATION', 'THICKNESS', \n",
" 'THICKNESS_UNCERTAINTY'], \n",
" radius=search_dist_north, method='nanmean', units='deg')\n",
"north_ttt['POINT_ID'] = range(1,len(north_ttt)+1)\n",
"north_ttt['GlaThiDa_ID'] = north['gid']\n",
"north_ttt['POLITICAL_UNIT'] = north['punit']\n",
"north_ttt['GLACIER_NAME'] = north['gname']\n",
"north_ttt['SURVEY_DATE'] = north['survey_date']\n",
"north_ttt['DATA_FLAG'] = north['dflag']\n",
"north_ttt['REMARKS'] = north['remarks_ttt'] + ' Point data have been thinned (mean) within a search distance of %s m.' % search_dist_north\n",
"\n",
"# do the reformatting again (doesn't work in combination with astype)\n",
"north_ttt['THICKNESS'] = north_ttt['THICKNESS'].round()\n",
"north_ttt['THICKNESS_UNCERTAINTY'] = north_ttt['THICKNESS_UNCERTAINTY'].round(2)\n",
"\n",
"north_t = north_t[['GlaThiDa_ID', 'POLITICAL_UNIT', 'GLACIER_NAME', 'SOURCE_ID', 'GLACIER_ID', 'LAT', 'LON', 'SURVEY_DATE', \n",
" 'DEM_DATE', 'AREA', 'MEAN_SLOPE', 'MEAN_THICKNESS', 'MEAN_THICKNESS_UNCERTAINTY', 'MAXIMUM_THICKNESS', \n",
" 'MAX_THICKNESS_UNCERTAINTY', 'SURVEY_METHOD', 'SURVEY_METHOD_DETAILS', 'NUMBER_OF_SURVEY_POINTS',\n",
" 'NUMBER_OF_SURVEY_PROFILES', 'TOTAL_LENGTH_OF_SURVEY_PROFILES', 'INTERPOLATION_METHOD', 'INVESTIGATOR', \n",
" 'SPONSORING_AGENCY', 'REFERENCES', 'DATA_FLAG', 'REMARKS']]\n",
"north_ttt = north_ttt[['GlaThiDa_ID','POLITICAL_UNIT','GLACIER_NAME','SURVEY_DATE','POINT_ID','POINT_LAT','POINT_LON',\n",
" 'ELEVATION','THICKNESS','THICKNESS_UNCERTAINTY','DATA_FLAG','REMARKS']]\n",
"\n",
"north_t.to_excel(os.path.join(os.path.dirname(north_path),north_path.split('.')[0]+'_Johannes_T.xls'), index=False)\n",
"north_ttt.to_excel(os.path.join(os.path.dirname(north_path),north_path.split('.')[0]+'_Johannes_TTT.xls'), index=False)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# South glacier"
]
},
{
"cell_type": "code",
"execution_count": 58,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"utm7n = pyproj.Proj(init='epsg:32607') # UTM 7N (WGS84)\n",
"latlon = pyproj.Proj(init='epsg:4326') #WGS84 lat/lon"
]
},
{
"cell_type": "code",
"execution_count": 59,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"# constants\n",
"south = {'punit': 'CA',\n",
"'gname': 'SOUTH',\n",
"'src_id':'',\n",
"'src_g_id':'',\n",
"'survey_date': '20119999',\n",
"'dem_date': '20079999',\n",
"'gid': gtd_ids['south'],\n",
"'lat': 60.817771,\n",
"'lon': -139.124004,\n",
"'area': 5.3,\n",
"'mean_sl': np.nan,\n",
"'mean_th': np.nan,\n",
"'mean_th_unc': np.nan,\n",
"'max_th': np.nan,\n",
"'max_th_unc': np.nan,\n",
"'surv_meth': 'OTH',\n",
"'surv_meth_det': 'Radar sounding data at center frequencies of 10, 35, and 50MHz were collected using the ice-penetrating radar system described by Mingo and Flowers [2010].',\n",
"'no_points': 9618,\n",
"'no_prfls': np.nan,\n",
"'length_prfls': np.nan,\n",
"'interp_meth':'KRG',\n",
"'investig':'Nat WILSON, Glenn FLOWERS, Laurent MINGO',\n",
"'spons_ag':'',\n",
"'ref':'Wilson, N. J., Flowers, G. E., & Mingo, L. (2013). Journal of Geophysical Research: Earth Surface, 118(3), pp.1443-1459. DOI:10.1002/jgrf.20096',\n",
"'dflag':np.nan,\n",
"'remarks_t':'Lat/lon from Google Earth. Survey methods: GPRt and DRIh',\n",
"'remarks_ttt':''\n",
" }\n",
"\n",
"search_dist_south = 10"
]
},
{
"cell_type": "code",
"execution_count": 60,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"9619\n",
"100 9347\n",
"200 9125\n",
"300 8840\n",
"400 8535\n",
"500 8158\n",
"600 7821\n",
"700 7517\n",
"800 7212\n",
"900 6900\n",
"1000 6674\n",
"1100 6371\n",
"1200 6039\n",
"1300 5807\n",
"1400 5525\n",
"1500 5357\n",
"1600 5126\n",
"1700 4758\n",
"1800 4445\n",
"1900 4187\n",
"2000 3875\n",
"2100 3599\n",
"2200 3331\n",
"2300 3082\n",
"2400 2797\n",
"2500 2524\n",
"2600 2269\n",
"2700 1848\n",
"2800 1666\n",
"2900 1403\n",
"3000 1152\n",
"3100 883\n",
"3200 610\n",
"3300 311\n",
"3400 71\n",
"3422\n"
]
}
],
"source": [
"south_path = 'C:\\\\Users\\\\jlandman\\\\Desktop\\\\newData\\\\WG_Farinotti_data_package\\\\north_and_south_glacier\\\\depth_GL1_080911.xyz'\n",
"\n",
"# read original data\n",
"south_dat = pd.read_csv(south_path, header=None, sep='\\t') # 0:easting (m), 1:northing (m), 2:depth (m), 3:estimated picking error (m) \n",
"\n",
"# convert New Real TM to WGS84 lat/lon\n",
"xs = south_dat[0].values\n",
"ys = south_dat[1].values\n",
"x1,y1 = utm7n(xs, ys)\n",
"lons, lats = pyproj.transform(utm7n,latlon,xs,ys)\n",
"\n",
"# fill T table\n",
"south_t = templ_t.copy()\n",
"south_t.loc[0,'GlaThiDa_ID'] = south['gid']\n",
"south_t.loc[0,'POLITICAL_UNIT'] = south['punit']\n",
"south_t.loc[0,'GLACIER_NAME'] = south['gname']\n",
"south_t.loc[0,'SOURCE_ID'] = south['src_id']\n",
"south_t.loc[0,'GLACIER_ID'] = south['src_g_id']\n",
"south_t.loc[0,'LAT'] = south['lat']\n",
"south_t.loc[0,'LON'] = south['lon']\n",
"south_t.loc[0,'SURVEY_DATE'] = south['survey_date']\n",
"south_t.loc[0,'DEM_DATE'] = south['dem_date']\n",
"south_t.loc[0,'AREA'] = south['area']\n",
"south_t.loc[0,'MEAN_SLOPE'] = south['mean_sl']\n",
"south_t.loc[0,'MEAN_THICKNESS'] = south['mean_th']\n",
"south_t.loc[0,'MEAN_THICKNESS_UNCERTAINTY'] = south['mean_th_unc']\n",
"south_t.loc[0,'MAXIMUM_THICKNESS'] = south['max_th']\n",
"south_t.loc[0,'MAX_THICKNESS_UNCERTAINTY'] = south['max_th_unc']\n",
"south_t.loc[0,'SURVEY_METHOD'] = south['surv_meth']\n",
"south_t.loc[0,'SURVEY_METHOD_DETAILS'] = south['surv_meth_det']\n",
"south_t.loc[0,'NUMBER_OF_SURVEY_POINTS'] = south['no_points']\n",
"south_t.loc[0,'NUMBER_OF_SURVEY_PROFILES'] = south['no_prfls']\n",
"south_t.loc[0,'TOTAL_LENGTH_OF_SURVEY_PROFILES'] = south['length_prfls']\n",
"south_t.loc[0,'INTERPOLATION_METHOD'] = south['interp_meth']\n",
"south_t.loc[0,'INVESTIGATOR'] = south['investig']\n",
"south_t.loc[0,'SPONSORING_AGENCY'] = south['spons_ag']\n",
"south_t.loc[0,'REFERENCES'] = south['ref']\n",
"south_t.loc[0,'DATA_FLAG'] = south['dflag']\n",
"south_t.loc[0,'REMARKS'] = south['remarks_t']\n",
"\n",
"\n",
"# fill TTT table and thin data\n",
"south_ttt = templ_ttt.copy()\n",
"south_ttt['POINT_ID'] = range(1,len(south_dat)+1)\n",
"south_ttt['POINT_LAT'] = lats\n",
"south_ttt['POINT_LON'] = lons\n",
"south_ttt['ELEVATION'] = np.nan\n",
"south_ttt['THICKNESS'] = (south_dat[2]).astype(np.float64)\n",
"south_ttt['THICKNESS_UNCERTAINTY'] = (south_dat[3]).astype(np.float64)\n",
"\n",
"# old point thinning \n",
"# south_ttt = south_ttt.groupby(south_ttt.index // thin_value_south).mean() # average over every x values as specified above\n",
"with warnings.catch_warnings():\n",
" warnings.simplefilter(\"ignore\", category=RuntimeWarning)\n",
" south_ttt = p_thin(south_ttt, xcol='POINT_LON', ycol='POINT_LAT', datacols=['ELEVATION', 'THICKNESS', \n",
" 'THICKNESS_UNCERTAINTY'], \n",
" radius=search_dist_south, method='nanmean', units='deg')\n",
"south_ttt['GlaThiDa_ID'] = south['gid']\n",
"south_ttt['POLITICAL_UNIT'] = south['punit']\n",
"south_ttt['GLACIER_NAME'] = south['gname']\n",
"south_ttt['SURVEY_DATE'] = south['survey_date']\n",
"south_ttt['DATA_FLAG'] = south['dflag']\n",
"south_ttt['REMARKS'] = south['remarks_ttt'] \n",
"\n",
"# do the reformatting again (doesn't work in combination with astype)\n",
"south_ttt['THICKNESS'] = south_ttt['THICKNESS'].round()\n",
"south_ttt['THICKNESS_UNCERTAINTY'] = south_ttt['THICKNESS_UNCERTAINTY'].round(2)\n",
"south_t = south_t[['GlaThiDa_ID', 'POLITICAL_UNIT', 'GLACIER_NAME', 'SOURCE_ID', 'GLACIER_ID', 'LAT', 'LON', 'SURVEY_DATE', \n",
" 'DEM_DATE', 'AREA', 'MEAN_SLOPE', 'MEAN_THICKNESS', 'MEAN_THICKNESS_UNCERTAINTY', 'MAXIMUM_THICKNESS', \n",
" 'MAX_THICKNESS_UNCERTAINTY', 'SURVEY_METHOD', 'SURVEY_METHOD_DETAILS', 'NUMBER_OF_SURVEY_POINTS',\n",
" 'NUMBER_OF_SURVEY_PROFILES', 'TOTAL_LENGTH_OF_SURVEY_PROFILES', 'INTERPOLATION_METHOD', 'INVESTIGATOR', \n",
" 'SPONSORING_AGENCY', 'REFERENCES', 'DATA_FLAG', 'REMARKS']]\n",
"south_ttt = south_ttt[['GlaThiDa_ID','POLITICAL_UNIT','GLACIER_NAME','SURVEY_DATE','POINT_ID','POINT_LAT','POINT_LON',\n",
" 'ELEVATION','THICKNESS','THICKNESS_UNCERTAINTY','DATA_FLAG','REMARKS']]\n",
"\n",
"south_t.to_excel(os.path.join(os.path.dirname(south_path),south_path.split('.')[0]+'_Johannes_T.xls'), index=False)\n",
"south_ttt.to_excel(os.path.join(os.path.dirname(south_path),south_path.split('.')[0]+'_Johannes_TTT.xls'), index=False)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Starbuck glacier GPRt and ICEBRIDGE measurements"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Starbuck GPRt"
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"aqps = pyproj.Proj(init='epsg:3031') # Antarctica polar stereographic\n",
"latlon = pyproj.Proj(init='epsg:4326') #WGS84 lat/lon"
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"# constants\n",
"starb = {'punit': 'AQ',\n",
"'gname': 'STARBUCK',\n",
"'src_id':'',\n",
"'src_g_id':'',\n",
"'survey_date': '20121299',\n",
"'dem_date': '20079999',\n",
"'gid': gtd_ids['starbuck_gprt'],\n",
"'lat': -65.614439,\n",
"'lon': -62.418465,\n",
"'area': 258,\n",
"'mean_sl': np.nan,\n",
"'mean_th': 312,\n",
"'mean_th_unc': 30,\n",
"'max_th': 1020,\n",
"'max_th_unc': np.nan,\n",
"'surv_meth': 'GPRt',\n",
"'surv_meth_det': 'Deep-Look Radio-Echo Sounder (DELORES), 1-4 MHz range (3MHz central frequ.)',\n",
"'no_points': 1315,\n",
"'no_prfls': 41,\n",
"'length_prfls': 90,\n",
"'interp_meth':'',\n",
"'investig':'Daniel FARINOTTI and colleagues',\n",
"'spons_ag':'',\n",
"'ref':'Farinotti, D. et al. (2014). Annals of Glaciology, 55(67), pp. 22-28. DOI:10.3189/2014AoG67A025',\n",
"'dflag':np.nan,\n",
"'remarks_t':'Lat/lon from Google Earth. DEM from Cook et al. (2012) (doi: 10.5194/essd-4-129-2012)',\n",
"'remarks_ttt':''\n",
" }\n",
"\n",
"search_dist_starbuck = 10"
]
},
{
"cell_type": "code",
"execution_count": 31,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"1315\n",
"100 1216\n",
"200 1116\n",
"300 1016\n",
"400 916\n",
"500 816\n",
"600 716\n",
"700 613\n",
"800 512\n",
"900 412\n",
"1000 311\n",
"1100 210\n",
"1200 109\n",
"1300 9\n",
"1308\n"
]
}
],
"source": [
"starb_path = 'C:\\\\Users\\\\jlandman\\\\Desktop\\\\newData\\\\WG_Farinotti_data_package\\\\starbuck\\\\starbuck_RES_data_including_MCoRDS.txt'\n",
"\n",
"# read original data\n",
"starb_dat = pd.read_csv(starb_path, comment='#', delim_whitespace=True) \n",
"\n",
"# select here the GPRt measurements\n",
"starb_dat = starb_dat[starb_dat.orgnm != 'icebridge']\n",
"\n",
"# convert New Real TM to WGS84 lat/lon\n",
"xs = starb_dat['lon'].values\n",
"ys = starb_dat['lat'].values\n",
"x1,y1 = aqps(xs, ys)\n",
"lons, lats = pyproj.transform(aqps,latlon,xs,ys)\n",
"\n",
"# fill T table\n",
"starb_t = templ_t.copy()\n",
"starb_t.loc[0,'GlaThiDa_ID'] = starb['gid']\n",
"starb_t.loc[0,'POLITICAL_UNIT'] = starb['punit']\n",
"starb_t.loc[0,'GLACIER_NAME'] = starb['gname']\n",
"starb_t.loc[0,'SOURCE_ID'] = starb['src_id']\n",
"starb_t.loc[0,'GLACIER_ID'] = starb['src_g_id']\n",
"starb_t.loc[0,'LAT'] = starb['lat']\n",
"starb_t.loc[0,'LON'] = starb['lon']\n",
"starb_t.loc[0,'SURVEY_DATE'] = starb['survey_date']\n",
"starb_t.loc[0,'DEM_DATE'] = starb['dem_date']\n",
"starb_t.loc[0,'AREA'] = starb['area']\n",
"starb_t.loc[0,'MEAN_SLOPE'] = starb['mean_sl']\n",
"starb_t.loc[0,'MEAN_THICKNESS'] = starb['mean_th']\n",
"starb_t.loc[0,'MEAN_THICKNESS_UNCERTAINTY'] = starb['mean_th_unc']\n",
"starb_t.loc[0,'MAXIMUM_THICKNESS'] = starb['max_th']\n",
"starb_t.loc[0,'MAX_THICKNESS_UNCERTAINTY'] = starb['max_th_unc']\n",
"starb_t.loc[0,'SURVEY_METHOD'] = starb['surv_meth']\n",
"starb_t.loc[0,'SURVEY_METHOD_DETAILS'] = starb['surv_meth_det']\n",
"starb_t.loc[0,'NUMBER_OF_SURVEY_POINTS'] = starb['no_points']\n",
"starb_t.loc[0,'NUMBER_OF_SURVEY_PROFILES'] = starb['no_prfls']\n",
"starb_t.loc[0,'TOTAL_LENGTH_OF_SURVEY_PROFILES'] = starb['length_prfls']\n",
"starb_t.loc[0,'INTERPOLATION_METHOD'] = starb['interp_meth']\n",
"starb_t.loc[0,'INVESTIGATOR'] = starb['investig']\n",
"starb_t.loc[0,'SPONSORING_AGENCY'] = starb['spons_ag']\n",
"starb_t.loc[0,'REFERENCES'] = starb['ref']\n",
"starb_t.loc[0,'DATA_FLAG'] = starb['dflag']\n",
"starb_t.loc[0,'REMARKS'] = starb['remarks_t']\n",
"\n",
"\n",
"# fill TTT table\n",
"starb_ttt = templ_ttt.copy()\n",
"starb_ttt['POINT_LAT'] = lats\n",
"starb_ttt['POINT_LON'] = lons\n",
"starb_ttt['ELEVATION'] = starb_dat['z_surf']\n",
"starb_ttt['THICKNESS'] = starb_dat['ice_th']\n",
"starb_ttt['THICKNESS_UNCERTAINTY'] = np.nan\n",
"\n",
"with warnings.catch_warnings():\n",
" warnings.simplefilter(\"ignore\", category=RuntimeWarning)\n",
" starb_ttt = p_thin(starb_ttt, xcol='POINT_LON', ycol='POINT_LAT', datacols=['ELEVATION', 'THICKNESS',\n",
" 'THICKNESS_UNCERTAINTY'], \n",
" radius=search_dist_starbuck, method='nanmean', units='deg')\n",
"\n",
"starb_ttt['THICKNESS'] = starb_ttt['THICKNESS'].round()\n",
"starb_ttt['THICKNESS_UNCERTAINTY'] = starb_ttt['THICKNESS_UNCERTAINTY'].round()\n",
"starb_ttt['ELEVATION'] = starb_ttt['ELEVATION'].round()\n",
"starb_ttt['POINT_ID'] = range(1,len(starb_ttt)+1)\n",
"starb_ttt['GlaThiDa_ID'] = starb['gid']\n",
"starb_ttt['POLITICAL_UNIT'] = starb['punit']\n",
"starb_ttt['GLACIER_NAME'] = starb['gname']\n",
"starb_ttt['SURVEY_DATE'] = starb['survey_date']\n",
"starb_ttt['DATA_FLAG'] = starb['dflag']\n",
"starb_ttt['REMARKS'] = starb['remarks_ttt'] + ' Point data have been thinned (mean) within a search distance of %s m.' % search_dist_starbuck\n",
"starb_t = starb_t[['GlaThiDa_ID', 'POLITICAL_UNIT', 'GLACIER_NAME', 'SOURCE_ID', 'GLACIER_ID', 'LAT', 'LON', 'SURVEY_DATE', \n",
" 'DEM_DATE', 'AREA', 'MEAN_SLOPE', 'MEAN_THICKNESS', 'MEAN_THICKNESS_UNCERTAINTY', 'MAXIMUM_THICKNESS', \n",
" 'MAX_THICKNESS_UNCERTAINTY', 'SURVEY_METHOD', 'SURVEY_METHOD_DETAILS', 'NUMBER_OF_SURVEY_POINTS',\n",
" 'NUMBER_OF_SURVEY_PROFILES', 'TOTAL_LENGTH_OF_SURVEY_PROFILES', 'INTERPOLATION_METHOD', 'INVESTIGATOR', \n",
" 'SPONSORING_AGENCY', 'REFERENCES', 'DATA_FLAG', 'REMARKS']]\n",
"starb_ttt = starb_ttt[['GlaThiDa_ID','POLITICAL_UNIT','GLACIER_NAME','SURVEY_DATE','POINT_ID','POINT_LAT','POINT_LON',\n",
" 'ELEVATION','THICKNESS','THICKNESS_UNCERTAINTY','DATA_FLAG','REMARKS']]\n",
"\n",
"starb_t.to_excel(os.path.join(os.path.dirname(starb_path),starb_path.split('.')[0]+'_GPRt_Johannes_T.xls'), index=False)\n",
"starb_ttt.to_excel(os.path.join(os.path.dirname(starb_path),starb_path.split('.')[0]+'_GPRt_Johannes_TTT.xls'), index=False)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Starbuck glacier ICEBRIDGE"
]
},
{
"cell_type": "code",
"execution_count": 16,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"# constants\n",
"starb_i = {'punit': 'AQ',\n",
"'gname': 'STARBUCK',\n",
"'src_id':'',\n",
"'src_g_id':'',\n",
"'survey_date': '20111014',\n",
"'dem_date': '20079999',\n",
"'gid': gtd_ids['starbuck_icebr'],\n",
"'lat': -65.614439,\n",
"'lon': -62.418465,\n",
"'area': 258,\n",
"'mean_sl': np.nan,\n",
"'mean_th': np.nan,\n",
"'mean_th_unc': np.nan,\n",
"'max_th': np.nan,\n",
"'max_th_unc': np.nan,\n",
"'surv_meth': 'OTH',\n",
"'surv_meth_det': 'Deep-Look Radio-Echo Sounder (DELORES), 1-4 MHz range (3MHz central frequ.)',\n",
"'no_points': 103,\n",
"'no_prfls': np.nan,\n",
"'length_prfls': np.nan,\n",
"'interp_meth':'',\n",
"'investig':'',\n",
"'spons_ag':'',\n",
"'ref':'Farinotti, D. et al. (2014). Annals of Glaciology, 55(67), pp. 22-28. DOI:10.3189/2014AoG67A025',\n",
"'dflag':np.nan,\n",
"'remarks_t':'Survey Method: NASA IceBridge project (MCoRDS sensor). Data used as base for cross-validation in given reference. DEM from Cook et al. (2012) (doi: 10.5194/essd-4-129-2012)',\n",
"'remarks_ttt':''\n",
" }"
]
},
{
"cell_type": "code",
"execution_count": 29,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
" pn lon lat z_surf ice_th qGPS orgnm\n",
"1315 42 -2379060.68 1247358.92 459.9 101.0 1 icebridge\n",
"1316 42 -2379126.81 1247461.36 454.4 179.2 1 icebridge\n",
"1317 42 -2379192.66 1247564.34 453.2 270.1 1 icebridge\n",
"1318 42 -2379258.07 1247667.41 451.8 344.4 1 icebridge\n",
"1319 42 -2379323.15 1247770.63 450.3 436.4 1 icebridge\n",
" GlaThiDa_ID POLITICAL_UNIT GLACIER_NAME SURVEY_DATE POINT_ID POINT_LAT \\\n",
"0 NaN NaN NaN NaN 1 -65.635913 \n",
"1 NaN NaN NaN NaN 2 -65.634978 \n",
"2 NaN NaN NaN NaN 3 -65.634043 \n",
"3 NaN NaN NaN NaN 4 -65.633111 \n",
"4 NaN NaN NaN NaN 5 -65.632181 \n",
"\n",
" POINT_LON ELEVATION THICKNESS THICKNESS_UNCERTAINTY DATA_FLAG REMARKS \n",
"0 -62.331649 460.0 101.0 NaN NaN NaN \n",
"1 -62.330368 454.0 179.0 NaN NaN NaN \n",
"2 -62.329075 453.0 270.0 NaN NaN NaN \n",
"3 -62.327777 452.0 344.0 NaN NaN NaN \n",
"4 -62.326472 450.0 436.0 NaN NaN NaN \n"
]
}
],
"source": [
"starb_path = 'C:\\\\Users\\\\jlandman\\\\Desktop\\\\newData\\\\WG_Farinotti_data_package\\\\starbuck\\\\starbuck_RES_data_including_MCoRDS.txt'\n",
"\n",
"# read original data\n",
"starb_i_dat = pd.read_csv(starb_path, comment='#', delim_whitespace=True) \n",
"\n",
"# select here the GPRt measurements\n",
"starb_i_dat = starb_i_dat[starb_i_dat.orgnm == 'icebridge']\n",
"\n",
"# convert New Real TM to WGS84 lat/lon\n",
"xs = starb_i_dat['lon'].values\n",
"ys = starb_i_dat['lat'].values\n",
"x1,y1 = aqps(xs, ys)\n",
"lons, lats = pyproj.transform(aqps,latlon,xs,ys)\n",
"\n",
"# fill T table\n",
"starb_i_t = templ_t.copy()\n",
"starb_i_t.loc[0,'GlaThiDa_ID'] = starb_i['gid']\n",
"starb_i_t.loc[0,'POLITICAL_UNIT'] = starb_i['punit']\n",
"starb_i_t.loc[0,'GLACIER_NAME'] = starb_i['gname']\n",
"starb_i_t.loc[0,'SOURCE_ID'] = starb_i['src_id']\n",
"starb_i_t.loc[0,'GLACIER_ID'] = starb_i['src_g_id']\n",
"starb_i_t.loc[0,'LAT'] = starb_i['lat']\n",
"starb_i_t.loc[0,'LON'] = starb_i['lon']\n",
"starb_i_t.loc[0,'SURVEY_DATE'] = starb_i['survey_date']\n",
"starb_i_t.loc[0,'DEM_DATE'] = starb_i['dem_date']\n",
"starb_i_t.loc[0,'AREA'] = starb_i['area']\n",
"starb_i_t.loc[0,'MEAN_SLOPE'] = starb_i['mean_sl']\n",
"starb_i_t.loc[0,'MEAN_THICKNESS'] = starb_i['mean_th']\n",
"starb_i_t.loc[0,'MEAN_THICKNESS_UNCERTAINTY'] = starb_i['mean_th_unc']\n",
"starb_i_t.loc[0,'MAXIMUM_THICKNESS'] = starb_i['max_th']\n",
"starb_i_t.loc[0,'MAX_THICKNESS_UNCERTAINTY'] = starb_i['max_th_unc']\n",
"starb_i_t.loc[0,'SURVEY_METHOD'] = starb_i['surv_meth']\n",
"starb_i_t.loc[0,'SURVEY_METHOD_DETAILS'] = starb_i['surv_meth_det']\n",
"starb_i_t.loc[0,'NUMBER_OF_SURVEY_POINTS'] = starb_i['no_points']\n",
"starb_i_t.loc[0,'NUMBER_OF_SURVEY_PROFILES'] = starb_i['no_prfls']\n",
"starb_i_t.loc[0,'TOTAL_LENGTH_OF_SURVEY_PROFILES'] = starb_i['length_prfls']\n",
"starb_i_t.loc[0,'INTERPOLATION_METHOD'] = starb_i['interp_meth']\n",
"starb_i_t.loc[0,'INVESTIGATOR'] = starb_i['investig']\n",
"starb_i_t.loc[0,'SPONSORING_AGENCY'] = starb_i['spons_ag']\n",
"starb_i_t.loc[0,'REFERENCES'] = starb_i['ref']\n",
"starb_i_t.loc[0,'DATA_FLAG'] = starb_i['dflag']\n",
"starb_i_t.loc[0,'REMARKS'] = starb_i['remarks_t']\n",
"\n",
"# fill TTT table\n",
"starb_i_ttt = templ_ttt.copy()\n",
"starb_i_ttt['POINT_ID'] = range(1,len(starb_i_dat)+1)\n",
"starb_i_ttt['POINT_LAT'] = lats\n",
"starb_i_ttt['POINT_LON'] = lons\n",
"starb_i_ttt['ELEVATION'] = starb_i_dat['z_surf'].values.round()\n",
"starb_i_ttt['THICKNESS'] = starb_i_dat['ice_th'].values.round()\n",
"starb_i_ttt['THICKNESS_UNCERTAINTY'] = np.nan\n",
"starb_i_ttt['GlaThiDa_ID'] = starb_i['gid']\n",
"starb_i_ttt['POLITICAL_UNIT'] = starb_i['punit']\n",
"starb_i_ttt['GLACIER_NAME'] = starb_i['gname']\n",
"starb_i_ttt['SURVEY_DATE'] = starb_i['survey_date']\n",
"starb_i_ttt['DATA_FLAG'] = starb_i['dflag']\n",
"starb_i_ttt['REMARKS'] = starb_i['remarks_ttt'] \n",
"\n",
"starb_i_t = starb_i_t[['GlaThiDa_ID', 'POLITICAL_UNIT', 'GLACIER_NAME', 'SOURCE_ID', 'GLACIER_ID', 'LAT', 'LON', 'SURVEY_DATE', \n",
" 'DEM_DATE', 'AREA', 'MEAN_SLOPE', 'MEAN_THICKNESS', 'MEAN_THICKNESS_UNCERTAINTY', 'MAXIMUM_THICKNESS', \n",
" 'MAX_THICKNESS_UNCERTAINTY', 'SURVEY_METHOD', 'SURVEY_METHOD_DETAILS', 'NUMBER_OF_SURVEY_POINTS',\n",
" 'NUMBER_OF_SURVEY_PROFILES', 'TOTAL_LENGTH_OF_SURVEY_PROFILES', 'INTERPOLATION_METHOD', 'INVESTIGATOR', \n",
" 'SPONSORING_AGENCY', 'REFERENCES', 'DATA_FLAG', 'REMARKS']]\n",
"starb_i_ttt = starb_i_ttt[['GlaThiDa_ID','POLITICAL_UNIT','GLACIER_NAME','SURVEY_DATE','POINT_ID','POINT_LAT','POINT_LON',\n",
" 'ELEVATION','THICKNESS','THICKNESS_UNCERTAINTY','DATA_FLAG','REMARKS']]\n",
"\n",
"starb_i_t.to_excel(os.path.join(os.path.dirname(starb_path),starb_path.split('.')[0]+'_IceBridge_Johannes_T.xls'), index=False)\n",
"starb_i_ttt.to_excel(os.path.join(os.path.dirname(starb_path),starb_path.split('.')[0]+'_IceBridge_Johannes_TTT.xls'), index=False)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Tasman Glacier"
]
},
{
"cell_type": "code",
"execution_count": 66,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"nzgd = pyproj.Proj(init='epsg:2193') # NZGD2000_New_Zealand_Transverse_Mercator_2000\n",
"latlon = pyproj.Proj(init='epsg:4326') #WGS84 lat/lon"
]
},
{
"cell_type": "code",
"execution_count": 67,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"# constants\n",
"tasman = {'punit': 'NZ',\n",
"'gname': 'TASMAN',\n",
"'src_id':'',\n",
"'src_g_id':'',\n",
"'survey_date': '',\n",
"'dem_date': '19869999',\n",
"'gid': gtd_ids['tasman'],\n",
"'lat': -43.52,\n",
"'lon': 170.32,\n",
"'area': 100.3,\n",
"'mean_sl': 19,\n",
"'mean_th': np.nan,\n",
"'mean_th_unc': np.nan,\n",
"'max_th': np.nan,\n",
"'max_th_unc': np.nan,\n",
"'surv_meth': 'SEI',\n",
"'surv_meth_det': 'Two generalised cross profiles of ice thickness are inferred from reflection seismics. x, y and ice thickness values are taken at points spaced along the published profiles (Figure 18, p23). A map of interpolated ice thickness (Figure 19, p26), but it is only based on measurements at the two widely-spaced profiles',\n",
"'no_points': np.nan,\n",
"'no_prfls': 2,\n",
"'length_prfls': 3,\n",
"'interp_meth':'',\n",
"'investig':'',\n",
"'spons_ag':'',\n",
"'ref':'Anderton, P. W. (1975). Hydrological Research Annual Report 33. Ministry of Works and Development.',\n",
"'dflag':np.nan,\n",
"'remarks_t':'No mean/max thickness given as there are only highly varying estimates for two cross-sections of the glacier in the reference. Mean slope calculated from DEM by WGMS.',\n",
"'remarks_ttt':''\n",
" }"
]
},
{
"cell_type": "code",
"execution_count": 68,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"tasman_path = 'C:\\\\Users\\\\jlandman\\\\Desktop\\\\newData\\\\WG_Farinotti_data_package\\\\tasman\\\\tasman_anderton_bed_xyz.csv'\n",
"\n",
"# read original data\n",
"tasman_dat = pd.read_csv(tasman_path)\n",
"\n",
"# convert New Real TM to WGS84 lat/lon\n",
"xs = tasman_dat['nztm_easting (m)'].values\n",
"ys = tasman_dat[' nztm_northing (m)'].values\n",
"x1,y1 = nzgd(xs, ys)\n",
"lons, lats = pyproj.transform(nzgd,latlon,xs,ys)\n",
"\n",
"# fill T table\n",
"tasman_t = templ_t.copy()\n",
"tasman_t.loc[0,'GlaThiDa_ID'] = tasman['gid']\n",
"tasman_t.loc[0,'POLITICAL_UNIT'] = tasman['punit']\n",
"tasman_t.loc[0,'GLACIER_NAME'] = tasman['gname']\n",
"tasman_t.loc[0,'SOURCE_ID'] = tasman['src_id']\n",
"tasman_t.loc[0,'GLACIER_ID'] = tasman['src_g_id']\n",
"tasman_t.loc[0,'LAT'] = tasman['lat']\n",
"tasman_t.loc[0,'LON'] = tasman['lon']\n",
"tasman_t.loc[0,'SURVEY_DATE'] = tasman['survey_date']\n",
"tasman_t.loc[0,'DEM_DATE'] = tasman['dem_date']\n",
"tasman_t.loc[0,'AREA'] = tasman['area']\n",
"tasman_t.loc[0,'MEAN_SLOPE'] = tasman['mean_sl']\n",
"tasman_t.loc[0,'MEAN_THICKNESS'] = tasman['mean_th']\n",
"tasman_t.loc[0,'MEAN_THICKNESS_UNCERTAINTY'] = tasman['mean_th_unc']\n",
"tasman_t.loc[0,'MAXIMUM_THICKNESS'] = tasman['max_th']\n",
"tasman_t.loc[0,'MAX_THICKNESS_UNCERTAINTY'] = tasman['max_th_unc']\n",
"tasman_t.loc[0,'SURVEY_METHOD'] = tasman['surv_meth']\n",
"tasman_t.loc[0,'SURVEY_METHOD_DETAILS'] = tasman['surv_meth_det']\n",
"tasman_t.loc[0,'NUMBER_OF_SURVEY_POINTS'] = tasman['no_points']\n",
"tasman_t.loc[0,'NUMBER_OF_SURVEY_PROFILES'] = tasman['no_prfls']\n",
"tasman_t.loc[0,'TOTAL_LENGTH_OF_SURVEY_PROFILES'] = tasman['length_prfls']\n",
"tasman_t.loc[0,'INTERPOLATION_METHOD'] = tasman['interp_meth']\n",
"tasman_t.loc[0,'INVESTIGATOR'] = tasman['investig']\n",
"tasman_t.loc[0,'SPONSORING_AGENCY'] = tasman['spons_ag']\n",
"tasman_t.loc[0,'REFERENCES'] = tasman['ref']\n",
"tasman_t.loc[0,'DATA_FLAG'] = tasman['dflag']\n",
"tasman_t.loc[0,'REMARKS'] = tasman['remarks_t']\n",
"\n",
"# fill TTT table\n",
"tasman_ttt = templ_ttt.copy()\n",
"tasman_ttt['POINT_ID'] = range(1,len(tasman_dat)+1)\n",
"tasman_ttt['POINT_LAT'] = lats\n",
"tasman_ttt['POINT_LON'] = lons\n",
"tasman_ttt['ELEVATION'] = tasman_dat[' surface_elevation (m asl)'].round()\n",
"tasman_ttt['THICKNESS'] = tasman_dat[' ice_thickness (m)'].round()\n",
"tasman_ttt['THICKNESS_UNCERTAINTY'] = np.nan\n",
"tasman_ttt['GlaThiDa_ID'] = tasman['gid']\n",
"tasman_ttt['POLITICAL_UNIT'] = tasman['punit']\n",
"tasman_ttt['GLACIER_NAME'] = tasman['gname']\n",
"tasman_ttt['SURVEY_DATE'] = tasman['survey_date']\n",
"tasman_ttt['DATA_FLAG'] = tasman['dflag']\n",
"tasman_ttt['REMARKS'] = tasman['remarks_ttt'] \n",
"\n",
"tasman_t = tasman_t[['GlaThiDa_ID', 'POLITICAL_UNIT', 'GLACIER_NAME', 'SOURCE_ID', 'GLACIER_ID', 'LAT', 'LON', 'SURVEY_DATE', \n",
" 'DEM_DATE', 'AREA', 'MEAN_SLOPE', 'MEAN_THICKNESS', 'MEAN_THICKNESS_UNCERTAINTY', 'MAXIMUM_THICKNESS', \n",
" 'MAX_THICKNESS_UNCERTAINTY', 'SURVEY_METHOD', 'SURVEY_METHOD_DETAILS', 'NUMBER_OF_SURVEY_POINTS',\n",
" 'NUMBER_OF_SURVEY_PROFILES', 'TOTAL_LENGTH_OF_SURVEY_PROFILES', 'INTERPOLATION_METHOD', 'INVESTIGATOR', \n",
" 'SPONSORING_AGENCY', 'REFERENCES', 'DATA_FLAG', 'REMARKS']]\n",
"tasman_ttt = tasman_ttt[['GlaThiDa_ID','POLITICAL_UNIT','GLACIER_NAME','SURVEY_DATE','POINT_ID','POINT_LAT','POINT_LON',\n",
" 'ELEVATION','THICKNESS','THICKNESS_UNCERTAINTY','DATA_FLAG','REMARKS']]\n",
"\n",
"tasman_t.to_excel(os.path.join(os.path.dirname(tasman_path),tasman_path.split('.')[0]+'_Johannes_T.xls'), index=False)\n",
"tasman_ttt.to_excel(os.path.join(os.path.dirname(tasman_path),tasman_path.split('.')[0]+'_Johannes_TTT.xls'), index=False)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Urumqi"
]
},
{
"cell_type": "code",
"execution_count": 69,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"# constants\n",
"urum = {'punit': 'CN',\n",
"'gname': 'URUMQI NO.1',\n",
"'src_id':'OTH',\n",
"'src_g_id':'',\n",
"'survey_date': '20149999',\n",
"'dem_date': '20129999',\n",
"'gid': gtd_ids['urumqui'],\n",
"'lat': 43.111240,\n",
"'lon': 86.810015,\n",
"'area': 1.59,\n",
"'mean_sl': 20,\n",
"'mean_th': 44.5,\n",
"'mean_th_unc': np.nan,\n",
"'max_th': 141,\n",
"'max_th_unc': np.nan,\n",
"'surv_meth': 'GPRt',\n",
"'surv_meth_det': '',\n",
"'no_points': 1387,\n",
"'no_prfls': 16,\n",
"'length_prfls': np.nan,\n",
"'interp_meth':'KRG',\n",
"'investig':'',\n",
"'spons_ag':'',\n",
"'ref':'Wang, P. et al. (2016). Environmental Earth Sciences, 75(8), pp. 1-11. DOI: 10.1007/s12665-016-5551-3',\n",
"'dflag':np.nan,\n",
"'remarks_t':'Lat/lon from Google Earth and located on upper tongue of eastern branch. Mean slope calculated from DEM by WGMS.',\n",
"'remarks_ttt':''\n",
" }"
]
},
{
"cell_type": "code",
"execution_count": 70,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"urum_path_w = 'C:\\\\Users\\\\jlandman\\\\Desktop\\\\newData\\\\WG_Farinotti_data_package\\\\urumqui\\\\west_branch\\\\original_west.shp'\n",
"urum_path_e = 'C:\\\\Users\\\\jlandman\\\\Desktop\\\\newData\\\\WG_Farinotti_data_package\\\\urumqui\\\\east_branch\\\\original_east.shp'\n",
"urum_out = 'C:\\\\Users\\\\jlandman\\\\Desktop\\\\newData\\\\WG_Farinotti_data_package\\\\urumqui\\\\'\n",
"\n",
"# read original data\n",
"urumw_dat = gpd.read_file(urum_path_w)\n",
"urume_dat = gpd.read_file(urum_path_e)\n",
"\n",
"urumall_dat = urumw_dat.append(urume_dat,ignore_index=True)\n",
"urumall_dat = urumall_dat.reindex(range(1,len(urumall_dat)+1))\n",
"\n",
"\n",
"lats = urumall_dat.N\n",
"lons = urumall_dat.E\n",
"elev = urumall_dat.a_s_l.round()\n",
"ice_th = urumall_dat['冰川厚度（'].round()\n",
"\n",
"\n",
"# fill T table\n",
"urum_t = templ_t.copy()\n",
"urum_t.loc[0,'GlaThiDa_ID'] = urum['gid']\n",
"urum_t.loc[0,'POLITICAL_UNIT'] = urum['punit']\n",
"urum_t.loc[0,'GLACIER_NAME'] = urum['gname']\n",
"urum_t.loc[0,'SOURCE_ID'] = urum['src_id']\n",
"urum_t.loc[0,'GLACIER_ID'] = urum['src_g_id']\n",
"urum_t.loc[0,'LAT'] = urum['lat']\n",
"urum_t.loc[0,'LON'] = urum['lon']\n",
"urum_t.loc[0,'SURVEY_DATE'] = urum['survey_date']\n",
"urum_t.loc[0,'DEM_DATE'] = urum['dem_date']\n",
"urum_t.loc[0,'AREA'] = urum['area']\n",
"urum_t.loc[0,'MEAN_SLOPE'] = urum['mean_sl']\n",
"urum_t.loc[0,'MEAN_THICKNESS'] = urum['mean_th']\n",
"urum_t.loc[0,'MEAN_THICKNESS_UNCERTAINTY'] = urum['mean_th_unc']\n",
"urum_t.loc[0,'MAXIMUM_THICKNESS'] = urum['max_th']\n",
"urum_t.loc[0,'MAX_THICKNESS_UNCERTAINTY'] = urum['max_th_unc']\n",
"urum_t.loc[0,'SURVEY_METHOD'] = urum['surv_meth']\n",
"urum_t.loc[0,'SURVEY_METHOD_DETAILS'] = urum['surv_meth_det']\n",
"urum_t.loc[0,'NUMBER_OF_SURVEY_POINTS'] = urum['no_points']\n",
"urum_t.loc[0,'NUMBER_OF_SURVEY_PROFILES'] = urum['no_prfls']\n",
"urum_t.loc[0,'TOTAL_LENGTH_OF_SURVEY_PROFILES'] = urum['length_prfls']\n",
"urum_t.loc[0,'INTERPOLATION_METHOD'] = urum['interp_meth']\n",
"urum_t.loc[0,'INVESTIGATOR'] = urum['investig']\n",
"urum_t.loc[0,'SPONSORING_AGENCY'] = urum['spons_ag']\n",
"urum_t.loc[0,'REFERENCES'] = urum['ref']\n",
"urum_t.loc[0,'DATA_FLAG'] = urum['dflag']\n",
"urum_t.loc[0,'REMARKS'] = urum['remarks_t']\n",
"\n",
"# fill TTT table\n",
"urum_ttt = templ_ttt.copy()\n",
"urum_ttt['POINT_ID'] = range(1,len(urumw_dat)+len(urume_dat)+1)\n",
"urum_ttt['POINT_LAT'] = lats\n",
"urum_ttt['POINT_LON'] = lons\n",
"urum_ttt['ELEVATION'] = elev\n",
"urum_ttt['THICKNESS'] = ice_th\n",
"urum_ttt['THICKNESS_UNCERTAINTY'] = np.nan\n",
"urum_ttt['GlaThiDa_ID'] = urum['gid']\n",
"urum_ttt['POLITICAL_UNIT'] = urum['punit']\n",
"urum_ttt['GLACIER_NAME'] = urum['gname']\n",
"urum_ttt['SURVEY_DATE'] = urum['survey_date']\n",
"urum_ttt['DATA_FLAG'] = urum['dflag']\n",
"urum_ttt['REMARKS'] = urum['remarks_ttt'] \n",
"\n",
"urum_t = urum_t[['GlaThiDa_ID', 'POLITICAL_UNIT', 'GLACIER_NAME', 'SOURCE_ID', 'GLACIER_ID', 'LAT', 'LON', 'SURVEY_DATE', \n",
" 'DEM_DATE', 'AREA', 'MEAN_SLOPE', 'MEAN_THICKNESS', 'MEAN_THICKNESS_UNCERTAINTY', 'MAXIMUM_THICKNESS', \n",
" 'MAX_THICKNESS_UNCERTAINTY', 'SURVEY_METHOD', 'SURVEY_METHOD_DETAILS', 'NUMBER_OF_SURVEY_POINTS',\n",
" 'NUMBER_OF_SURVEY_PROFILES', 'TOTAL_LENGTH_OF_SURVEY_PROFILES', 'INTERPOLATION_METHOD', 'INVESTIGATOR', \n",
" 'SPONSORING_AGENCY', 'REFERENCES', 'DATA_FLAG', 'REMARKS']]\n",
"urum_ttt = urum_ttt[['GlaThiDa_ID','POLITICAL_UNIT','GLACIER_NAME','SURVEY_DATE','POINT_ID','POINT_LAT','POINT_LON',\n",
" 'ELEVATION','THICKNESS','THICKNESS_UNCERTAINTY','DATA_FLAG','REMARKS']]\n",
"\n",
"urum_t.to_excel(os.path.join(urum_out,'Urumqi_Johannes_T.xls'), index=False)\n",
"urum_ttt.to_excel(os.path.join(urum_out,'Urumqi_Johannes_TTT.xls'), index=False)\n"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# West Washmawapta"
]
},
{
"cell_type": "code",
"execution_count": 71,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"# constants\n",
"wwmw = {'punit': 'CA',\n",
"'gname': 'WEST WASHMAWAPTA',\n",
"'src_id':'',\n",
"'src_g_id':'',\n",
"'survey_date': '20069999',\n",
"'dem_date': '20079999',\n",
"'gid': gtd_ids['west_washmawapta'],\n",
"'lat': 51.1833,\n",
"'lon': -116.3189,\n",
"'area': 1,\n",
"'mean_sl': 16,\n",
"'mean_th': 70,\n",
"'mean_th_unc': 7,\n",
"'max_th': 185,\n",
"'max_th_unc': 13,\n",
"'surv_meth': 'GPRt',\n",
"'surv_meth_det': 'Icefield Instruments icepenetrating radar with 5 MHz center frequency and 10m antennae 50m apart. Assuming a signal propagation speed of 1.68*10^8 m/s, and migrating individual profiles using the circle-tangent technique.',\n",
"'no_points': 199,\n",
"'no_prfls': 21,\n",
"'length_prfls': np.nan,\n",
"'interp_meth':'',\n",
"'investig':'',\n",
"'spons_ag':'',\n",
"'ref':'Sanders, J. W. et al. (2010). American Journal of Science, 310(8), pp.753-773. DOI: 10.2475/08.2010.03]',\n",
"'dflag':np.nan,\n",
"'remarks_t':'Near the steeply-sloping marginal walls, where the ice is thin and the topography complex, errors increased to 13 +- 2 m (27 +- 8%). Mean slope calculated from DEM by WGMS.',\n",
"'remarks_ttt':''\n",
" }"
]
},
{
"cell_type": "code",
"execution_count": 72,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"utm11n = pyproj.Proj(init='epsg:32611') # UTM 11N (WGS84)\n",
"latlon = pyproj.Proj(init='epsg:4326') #WGS84 lat/lon"
]
},
{
"cell_type": "code",
"execution_count": 73,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"wwmw_path = 'C:\\\\Users\\\\jlandman\\\\Desktop\\\\newData\\\\WG_Farinotti_data_package\\\\west_washmawapta\\\\radar_table.txt'\n",
"\n",
"# read original data\n",
"wwmw_dat = pd.read_csv(wwmw_path)\n",
"\n",
"\n",
"\n",
"ice_th = (wwmw_dat['SurfaceElev'] - wwmw_dat['BedrockElev']).round()\n",
"\n",
"# convert New Real TM to WGS84 lat/lon\n",
"xs = wwmw_dat['Easting'].values\n",
"ys = wwmw_dat['Northing'].values\n",
"x1,y1 = utm11n(xs, ys)\n",
"lons, lats = pyproj.transform(utm11n,latlon,xs,ys)\n",
"\n",
"\n",
"# fill T table\n",
"wwmw_t = templ_t.copy()\n",
"wwmw_t.loc[0,'GlaThiDa_ID'] = wwmw['gid']\n",
"wwmw_t.loc[0,'POLITICAL_UNIT'] = wwmw['punit']\n",
"wwmw_t.loc[0,'GLACIER_NAME'] = wwmw['gname']\n",
"wwmw_t.loc[0,'SOURCE_ID'] = wwmw['src_id']\n",
"wwmw_t.loc[0,'GLACIER_ID'] = wwmw['src_g_id']\n",
"wwmw_t.loc[0,'LAT'] = wwmw['lat']\n",
"wwmw_t.loc[0,'LON'] = wwmw['lon']\n",
"wwmw_t.loc[0,'SURVEY_DATE'] = wwmw['survey_date']\n",
"wwmw_t.loc[0,'DEM_DATE'] = wwmw['dem_date']\n",
"wwmw_t.loc[0,'AREA'] = wwmw['area']\n",
"wwmw_t.loc[0,'MEAN_SLOPE'] = wwmw['mean_sl']\n",
"wwmw_t.loc[0,'MEAN_THICKNESS'] = wwmw['mean_th']\n",
"wwmw_t.loc[0,'MEAN_THICKNESS_UNCERTAINTY'] = wwmw['mean_th_unc']\n",
"wwmw_t.loc[0,'MAXIMUM_THICKNESS'] = wwmw['max_th']\n",
"wwmw_t.loc[0,'MAX_THICKNESS_UNCERTAINTY'] = wwmw['max_th_unc']\n",
"wwmw_t.loc[0,'SURVEY_METHOD'] = wwmw['surv_meth']\n",
"wwmw_t.loc[0,'SURVEY_METHOD_DETAILS'] = wwmw['surv_meth_det']\n",
"wwmw_t.loc[0,'NUMBER_OF_SURVEY_POINTS'] = wwmw['no_points']\n",
"wwmw_t.loc[0,'NUMBER_OF_SURVEY_PROFILES'] = wwmw['no_prfls']\n",
"wwmw_t.loc[0,'TOTAL_LENGTH_OF_SURVEY_PROFILES'] = wwmw['length_prfls']\n",
"wwmw_t.loc[0,'INTERPOLATION_METHOD'] = wwmw['interp_meth']\n",
"wwmw_t.loc[0,'INVESTIGATOR'] = wwmw['investig']\n",
"wwmw_t.loc[0,'SPONSORING_AGENCY'] = wwmw['spons_ag']\n",
"wwmw_t.loc[0,'REFERENCES'] = wwmw['ref']\n",
"wwmw_t.loc[0,'DATA_FLAG'] = wwmw['dflag']\n",
"wwmw_t.loc[0,'REMARKS'] = wwmw['remarks_t']\n",
"\n",
"# fill TTT table\n",
"wwmw_ttt = templ_ttt.copy()\n",
"wwmw_ttt['POINT_ID'] = range(1,len(wwmw_dat)+1)\n",
"wwmw_ttt['POINT_LAT'] = lats\n",
"wwmw_ttt['POINT_LON'] = lons\n",
"wwmw_ttt['ELEVATION'] = wwmw_dat['SurfaceElev'].round()\n",
"wwmw_ttt['THICKNESS'] = ice_th\n",
"wwmw_ttt['THICKNESS_UNCERTAINTY'] = np.nan\n",
"wwmw_ttt['GlaThiDa_ID'] = wwmw['gid']\n",
"wwmw_ttt['POLITICAL_UNIT'] = wwmw['punit']\n",
"wwmw_ttt['GLACIER_NAME'] = wwmw['gname']\n",
"wwmw_ttt['SURVEY_DATE'] = wwmw['survey_date']\n",
"wwmw_ttt['DATA_FLAG'] = wwmw['dflag']\n",
"wwmw_ttt['REMARKS'] = wwmw['remarks_ttt'] \n",
"\n",
"wwmw_t = wwmw_t[['GlaThiDa_ID', 'POLITICAL_UNIT', 'GLACIER_NAME', 'SOURCE_ID', 'GLACIER_ID', 'LAT', 'LON', 'SURVEY_DATE', \n",
" 'DEM_DATE', 'AREA', 'MEAN_SLOPE', 'MEAN_THICKNESS', 'MEAN_THICKNESS_UNCERTAINTY', 'MAXIMUM_THICKNESS', \n",
" 'MAX_THICKNESS_UNCERTAINTY', 'SURVEY_METHOD', 'SURVEY_METHOD_DETAILS', 'NUMBER_OF_SURVEY_POINTS',\n",
" 'NUMBER_OF_SURVEY_PROFILES', 'TOTAL_LENGTH_OF_SURVEY_PROFILES', 'INTERPOLATION_METHOD', 'INVESTIGATOR', \n",
" 'SPONSORING_AGENCY', 'REFERENCES', 'DATA_FLAG', 'REMARKS']]\n",
"wwmw_ttt = wwmw_ttt[['GlaThiDa_ID','POLITICAL_UNIT','GLACIER_NAME','SURVEY_DATE','POINT_ID','POINT_LAT','POINT_LON',\n",
" 'ELEVATION','THICKNESS','THICKNESS_UNCERTAINTY','DATA_FLAG','REMARKS']]\n",
"\n",
"wwmw_t.to_excel(os.path.join(os.path.dirname(wwmw_path),'West_Washmawapta_Johannes_T.xls'), index=False)\n",
"wwmw_ttt.to_excel(os.path.join(os.path.dirname(wwmw_path),'West_Washmawapta_Johannes_TTT.xls'), index=False)\n"
]
}
],
"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": "3.5.1"
}
},
"nbformat": 4,
"nbformat_minor": 0
}
| gpl-3.0 |
AeroPython/Taller-PyConEs-2015 | Teoria III - Exploracion-Explotacion.ipynb | 1 | 590821 | {
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
""
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"#Simplifica tu vida con sistemas complejos y algoritmos genéticos"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Parte 3 - El dilema Exploración - Explotación: feedback positivo y negativo"
]
},
{
"cell_type": "markdown",
"metadata": {
"collapsed": true
},
"source": [
"Cuando usamos algoritmos genéticos y sistemas complejos, en general, estaremos buscando optimizar funciones muy complicadas, de varios parámetros, a menudo incluso implícitas (como la optimización de un avión mediante CFD). Estas funciones normalmente tendrán óptimos locales, soluciones *buenas*, pero que no son el máximo global, la mejor solución, que es lo que buscamos.\n",
"\n",
"Hagamos un pequeño esquema para verlo claramente!"
]
},
{
"cell_type": "code",
"execution_count": 1,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"%matplotlib inline \n",
"import numpy as np # Usaremos arrays\n",
"import matplotlib.pyplot as plt # Para pintar resultados"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Supongamos que esta curva representa a una función cuyo máximo buscamos, y supongamos que el eje x representa parámetros de los que la función depende."
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"[<matplotlib.lines.Line2D at 0x7f2666632400>]"
]
},
"execution_count": 2,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"image/png": "iVBORw0KGgoAAAANSUhEUgAAAlwAAAEzCAYAAADkVt2iAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nEjEUXIQQQgghEUPBRQghhBASMf8fb6rTiwhAWgQAAAAASUVORK5CYII=\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x7f2666623a90>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"x = np.linspace(0,50,500)\n",
"y = np.sin(x) * np.sin(x/17)\n",
"plt.figure(None, figsize=(10,5))\n",
"plt.ylim(-1.1, 1.1)\n",
"plt.plot(x,y)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Supongamos que con un algoritmo hemos encontrado un punto alto, pero que corresponde a un óptimo local, por ejemplo:"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"[<matplotlib.lines.Line2D at 0x7f26642f1f60>]"
]
},
"execution_count": 3,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"image/png": "iVBORw0KGgoAAAANSUhEUgAAAlwAAAEzCAYAAADkVt2iAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nTkSuQmCC\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x7f266436f4e0>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"plt.figure(None, figsize=(10,5))\n",
"plt.ylim(-1.1, 1.1)\n",
"plt.plot(x,y)\n",
"plt.plot([21,21],[0,1],'r--')\n",
"plt.plot(21, 0.75, 'ko')"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"El dilema Exploración-Explotación hace referencia a a dos fuerzas contrapuestas que necesitamos equilibrar cuidadosamente cuando usemos estos tipos de algoritmos.\n",
"\n",
"La **Exploración** se refiere a buscar soluciones alejadas de lo que tenemos, abrir nuestro abanico de búsqueda.\n",
"\n",
"- Nos permite escapar de máximos locales y encontrar el global.\n",
"- Nos permite encontrar soluciones atípicas y novedosas a problemas complicados.\n",
"- Demasiada exploración nos impedirá guardar nuestras soluciones y refinarlas, y tendremos a nuestro algoritmo saltando de un lado a otro sin sacar nada en claro.\n",
"\n",
"La **Explotación** se refiere a la capacidad de nuestro algoritmo de mantener las soluciones buenas que ha encontrado y refinarlas, buscando en entornos cercanos.\n",
"\n",
"- Nos permite encontrar máximos de la función y mantenerlos.\n",
"- Demasiada Explotación nos bloqueará en máximos locales y nos impedirá encontrar el global."
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"[<matplotlib.lines.Line2D at 0x7f266426ab38>]"
]
},
"execution_count": 4,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"image/png": "iVBORw0KGgoAAAANSUhEUgAAAlwAAAEzCAYAAADkVt2iAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x7f2664204f98>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"# EJEMPLO DE RESULTADO CON DEMASIADA EXPLORACIÓN: NO SE ENCUENTRA NADA\n",
"\n",
"\n",
"x2 = np.array([7,8,12,28,31,35,40,49])\n",
"y2 = np.sin(x2) * np.sin(x2/17)\n",
"\n",
"plt.figure(None, figsize=(10,5))\n",
"plt.ylim(-1.1, 1.1)\n",
"plt.plot(x,y)\n",
"plt.plot([21,21],[0,1],'r--')\n",
"plt.plot(21, 0.75, 'ko')\n",
"plt.plot(x2, y2, 'go')"
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"[<matplotlib.lines.Line2D at 0x7f26641d3c18>]"
]
},
"execution_count": 5,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"image/png": "iVBORw0KGgoAAAANSUhEUgAAAlwAAAEzCAYAAADkVt2iAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nXAzDMAzDMIr5//z+eVyLgQ/8AAAAAElFTkSuQmCC\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x7f26642bae80>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"# EJEMPLO DE RESULTADO CON DEMASIADA EXPLOTACIÓN: SÓLO SE LLEGA AL LOCAL\n",
"\n",
"\n",
"x2 = np.linspace(20.2, 21, 10)\n",
"y2 = np.sin(x2) * np.sin(x2/17)\n",
"\n",
"plt.figure(None, figsize=(10,5))\n",
"plt.ylim(-1.1, 1.1)\n",
"plt.plot(x,y)\n",
"plt.plot([21,21],[0,1],'r--')\n",
"plt.plot(21, 0.75, 'ko')\n",
"plt.plot(x2, y2, 'go')"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Este tipo de estrategias se modulan mediante todos los parámetros de los algoritmos, pero quizás el parámetro que más claramente influye en este equilibrio es el de la mutación en los algoritmos genéticos: Reduciendo el índice de mutación potenciaremos la explotación, mientras que si lo aumentamos, potenciamos la exploración."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"##Ejemplo: Laberinto"
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"#Usaremos el paquete en el ejercicio del laberinto\n",
"import Ejercicios.Laberinto.laberinto.laberinto as lab\n",
"ag = lab.ag"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Supongamos que tenemos el siguiente laberinto, al que accedemos por la izquierda y que queremos resolver:"
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"image/png": "iVBORw0KGgoAAAANSUhEUgAAAlAAAAJPCAYAAACzYuoLAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nKOS8Ys3i7gAAAAAASUVORK5CYII=\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x7f268455eb38>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"mapa1 = lab.Map()\n",
"mapa1.draw_tablero()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"En el ejercicio se detalla más el proceso, llamemos aquí simplemente al algoritmo genético que lo resuelve:"
]
},
{
"cell_type": "code",
"execution_count": 8,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"1·2·3·4·5·6·7·8·9·10·11·12·13·14·15·16·17·18·19·20·21·22·23·24·25·26·27·28·29·30·31·32·33·34·35·36·37·38·39·40·41·42·43·44·45·46·47·48·49·50·51·52·53·54·55·56·57·58·59·60·61·62·63·64·65·66·67·68·69·70·71·72·73·74·75·76·77·78·79·80·81·82·83·84·85·86·87·88·89·90·91·92·93·94·95·96·97·98·99·100·"
]
},
{
"data": {
"image/png": "iVBORw0KGgoAAAANSUhEUgAAAlAAAAJPCAYAAACzYuoLAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nAAAAOSJAAQAA5Oi/ADtVUwF0L0jYAAAAAElFTkSuQmCC\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x7f266420f588>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"image/png": "iVBORw0KGgoAAAANSUhEUgAAAmIAAAHpCAYAAAAhyVBgAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nIqKIMBAjIiIiiggDMSIiIqKI/H+pUe0eqJZH5wAAAABJRU5ErkJggg==\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x7f2663d755c0>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"mapa1 = lab.Map()\n",
"lab.avanzar(mapa1)\n",
"lab.draw_all(mapa1)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Lo más probable es que hayas obtenido una solución o un camino cerrado en un bucle. Puedes ejecutar la celda superior varias veces para hecerte una idea aproximada de con qué frecuencia aparece cada situación. Pero, ¿por qué aparecen estos bucles?\n",
"\n",
"Examinemos qué aspecto tiene una solución:\n",
"\n",
"Cada casilla contiene una flecha que indica cuál es la siguiente casilla a la que cruzar. Esto es lo que se describe en el genoma de cada camino.\n",
"\n",
"Si la casilla apunta a una pared, el programa intentará cruzar de todos modos a una casilla aleatoria diferente."
]
},
{
"cell_type": "code",
"execution_count": 9,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"image/png": "iVBORw0KGgoAAAANSUhEUgAAAlAAAAJPCAYAAACzYuoLAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nAEVERERk0P8HniMW2bZd0IAAAAAASUVORK5CYII=\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x7f2664136128>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"mapa1.list_caminos[0].draw_directions()\n",
"mapa1.list_caminos[0].draw_path(0.7)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"La respuesta a por qué se forman bucles está en cómo se define la función de fitness o puntuación de cada camino:\n",
"\n",
"- Se recorren 50 casillas, intentando seguir el camino que determinan las flechas\n",
"- Cada vez que se **choca con una pared**, o que se vuelve a la casilla anterior (por ejemplo, si dos flechas se apuntan mutuamente), **se pierden puntos**.\n",
"- Se obtiene una puntuación mejor cuanto más a la derecha acabe el caminante.\n",
"- Se obtiene una gran bonificación si se llega a la salida\n",
"\n",
"En este ejercicio, un bucle es un optimo local: Al no chocarse con nada al recorrerlo, la puntuación es mejor que la de caminos ligeramente diferentes, que terminarían chocando con las paredes varias veces.\n",
"\n",
"Sin embargo, no es la solución que buscamos. Tenemos que potenciar la exploración lejos de estos máximos locales.\n",
"\n",
"Una manera de hacerlo es con feromonas, parecido a lo que hicimos con las hormigas.\n",
"\n",
"Supongamos que cada persona que camina por el laberinto, deja por cada casilla por la que pasa un olor desagradable, que hace que los que vuelvan a pasar por allí intenten evitar ese camino. La manera de implementar esto en el algoritmo es añadir un rastro de feromonas, y luego tener en cuenta la cantidad de feromonas encontradas al calcular la puntuación. ¿Cómo crees que eso afectaría a los bucles?\n",
"\n",
"Probémoslo!"
]
},
{
"cell_type": "code",
"execution_count": 12,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"1·2·3·4·5·6·7·8·9·10·11·12·13·14·15·16·17·18·19·20·21·22·23·24·25·26·27·28·29·30·31·32·33·34·35·36·37·38·39·40·41·42·43·44·45·46·47·48·49·50·51·52·53·54·55·56·57·58·59·60·61·62·63·64·65·66·67·68·69·70·71·72·73·74·75·76·77·78·79·80·81·82·83·84·85·86·87·88·89·90·91·92·93·94·95·96·97·98·99·100·"
]
},
{
"data": {
"image/png": "iVBORw0KGgoAAAANSUhEUgAAAlAAAAJPCAYAAACzYuoLAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\ns+5ZaEgAAAAASUVORK5CYII=\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x7f2664075c18>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"image/png": "iVBORw0KGgoAAAANSUhEUgAAAmIAAAHpCAYAAAAhyVBgAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nkkIsIiIioiYpxCIiIiJq8i8VnIsZtAoyGwAAAABJRU5ErkJggg==\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x7f2663896588>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"mapa1 = lab.Map(veneno=1)\n",
"lab.avanzar(mapa1)\n",
"lab.draw_all(mapa1)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Prueba e ejecutarlo varias veces. ¿Notas si ha cambiado la cantidad de bucles?\n",
"\n",
"Por último, veamos que ocurre si potenciamos la exploración demasiado:"
]
},
{
"cell_type": "code",
"execution_count": 11,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"1·2·3·4·5·6·7·8·9·10·11·12·13·14·15·16·17·18·19·20·21·22·23·24·25·26·27·28·29·30·31·32·33·34·35·36·37·38·39·40·41·42·43·44·45·46·47·48·49·50·51·52·53·54·55·56·57·58·59·60·61·62·63·64·65·66·67·68·69·70·71·72·73·74·75·76·77·78·79·80·81·82·83·84·85·86·87·88·89·90·91·92·93·94·95·96·97·98·99·100·"
]
},
{
"data": {
"image/png": "iVBORw0KGgoAAAANSUhEUgAAAlAAAAJPCAYAAACzYuoLAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x7f2663df50f0>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"image/png": "iVBORw0KGgoAAAANSUhEUgAAAm8AAAHpCAYAAADUN9vQAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nw5skSVKPGN4kSZJ65L/dWiCeP7k7DAAAAABJRU5ErkJggg==\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x7f266384d4a8>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"mapa1 = lab.Map(veneno=100)\n",
"lab.avanzar(mapa1)\n",
"lab.draw_all(mapa1)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"¿Cómo explicas lo que ocurre? ¿Por qué hemos perdido la capacidad para encontrar soluciones al potenciar demasiado la exploración?"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": []
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": []
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Siro Moreno, Aeropython, 19 de Noviembre de 2015\n"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"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": "3.4.3"
}
},
"nbformat": 4,
"nbformat_minor": 0
}
| mit |
esa/pykep | doc/sphinx/examples/gravity_spherical_harmonic.ipynb | 2 | 65586 | {
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Lunar orbit propagation with degree 20 spherical harmonics gravity model\n",
"In this example, the use of the gravity_spherical_harmonics functions will be demonstrated. Using Goddard Lunar Gravity Model 3, the orbit of a satellite in low Lunar orbit will be propagated. Due to time constraints, relatively low degree and order of the gravity model are used as well as relatively large tolerances in Scipy's RK45 integrator."
]
},
{
"cell_type": "code",
"execution_count": 1,
"metadata": {},
"outputs": [],
"source": [
"# Imports\n",
"import matplotlib.pyplot as plt\n",
"import pykep as pk\n",
"import scipy as sp\n",
"import numpy as np\n",
"%matplotlib inline"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {},
"outputs": [],
"source": [
"# Load the gravity model\n",
"r_m, mu_m, c, s, max_degree, max_order = pk.util.load_gravity_model(\"glgm3_150.txt\")"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {},
"outputs": [],
"source": [
"# Define initial orbital parameters\n",
"\n",
"# a, e, i, RAAN, AOP, E\n",
"kepl_init_state = np.array([1.882105e06, 0.05, np.radians(92), 0, 3*np.pi/2, 0])\n",
"\n",
"# Convert to cartesian state\n",
"cart_init_state = np.concatenate(pk.par2ic(kepl_init_state, mu_m))"
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {},
"outputs": [],
"source": [
"# Function to calculate the state derivative.\n",
"def propagate(t, state):\n",
" # Rotational rate of the Moon\n",
" w_m = 2.661695728e-6 # rad/s\n",
" \n",
" # Get the absolute rotation (assuming alpha(t0) = 0)\n",
" alpha = w_m * t\n",
" \n",
" # Build the rotation matrix\n",
" sin = np.sin(alpha)\n",
" cos = np.cos(alpha)\n",
" \n",
" transformation = np.array([[cos, sin, 0], \n",
" [-sin, cos, 0],\n",
" [0, 0, 1]])\n",
" \n",
" # Get the state in the inertial reference frame\n",
" rotated_pos = np.dot(transformation, state[0:3])\n",
" \n",
" # gravity_spherical_harmonic needs an (N x 3) input array\n",
" acc = pk.util.gravity_spherical_harmonic(np.array([rotated_pos]), r_m, mu_m, c, s, 10, 0)[0]\n",
" \n",
" delta_state = np.array([state[3],\n",
" state[4],\n",
" state[5],\n",
" acc[0],\n",
" acc[1],\n",
" acc[2]])\n",
" \n",
" return delta_state"
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {
"scrolled": false
},
"outputs": [],
"source": [
"# Run the integration of the orbit for one day\n",
"solution = sp.integrate.solve_ivp(propagate, (0, 3600*24), cart_init_state, rtol=1e-6, atol=1e-6)\n",
"\n",
"time_hist = solution['t']\n",
"state_hist = np.transpose(solution['y'])"
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {},
"outputs": [
{
"data": {
"image/png": "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\n",
"text/plain": [
"<Figure size 1152x216 with 6 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"# Transform cartesian to modified equinoctial\n",
"eq_state_hist = np.zeros(state_hist.shape)\n",
"for i, state in enumerate(state_hist):\n",
" eq_state_hist[i] = pk.ic2eq(state[0:3], state[3:6], mu_m)\n",
"\n",
"labels = [r\"$a \\cdot \\left( 1 - e^2 \\right)$\", r\"$h$\", r\"$k$\", r\"$p$\", r\"$q$\", r\"$L$\"]\n",
"# Plot the orbit\n",
"plt.figure(1, figsize=(16, 3))\n",
"for i in range(6):\n",
" plt.subplot(1, 6, i + 1)\n",
" plt.plot(time_hist, eq_state_hist[:, i] - eq_state_hist[0, i])\n",
" plt.xlabel(labels[i])"
]
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.7.6"
}
},
"nbformat": 4,
"nbformat_minor": 2
}
| gpl-3.0 |
Resly/pipeline | jupyterhub.ml/notebooks/spark/run_pyspark_from_airflow.ipynb | 1 | 798 | {
"cells": [
{
"cell_type": "code",
"execution_count": 1,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"## TODO: Trigger pyspark_submit_pi DAG from Airflow REST API"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": []
}
],
"metadata": {
"kernelspec": {
"display_name": "Python [default]",
"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": "3.5.3"
}
},
"nbformat": 4,
"nbformat_minor": 2
}
| apache-2.0 |
transcranial/keras-js | notebooks/layers/pooling/MaxPooling1D.ipynb | 1 | 28688 | {
"cells": [
{
"cell_type": "code",
"execution_count": 1,
"metadata": {},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"Using TensorFlow backend.\n"
]
}
],
"source": [
"import numpy as np\n",
"from keras.models import Model\n",
"from keras.layers import Input\n",
"from keras.layers.pooling import MaxPooling1D\n",
"from keras import backend as K\n",
"import json\n",
"from collections import OrderedDict"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"def format_decimal(arr, places=6):\n",
" return [round(x * 10**places) / 10**places for x in arr]"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"DATA = OrderedDict()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### MaxPooling1D"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"**[pooling.MaxPooling1D.0] input 6x6, pool_size=2, strides=None, padding='valid'**"
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"\n",
"in shape: (6, 6)\n",
"in: [-0.570441, -0.454673, -0.285321, 0.237249, 0.282682, 0.428035, 0.160547, -0.332203, 0.546391, 0.272735, 0.010827, -0.763164, -0.442696, 0.381948, -0.676994, 0.753553, -0.031788, 0.915329, -0.738844, 0.269075, 0.434091, 0.991585, -0.944288, 0.258834, 0.162138, 0.565201, -0.492094, 0.170854, -0.139788, -0.710674, 0.406968, 0.705926, -0.094137, -0.793497, -0.040684, 0.522292]\n",
"out shape: (3, 6)\n",
"out: [0.160547, -0.332203, 0.546391, 0.272735, 0.282682, 0.428035, -0.442696, 0.381948, 0.434091, 0.991585, -0.031788, 0.915329, 0.406968, 0.705926, -0.094137, 0.170854, -0.040684, 0.522292]\n"
]
}
],
"source": [
"data_in_shape = (6, 6)\n",
"L = MaxPooling1D(pool_size=2, strides=None, padding='valid')\n",
"\n",
"layer_0 = Input(shape=data_in_shape)\n",
"layer_1 = L(layer_0)\n",
"model = Model(inputs=layer_0, outputs=layer_1)\n",
"\n",
"# set weights to random (use seed for reproducibility)\n",
"np.random.seed(250)\n",
"data_in = 2 * np.random.random(data_in_shape) - 1\n",
"result = model.predict(np.array([data_in]))\n",
"data_out_shape = result[0].shape\n",
"data_in_formatted = format_decimal(data_in.ravel().tolist())\n",
"data_out_formatted = format_decimal(result[0].ravel().tolist())\n",
"print('')\n",
"print('in shape:', data_in_shape)\n",
"print('in:', data_in_formatted)\n",
"print('out shape:', data_out_shape)\n",
"print('out:', data_out_formatted)\n",
"\n",
"DATA['pooling.MaxPooling1D.0'] = {\n",
" 'input': {'data': data_in_formatted, 'shape': data_in_shape},\n",
" 'expected': {'data': data_out_formatted, 'shape': data_out_shape}\n",
"}"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"**[pooling.MaxPooling1D.1] input 6x6, pool_size=2, strides=1, padding='valid'**"
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"\n",
"in shape: (6, 6)\n",
"in: [0.275222, -0.793967, -0.468107, -0.841484, -0.295362, 0.78175, 0.068787, -0.261747, -0.625733, -0.042907, 0.861141, 0.85267, 0.956439, 0.717838, -0.99869, -0.963008, 0.013277, -0.180306, 0.832137, -0.385252, -0.524308, 0.659706, -0.905127, 0.526292, 0.832569, 0.084455, 0.23838, -0.046178, -0.735871, 0.776883, -0.394643, 0.498903, 0.029584, -0.17332, 0.628159, 0.445074]\n",
"out shape: (5, 6)\n",
"out: [0.275222, -0.261747, -0.468107, -0.042907, 0.861141, 0.85267, 0.956439, 0.717838, -0.625733, -0.042907, 0.861141, 0.85267, 0.956439, 0.717838, -0.524308, 0.659706, 0.013277, 0.526292, 0.832569, 0.084455, 0.23838, 0.659706, -0.735871, 0.776883, 0.832569, 0.498903, 0.23838, -0.046178, 0.628159, 0.776883]\n"
]
}
],
"source": [
"data_in_shape = (6, 6)\n",
"L = MaxPooling1D(pool_size=2, strides=1, padding='valid')\n",
"\n",
"layer_0 = Input(shape=data_in_shape)\n",
"layer_1 = L(layer_0)\n",
"model = Model(inputs=layer_0, outputs=layer_1)\n",
"\n",
"# set weights to random (use seed for reproducibility)\n",
"np.random.seed(251)\n",
"data_in = 2 * np.random.random(data_in_shape) - 1\n",
"result = model.predict(np.array([data_in]))\n",
"data_out_shape = result[0].shape\n",
"data_in_formatted = format_decimal(data_in.ravel().tolist())\n",
"data_out_formatted = format_decimal(result[0].ravel().tolist())\n",
"print('')\n",
"print('in shape:', data_in_shape)\n",
"print('in:', data_in_formatted)\n",
"print('out shape:', data_out_shape)\n",
"print('out:', data_out_formatted)\n",
"\n",
"DATA['pooling.MaxPooling1D.1'] = {\n",
" 'input': {'data': data_in_formatted, 'shape': data_in_shape},\n",
" 'expected': {'data': data_out_formatted, 'shape': data_out_shape}\n",
"}"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"**[pooling.MaxPooling1D.2] input 6x6, pool_size=2, strides=3, padding='valid'**"
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"\n",
"in shape: (6, 6)\n",
"in: [-0.989173, -0.133618, -0.505338, 0.023259, 0.503982, -0.303769, -0.436321, 0.793911, 0.416102, 0.806405, -0.098342, -0.738022, -0.982676, 0.805073, 0.741244, -0.941634, -0.253526, -0.136544, -0.295772, 0.207565, -0.517246, -0.686963, -0.176235, -0.354111, -0.862411, -0.969822, 0.200074, 0.290718, -0.038623, 0.294839, 0.247968, 0.557946, -0.455596, 0.6624, 0.879529, -0.466772]\n",
"out shape: (2, 6)\n",
"out: [-0.436321, 0.793911, 0.416102, 0.806405, 0.503982, -0.303769, -0.295772, 0.207565, 0.200074, 0.290718, -0.038623, 0.294839]\n"
]
}
],
"source": [
"data_in_shape = (6, 6)\n",
"L = MaxPooling1D(pool_size=2, strides=3, padding='valid')\n",
"\n",
"layer_0 = Input(shape=data_in_shape)\n",
"layer_1 = L(layer_0)\n",
"model = Model(inputs=layer_0, outputs=layer_1)\n",
"\n",
"# set weights to random (use seed for reproducibility)\n",
"np.random.seed(252)\n",
"data_in = 2 * np.random.random(data_in_shape) - 1\n",
"result = model.predict(np.array([data_in]))\n",
"data_out_shape = result[0].shape\n",
"data_in_formatted = format_decimal(data_in.ravel().tolist())\n",
"data_out_formatted = format_decimal(result[0].ravel().tolist())\n",
"print('')\n",
"print('in shape:', data_in_shape)\n",
"print('in:', data_in_formatted)\n",
"print('out shape:', data_out_shape)\n",
"print('out:', data_out_formatted)\n",
"\n",
"DATA['pooling.MaxPooling1D.2'] = {\n",
" 'input': {'data': data_in_formatted, 'shape': data_in_shape},\n",
" 'expected': {'data': data_out_formatted, 'shape': data_out_shape}\n",
"}"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"**[pooling.MaxPooling1D.3] input 6x6, pool_size=2, strides=None, padding='same'**"
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"\n",
"in shape: (6, 6)\n",
"in: [-0.47588, 0.366985, 0.040173, 0.015578, -0.906159, 0.241982, -0.771299, -0.443554, -0.56404, -0.17751, 0.541277, -0.233327, 0.024369, 0.858275, 0.496191, 0.980574, -0.59522, 0.480899, 0.392553, -0.191718, 0.055121, 0.289836, -0.498339, 0.800408, 0.132679, -0.716649, 0.840092, -0.088837, -0.538209, -0.580887, -0.370128, -0.924933, -0.161736, -0.205619, 0.793729, -0.354472]\n",
"out shape: (3, 6)\n",
"out: [-0.47588, 0.366985, 0.040173, 0.015578, 0.541277, 0.241982, 0.392553, 0.858275, 0.496191, 0.980574, -0.498339, 0.800408, 0.132679, -0.716649, 0.840092, -0.088837, 0.793729, -0.354472]\n"
]
}
],
"source": [
"data_in_shape = (6, 6)\n",
"L = MaxPooling1D(pool_size=2, strides=None, padding='same')\n",
"\n",
"layer_0 = Input(shape=data_in_shape)\n",
"layer_1 = L(layer_0)\n",
"model = Model(inputs=layer_0, outputs=layer_1)\n",
"\n",
"# set weights to random (use seed for reproducibility)\n",
"np.random.seed(253)\n",
"data_in = 2 * np.random.random(data_in_shape) - 1\n",
"result = model.predict(np.array([data_in]))\n",
"data_out_shape = result[0].shape\n",
"data_in_formatted = format_decimal(data_in.ravel().tolist())\n",
"data_out_formatted = format_decimal(result[0].ravel().tolist())\n",
"print('')\n",
"print('in shape:', data_in_shape)\n",
"print('in:', data_in_formatted)\n",
"print('out shape:', data_out_shape)\n",
"print('out:', data_out_formatted)\n",
"\n",
"DATA['pooling.MaxPooling1D.3'] = {\n",
" 'input': {'data': data_in_formatted, 'shape': data_in_shape},\n",
" 'expected': {'data': data_out_formatted, 'shape': data_out_shape}\n",
"}"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"**[pooling.MaxPooling1D.4] input 6x6, pool_size=2, strides=1, padding='same'**"
]
},
{
"cell_type": "code",
"execution_count": 8,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"\n",
"in shape: (6, 6)\n",
"in: [0.024124, 0.280236, -0.680013, -0.042458, -0.164273, 0.358409, 0.511014, -0.585272, -0.481578, 0.692702, 0.64189, -0.400252, -0.922248, -0.735105, -0.533918, 0.071402, 0.310474, 0.369868, 0.767931, -0.842066, -0.091189, 0.835301, -0.480484, 0.950819, -0.002131, 0.086491, -0.480947, 0.405572, -0.083803, -0.921447, -0.291545, 0.674087, -0.560444, 0.881432, 0.076544, 0.63549]\n",
"out shape: (6, 6)\n",
"out: [0.511014, 0.280236, -0.481578, 0.692702, 0.64189, 0.358409, 0.511014, -0.585272, -0.481578, 0.692702, 0.64189, 0.369868, 0.767931, -0.735105, -0.091189, 0.835301, 0.310474, 0.950819, 0.767931, 0.086491, -0.091189, 0.835301, -0.083803, 0.950819, -0.002131, 0.674087, -0.480947, 0.881432, 0.076544, 0.63549, -0.291545, 0.674087, -0.560444, 0.881432, 0.076544, 0.63549]\n"
]
}
],
"source": [
"data_in_shape = (6, 6)\n",
"L = MaxPooling1D(pool_size=2, strides=1, padding='same')\n",
"\n",
"layer_0 = Input(shape=data_in_shape)\n",
"layer_1 = L(layer_0)\n",
"model = Model(inputs=layer_0, outputs=layer_1)\n",
"\n",
"# set weights to random (use seed for reproducibility)\n",
"np.random.seed(254)\n",
"data_in = 2 * np.random.random(data_in_shape) - 1\n",
"result = model.predict(np.array([data_in]))\n",
"data_out_shape = result[0].shape\n",
"data_in_formatted = format_decimal(data_in.ravel().tolist())\n",
"data_out_formatted = format_decimal(result[0].ravel().tolist())\n",
"print('')\n",
"print('in shape:', data_in_shape)\n",
"print('in:', data_in_formatted)\n",
"print('out shape:', data_out_shape)\n",
"print('out:', data_out_formatted)\n",
"\n",
"DATA['pooling.MaxPooling1D.4'] = {\n",
" 'input': {'data': data_in_formatted, 'shape': data_in_shape},\n",
" 'expected': {'data': data_out_formatted, 'shape': data_out_shape}\n",
"}"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"**[pooling.MaxPooling1D.5] input 6x6, pool_size=2, strides=3, padding='same'**"
]
},
{
"cell_type": "code",
"execution_count": 9,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"\n",
"in shape: (6, 6)\n",
"in: [-0.072127, -0.553929, -0.355552, -0.936405, 0.556627, -0.482815, -0.225337, -0.640315, 0.023246, -0.638412, -0.797304, 0.284959, -0.569771, -0.685286, 0.002481, 0.398436, 0.11345, 0.416629, -0.526713, 0.962183, 0.021732, 0.922994, 0.07991, -0.164385, 0.461494, -0.982877, -0.142158, 0.175741, -0.124041, -0.875609, -0.528708, -0.911127, 0.782257, -0.509403, 0.573973, -0.151309]\n",
"out shape: (2, 6)\n",
"out: [-0.072127, -0.553929, 0.023246, -0.638412, 0.556627, 0.284959, 0.461494, 0.962183, 0.021732, 0.922994, 0.07991, -0.164385]\n"
]
}
],
"source": [
"data_in_shape = (6, 6)\n",
"L = MaxPooling1D(pool_size=2, strides=3, padding='same')\n",
"\n",
"layer_0 = Input(shape=data_in_shape)\n",
"layer_1 = L(layer_0)\n",
"model = Model(inputs=layer_0, outputs=layer_1)\n",
"\n",
"# set weights to random (use seed for reproducibility)\n",
"np.random.seed(255)\n",
"data_in = 2 * np.random.random(data_in_shape) - 1\n",
"result = model.predict(np.array([data_in]))\n",
"data_out_shape = result[0].shape\n",
"data_in_formatted = format_decimal(data_in.ravel().tolist())\n",
"data_out_formatted = format_decimal(result[0].ravel().tolist())\n",
"print('')\n",
"print('in shape:', data_in_shape)\n",
"print('in:', data_in_formatted)\n",
"print('out shape:', data_out_shape)\n",
"print('out:', data_out_formatted)\n",
"\n",
"DATA['pooling.MaxPooling1D.5'] = {\n",
" 'input': {'data': data_in_formatted, 'shape': data_in_shape},\n",
" 'expected': {'data': data_out_formatted, 'shape': data_out_shape}\n",
"}"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"**[pooling.MaxPooling1D.6] input 6x6, pool_size=3, strides=None, padding='valid'**"
]
},
{
"cell_type": "code",
"execution_count": 10,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"\n",
"in shape: (6, 6)\n",
"in: [-0.908432, 0.172241, -0.59352, -0.831514, -0.948016, -0.194126, -0.242576, -0.89432, 0.610714, -0.24071, -0.245859, 0.500851, 0.088791, 0.04635, 0.908568, -0.232197, -0.175815, -0.177919, -0.535898, 0.04802, 0.512585, 0.854168, 0.283045, 0.282488, -0.126263, 0.772568, 0.403228, 0.721107, -0.043311, -0.799013, -0.683105, -0.52703, 0.838417, 0.915738, 0.180207, -0.181716]\n",
"out shape: (2, 6)\n",
"out: [0.088791, 0.172241, 0.908568, -0.232197, -0.175815, 0.500851, -0.126263, 0.772568, 0.838417, 0.915738, 0.283045, 0.282488]\n"
]
}
],
"source": [
"data_in_shape = (6, 6)\n",
"L = MaxPooling1D(pool_size=3, strides=None, padding='valid')\n",
"\n",
"layer_0 = Input(shape=data_in_shape)\n",
"layer_1 = L(layer_0)\n",
"model = Model(inputs=layer_0, outputs=layer_1)\n",
"\n",
"# set weights to random (use seed for reproducibility)\n",
"np.random.seed(256)\n",
"data_in = 2 * np.random.random(data_in_shape) - 1\n",
"result = model.predict(np.array([data_in]))\n",
"data_out_shape = result[0].shape\n",
"data_in_formatted = format_decimal(data_in.ravel().tolist())\n",
"data_out_formatted = format_decimal(result[0].ravel().tolist())\n",
"print('')\n",
"print('in shape:', data_in_shape)\n",
"print('in:', data_in_formatted)\n",
"print('out shape:', data_out_shape)\n",
"print('out:', data_out_formatted)\n",
"\n",
"DATA['pooling.MaxPooling1D.6'] = {\n",
" 'input': {'data': data_in_formatted, 'shape': data_in_shape},\n",
" 'expected': {'data': data_out_formatted, 'shape': data_out_shape}\n",
"}"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"**[pooling.MaxPooling1D.7] input 7x7, pool_size=3, strides=1, padding='same'**"
]
},
{
"cell_type": "code",
"execution_count": 11,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"\n",
"in shape: (7, 7)\n",
"in: [0.859653, 0.613312, 0.262871, 0.484585, 0.518061, -0.718848, -0.351388, -0.501557, 0.017192, -0.026869, -0.768317, -0.476893, -0.895809, 0.764782, 0.862057, 0.021243, 0.004039, 0.760431, 0.72102, 0.395305, 0.930351, 0.425255, -0.000952, -0.060338, -0.095258, 0.173776, -0.645557, 0.196502, 0.27885, -0.6868, -0.551196, 0.726361, -0.382779, 0.61877, 0.023847, -0.451251, 0.065412, -0.708225, -0.815011, -0.926643, 0.323493, -0.063352, 0.16365, -0.030438, -0.054635, 0.193949, -0.574495, 0.022988, 0.36335]\n",
"out shape: (7, 7)\n",
"out: [0.859653, 0.613312, 0.262871, 0.484585, 0.518061, -0.718848, 0.764782, 0.862057, 0.613312, 0.262871, 0.760431, 0.72102, 0.395305, 0.930351, 0.862057, 0.021243, 0.004039, 0.760431, 0.72102, 0.395305, 0.930351, 0.862057, 0.021243, 0.004039, 0.760431, 0.72102, 0.61877, 0.930351, 0.425255, 0.065412, -0.060338, 0.726361, 0.173776, 0.61877, 0.196502, 0.27885, 0.065412, -0.054635, 0.726361, -0.382779, 0.61877, 0.36335, 0.16365, 0.065412, -0.054635, 0.193949, -0.574495, 0.323493, 0.36335]\n"
]
}
],
"source": [
"data_in_shape = (7, 7)\n",
"L = MaxPooling1D(pool_size=3, strides=1, padding='same')\n",
"\n",
"layer_0 = Input(shape=data_in_shape)\n",
"layer_1 = L(layer_0)\n",
"model = Model(inputs=layer_0, outputs=layer_1)\n",
"\n",
"# set weights to random (use seed for reproducibility)\n",
"np.random.seed(257)\n",
"data_in = 2 * np.random.random(data_in_shape) - 1\n",
"result = model.predict(np.array([data_in]))\n",
"data_out_shape = result[0].shape\n",
"data_in_formatted = format_decimal(data_in.ravel().tolist())\n",
"data_out_formatted = format_decimal(result[0].ravel().tolist())\n",
"print('')\n",
"print('in shape:', data_in_shape)\n",
"print('in:', data_in_formatted)\n",
"print('out shape:', data_out_shape)\n",
"print('out:', data_out_formatted)\n",
"\n",
"DATA['pooling.MaxPooling1D.7'] = {\n",
" 'input': {'data': data_in_formatted, 'shape': data_in_shape},\n",
" 'expected': {'data': data_out_formatted, 'shape': data_out_shape}\n",
"}"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"**[pooling.MaxPooling1D.8] input 7x7, pool_size=3, strides=3, padding='same'**"
]
},
{
"cell_type": "code",
"execution_count": 12,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"\n",
"in shape: (7, 7)\n",
"in: [-0.830746, 0.315868, -0.173506, 0.415541, -0.957882, 0.658995, 0.795264, -0.147083, -0.042061, 0.230065, 0.388847, -0.277524, -0.268423, 0.35691, -0.515291, -0.37555, 0.367489, 0.753251, -0.60764, -0.16741, -0.893275, -0.814508, -0.437352, 0.062193, -0.003077, 0.560767, -0.646034, -0.283879, 0.097661, 0.401756, -0.236235, -0.199824, -0.252007, -0.335503, 0.414988, 0.301686, 0.309765, -0.349835, -0.274081, 0.383308, -0.782973, -0.667924, 0.282556, -0.932491, 0.954125, 0.837689, 0.219229, -0.583405, -0.018424]\n",
"out shape: (3, 7)\n",
"out: [-0.147083, 0.315868, 0.230065, 0.415541, -0.277524, 0.658995, 0.795264, 0.097661, 0.401756, 0.367489, 0.753251, 0.560767, -0.16741, 0.414988, 0.301686, 0.309765, 0.954125, 0.837689, 0.383308, -0.583405, -0.018424]\n"
]
}
],
"source": [
"data_in_shape = (7, 7)\n",
"L = MaxPooling1D(pool_size=3, strides=3, padding='same')\n",
"\n",
"layer_0 = Input(shape=data_in_shape)\n",
"layer_1 = L(layer_0)\n",
"model = Model(inputs=layer_0, outputs=layer_1)\n",
"\n",
"# set weights to random (use seed for reproducibility)\n",
"np.random.seed(258)\n",
"data_in = 2 * np.random.random(data_in_shape) - 1\n",
"result = model.predict(np.array([data_in]))\n",
"data_out_shape = result[0].shape\n",
"data_in_formatted = format_decimal(data_in.ravel().tolist())\n",
"data_out_formatted = format_decimal(result[0].ravel().tolist())\n",
"print('')\n",
"print('in shape:', data_in_shape)\n",
"print('in:', data_in_formatted)\n",
"print('out shape:', data_out_shape)\n",
"print('out:', data_out_formatted)\n",
"\n",
"DATA['pooling.MaxPooling1D.8'] = {\n",
" 'input': {'data': data_in_formatted, 'shape': data_in_shape},\n",
" 'expected': {'data': data_out_formatted, 'shape': data_out_shape}\n",
"}"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### export for Keras.js tests"
]
},
{
"cell_type": "code",
"execution_count": 13,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"import os\n",
"\n",
"filename = '../../../test/data/layers/pooling/MaxPooling1D.json'\n",
"if not os.path.exists(os.path.dirname(filename)):\n",
" os.makedirs(os.path.dirname(filename))\n",
"with open(filename, 'w') as f:\n",
" json.dump(DATA, f)"
]
},
{
"cell_type": "code",
"execution_count": 14,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"{\"pooling.MaxPooling1D.0\": {\"input\": {\"data\": [-0.570441, -0.454673, -0.285321, 0.237249, 0.282682, 0.428035, 0.160547, -0.332203, 0.546391, 0.272735, 0.010827, -0.763164, -0.442696, 0.381948, -0.676994, 0.753553, -0.031788, 0.915329, -0.738844, 0.269075, 0.434091, 0.991585, -0.944288, 0.258834, 0.162138, 0.565201, -0.492094, 0.170854, -0.139788, -0.710674, 0.406968, 0.705926, -0.094137, -0.793497, -0.040684, 0.522292], \"shape\": [6, 6]}, \"expected\": {\"data\": [0.160547, -0.332203, 0.546391, 0.272735, 0.282682, 0.428035, -0.442696, 0.381948, 0.434091, 0.991585, -0.031788, 0.915329, 0.406968, 0.705926, -0.094137, 0.170854, -0.040684, 0.522292], \"shape\": [3, 6]}}, \"pooling.MaxPooling1D.1\": {\"input\": {\"data\": [0.275222, -0.793967, -0.468107, -0.841484, -0.295362, 0.78175, 0.068787, -0.261747, -0.625733, -0.042907, 0.861141, 0.85267, 0.956439, 0.717838, -0.99869, -0.963008, 0.013277, -0.180306, 0.832137, -0.385252, -0.524308, 0.659706, -0.905127, 0.526292, 0.832569, 0.084455, 0.23838, -0.046178, -0.735871, 0.776883, -0.394643, 0.498903, 0.029584, -0.17332, 0.628159, 0.445074], \"shape\": [6, 6]}, \"expected\": {\"data\": [0.275222, -0.261747, -0.468107, -0.042907, 0.861141, 0.85267, 0.956439, 0.717838, -0.625733, -0.042907, 0.861141, 0.85267, 0.956439, 0.717838, -0.524308, 0.659706, 0.013277, 0.526292, 0.832569, 0.084455, 0.23838, 0.659706, -0.735871, 0.776883, 0.832569, 0.498903, 0.23838, -0.046178, 0.628159, 0.776883], \"shape\": [5, 6]}}, \"pooling.MaxPooling1D.2\": {\"input\": {\"data\": [-0.989173, -0.133618, -0.505338, 0.023259, 0.503982, -0.303769, -0.436321, 0.793911, 0.416102, 0.806405, -0.098342, -0.738022, -0.982676, 0.805073, 0.741244, -0.941634, -0.253526, -0.136544, -0.295772, 0.207565, -0.517246, -0.686963, -0.176235, -0.354111, -0.862411, -0.969822, 0.200074, 0.290718, -0.038623, 0.294839, 0.247968, 0.557946, -0.455596, 0.6624, 0.879529, -0.466772], \"shape\": [6, 6]}, \"expected\": {\"data\": [-0.436321, 0.793911, 0.416102, 0.806405, 0.503982, -0.303769, -0.295772, 0.207565, 0.200074, 0.290718, -0.038623, 0.294839], \"shape\": [2, 6]}}, \"pooling.MaxPooling1D.3\": {\"input\": {\"data\": [-0.47588, 0.366985, 0.040173, 0.015578, -0.906159, 0.241982, -0.771299, -0.443554, -0.56404, -0.17751, 0.541277, -0.233327, 0.024369, 0.858275, 0.496191, 0.980574, -0.59522, 0.480899, 0.392553, -0.191718, 0.055121, 0.289836, -0.498339, 0.800408, 0.132679, -0.716649, 0.840092, -0.088837, -0.538209, -0.580887, -0.370128, -0.924933, -0.161736, -0.205619, 0.793729, -0.354472], \"shape\": [6, 6]}, \"expected\": {\"data\": [-0.47588, 0.366985, 0.040173, 0.015578, 0.541277, 0.241982, 0.392553, 0.858275, 0.496191, 0.980574, -0.498339, 0.800408, 0.132679, -0.716649, 0.840092, -0.088837, 0.793729, -0.354472], \"shape\": [3, 6]}}, \"pooling.MaxPooling1D.4\": {\"input\": {\"data\": [0.024124, 0.280236, -0.680013, -0.042458, -0.164273, 0.358409, 0.511014, -0.585272, -0.481578, 0.692702, 0.64189, -0.400252, -0.922248, -0.735105, -0.533918, 0.071402, 0.310474, 0.369868, 0.767931, -0.842066, -0.091189, 0.835301, -0.480484, 0.950819, -0.002131, 0.086491, -0.480947, 0.405572, -0.083803, -0.921447, -0.291545, 0.674087, -0.560444, 0.881432, 0.076544, 0.63549], \"shape\": [6, 6]}, \"expected\": {\"data\": [0.511014, 0.280236, -0.481578, 0.692702, 0.64189, 0.358409, 0.511014, -0.585272, -0.481578, 0.692702, 0.64189, 0.369868, 0.767931, -0.735105, -0.091189, 0.835301, 0.310474, 0.950819, 0.767931, 0.086491, -0.091189, 0.835301, -0.083803, 0.950819, -0.002131, 0.674087, -0.480947, 0.881432, 0.076544, 0.63549, -0.291545, 0.674087, -0.560444, 0.881432, 0.076544, 0.63549], \"shape\": [6, 6]}}, \"pooling.MaxPooling1D.5\": {\"input\": {\"data\": [-0.072127, -0.553929, -0.355552, -0.936405, 0.556627, -0.482815, -0.225337, -0.640315, 0.023246, -0.638412, -0.797304, 0.284959, -0.569771, -0.685286, 0.002481, 0.398436, 0.11345, 0.416629, -0.526713, 0.962183, 0.021732, 0.922994, 0.07991, -0.164385, 0.461494, -0.982877, -0.142158, 0.175741, -0.124041, -0.875609, -0.528708, -0.911127, 0.782257, -0.509403, 0.573973, -0.151309], \"shape\": [6, 6]}, \"expected\": {\"data\": [-0.072127, -0.553929, 0.023246, -0.638412, 0.556627, 0.284959, 0.461494, 0.962183, 0.021732, 0.922994, 0.07991, -0.164385], \"shape\": [2, 6]}}, \"pooling.MaxPooling1D.6\": {\"input\": {\"data\": [-0.908432, 0.172241, -0.59352, -0.831514, -0.948016, -0.194126, -0.242576, -0.89432, 0.610714, -0.24071, -0.245859, 0.500851, 0.088791, 0.04635, 0.908568, -0.232197, -0.175815, -0.177919, -0.535898, 0.04802, 0.512585, 0.854168, 0.283045, 0.282488, -0.126263, 0.772568, 0.403228, 0.721107, -0.043311, -0.799013, -0.683105, -0.52703, 0.838417, 0.915738, 0.180207, -0.181716], \"shape\": [6, 6]}, \"expected\": {\"data\": [0.088791, 0.172241, 0.908568, -0.232197, -0.175815, 0.500851, -0.126263, 0.772568, 0.838417, 0.915738, 0.283045, 0.282488], \"shape\": [2, 6]}}, \"pooling.MaxPooling1D.7\": {\"input\": {\"data\": [0.859653, 0.613312, 0.262871, 0.484585, 0.518061, -0.718848, -0.351388, -0.501557, 0.017192, -0.026869, -0.768317, -0.476893, -0.895809, 0.764782, 0.862057, 0.021243, 0.004039, 0.760431, 0.72102, 0.395305, 0.930351, 0.425255, -0.000952, -0.060338, -0.095258, 0.173776, -0.645557, 0.196502, 0.27885, -0.6868, -0.551196, 0.726361, -0.382779, 0.61877, 0.023847, -0.451251, 0.065412, -0.708225, -0.815011, -0.926643, 0.323493, -0.063352, 0.16365, -0.030438, -0.054635, 0.193949, -0.574495, 0.022988, 0.36335], \"shape\": [7, 7]}, \"expected\": {\"data\": [0.859653, 0.613312, 0.262871, 0.484585, 0.518061, -0.718848, 0.764782, 0.862057, 0.613312, 0.262871, 0.760431, 0.72102, 0.395305, 0.930351, 0.862057, 0.021243, 0.004039, 0.760431, 0.72102, 0.395305, 0.930351, 0.862057, 0.021243, 0.004039, 0.760431, 0.72102, 0.61877, 0.930351, 0.425255, 0.065412, -0.060338, 0.726361, 0.173776, 0.61877, 0.196502, 0.27885, 0.065412, -0.054635, 0.726361, -0.382779, 0.61877, 0.36335, 0.16365, 0.065412, -0.054635, 0.193949, -0.574495, 0.323493, 0.36335], \"shape\": [7, 7]}}, \"pooling.MaxPooling1D.8\": {\"input\": {\"data\": [-0.830746, 0.315868, -0.173506, 0.415541, -0.957882, 0.658995, 0.795264, -0.147083, -0.042061, 0.230065, 0.388847, -0.277524, -0.268423, 0.35691, -0.515291, -0.37555, 0.367489, 0.753251, -0.60764, -0.16741, -0.893275, -0.814508, -0.437352, 0.062193, -0.003077, 0.560767, -0.646034, -0.283879, 0.097661, 0.401756, -0.236235, -0.199824, -0.252007, -0.335503, 0.414988, 0.301686, 0.309765, -0.349835, -0.274081, 0.383308, -0.782973, -0.667924, 0.282556, -0.932491, 0.954125, 0.837689, 0.219229, -0.583405, -0.018424], \"shape\": [7, 7]}, \"expected\": {\"data\": [-0.147083, 0.315868, 0.230065, 0.415541, -0.277524, 0.658995, 0.795264, 0.097661, 0.401756, 0.367489, 0.753251, 0.560767, -0.16741, 0.414988, 0.301686, 0.309765, 0.954125, 0.837689, 0.383308, -0.583405, -0.018424], \"shape\": [3, 7]}}}\n"
]
}
],
"source": [
"print(json.dumps(DATA))"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": []
}
],
"metadata": {
"anaconda-cloud": {},
"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": "3.6.3"
}
},
"nbformat": 4,
"nbformat_minor": 1
}
| mit |
yuyichao/ApproxFun.jl | examples/Time Evolution PDEs on Interval.ipynb | 1 | 226954 | {
"cells": [
{
"cell_type": "code",
"execution_count": 2,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"using ApproxFun,Interact,Reactive"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# KdV with Neumann $$u_t + 6uu_x + u_{xxx}=0$$"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Evaluate the first cell to start the plot, then the second cell to evolve the plot. Evaluating the second cell will continue evolving the solution. It may take two times before it works. "
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"image/png": "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",
"image/svg+xml": [
"<?xml version=\"1.0\" encoding=\"UTF-8\"?>\n",
"<svg xmlns=\"http://www.w3.org/2000/svg\"\n",
" xmlns:xlink=\"http://www.w3.org/1999/xlink\"\n",
" xmlns:gadfly=\"http://www.gadflyjl.org/ns\"\n",
" version=\"1.2\"\n",
" width=\"141.42mm\" height=\"100mm\" viewBox=\"0 0 141.42 100\"\n",
" stroke=\"none\"\n",
" fill=\"#000000\"\n",
" stroke-width=\"0.3\"\n",
" font-size=\"3.88\"\n",
">\n",
"<g class=\"plotroot xscalable yscalable\" id=\"fig-31e0052996db4361a264c4686e0076eb-element-1\">\n",
" <g font-size=\"3.88\" font-family=\"'PT Sans','Helvetica Neue','Helvetica',sans-serif\" fill=\"#564A55\" stroke=\"#000000\" stroke-opacity=\"0.000\" id=\"fig-31e0052996db4361a264c4686e0076eb-element-2\">\n",
" <text x=\"76.01\" y=\"88.39\" text-anchor=\"middle\" dy=\"0.6em\">x</text>\n",
" </g>\n",
" <g class=\"guide xlabels\" font-size=\"2.82\" font-family=\"'PT Sans Caption','Helvetica Neue','Helvetica',sans-serif\" fill=\"#6C606B\" id=\"fig-31e0052996db4361a264c4686e0076eb-element-3\">\n",
" <text x=\"17.59\" y=\"81.72\" text-anchor=\"middle\" dy=\"0.6em\">-10</text>\n",
" <text x=\"46.8\" y=\"81.72\" text-anchor=\"middle\" dy=\"0.6em\">-5</text>\n",
" <text x=\"76.01\" y=\"81.72\" text-anchor=\"middle\" dy=\"0.6em\">0</text>\n",
" <text x=\"105.21\" y=\"81.72\" text-anchor=\"middle\" dy=\"0.6em\">5</text>\n",
" <text x=\"134.42\" y=\"81.72\" text-anchor=\"middle\" dy=\"0.6em\">10</text>\n",
" </g>\n",
" <g clip-path=\"url(#fig-31e0052996db4361a264c4686e0076eb-element-5)\" id=\"fig-31e0052996db4361a264c4686e0076eb-element-4\">\n",
" <g pointer-events=\"visible\" opacity=\"1\" fill=\"#000000\" fill-opacity=\"0.000\" stroke=\"#000000\" stroke-opacity=\"0.000\" class=\"guide background\" id=\"fig-31e0052996db4361a264c4686e0076eb-element-6\">\n",
" <rect x=\"15.59\" y=\"5\" width=\"120.83\" height=\"75.72\"/>\n",
" </g>\n",
" <g class=\"guide ygridlines xfixed\" stroke-dasharray=\"0.5,0.5\" stroke-width=\"0.2\" stroke=\"#D0D0E0\" id=\"fig-31e0052996db4361a264c4686e0076eb-element-7\">\n",
" <path fill=\"none\" d=\"M15.59,78.71 L 136.42 78.71\"/>\n",
" <path fill=\"none\" d=\"M15.59,60.79 L 136.42 60.79\"/>\n",
" <path fill=\"none\" d=\"M15.59,42.86 L 136.42 42.86\"/>\n",
" <path fill=\"none\" d=\"M15.59,24.93 L 136.42 24.93\"/>\n",
" <path fill=\"none\" d=\"M15.59,7 L 136.42 7\"/>\n",
" </g>\n",
" <g class=\"guide xgridlines yfixed\" stroke-dasharray=\"0.5,0.5\" stroke-width=\"0.2\" stroke=\"#D0D0E0\" id=\"fig-31e0052996db4361a264c4686e0076eb-element-8\">\n",
" <path fill=\"none\" d=\"M17.59,5 L 17.59 80.72\"/>\n",
" <path fill=\"none\" d=\"M46.8,5 L 46.8 80.72\"/>\n",
" <path fill=\"none\" d=\"M76.01,5 L 76.01 80.72\"/>\n",
" <path fill=\"none\" d=\"M105.21,5 L 105.21 80.72\"/>\n",
" <path fill=\"none\" d=\"M134.42,5 L 134.42 80.72\"/>\n",
" </g>\n",
" <g class=\"plotpanel\" id=\"fig-31e0052996db4361a264c4686e0076eb-element-9\">\n",
" <g stroke-width=\"0.3\" fill=\"#000000\" fill-opacity=\"0.000\" class=\"geometry\" stroke-dasharray=\"none\" stroke=\"#00BFFF\" id=\"fig-31e0052996db4361a264c4686e0076eb-element-10\">\n",
" <path fill=\"none\" d=\"M17.59,60.79 L 17.6 60.79 17.6 60.79 17.61 60.79 17.63 60.79 17.65 60.79 17.67 60.79 17.69 60.79 17.72 60.79 17.75 60.79 17.78 60.79 17.82 60.79 17.86 60.79 17.91 60.79 17.96 60.79 18.01 60.79 18.07 60.79 18.12 60.79 18.19 60.79 18.25 60.79 18.32 60.79 18.4 60.79 18.47 60.79 18.55 60.79 18.63 60.79 18.72 60.79 18.81 60.79 18.9 60.79 19 60.79 19.1 60.79 19.2 60.79 19.31 60.79 19.42 60.79 19.53 60.79 19.65 60.79 19.77 60.79 19.9 60.79 20.02 60.79 20.15 60.79 20.29 60.79 20.42 60.79 20.56 60.79 20.71 60.79 20.85 60.79 21 60.79 21.16 60.79 21.32 60.79 21.48 60.79 21.64 60.79 21.8 60.79 21.97 60.79 22.15 60.79 22.32 60.79 22.5 60.79 22.69 60.79 22.87 60.79 23.06 60.79 23.25 60.79 23.45 60.79 23.65 60.79 23.85 60.79 24.05 60.79 24.26 60.79 24.47 60.79 24.68 60.79 24.9 60.79 25.12 60.79 25.34 60.79 25.57 60.79 25.8 60.79 26.03 60.79 26.27 60.79 26.5 60.79 26.74 60.79 26.99 60.79 27.23 60.79 27.48 60.79 27.74 60.79 27.99 60.79 28.25 60.79 28.51 60.79 28.78 60.79 29.04 60.79 29.31 60.79 29.58 60.79 29.86 60.79 30.14 60.79 30.42 60.79 30.7 60.79 30.99 60.79 31.27 60.79 31.57 60.79 31.86 60.79 32.16 60.79 32.46 60.79 32.76 60.79 33.06 60.79 33.37 60.79 33.68 60.79 33.99 60.79 34.31 60.79 34.62 60.79 34.94 60.79 35.26 60.79 35.59 60.79 35.91 60.79 36.24 60.79 36.58 60.79 36.91 60.79 37.25 60.79 37.58 60.79 37.92 60.79 38.27 60.79 38.61 60.79 38.96 60.79 39.31 60.79 39.66 60.79 40.02 60.79 40.37 60.79 40.73 60.79 41.09 60.79 41.45 60.79 41.82 60.79 42.18 60.79 42.55 60.79 42.92 60.79 43.3 60.79 43.67 60.79 44.05 60.79 44.43 60.79 44.81 60.79 45.19 60.79 45.57 60.79 45.96 60.79 46.35 60.79 46.73 60.79 47.13 60.79 47.52 60.79 47.91 60.79 48.31 60.79 48.71 60.79 49.11 60.79 49.51 60.79 49.91 60.79 50.31 60.79 50.72 60.79 51.13 60.79 51.54 60.79 51.95 60.79 52.36 60.79 52.77 60.79 53.18 60.79 53.6 60.79 54.02 60.79 54.44 60.79 54.86 60.79 55.28 60.79 55.7 60.79 56.12 60.79 56.55 60.79 56.97 60.79 57.4 60.78 57.83 60.78 58.26 60.78 58.69 60.78 59.12 60.78 59.55 60.77 59.98 60.77 60.42 60.76 60.85 60.74 61.29 60.72 61.72 60.7 62.16 60.66 62.6 60.6 63.04 60.53 63.48 60.43 63.92 60.29 64.36 60.11 64.8 59.88 65.25 59.58 65.69 59.2 66.13 58.72 66.58 58.14 67.02 57.42 67.47 56.55 67.92 55.52 68.36 54.31 68.81 52.92 69.26 51.35 69.71 49.58 70.16 47.64 70.6 45.54 71.05 43.32 71.5 41 71.95 38.64 72.4 36.29 72.85 34 73.3 31.85 73.75 29.89 74.2 28.19 74.65 26.8 75.11 25.77 75.56 25.14 76.01 24.93 76.46 25.14 76.91 25.77 77.36 26.8 77.81 28.19 78.26 29.89 78.71 31.85 79.16 34 79.61 36.29 80.06 38.64 80.51 41 80.96 43.32 81.41 45.54 81.86 47.64 82.31 49.58 82.76 51.35 83.2 52.92 83.65 54.31 84.1 55.52 84.54 56.55 84.99 57.42 85.43 58.14 85.88 58.72 86.32 59.2 86.77 59.58 87.21 59.88 87.65 60.11 88.09 60.29 88.53 60.43 88.97 60.53 89.41 60.6 89.85 60.66 90.29 60.7 90.73 60.72 91.16 60.74 91.6 60.76 92.03 60.77 92.46 60.77 92.9 60.78 93.33 60.78 93.76 60.78 94.19 60.78 94.61 60.78 95.04 60.79 95.47 60.79 95.89 60.79 96.32 60.79 96.74 60.79 97.16 60.79 97.58 60.79 98 60.79 98.41 60.79 98.83 60.79 99.24 60.79 99.66 60.79 100.07 60.79 100.48 60.79 100.89 60.79 101.29 60.79 101.7 60.79 102.1 60.79 102.51 60.79 102.91 60.79 103.31 60.79 103.7 60.79 104.1 60.79 104.5 60.79 104.89 60.79 105.28 60.79 105.67 60.79 106.06 60.79 106.44 60.79 106.83 60.79 107.21 60.79 107.59 60.79 107.97 60.79 108.34 60.79 108.72 60.79 109.09 60.79 109.46 60.79 109.83 60.79 110.2 60.79 110.56 60.79 110.92 60.79 111.28 60.79 111.64 60.79 112 60.79 112.35 60.79 112.7 60.79 113.05 60.79 113.4 60.79 113.75 60.79 114.09 60.79 114.43 60.79 114.77 60.79 115.1 60.79 115.44 60.79 115.77 60.79 116.1 60.79 116.43 60.79 116.75 60.79 117.07 60.79 117.39 60.79 117.71 60.79 118.02 60.79 118.34 60.79 118.64 60.79 118.95 60.79 119.26 60.79 119.56 60.79 119.86 60.79 120.15 60.79 120.45 60.79 120.74 60.79 121.03 60.79 121.31 60.79 121.6 60.79 121.88 60.79 122.16 60.79 122.43 60.79 122.7 60.79 122.97 60.79 123.24 60.79 123.5 60.79 123.76 60.79 124.02 60.79 124.28 60.79 124.53 60.79 124.78 60.79 125.03 60.79 125.27 60.79 125.51 60.79 125.75 60.79 125.98 60.79 126.22 60.79 126.44 60.79 126.67 60.79 126.89 60.79 127.11 60.79 127.33 60.79 127.54 60.79 127.75 60.79 127.96 60.79 128.17 60.79 128.37 60.79 128.57 60.79 128.76 60.79 128.95 60.79 129.14 60.79 129.33 60.79 129.51 60.79 129.69 60.79 129.87 60.79 130.04 60.79 130.21 60.79 130.38 60.79 130.54 60.79 130.7 60.79 130.86 60.79 131.01 60.79 131.16 60.79 131.31 60.79 131.45 60.79 131.59 60.79 131.73 60.79 131.86 60.79 131.99 60.79 132.12 60.79 132.24 60.79 132.36 60.79 132.48 60.79 132.59 60.79 132.7 60.79 132.81 60.79 132.91 60.79 133.01 60.79 133.11 60.79 133.2 60.79 133.29 60.79 133.38 60.79 133.46 60.79 133.54 60.79 133.62 60.79 133.69 60.79 133.76 60.79 133.83 60.79 133.89 60.79 133.95 60.79 134 60.79 134.06 60.79 134.1 60.79 134.15 60.79 134.19 60.79 134.23 60.79 134.26 60.79 134.3 60.79 134.32 60.79 134.35 60.79 134.37 60.79 134.39 60.79 134.4 60.79 134.41 60.79 134.42 60.79 134.42 60.79\"/>\n",
" </g>\n",
" </g>\n",
" </g>\n",
" <g class=\"guide ylabels\" font-size=\"2.82\" font-family=\"'PT Sans Caption','Helvetica Neue','Helvetica',sans-serif\" fill=\"#6C606B\" id=\"fig-31e0052996db4361a264c4686e0076eb-element-11\">\n",
" <text x=\"14.59\" y=\"78.71\" text-anchor=\"end\" dy=\"0.35em\">-1</text>\n",
" <text x=\"14.59\" y=\"60.79\" text-anchor=\"end\" dy=\"0.35em\">0</text>\n",
" <text x=\"14.59\" y=\"42.86\" text-anchor=\"end\" dy=\"0.35em\">1</text>\n",
" <text x=\"14.59\" y=\"24.93\" text-anchor=\"end\" dy=\"0.35em\">2</text>\n",
" <text x=\"14.59\" y=\"7\" text-anchor=\"end\" dy=\"0.35em\">3</text>\n",
" </g>\n",
" <g font-size=\"3.88\" font-family=\"'PT Sans','Helvetica Neue','Helvetica',sans-serif\" fill=\"#564A55\" stroke=\"#000000\" stroke-opacity=\"0.000\" id=\"fig-31e0052996db4361a264c4686e0076eb-element-12\">\n",
" <text x=\"8.81\" y=\"42.86\" text-anchor=\"end\" dy=\"0.35em\">y</text>\n",
" </g>\n",
"</g>\n",
"<defs>\n",
"<clipPath id=\"fig-31e0052996db4361a264c4686e0076eb-element-5\">\n",
" <path d=\"M15.59,5 L 136.42 5 136.42 80.72 15.59 80.72\" />\n",
"</clipPath\n",
"></defs>\n",
"</svg>\n"
],
"text/html": [
"<?xml version=\"1.0\" encoding=\"UTF-8\"?>\n",
"<svg xmlns=\"http://www.w3.org/2000/svg\"\n",
" xmlns:xlink=\"http://www.w3.org/1999/xlink\"\n",
" xmlns:gadfly=\"http://www.gadflyjl.org/ns\"\n",
" version=\"1.2\"\n",
" width=\"141.42mm\" height=\"100mm\" viewBox=\"0 0 141.42 100\"\n",
" stroke=\"none\"\n",
" fill=\"#000000\"\n",
" stroke-width=\"0.3\"\n",
" font-size=\"3.88\"\n",
"\n",
" id=\"fig-24ef7475d29e43d3b5056099cb85fa82\">\n",
"<g class=\"plotroot xscalable yscalable\" id=\"fig-24ef7475d29e43d3b5056099cb85fa82-element-1\">\n",
" <g font-size=\"3.88\" font-family=\"'PT Sans','Helvetica Neue','Helvetica',sans-serif\" fill=\"#564A55\" stroke=\"#000000\" stroke-opacity=\"0.000\" id=\"fig-24ef7475d29e43d3b5056099cb85fa82-element-2\">\n",
" <text x=\"76.01\" y=\"88.39\" text-anchor=\"middle\" dy=\"0.6em\">x</text>\n",
" </g>\n",
" <g class=\"guide xlabels\" font-size=\"2.82\" font-family=\"'PT Sans Caption','Helvetica Neue','Helvetica',sans-serif\" fill=\"#6C606B\" id=\"fig-24ef7475d29e43d3b5056099cb85fa82-element-3\">\n",
" <text x=\"-128.44\" y=\"84.39\" text-anchor=\"middle\" visibility=\"hidden\" gadfly:scale=\"1.0\">-35</text>\n",
" <text x=\"-99.24\" y=\"84.39\" text-anchor=\"middle\" visibility=\"hidden\" gadfly:scale=\"1.0\">-30</text>\n",
" <text x=\"-70.03\" y=\"84.39\" text-anchor=\"middle\" visibility=\"hidden\" gadfly:scale=\"1.0\">-25</text>\n",
" <text x=\"-40.82\" y=\"84.39\" text-anchor=\"middle\" visibility=\"hidden\" gadfly:scale=\"1.0\">-20</text>\n",
" <text x=\"-11.61\" y=\"84.39\" text-anchor=\"middle\" visibility=\"hidden\" gadfly:scale=\"1.0\">-15</text>\n",
" <text x=\"17.59\" y=\"84.39\" text-anchor=\"middle\" visibility=\"visible\" gadfly:scale=\"1.0\">-10</text>\n",
" <text x=\"46.8\" y=\"84.39\" text-anchor=\"middle\" visibility=\"visible\" gadfly:scale=\"1.0\">-5</text>\n",
" <text x=\"76.01\" y=\"84.39\" text-anchor=\"middle\" visibility=\"visible\" gadfly:scale=\"1.0\">0</text>\n",
" <text x=\"105.21\" y=\"84.39\" text-anchor=\"middle\" visibility=\"visible\" gadfly:scale=\"1.0\">5</text>\n",
" <text x=\"134.42\" y=\"84.39\" text-anchor=\"middle\" visibility=\"visible\" gadfly:scale=\"1.0\">10</text>\n",
" <text x=\"163.63\" y=\"84.39\" text-anchor=\"middle\" visibility=\"hidden\" gadfly:scale=\"1.0\">15</text>\n",
" <text x=\"192.84\" y=\"84.39\" text-anchor=\"middle\" visibility=\"hidden\" gadfly:scale=\"1.0\">20</text>\n",
" <text x=\"222.04\" y=\"84.39\" text-anchor=\"middle\" visibility=\"hidden\" gadfly:scale=\"1.0\">25</text>\n",
" <text x=\"251.25\" y=\"84.39\" text-anchor=\"middle\" visibility=\"hidden\" gadfly:scale=\"1.0\">30</text>\n",
" <text x=\"280.46\" y=\"84.39\" text-anchor=\"middle\" visibility=\"hidden\" gadfly:scale=\"1.0\">35</text>\n",
" <text x=\"-99.24\" y=\"84.39\" text-anchor=\"middle\" visibility=\"hidden\" gadfly:scale=\"10.0\">-30</text>\n",
" <text x=\"-93.39\" y=\"84.39\" text-anchor=\"middle\" visibility=\"hidden\" gadfly:scale=\"10.0\">-29</text>\n",
" <text x=\"-87.55\" y=\"84.39\" text-anchor=\"middle\" visibility=\"hidden\" gadfly:scale=\"10.0\">-28</text>\n",
" <text x=\"-81.71\" y=\"84.39\" text-anchor=\"middle\" visibility=\"hidden\" gadfly:scale=\"10.0\">-27</text>\n",
" <text x=\"-75.87\" y=\"84.39\" text-anchor=\"middle\" visibility=\"hidden\" gadfly:scale=\"10.0\">-26</text>\n",
" <text x=\"-70.03\" y=\"84.39\" text-anchor=\"middle\" visibility=\"hidden\" gadfly:scale=\"10.0\">-25</text>\n",
" <text x=\"-64.19\" y=\"84.39\" text-anchor=\"middle\" visibility=\"hidden\" gadfly:scale=\"10.0\">-24</text>\n",
" <text x=\"-58.35\" y=\"84.39\" text-anchor=\"middle\" visibility=\"hidden\" gadfly:scale=\"10.0\">-23</text>\n",
" <text x=\"-52.5\" y=\"84.39\" text-anchor=\"middle\" visibility=\"hidden\" gadfly:scale=\"10.0\">-22</text>\n",
" <text x=\"-46.66\" y=\"84.39\" text-anchor=\"middle\" visibility=\"hidden\" gadfly:scale=\"10.0\">-21</text>\n",
" <text x=\"-40.82\" y=\"84.39\" text-anchor=\"middle\" visibility=\"hidden\" gadfly:scale=\"10.0\">-20</text>\n",
" <text x=\"-34.98\" y=\"84.39\" text-anchor=\"middle\" visibility=\"hidden\" gadfly:scale=\"10.0\">-19</text>\n",
" <text x=\"-29.14\" y=\"84.39\" text-anchor=\"middle\" visibility=\"hidden\" gadfly:scale=\"10.0\">-18</text>\n",
" <text x=\"-23.3\" y=\"84.39\" text-anchor=\"middle\" visibility=\"hidden\" gadfly:scale=\"10.0\">-17</text>\n",
" <text x=\"-17.46\" y=\"84.39\" text-anchor=\"middle\" visibility=\"hidden\" gadfly:scale=\"10.0\">-16</text>\n",
" <text x=\"-11.61\" y=\"84.39\" text-anchor=\"middle\" visibility=\"hidden\" gadfly:scale=\"10.0\">-15</text>\n",
" <text x=\"-5.77\" y=\"84.39\" text-anchor=\"middle\" visibility=\"hidden\" gadfly:scale=\"10.0\">-14</text>\n",
" <text x=\"0.07\" y=\"84.39\" text-anchor=\"middle\" visibility=\"hidden\" gadfly:scale=\"10.0\">-13</text>\n",
" <text x=\"5.91\" y=\"84.39\" text-anchor=\"middle\" visibility=\"hidden\" gadfly:scale=\"10.0\">-12</text>\n",
" <text x=\"11.75\" y=\"84.39\" text-anchor=\"middle\" visibility=\"hidden\" gadfly:scale=\"10.0\">-11</text>\n",
" <text x=\"17.59\" y=\"84.39\" text-anchor=\"middle\" visibility=\"hidden\" gadfly:scale=\"10.0\">-10</text>\n",
" <text x=\"23.43\" y=\"84.39\" text-anchor=\"middle\" visibility=\"hidden\" gadfly:scale=\"10.0\">-9</text>\n",
" <text x=\"29.28\" y=\"84.39\" text-anchor=\"middle\" visibility=\"hidden\" gadfly:scale=\"10.0\">-8</text>\n",
" <text x=\"35.12\" y=\"84.39\" text-anchor=\"middle\" visibility=\"hidden\" gadfly:scale=\"10.0\">-7</text>\n",
" <text x=\"40.96\" y=\"84.39\" text-anchor=\"middle\" visibility=\"hidden\" gadfly:scale=\"10.0\">-6</text>\n",
" <text x=\"46.8\" y=\"84.39\" text-anchor=\"middle\" visibility=\"hidden\" gadfly:scale=\"10.0\">-5</text>\n",
" <text x=\"52.64\" y=\"84.39\" text-anchor=\"middle\" visibility=\"hidden\" gadfly:scale=\"10.0\">-4</text>\n",
" <text x=\"58.48\" y=\"84.39\" text-anchor=\"middle\" visibility=\"hidden\" gadfly:scale=\"10.0\">-3</text>\n",
" <text x=\"64.32\" y=\"84.39\" text-anchor=\"middle\" visibility=\"hidden\" gadfly:scale=\"10.0\">-2</text>\n",
" <text x=\"70.17\" y=\"84.39\" text-anchor=\"middle\" visibility=\"hidden\" gadfly:scale=\"10.0\">-1</text>\n",
" <text x=\"76.01\" y=\"84.39\" text-anchor=\"middle\" visibility=\"hidden\" gadfly:scale=\"10.0\">0</text>\n",
" <text x=\"81.85\" y=\"84.39\" text-anchor=\"middle\" visibility=\"hidden\" gadfly:scale=\"10.0\">1</text>\n",
" <text x=\"87.69\" y=\"84.39\" text-anchor=\"middle\" visibility=\"hidden\" gadfly:scale=\"10.0\">2</text>\n",
" <text x=\"93.53\" y=\"84.39\" text-anchor=\"middle\" visibility=\"hidden\" gadfly:scale=\"10.0\">3</text>\n",
" <text x=\"99.37\" y=\"84.39\" text-anchor=\"middle\" visibility=\"hidden\" gadfly:scale=\"10.0\">4</text>\n",
" <text x=\"105.21\" y=\"84.39\" text-anchor=\"middle\" visibility=\"hidden\" gadfly:scale=\"10.0\">5</text>\n",
" <text x=\"111.06\" y=\"84.39\" text-anchor=\"middle\" visibility=\"hidden\" gadfly:scale=\"10.0\">6</text>\n",
" <text x=\"116.9\" y=\"84.39\" text-anchor=\"middle\" visibility=\"hidden\" gadfly:scale=\"10.0\">7</text>\n",
" <text x=\"122.74\" y=\"84.39\" text-anchor=\"middle\" visibility=\"hidden\" gadfly:scale=\"10.0\">8</text>\n",
" <text x=\"128.58\" y=\"84.39\" text-anchor=\"middle\" visibility=\"hidden\" gadfly:scale=\"10.0\">9</text>\n",
" <text x=\"134.42\" y=\"84.39\" text-anchor=\"middle\" visibility=\"hidden\" gadfly:scale=\"10.0\">10</text>\n",
" <text x=\"140.26\" y=\"84.39\" text-anchor=\"middle\" visibility=\"hidden\" gadfly:scale=\"10.0\">11</text>\n",
" <text x=\"146.1\" y=\"84.39\" text-anchor=\"middle\" visibility=\"hidden\" gadfly:scale=\"10.0\">12</text>\n",
" <text x=\"151.95\" y=\"84.39\" text-anchor=\"middle\" visibility=\"hidden\" gadfly:scale=\"10.0\">13</text>\n",
" <text x=\"157.79\" y=\"84.39\" text-anchor=\"middle\" visibility=\"hidden\" gadfly:scale=\"10.0\">14</text>\n",
" <text x=\"163.63\" y=\"84.39\" text-anchor=\"middle\" visibility=\"hidden\" gadfly:scale=\"10.0\">15</text>\n",
" <text x=\"169.47\" y=\"84.39\" text-anchor=\"middle\" visibility=\"hidden\" gadfly:scale=\"10.0\">16</text>\n",
" <text x=\"175.31\" y=\"84.39\" text-anchor=\"middle\" visibility=\"hidden\" gadfly:scale=\"10.0\">17</text>\n",
" <text x=\"181.15\" y=\"84.39\" text-anchor=\"middle\" visibility=\"hidden\" gadfly:scale=\"10.0\">18</text>\n",
" <text x=\"186.99\" y=\"84.39\" text-anchor=\"middle\" visibility=\"hidden\" gadfly:scale=\"10.0\">19</text>\n",
" <text x=\"192.84\" y=\"84.39\" text-anchor=\"middle\" visibility=\"hidden\" gadfly:scale=\"10.0\">20</text>\n",
" <text x=\"198.68\" y=\"84.39\" text-anchor=\"middle\" visibility=\"hidden\" gadfly:scale=\"10.0\">21</text>\n",
" <text x=\"204.52\" y=\"84.39\" text-anchor=\"middle\" visibility=\"hidden\" gadfly:scale=\"10.0\">22</text>\n",
" <text x=\"210.36\" y=\"84.39\" text-anchor=\"middle\" visibility=\"hidden\" gadfly:scale=\"10.0\">23</text>\n",
" <text x=\"216.2\" y=\"84.39\" text-anchor=\"middle\" visibility=\"hidden\" gadfly:scale=\"10.0\">24</text>\n",
" <text x=\"222.04\" y=\"84.39\" text-anchor=\"middle\" visibility=\"hidden\" gadfly:scale=\"10.0\">25</text>\n",
" <text x=\"227.88\" y=\"84.39\" text-anchor=\"middle\" visibility=\"hidden\" gadfly:scale=\"10.0\">26</text>\n",
" <text x=\"233.73\" y=\"84.39\" text-anchor=\"middle\" visibility=\"hidden\" gadfly:scale=\"10.0\">27</text>\n",
" <text x=\"239.57\" y=\"84.39\" text-anchor=\"middle\" visibility=\"hidden\" gadfly:scale=\"10.0\">28</text>\n",
" <text x=\"245.41\" y=\"84.39\" text-anchor=\"middle\" visibility=\"hidden\" gadfly:scale=\"10.0\">29</text>\n",
" <text x=\"251.25\" y=\"84.39\" text-anchor=\"middle\" visibility=\"hidden\" gadfly:scale=\"10.0\">30</text>\n",
" <text x=\"-157.65\" y=\"84.39\" text-anchor=\"middle\" visibility=\"hidden\" gadfly:scale=\"0.5\">-40</text>\n",
" <text x=\"-40.82\" y=\"84.39\" text-anchor=\"middle\" visibility=\"hidden\" gadfly:scale=\"0.5\">-20</text>\n",
" <text x=\"76.01\" y=\"84.39\" text-anchor=\"middle\" visibility=\"hidden\" gadfly:scale=\"0.5\">0</text>\n",
" <text x=\"192.84\" y=\"84.39\" text-anchor=\"middle\" visibility=\"hidden\" gadfly:scale=\"0.5\">20</text>\n",
" <text x=\"309.66\" y=\"84.39\" text-anchor=\"middle\" visibility=\"hidden\" gadfly:scale=\"0.5\">40</text>\n",
" <text x=\"-99.24\" y=\"84.39\" text-anchor=\"middle\" visibility=\"hidden\" gadfly:scale=\"5.0\">-30</text>\n",
" <text x=\"-87.55\" y=\"84.39\" text-anchor=\"middle\" visibility=\"hidden\" gadfly:scale=\"5.0\">-28</text>\n",
" <text x=\"-75.87\" y=\"84.39\" text-anchor=\"middle\" visibility=\"hidden\" gadfly:scale=\"5.0\">-26</text>\n",
" <text x=\"-64.19\" y=\"84.39\" text-anchor=\"middle\" visibility=\"hidden\" gadfly:scale=\"5.0\">-24</text>\n",
" <text x=\"-52.5\" y=\"84.39\" text-anchor=\"middle\" visibility=\"hidden\" gadfly:scale=\"5.0\">-22</text>\n",
" <text x=\"-40.82\" y=\"84.39\" text-anchor=\"middle\" visibility=\"hidden\" gadfly:scale=\"5.0\">-20</text>\n",
" <text x=\"-29.14\" y=\"84.39\" text-anchor=\"middle\" visibility=\"hidden\" gadfly:scale=\"5.0\">-18</text>\n",
" <text x=\"-17.46\" y=\"84.39\" text-anchor=\"middle\" visibility=\"hidden\" gadfly:scale=\"5.0\">-16</text>\n",
" <text x=\"-5.77\" y=\"84.39\" text-anchor=\"middle\" visibility=\"hidden\" gadfly:scale=\"5.0\">-14</text>\n",
" <text x=\"5.91\" y=\"84.39\" text-anchor=\"middle\" visibility=\"hidden\" gadfly:scale=\"5.0\">-12</text>\n",
" <text x=\"17.59\" y=\"84.39\" text-anchor=\"middle\" visibility=\"hidden\" gadfly:scale=\"5.0\">-10</text>\n",
" <text x=\"29.28\" y=\"84.39\" text-anchor=\"middle\" visibility=\"hidden\" gadfly:scale=\"5.0\">-8</text>\n",
" <text x=\"40.96\" y=\"84.39\" text-anchor=\"middle\" visibility=\"hidden\" gadfly:scale=\"5.0\">-6</text>\n",
" <text x=\"52.64\" y=\"84.39\" text-anchor=\"middle\" visibility=\"hidden\" gadfly:scale=\"5.0\">-4</text>\n",
" <text x=\"64.32\" y=\"84.39\" text-anchor=\"middle\" visibility=\"hidden\" gadfly:scale=\"5.0\">-2</text>\n",
" <text x=\"76.01\" y=\"84.39\" text-anchor=\"middle\" visibility=\"hidden\" gadfly:scale=\"5.0\">0</text>\n",
" <text x=\"87.69\" y=\"84.39\" text-anchor=\"middle\" visibility=\"hidden\" gadfly:scale=\"5.0\">2</text>\n",
" <text x=\"99.37\" y=\"84.39\" text-anchor=\"middle\" visibility=\"hidden\" gadfly:scale=\"5.0\">4</text>\n",
" <text x=\"111.06\" y=\"84.39\" text-anchor=\"middle\" visibility=\"hidden\" gadfly:scale=\"5.0\">6</text>\n",
" <text x=\"122.74\" y=\"84.39\" text-anchor=\"middle\" visibility=\"hidden\" gadfly:scale=\"5.0\">8</text>\n",
" <text x=\"134.42\" y=\"84.39\" text-anchor=\"middle\" visibility=\"hidden\" gadfly:scale=\"5.0\">10</text>\n",
" <text x=\"146.1\" y=\"84.39\" text-anchor=\"middle\" visibility=\"hidden\" gadfly:scale=\"5.0\">12</text>\n",
" <text x=\"157.79\" y=\"84.39\" text-anchor=\"middle\" visibility=\"hidden\" gadfly:scale=\"5.0\">14</text>\n",
" <text x=\"169.47\" y=\"84.39\" text-anchor=\"middle\" visibility=\"hidden\" gadfly:scale=\"5.0\">16</text>\n",
" <text x=\"181.15\" y=\"84.39\" text-anchor=\"middle\" visibility=\"hidden\" gadfly:scale=\"5.0\">18</text>\n",
" <text x=\"192.84\" y=\"84.39\" text-anchor=\"middle\" visibility=\"hidden\" gadfly:scale=\"5.0\">20</text>\n",
" <text x=\"204.52\" y=\"84.39\" text-anchor=\"middle\" visibility=\"hidden\" gadfly:scale=\"5.0\">22</text>\n",
" <text x=\"216.2\" y=\"84.39\" text-anchor=\"middle\" visibility=\"hidden\" gadfly:scale=\"5.0\">24</text>\n",
" <text x=\"227.88\" y=\"84.39\" text-anchor=\"middle\" visibility=\"hidden\" gadfly:scale=\"5.0\">26</text>\n",
" <text x=\"239.57\" y=\"84.39\" text-anchor=\"middle\" visibility=\"hidden\" gadfly:scale=\"5.0\">28</text>\n",
" <text x=\"251.25\" y=\"84.39\" text-anchor=\"middle\" visibility=\"hidden\" gadfly:scale=\"5.0\">30</text>\n",
" </g>\n",
" <g clip-path=\"url(#fig-24ef7475d29e43d3b5056099cb85fa82-element-5)\" id=\"fig-24ef7475d29e43d3b5056099cb85fa82-element-4\">\n",
" <g pointer-events=\"visible\" opacity=\"1\" fill=\"#000000\" fill-opacity=\"0.000\" stroke=\"#000000\" stroke-opacity=\"0.000\" class=\"guide background\" id=\"fig-24ef7475d29e43d3b5056099cb85fa82-element-6\">\n",
" <rect x=\"15.59\" y=\"5\" width=\"120.83\" height=\"75.72\"/>\n",
" </g>\n",
" <g class=\"guide ygridlines xfixed\" stroke-dasharray=\"0.5,0.5\" stroke-width=\"0.2\" stroke=\"#D0D0E0\" id=\"fig-24ef7475d29e43d3b5056099cb85fa82-element-7\">\n",
" <path fill=\"none\" d=\"M15.59,168.36 L 136.42 168.36\" visibility=\"hidden\" gadfly:scale=\"1.0\"/>\n",
" <path fill=\"none\" d=\"M15.59,150.43 L 136.42 150.43\" visibility=\"hidden\" gadfly:scale=\"1.0\"/>\n",
" <path fill=\"none\" d=\"M15.59,132.5 L 136.42 132.5\" visibility=\"hidden\" gadfly:scale=\"1.0\"/>\n",
" <path fill=\"none\" d=\"M15.59,114.57 L 136.42 114.57\" visibility=\"hidden\" gadfly:scale=\"1.0\"/>\n",
" <path fill=\"none\" d=\"M15.59,96.64 L 136.42 96.64\" visibility=\"hidden\" gadfly:scale=\"1.0\"/>\n",
" <path fill=\"none\" d=\"M15.59,78.71 L 136.42 78.71\" visibility=\"visible\" gadfly:scale=\"1.0\"/>\n",
" <path fill=\"none\" d=\"M15.59,60.79 L 136.42 60.79\" visibility=\"visible\" gadfly:scale=\"1.0\"/>\n",
" <path fill=\"none\" d=\"M15.59,42.86 L 136.42 42.86\" visibility=\"visible\" gadfly:scale=\"1.0\"/>\n",
" <path fill=\"none\" d=\"M15.59,24.93 L 136.42 24.93\" visibility=\"visible\" gadfly:scale=\"1.0\"/>\n",
" <path fill=\"none\" d=\"M15.59,7 L 136.42 7\" visibility=\"visible\" gadfly:scale=\"1.0\"/>\n",
" <path fill=\"none\" d=\"M15.59,-10.93 L 136.42 -10.93\" visibility=\"hidden\" gadfly:scale=\"1.0\"/>\n",
" <path fill=\"none\" d=\"M15.59,-28.86 L 136.42 -28.86\" visibility=\"hidden\" gadfly:scale=\"1.0\"/>\n",
" <path fill=\"none\" d=\"M15.59,-46.79 L 136.42 -46.79\" visibility=\"hidden\" gadfly:scale=\"1.0\"/>\n",
" <path fill=\"none\" d=\"M15.59,-64.71 L 136.42 -64.71\" visibility=\"hidden\" gadfly:scale=\"1.0\"/>\n",
" <path fill=\"none\" d=\"M15.59,-82.64 L 136.42 -82.64\" visibility=\"hidden\" gadfly:scale=\"1.0\"/>\n",
" <path fill=\"none\" d=\"M15.59,150.43 L 136.42 150.43\" visibility=\"hidden\" gadfly:scale=\"10.0\"/>\n",
" <path fill=\"none\" d=\"M15.59,146.84 L 136.42 146.84\" visibility=\"hidden\" gadfly:scale=\"10.0\"/>\n",
" <path fill=\"none\" d=\"M15.59,143.26 L 136.42 143.26\" visibility=\"hidden\" gadfly:scale=\"10.0\"/>\n",
" <path fill=\"none\" d=\"M15.59,139.67 L 136.42 139.67\" visibility=\"hidden\" gadfly:scale=\"10.0\"/>\n",
" <path fill=\"none\" d=\"M15.59,136.09 L 136.42 136.09\" visibility=\"hidden\" gadfly:scale=\"10.0\"/>\n",
" <path fill=\"none\" d=\"M15.59,132.5 L 136.42 132.5\" visibility=\"hidden\" gadfly:scale=\"10.0\"/>\n",
" <path fill=\"none\" d=\"M15.59,128.92 L 136.42 128.92\" visibility=\"hidden\" gadfly:scale=\"10.0\"/>\n",
" <path fill=\"none\" d=\"M15.59,125.33 L 136.42 125.33\" visibility=\"hidden\" gadfly:scale=\"10.0\"/>\n",
" <path fill=\"none\" d=\"M15.59,121.74 L 136.42 121.74\" visibility=\"hidden\" gadfly:scale=\"10.0\"/>\n",
" <path fill=\"none\" d=\"M15.59,118.16 L 136.42 118.16\" visibility=\"hidden\" gadfly:scale=\"10.0\"/>\n",
" <path fill=\"none\" d=\"M15.59,114.57 L 136.42 114.57\" visibility=\"hidden\" gadfly:scale=\"10.0\"/>\n",
" <path fill=\"none\" d=\"M15.59,110.99 L 136.42 110.99\" visibility=\"hidden\" gadfly:scale=\"10.0\"/>\n",
" <path fill=\"none\" d=\"M15.59,107.4 L 136.42 107.4\" visibility=\"hidden\" gadfly:scale=\"10.0\"/>\n",
" <path fill=\"none\" d=\"M15.59,103.82 L 136.42 103.82\" visibility=\"hidden\" gadfly:scale=\"10.0\"/>\n",
" <path fill=\"none\" d=\"M15.59,100.23 L 136.42 100.23\" visibility=\"hidden\" gadfly:scale=\"10.0\"/>\n",
" <path fill=\"none\" d=\"M15.59,96.64 L 136.42 96.64\" visibility=\"hidden\" gadfly:scale=\"10.0\"/>\n",
" <path fill=\"none\" d=\"M15.59,93.06 L 136.42 93.06\" visibility=\"hidden\" gadfly:scale=\"10.0\"/>\n",
" <path fill=\"none\" d=\"M15.59,89.47 L 136.42 89.47\" visibility=\"hidden\" gadfly:scale=\"10.0\"/>\n",
" <path fill=\"none\" d=\"M15.59,85.89 L 136.42 85.89\" visibility=\"hidden\" gadfly:scale=\"10.0\"/>\n",
" <path fill=\"none\" d=\"M15.59,82.3 L 136.42 82.3\" visibility=\"hidden\" gadfly:scale=\"10.0\"/>\n",
" <path fill=\"none\" d=\"M15.59,78.71 L 136.42 78.71\" visibility=\"hidden\" gadfly:scale=\"10.0\"/>\n",
" <path fill=\"none\" d=\"M15.59,75.13 L 136.42 75.13\" visibility=\"hidden\" gadfly:scale=\"10.0\"/>\n",
" <path fill=\"none\" d=\"M15.59,71.54 L 136.42 71.54\" visibility=\"hidden\" gadfly:scale=\"10.0\"/>\n",
" <path fill=\"none\" d=\"M15.59,67.96 L 136.42 67.96\" visibility=\"hidden\" gadfly:scale=\"10.0\"/>\n",
" <path fill=\"none\" d=\"M15.59,64.37 L 136.42 64.37\" visibility=\"hidden\" gadfly:scale=\"10.0\"/>\n",
" <path fill=\"none\" d=\"M15.59,60.79 L 136.42 60.79\" visibility=\"hidden\" gadfly:scale=\"10.0\"/>\n",
" <path fill=\"none\" d=\"M15.59,57.2 L 136.42 57.2\" visibility=\"hidden\" gadfly:scale=\"10.0\"/>\n",
" <path fill=\"none\" d=\"M15.59,53.61 L 136.42 53.61\" visibility=\"hidden\" gadfly:scale=\"10.0\"/>\n",
" <path fill=\"none\" d=\"M15.59,50.03 L 136.42 50.03\" visibility=\"hidden\" gadfly:scale=\"10.0\"/>\n",
" <path fill=\"none\" d=\"M15.59,46.44 L 136.42 46.44\" visibility=\"hidden\" gadfly:scale=\"10.0\"/>\n",
" <path fill=\"none\" d=\"M15.59,42.86 L 136.42 42.86\" visibility=\"hidden\" gadfly:scale=\"10.0\"/>\n",
" <path fill=\"none\" d=\"M15.59,39.27 L 136.42 39.27\" visibility=\"hidden\" gadfly:scale=\"10.0\"/>\n",
" <path fill=\"none\" d=\"M15.59,35.69 L 136.42 35.69\" visibility=\"hidden\" gadfly:scale=\"10.0\"/>\n",
" <path fill=\"none\" d=\"M15.59,32.1 L 136.42 32.1\" visibility=\"hidden\" gadfly:scale=\"10.0\"/>\n",
" <path fill=\"none\" d=\"M15.59,28.51 L 136.42 28.51\" visibility=\"hidden\" gadfly:scale=\"10.0\"/>\n",
" <path fill=\"none\" d=\"M15.59,24.93 L 136.42 24.93\" visibility=\"hidden\" gadfly:scale=\"10.0\"/>\n",
" <path fill=\"none\" d=\"M15.59,21.34 L 136.42 21.34\" visibility=\"hidden\" gadfly:scale=\"10.0\"/>\n",
" <path fill=\"none\" d=\"M15.59,17.76 L 136.42 17.76\" visibility=\"hidden\" gadfly:scale=\"10.0\"/>\n",
" <path fill=\"none\" d=\"M15.59,14.17 L 136.42 14.17\" visibility=\"hidden\" gadfly:scale=\"10.0\"/>\n",
" <path fill=\"none\" d=\"M15.59,10.59 L 136.42 10.59\" visibility=\"hidden\" gadfly:scale=\"10.0\"/>\n",
" <path fill=\"none\" d=\"M15.59,7 L 136.42 7\" visibility=\"hidden\" gadfly:scale=\"10.0\"/>\n",
" <path fill=\"none\" d=\"M15.59,3.41 L 136.42 3.41\" visibility=\"hidden\" gadfly:scale=\"10.0\"/>\n",
" <path fill=\"none\" d=\"M15.59,-0.17 L 136.42 -0.17\" visibility=\"hidden\" gadfly:scale=\"10.0\"/>\n",
" <path fill=\"none\" d=\"M15.59,-3.76 L 136.42 -3.76\" visibility=\"hidden\" gadfly:scale=\"10.0\"/>\n",
" <path fill=\"none\" d=\"M15.59,-7.34 L 136.42 -7.34\" visibility=\"hidden\" gadfly:scale=\"10.0\"/>\n",
" <path fill=\"none\" d=\"M15.59,-10.93 L 136.42 -10.93\" visibility=\"hidden\" gadfly:scale=\"10.0\"/>\n",
" <path fill=\"none\" d=\"M15.59,-14.51 L 136.42 -14.51\" visibility=\"hidden\" gadfly:scale=\"10.0\"/>\n",
" <path fill=\"none\" d=\"M15.59,-18.1 L 136.42 -18.1\" visibility=\"hidden\" gadfly:scale=\"10.0\"/>\n",
" <path fill=\"none\" d=\"M15.59,-21.69 L 136.42 -21.69\" visibility=\"hidden\" gadfly:scale=\"10.0\"/>\n",
" <path fill=\"none\" d=\"M15.59,-25.27 L 136.42 -25.27\" visibility=\"hidden\" gadfly:scale=\"10.0\"/>\n",
" <path fill=\"none\" d=\"M15.59,-28.86 L 136.42 -28.86\" visibility=\"hidden\" gadfly:scale=\"10.0\"/>\n",
" <path fill=\"none\" d=\"M15.59,-32.44 L 136.42 -32.44\" visibility=\"hidden\" gadfly:scale=\"10.0\"/>\n",
" <path fill=\"none\" d=\"M15.59,-36.03 L 136.42 -36.03\" visibility=\"hidden\" gadfly:scale=\"10.0\"/>\n",
" <path fill=\"none\" d=\"M15.59,-39.61 L 136.42 -39.61\" visibility=\"hidden\" gadfly:scale=\"10.0\"/>\n",
" <path fill=\"none\" d=\"M15.59,-43.2 L 136.42 -43.2\" visibility=\"hidden\" gadfly:scale=\"10.0\"/>\n",
" <path fill=\"none\" d=\"M15.59,-46.79 L 136.42 -46.79\" visibility=\"hidden\" gadfly:scale=\"10.0\"/>\n",
" <path fill=\"none\" d=\"M15.59,-50.37 L 136.42 -50.37\" visibility=\"hidden\" gadfly:scale=\"10.0\"/>\n",
" <path fill=\"none\" d=\"M15.59,-53.96 L 136.42 -53.96\" visibility=\"hidden\" gadfly:scale=\"10.0\"/>\n",
" <path fill=\"none\" d=\"M15.59,-57.54 L 136.42 -57.54\" visibility=\"hidden\" gadfly:scale=\"10.0\"/>\n",
" <path fill=\"none\" d=\"M15.59,-61.13 L 136.42 -61.13\" visibility=\"hidden\" gadfly:scale=\"10.0\"/>\n",
" <path fill=\"none\" d=\"M15.59,-64.71 L 136.42 -64.71\" visibility=\"hidden\" gadfly:scale=\"10.0\"/>\n",
" <path fill=\"none\" d=\"M15.59,150.43 L 136.42 150.43\" visibility=\"hidden\" gadfly:scale=\"0.5\"/>\n",
" <path fill=\"none\" d=\"M15.59,60.79 L 136.42 60.79\" visibility=\"hidden\" gadfly:scale=\"0.5\"/>\n",
" <path fill=\"none\" d=\"M15.59,-28.86 L 136.42 -28.86\" visibility=\"hidden\" gadfly:scale=\"0.5\"/>\n",
" <path fill=\"none\" d=\"M15.59,-118.5 L 136.42 -118.5\" visibility=\"hidden\" gadfly:scale=\"0.5\"/>\n",
" <path fill=\"none\" d=\"M15.59,150.43 L 136.42 150.43\" visibility=\"hidden\" gadfly:scale=\"5.0\"/>\n",
" <path fill=\"none\" d=\"M15.59,141.47 L 136.42 141.47\" visibility=\"hidden\" gadfly:scale=\"5.0\"/>\n",
" <path fill=\"none\" d=\"M15.59,132.5 L 136.42 132.5\" visibility=\"hidden\" gadfly:scale=\"5.0\"/>\n",
" <path fill=\"none\" d=\"M15.59,123.54 L 136.42 123.54\" visibility=\"hidden\" gadfly:scale=\"5.0\"/>\n",
" <path fill=\"none\" d=\"M15.59,114.57 L 136.42 114.57\" visibility=\"hidden\" gadfly:scale=\"5.0\"/>\n",
" <path fill=\"none\" d=\"M15.59,105.61 L 136.42 105.61\" visibility=\"hidden\" gadfly:scale=\"5.0\"/>\n",
" <path fill=\"none\" d=\"M15.59,96.64 L 136.42 96.64\" visibility=\"hidden\" gadfly:scale=\"5.0\"/>\n",
" <path fill=\"none\" d=\"M15.59,87.68 L 136.42 87.68\" visibility=\"hidden\" gadfly:scale=\"5.0\"/>\n",
" <path fill=\"none\" d=\"M15.59,78.71 L 136.42 78.71\" visibility=\"hidden\" gadfly:scale=\"5.0\"/>\n",
" <path fill=\"none\" d=\"M15.59,69.75 L 136.42 69.75\" visibility=\"hidden\" gadfly:scale=\"5.0\"/>\n",
" <path fill=\"none\" d=\"M15.59,60.79 L 136.42 60.79\" visibility=\"hidden\" gadfly:scale=\"5.0\"/>\n",
" <path fill=\"none\" d=\"M15.59,51.82 L 136.42 51.82\" visibility=\"hidden\" gadfly:scale=\"5.0\"/>\n",
" <path fill=\"none\" d=\"M15.59,42.86 L 136.42 42.86\" visibility=\"hidden\" gadfly:scale=\"5.0\"/>\n",
" <path fill=\"none\" d=\"M15.59,33.89 L 136.42 33.89\" visibility=\"hidden\" gadfly:scale=\"5.0\"/>\n",
" <path fill=\"none\" d=\"M15.59,24.93 L 136.42 24.93\" visibility=\"hidden\" gadfly:scale=\"5.0\"/>\n",
" <path fill=\"none\" d=\"M15.59,15.96 L 136.42 15.96\" visibility=\"hidden\" gadfly:scale=\"5.0\"/>\n",
" <path fill=\"none\" d=\"M15.59,7 L 136.42 7\" visibility=\"hidden\" gadfly:scale=\"5.0\"/>\n",
" <path fill=\"none\" d=\"M15.59,-1.96 L 136.42 -1.96\" visibility=\"hidden\" gadfly:scale=\"5.0\"/>\n",
" <path fill=\"none\" d=\"M15.59,-10.93 L 136.42 -10.93\" visibility=\"hidden\" gadfly:scale=\"5.0\"/>\n",
" <path fill=\"none\" d=\"M15.59,-19.89 L 136.42 -19.89\" visibility=\"hidden\" gadfly:scale=\"5.0\"/>\n",
" <path fill=\"none\" d=\"M15.59,-28.86 L 136.42 -28.86\" visibility=\"hidden\" gadfly:scale=\"5.0\"/>\n",
" <path fill=\"none\" d=\"M15.59,-37.82 L 136.42 -37.82\" visibility=\"hidden\" gadfly:scale=\"5.0\"/>\n",
" <path fill=\"none\" d=\"M15.59,-46.79 L 136.42 -46.79\" visibility=\"hidden\" gadfly:scale=\"5.0\"/>\n",
" <path fill=\"none\" d=\"M15.59,-55.75 L 136.42 -55.75\" visibility=\"hidden\" gadfly:scale=\"5.0\"/>\n",
" <path fill=\"none\" d=\"M15.59,-64.71 L 136.42 -64.71\" visibility=\"hidden\" gadfly:scale=\"5.0\"/>\n",
" </g>\n",
" <g class=\"guide xgridlines yfixed\" stroke-dasharray=\"0.5,0.5\" stroke-width=\"0.2\" stroke=\"#D0D0E0\" id=\"fig-24ef7475d29e43d3b5056099cb85fa82-element-8\">\n",
" <path fill=\"none\" d=\"M-128.44,5 L -128.44 80.72\" visibility=\"hidden\" gadfly:scale=\"1.0\"/>\n",
" <path fill=\"none\" d=\"M-99.24,5 L -99.24 80.72\" visibility=\"hidden\" gadfly:scale=\"1.0\"/>\n",
" <path fill=\"none\" d=\"M-70.03,5 L -70.03 80.72\" visibility=\"hidden\" gadfly:scale=\"1.0\"/>\n",
" <path fill=\"none\" d=\"M-40.82,5 L -40.82 80.72\" visibility=\"hidden\" gadfly:scale=\"1.0\"/>\n",
" <path fill=\"none\" d=\"M-11.61,5 L -11.61 80.72\" visibility=\"hidden\" gadfly:scale=\"1.0\"/>\n",
" <path fill=\"none\" d=\"M17.59,5 L 17.59 80.72\" visibility=\"visible\" gadfly:scale=\"1.0\"/>\n",
" <path fill=\"none\" d=\"M46.8,5 L 46.8 80.72\" visibility=\"visible\" gadfly:scale=\"1.0\"/>\n",
" <path fill=\"none\" d=\"M76.01,5 L 76.01 80.72\" visibility=\"visible\" gadfly:scale=\"1.0\"/>\n",
" <path fill=\"none\" d=\"M105.21,5 L 105.21 80.72\" visibility=\"visible\" gadfly:scale=\"1.0\"/>\n",
" <path fill=\"none\" d=\"M134.42,5 L 134.42 80.72\" visibility=\"visible\" gadfly:scale=\"1.0\"/>\n",
" <path fill=\"none\" d=\"M163.63,5 L 163.63 80.72\" visibility=\"hidden\" gadfly:scale=\"1.0\"/>\n",
" <path fill=\"none\" d=\"M192.84,5 L 192.84 80.72\" visibility=\"hidden\" gadfly:scale=\"1.0\"/>\n",
" <path fill=\"none\" d=\"M222.04,5 L 222.04 80.72\" visibility=\"hidden\" gadfly:scale=\"1.0\"/>\n",
" <path fill=\"none\" d=\"M251.25,5 L 251.25 80.72\" visibility=\"hidden\" gadfly:scale=\"1.0\"/>\n",
" <path fill=\"none\" d=\"M280.46,5 L 280.46 80.72\" visibility=\"hidden\" gadfly:scale=\"1.0\"/>\n",
" <path fill=\"none\" d=\"M-99.24,5 L -99.24 80.72\" visibility=\"hidden\" gadfly:scale=\"10.0\"/>\n",
" <path fill=\"none\" d=\"M-93.39,5 L -93.39 80.72\" visibility=\"hidden\" gadfly:scale=\"10.0\"/>\n",
" <path fill=\"none\" d=\"M-87.55,5 L -87.55 80.72\" visibility=\"hidden\" gadfly:scale=\"10.0\"/>\n",
" <path fill=\"none\" d=\"M-81.71,5 L -81.71 80.72\" visibility=\"hidden\" gadfly:scale=\"10.0\"/>\n",
" <path fill=\"none\" d=\"M-75.87,5 L -75.87 80.72\" visibility=\"hidden\" gadfly:scale=\"10.0\"/>\n",
" <path fill=\"none\" d=\"M-70.03,5 L -70.03 80.72\" visibility=\"hidden\" gadfly:scale=\"10.0\"/>\n",
" <path fill=\"none\" d=\"M-64.19,5 L -64.19 80.72\" visibility=\"hidden\" gadfly:scale=\"10.0\"/>\n",
" <path fill=\"none\" d=\"M-58.35,5 L -58.35 80.72\" visibility=\"hidden\" gadfly:scale=\"10.0\"/>\n",
" <path fill=\"none\" d=\"M-52.5,5 L -52.5 80.72\" visibility=\"hidden\" gadfly:scale=\"10.0\"/>\n",
" <path fill=\"none\" d=\"M-46.66,5 L -46.66 80.72\" visibility=\"hidden\" gadfly:scale=\"10.0\"/>\n",
" <path fill=\"none\" d=\"M-40.82,5 L -40.82 80.72\" visibility=\"hidden\" gadfly:scale=\"10.0\"/>\n",
" <path fill=\"none\" d=\"M-34.98,5 L -34.98 80.72\" visibility=\"hidden\" gadfly:scale=\"10.0\"/>\n",
" <path fill=\"none\" d=\"M-29.14,5 L -29.14 80.72\" visibility=\"hidden\" gadfly:scale=\"10.0\"/>\n",
" <path fill=\"none\" d=\"M-23.3,5 L -23.3 80.72\" visibility=\"hidden\" gadfly:scale=\"10.0\"/>\n",
" <path fill=\"none\" d=\"M-17.46,5 L -17.46 80.72\" visibility=\"hidden\" gadfly:scale=\"10.0\"/>\n",
" <path fill=\"none\" d=\"M-11.61,5 L -11.61 80.72\" visibility=\"hidden\" gadfly:scale=\"10.0\"/>\n",
" <path fill=\"none\" d=\"M-5.77,5 L -5.77 80.72\" visibility=\"hidden\" gadfly:scale=\"10.0\"/>\n",
" <path fill=\"none\" d=\"M0.07,5 L 0.07 80.72\" visibility=\"hidden\" gadfly:scale=\"10.0\"/>\n",
" <path fill=\"none\" d=\"M5.91,5 L 5.91 80.72\" visibility=\"hidden\" gadfly:scale=\"10.0\"/>\n",
" <path fill=\"none\" d=\"M11.75,5 L 11.75 80.72\" visibility=\"hidden\" gadfly:scale=\"10.0\"/>\n",
" <path fill=\"none\" d=\"M17.59,5 L 17.59 80.72\" visibility=\"hidden\" gadfly:scale=\"10.0\"/>\n",
" <path fill=\"none\" d=\"M23.43,5 L 23.43 80.72\" visibility=\"hidden\" gadfly:scale=\"10.0\"/>\n",
" <path fill=\"none\" d=\"M29.28,5 L 29.28 80.72\" visibility=\"hidden\" gadfly:scale=\"10.0\"/>\n",
" <path fill=\"none\" d=\"M35.12,5 L 35.12 80.72\" visibility=\"hidden\" gadfly:scale=\"10.0\"/>\n",
" <path fill=\"none\" d=\"M40.96,5 L 40.96 80.72\" visibility=\"hidden\" gadfly:scale=\"10.0\"/>\n",
" <path fill=\"none\" d=\"M46.8,5 L 46.8 80.72\" visibility=\"hidden\" gadfly:scale=\"10.0\"/>\n",
" <path fill=\"none\" d=\"M52.64,5 L 52.64 80.72\" visibility=\"hidden\" gadfly:scale=\"10.0\"/>\n",
" <path fill=\"none\" d=\"M58.48,5 L 58.48 80.72\" visibility=\"hidden\" gadfly:scale=\"10.0\"/>\n",
" <path fill=\"none\" d=\"M64.32,5 L 64.32 80.72\" visibility=\"hidden\" gadfly:scale=\"10.0\"/>\n",
" <path fill=\"none\" d=\"M70.17,5 L 70.17 80.72\" visibility=\"hidden\" gadfly:scale=\"10.0\"/>\n",
" <path fill=\"none\" d=\"M76.01,5 L 76.01 80.72\" visibility=\"hidden\" gadfly:scale=\"10.0\"/>\n",
" <path fill=\"none\" d=\"M81.85,5 L 81.85 80.72\" visibility=\"hidden\" gadfly:scale=\"10.0\"/>\n",
" <path fill=\"none\" d=\"M87.69,5 L 87.69 80.72\" visibility=\"hidden\" gadfly:scale=\"10.0\"/>\n",
" <path fill=\"none\" d=\"M93.53,5 L 93.53 80.72\" visibility=\"hidden\" gadfly:scale=\"10.0\"/>\n",
" <path fill=\"none\" d=\"M99.37,5 L 99.37 80.72\" visibility=\"hidden\" gadfly:scale=\"10.0\"/>\n",
" <path fill=\"none\" d=\"M105.21,5 L 105.21 80.72\" visibility=\"hidden\" gadfly:scale=\"10.0\"/>\n",
" <path fill=\"none\" d=\"M111.06,5 L 111.06 80.72\" visibility=\"hidden\" gadfly:scale=\"10.0\"/>\n",
" <path fill=\"none\" d=\"M116.9,5 L 116.9 80.72\" visibility=\"hidden\" gadfly:scale=\"10.0\"/>\n",
" <path fill=\"none\" d=\"M122.74,5 L 122.74 80.72\" visibility=\"hidden\" gadfly:scale=\"10.0\"/>\n",
" <path fill=\"none\" d=\"M128.58,5 L 128.58 80.72\" visibility=\"hidden\" gadfly:scale=\"10.0\"/>\n",
" <path fill=\"none\" d=\"M134.42,5 L 134.42 80.72\" visibility=\"hidden\" gadfly:scale=\"10.0\"/>\n",
" <path fill=\"none\" d=\"M140.26,5 L 140.26 80.72\" visibility=\"hidden\" gadfly:scale=\"10.0\"/>\n",
" <path fill=\"none\" d=\"M146.1,5 L 146.1 80.72\" visibility=\"hidden\" gadfly:scale=\"10.0\"/>\n",
" <path fill=\"none\" d=\"M151.95,5 L 151.95 80.72\" visibility=\"hidden\" gadfly:scale=\"10.0\"/>\n",
" <path fill=\"none\" d=\"M157.79,5 L 157.79 80.72\" visibility=\"hidden\" gadfly:scale=\"10.0\"/>\n",
" <path fill=\"none\" d=\"M163.63,5 L 163.63 80.72\" visibility=\"hidden\" gadfly:scale=\"10.0\"/>\n",
" <path fill=\"none\" d=\"M169.47,5 L 169.47 80.72\" visibility=\"hidden\" gadfly:scale=\"10.0\"/>\n",
" <path fill=\"none\" d=\"M175.31,5 L 175.31 80.72\" visibility=\"hidden\" gadfly:scale=\"10.0\"/>\n",
" <path fill=\"none\" d=\"M181.15,5 L 181.15 80.72\" visibility=\"hidden\" gadfly:scale=\"10.0\"/>\n",
" <path fill=\"none\" d=\"M186.99,5 L 186.99 80.72\" visibility=\"hidden\" gadfly:scale=\"10.0\"/>\n",
" <path fill=\"none\" d=\"M192.84,5 L 192.84 80.72\" visibility=\"hidden\" gadfly:scale=\"10.0\"/>\n",
" <path fill=\"none\" d=\"M198.68,5 L 198.68 80.72\" visibility=\"hidden\" gadfly:scale=\"10.0\"/>\n",
" <path fill=\"none\" d=\"M204.52,5 L 204.52 80.72\" visibility=\"hidden\" gadfly:scale=\"10.0\"/>\n",
" <path fill=\"none\" d=\"M210.36,5 L 210.36 80.72\" visibility=\"hidden\" gadfly:scale=\"10.0\"/>\n",
" <path fill=\"none\" d=\"M216.2,5 L 216.2 80.72\" visibility=\"hidden\" gadfly:scale=\"10.0\"/>\n",
" <path fill=\"none\" d=\"M222.04,5 L 222.04 80.72\" visibility=\"hidden\" gadfly:scale=\"10.0\"/>\n",
" <path fill=\"none\" d=\"M227.88,5 L 227.88 80.72\" visibility=\"hidden\" gadfly:scale=\"10.0\"/>\n",
" <path fill=\"none\" d=\"M233.73,5 L 233.73 80.72\" visibility=\"hidden\" gadfly:scale=\"10.0\"/>\n",
" <path fill=\"none\" d=\"M239.57,5 L 239.57 80.72\" visibility=\"hidden\" gadfly:scale=\"10.0\"/>\n",
" <path fill=\"none\" d=\"M245.41,5 L 245.41 80.72\" visibility=\"hidden\" gadfly:scale=\"10.0\"/>\n",
" <path fill=\"none\" d=\"M251.25,5 L 251.25 80.72\" visibility=\"hidden\" gadfly:scale=\"10.0\"/>\n",
" <path fill=\"none\" d=\"M-157.65,5 L -157.65 80.72\" visibility=\"hidden\" gadfly:scale=\"0.5\"/>\n",
" <path fill=\"none\" d=\"M-40.82,5 L -40.82 80.72\" visibility=\"hidden\" gadfly:scale=\"0.5\"/>\n",
" <path fill=\"none\" d=\"M76.01,5 L 76.01 80.72\" visibility=\"hidden\" gadfly:scale=\"0.5\"/>\n",
" <path fill=\"none\" d=\"M192.84,5 L 192.84 80.72\" visibility=\"hidden\" gadfly:scale=\"0.5\"/>\n",
" <path fill=\"none\" d=\"M309.66,5 L 309.66 80.72\" visibility=\"hidden\" gadfly:scale=\"0.5\"/>\n",
" <path fill=\"none\" d=\"M-99.24,5 L -99.24 80.72\" visibility=\"hidden\" gadfly:scale=\"5.0\"/>\n",
" <path fill=\"none\" d=\"M-87.55,5 L -87.55 80.72\" visibility=\"hidden\" gadfly:scale=\"5.0\"/>\n",
" <path fill=\"none\" d=\"M-75.87,5 L -75.87 80.72\" visibility=\"hidden\" gadfly:scale=\"5.0\"/>\n",
" <path fill=\"none\" d=\"M-64.19,5 L -64.19 80.72\" visibility=\"hidden\" gadfly:scale=\"5.0\"/>\n",
" <path fill=\"none\" d=\"M-52.5,5 L -52.5 80.72\" visibility=\"hidden\" gadfly:scale=\"5.0\"/>\n",
" <path fill=\"none\" d=\"M-40.82,5 L -40.82 80.72\" visibility=\"hidden\" gadfly:scale=\"5.0\"/>\n",
" <path fill=\"none\" d=\"M-29.14,5 L -29.14 80.72\" visibility=\"hidden\" gadfly:scale=\"5.0\"/>\n",
" <path fill=\"none\" d=\"M-17.46,5 L -17.46 80.72\" visibility=\"hidden\" gadfly:scale=\"5.0\"/>\n",
" <path fill=\"none\" d=\"M-5.77,5 L -5.77 80.72\" visibility=\"hidden\" gadfly:scale=\"5.0\"/>\n",
" <path fill=\"none\" d=\"M5.91,5 L 5.91 80.72\" visibility=\"hidden\" gadfly:scale=\"5.0\"/>\n",
" <path fill=\"none\" d=\"M17.59,5 L 17.59 80.72\" visibility=\"hidden\" gadfly:scale=\"5.0\"/>\n",
" <path fill=\"none\" d=\"M29.28,5 L 29.28 80.72\" visibility=\"hidden\" gadfly:scale=\"5.0\"/>\n",
" <path fill=\"none\" d=\"M40.96,5 L 40.96 80.72\" visibility=\"hidden\" gadfly:scale=\"5.0\"/>\n",
" <path fill=\"none\" d=\"M52.64,5 L 52.64 80.72\" visibility=\"hidden\" gadfly:scale=\"5.0\"/>\n",
" <path fill=\"none\" d=\"M64.32,5 L 64.32 80.72\" visibility=\"hidden\" gadfly:scale=\"5.0\"/>\n",
" <path fill=\"none\" d=\"M76.01,5 L 76.01 80.72\" visibility=\"hidden\" gadfly:scale=\"5.0\"/>\n",
" <path fill=\"none\" d=\"M87.69,5 L 87.69 80.72\" visibility=\"hidden\" gadfly:scale=\"5.0\"/>\n",
" <path fill=\"none\" d=\"M99.37,5 L 99.37 80.72\" visibility=\"hidden\" gadfly:scale=\"5.0\"/>\n",
" <path fill=\"none\" d=\"M111.06,5 L 111.06 80.72\" visibility=\"hidden\" gadfly:scale=\"5.0\"/>\n",
" <path fill=\"none\" d=\"M122.74,5 L 122.74 80.72\" visibility=\"hidden\" gadfly:scale=\"5.0\"/>\n",
" <path fill=\"none\" d=\"M134.42,5 L 134.42 80.72\" visibility=\"hidden\" gadfly:scale=\"5.0\"/>\n",
" <path fill=\"none\" d=\"M146.1,5 L 146.1 80.72\" visibility=\"hidden\" gadfly:scale=\"5.0\"/>\n",
" <path fill=\"none\" d=\"M157.79,5 L 157.79 80.72\" visibility=\"hidden\" gadfly:scale=\"5.0\"/>\n",
" <path fill=\"none\" d=\"M169.47,5 L 169.47 80.72\" visibility=\"hidden\" gadfly:scale=\"5.0\"/>\n",
" <path fill=\"none\" d=\"M181.15,5 L 181.15 80.72\" visibility=\"hidden\" gadfly:scale=\"5.0\"/>\n",
" <path fill=\"none\" d=\"M192.84,5 L 192.84 80.72\" visibility=\"hidden\" gadfly:scale=\"5.0\"/>\n",
" <path fill=\"none\" d=\"M204.52,5 L 204.52 80.72\" visibility=\"hidden\" gadfly:scale=\"5.0\"/>\n",
" <path fill=\"none\" d=\"M216.2,5 L 216.2 80.72\" visibility=\"hidden\" gadfly:scale=\"5.0\"/>\n",
" <path fill=\"none\" d=\"M227.88,5 L 227.88 80.72\" visibility=\"hidden\" gadfly:scale=\"5.0\"/>\n",
" <path fill=\"none\" d=\"M239.57,5 L 239.57 80.72\" visibility=\"hidden\" gadfly:scale=\"5.0\"/>\n",
" <path fill=\"none\" d=\"M251.25,5 L 251.25 80.72\" visibility=\"hidden\" gadfly:scale=\"5.0\"/>\n",
" </g>\n",
" <g class=\"plotpanel\" id=\"fig-24ef7475d29e43d3b5056099cb85fa82-element-9\">\n",
" <g stroke-width=\"0.3\" fill=\"#000000\" fill-opacity=\"0.000\" class=\"geometry\" stroke-dasharray=\"none\" stroke=\"#00BFFF\" id=\"fig-24ef7475d29e43d3b5056099cb85fa82-element-10\">\n",
" <path fill=\"none\" d=\"M17.59,60.79 L 17.6 60.79 17.6 60.79 17.61 60.79 17.63 60.79 17.65 60.79 17.67 60.79 17.69 60.79 17.72 60.79 17.75 60.79 17.78 60.79 17.82 60.79 17.86 60.79 17.91 60.79 17.96 60.79 18.01 60.79 18.07 60.79 18.12 60.79 18.19 60.79 18.25 60.79 18.32 60.79 18.4 60.79 18.47 60.79 18.55 60.79 18.63 60.79 18.72 60.79 18.81 60.79 18.9 60.79 19 60.79 19.1 60.79 19.2 60.79 19.31 60.79 19.42 60.79 19.53 60.79 19.65 60.79 19.77 60.79 19.9 60.79 20.02 60.79 20.15 60.79 20.29 60.79 20.42 60.79 20.56 60.79 20.71 60.79 20.85 60.79 21 60.79 21.16 60.79 21.32 60.79 21.48 60.79 21.64 60.79 21.8 60.79 21.97 60.79 22.15 60.79 22.32 60.79 22.5 60.79 22.69 60.79 22.87 60.79 23.06 60.79 23.25 60.79 23.45 60.79 23.65 60.79 23.85 60.79 24.05 60.79 24.26 60.79 24.47 60.79 24.68 60.79 24.9 60.79 25.12 60.79 25.34 60.79 25.57 60.79 25.8 60.79 26.03 60.79 26.27 60.79 26.5 60.79 26.74 60.79 26.99 60.79 27.23 60.79 27.48 60.79 27.74 60.79 27.99 60.79 28.25 60.79 28.51 60.79 28.78 60.79 29.04 60.79 29.31 60.79 29.58 60.79 29.86 60.79 30.14 60.79 30.42 60.79 30.7 60.79 30.99 60.79 31.27 60.79 31.57 60.79 31.86 60.79 32.16 60.79 32.46 60.79 32.76 60.79 33.06 60.79 33.37 60.79 33.68 60.79 33.99 60.79 34.31 60.79 34.62 60.79 34.94 60.79 35.26 60.79 35.59 60.79 35.91 60.79 36.24 60.79 36.58 60.79 36.91 60.79 37.25 60.79 37.58 60.79 37.92 60.79 38.27 60.79 38.61 60.79 38.96 60.79 39.31 60.79 39.66 60.79 40.02 60.79 40.37 60.79 40.73 60.79 41.09 60.79 41.45 60.79 41.82 60.79 42.18 60.79 42.55 60.79 42.92 60.79 43.3 60.79 43.67 60.79 44.05 60.79 44.43 60.79 44.81 60.79 45.19 60.79 45.57 60.79 45.96 60.79 46.35 60.79 46.73 60.79 47.13 60.79 47.52 60.79 47.91 60.79 48.31 60.79 48.71 60.79 49.11 60.79 49.51 60.79 49.91 60.79 50.31 60.79 50.72 60.79 51.13 60.79 51.54 60.79 51.95 60.79 52.36 60.79 52.77 60.79 53.18 60.79 53.6 60.79 54.02 60.79 54.44 60.79 54.86 60.79 55.28 60.79 55.7 60.79 56.12 60.79 56.55 60.79 56.97 60.79 57.4 60.78 57.83 60.78 58.26 60.78 58.69 60.78 59.12 60.78 59.55 60.77 59.98 60.77 60.42 60.76 60.85 60.74 61.29 60.72 61.72 60.7 62.16 60.66 62.6 60.6 63.04 60.53 63.48 60.43 63.92 60.29 64.36 60.11 64.8 59.88 65.25 59.58 65.69 59.2 66.13 58.72 66.58 58.14 67.02 57.42 67.47 56.55 67.92 55.52 68.36 54.31 68.81 52.92 69.26 51.35 69.71 49.58 70.16 47.64 70.6 45.54 71.05 43.32 71.5 41 71.95 38.64 72.4 36.29 72.85 34 73.3 31.85 73.75 29.89 74.2 28.19 74.65 26.8 75.11 25.77 75.56 25.14 76.01 24.93 76.46 25.14 76.91 25.77 77.36 26.8 77.81 28.19 78.26 29.89 78.71 31.85 79.16 34 79.61 36.29 80.06 38.64 80.51 41 80.96 43.32 81.41 45.54 81.86 47.64 82.31 49.58 82.76 51.35 83.2 52.92 83.65 54.31 84.1 55.52 84.54 56.55 84.99 57.42 85.43 58.14 85.88 58.72 86.32 59.2 86.77 59.58 87.21 59.88 87.65 60.11 88.09 60.29 88.53 60.43 88.97 60.53 89.41 60.6 89.85 60.66 90.29 60.7 90.73 60.72 91.16 60.74 91.6 60.76 92.03 60.77 92.46 60.77 92.9 60.78 93.33 60.78 93.76 60.78 94.19 60.78 94.61 60.78 95.04 60.79 95.47 60.79 95.89 60.79 96.32 60.79 96.74 60.79 97.16 60.79 97.58 60.79 98 60.79 98.41 60.79 98.83 60.79 99.24 60.79 99.66 60.79 100.07 60.79 100.48 60.79 100.89 60.79 101.29 60.79 101.7 60.79 102.1 60.79 102.51 60.79 102.91 60.79 103.31 60.79 103.7 60.79 104.1 60.79 104.5 60.79 104.89 60.79 105.28 60.79 105.67 60.79 106.06 60.79 106.44 60.79 106.83 60.79 107.21 60.79 107.59 60.79 107.97 60.79 108.34 60.79 108.72 60.79 109.09 60.79 109.46 60.79 109.83 60.79 110.2 60.79 110.56 60.79 110.92 60.79 111.28 60.79 111.64 60.79 112 60.79 112.35 60.79 112.7 60.79 113.05 60.79 113.4 60.79 113.75 60.79 114.09 60.79 114.43 60.79 114.77 60.79 115.1 60.79 115.44 60.79 115.77 60.79 116.1 60.79 116.43 60.79 116.75 60.79 117.07 60.79 117.39 60.79 117.71 60.79 118.02 60.79 118.34 60.79 118.64 60.79 118.95 60.79 119.26 60.79 119.56 60.79 119.86 60.79 120.15 60.79 120.45 60.79 120.74 60.79 121.03 60.79 121.31 60.79 121.6 60.79 121.88 60.79 122.16 60.79 122.43 60.79 122.7 60.79 122.97 60.79 123.24 60.79 123.5 60.79 123.76 60.79 124.02 60.79 124.28 60.79 124.53 60.79 124.78 60.79 125.03 60.79 125.27 60.79 125.51 60.79 125.75 60.79 125.98 60.79 126.22 60.79 126.44 60.79 126.67 60.79 126.89 60.79 127.11 60.79 127.33 60.79 127.54 60.79 127.75 60.79 127.96 60.79 128.17 60.79 128.37 60.79 128.57 60.79 128.76 60.79 128.95 60.79 129.14 60.79 129.33 60.79 129.51 60.79 129.69 60.79 129.87 60.79 130.04 60.79 130.21 60.79 130.38 60.79 130.54 60.79 130.7 60.79 130.86 60.79 131.01 60.79 131.16 60.79 131.31 60.79 131.45 60.79 131.59 60.79 131.73 60.79 131.86 60.79 131.99 60.79 132.12 60.79 132.24 60.79 132.36 60.79 132.48 60.79 132.59 60.79 132.7 60.79 132.81 60.79 132.91 60.79 133.01 60.79 133.11 60.79 133.2 60.79 133.29 60.79 133.38 60.79 133.46 60.79 133.54 60.79 133.62 60.79 133.69 60.79 133.76 60.79 133.83 60.79 133.89 60.79 133.95 60.79 134 60.79 134.06 60.79 134.1 60.79 134.15 60.79 134.19 60.79 134.23 60.79 134.26 60.79 134.3 60.79 134.32 60.79 134.35 60.79 134.37 60.79 134.39 60.79 134.4 60.79 134.41 60.79 134.42 60.79 134.42 60.79\"/>\n",
" </g>\n",
" </g>\n",
" <g opacity=\"0\" class=\"guide zoomslider\" stroke=\"#000000\" stroke-opacity=\"0.000\" id=\"fig-24ef7475d29e43d3b5056099cb85fa82-element-11\">\n",
" <g fill=\"#EAEAEA\" stroke-width=\"0.3\" stroke-opacity=\"0\" stroke=\"#6A6A6A\" id=\"fig-24ef7475d29e43d3b5056099cb85fa82-element-12\">\n",
" <rect x=\"129.42\" y=\"8\" width=\"4\" height=\"4\"/>\n",
" <g class=\"button_logo\" fill=\"#6A6A6A\" id=\"fig-24ef7475d29e43d3b5056099cb85fa82-element-13\">\n",
" <path d=\"M130.22,9.6 L 131.02 9.6 131.02 8.8 131.82 8.8 131.82 9.6 132.62 9.6 132.62 10.4 131.82 10.4 131.82 11.2 131.02 11.2 131.02 10.4 130.22 10.4 z\"/>\n",
" </g>\n",
" </g>\n",
" <g fill=\"#EAEAEA\" id=\"fig-24ef7475d29e43d3b5056099cb85fa82-element-14\">\n",
" <rect x=\"109.92\" y=\"8\" width=\"19\" height=\"4\"/>\n",
" </g>\n",
" <g class=\"zoomslider_thumb\" fill=\"#6A6A6A\" id=\"fig-24ef7475d29e43d3b5056099cb85fa82-element-15\">\n",
" <rect x=\"118.42\" y=\"8\" width=\"2\" height=\"4\"/>\n",
" </g>\n",
" <g fill=\"#EAEAEA\" stroke-width=\"0.3\" stroke-opacity=\"0\" stroke=\"#6A6A6A\" id=\"fig-24ef7475d29e43d3b5056099cb85fa82-element-16\">\n",
" <rect x=\"105.42\" y=\"8\" width=\"4\" height=\"4\"/>\n",
" <g class=\"button_logo\" fill=\"#6A6A6A\" id=\"fig-24ef7475d29e43d3b5056099cb85fa82-element-17\">\n",
" <path d=\"M106.22,9.6 L 108.62 9.6 108.62 10.4 106.22 10.4 z\"/>\n",
" </g>\n",
" </g>\n",
" </g>\n",
" </g>\n",
" <g class=\"guide ylabels\" font-size=\"2.82\" font-family=\"'PT Sans Caption','Helvetica Neue','Helvetica',sans-serif\" fill=\"#6C606B\" id=\"fig-24ef7475d29e43d3b5056099cb85fa82-element-18\">\n",
" <text x=\"14.59\" y=\"168.36\" text-anchor=\"end\" dy=\"0.35em\" visibility=\"hidden\" gadfly:scale=\"1.0\">-6</text>\n",
" <text x=\"14.59\" y=\"150.43\" text-anchor=\"end\" dy=\"0.35em\" visibility=\"hidden\" gadfly:scale=\"1.0\">-5</text>\n",
" <text x=\"14.59\" y=\"132.5\" text-anchor=\"end\" dy=\"0.35em\" visibility=\"hidden\" gadfly:scale=\"1.0\">-4</text>\n",
" <text x=\"14.59\" y=\"114.57\" text-anchor=\"end\" dy=\"0.35em\" visibility=\"hidden\" gadfly:scale=\"1.0\">-3</text>\n",
" <text x=\"14.59\" y=\"96.64\" text-anchor=\"end\" dy=\"0.35em\" visibility=\"hidden\" gadfly:scale=\"1.0\">-2</text>\n",
" <text x=\"14.59\" y=\"78.71\" text-anchor=\"end\" dy=\"0.35em\" visibility=\"visible\" gadfly:scale=\"1.0\">-1</text>\n",
" <text x=\"14.59\" y=\"60.79\" text-anchor=\"end\" dy=\"0.35em\" visibility=\"visible\" gadfly:scale=\"1.0\">0</text>\n",
" <text x=\"14.59\" y=\"42.86\" text-anchor=\"end\" dy=\"0.35em\" visibility=\"visible\" gadfly:scale=\"1.0\">1</text>\n",
" <text x=\"14.59\" y=\"24.93\" text-anchor=\"end\" dy=\"0.35em\" visibility=\"visible\" gadfly:scale=\"1.0\">2</text>\n",
" <text x=\"14.59\" y=\"7\" text-anchor=\"end\" dy=\"0.35em\" visibility=\"visible\" gadfly:scale=\"1.0\">3</text>\n",
" <text x=\"14.59\" y=\"-10.93\" text-anchor=\"end\" dy=\"0.35em\" visibility=\"hidden\" gadfly:scale=\"1.0\">4</text>\n",
" <text x=\"14.59\" y=\"-28.86\" text-anchor=\"end\" dy=\"0.35em\" visibility=\"hidden\" gadfly:scale=\"1.0\">5</text>\n",
" <text x=\"14.59\" y=\"-46.79\" text-anchor=\"end\" dy=\"0.35em\" visibility=\"hidden\" gadfly:scale=\"1.0\">6</text>\n",
" <text x=\"14.59\" y=\"-64.71\" text-anchor=\"end\" dy=\"0.35em\" visibility=\"hidden\" gadfly:scale=\"1.0\">7</text>\n",
" <text x=\"14.59\" y=\"-82.64\" text-anchor=\"end\" dy=\"0.35em\" visibility=\"hidden\" gadfly:scale=\"1.0\">8</text>\n",
" <text x=\"14.59\" y=\"150.43\" text-anchor=\"end\" dy=\"0.35em\" visibility=\"hidden\" gadfly:scale=\"10.0\">-5.0</text>\n",
" <text x=\"14.59\" y=\"146.84\" text-anchor=\"end\" dy=\"0.35em\" visibility=\"hidden\" gadfly:scale=\"10.0\">-4.8</text>\n",
" <text x=\"14.59\" y=\"143.26\" text-anchor=\"end\" dy=\"0.35em\" visibility=\"hidden\" gadfly:scale=\"10.0\">-4.6</text>\n",
" <text x=\"14.59\" y=\"139.67\" text-anchor=\"end\" dy=\"0.35em\" visibility=\"hidden\" gadfly:scale=\"10.0\">-4.4</text>\n",
" <text x=\"14.59\" y=\"136.09\" text-anchor=\"end\" dy=\"0.35em\" visibility=\"hidden\" gadfly:scale=\"10.0\">-4.2</text>\n",
" <text x=\"14.59\" y=\"132.5\" text-anchor=\"end\" dy=\"0.35em\" visibility=\"hidden\" gadfly:scale=\"10.0\">-4.0</text>\n",
" <text x=\"14.59\" y=\"128.92\" text-anchor=\"end\" dy=\"0.35em\" visibility=\"hidden\" gadfly:scale=\"10.0\">-3.8</text>\n",
" <text x=\"14.59\" y=\"125.33\" text-anchor=\"end\" dy=\"0.35em\" visibility=\"hidden\" gadfly:scale=\"10.0\">-3.6</text>\n",
" <text x=\"14.59\" y=\"121.74\" text-anchor=\"end\" dy=\"0.35em\" visibility=\"hidden\" gadfly:scale=\"10.0\">-3.4</text>\n",
" <text x=\"14.59\" y=\"118.16\" text-anchor=\"end\" dy=\"0.35em\" visibility=\"hidden\" gadfly:scale=\"10.0\">-3.2</text>\n",
" <text x=\"14.59\" y=\"114.57\" text-anchor=\"end\" dy=\"0.35em\" visibility=\"hidden\" gadfly:scale=\"10.0\">-3.0</text>\n",
" <text x=\"14.59\" y=\"110.99\" text-anchor=\"end\" dy=\"0.35em\" visibility=\"hidden\" gadfly:scale=\"10.0\">-2.8</text>\n",
" <text x=\"14.59\" y=\"107.4\" text-anchor=\"end\" dy=\"0.35em\" visibility=\"hidden\" gadfly:scale=\"10.0\">-2.6</text>\n",
" <text x=\"14.59\" y=\"103.82\" text-anchor=\"end\" dy=\"0.35em\" visibility=\"hidden\" gadfly:scale=\"10.0\">-2.4</text>\n",
" <text x=\"14.59\" y=\"100.23\" text-anchor=\"end\" dy=\"0.35em\" visibility=\"hidden\" gadfly:scale=\"10.0\">-2.2</text>\n",
" <text x=\"14.59\" y=\"96.64\" text-anchor=\"end\" dy=\"0.35em\" visibility=\"hidden\" gadfly:scale=\"10.0\">-2.0</text>\n",
" <text x=\"14.59\" y=\"93.06\" text-anchor=\"end\" dy=\"0.35em\" visibility=\"hidden\" gadfly:scale=\"10.0\">-1.8</text>\n",
" <text x=\"14.59\" y=\"89.47\" text-anchor=\"end\" dy=\"0.35em\" visibility=\"hidden\" gadfly:scale=\"10.0\">-1.6</text>\n",
" <text x=\"14.59\" y=\"85.89\" text-anchor=\"end\" dy=\"0.35em\" visibility=\"hidden\" gadfly:scale=\"10.0\">-1.4</text>\n",
" <text x=\"14.59\" y=\"82.3\" text-anchor=\"end\" dy=\"0.35em\" visibility=\"hidden\" gadfly:scale=\"10.0\">-1.2</text>\n",
" <text x=\"14.59\" y=\"78.71\" text-anchor=\"end\" dy=\"0.35em\" visibility=\"hidden\" gadfly:scale=\"10.0\">-1.0</text>\n",
" <text x=\"14.59\" y=\"75.13\" text-anchor=\"end\" dy=\"0.35em\" visibility=\"hidden\" gadfly:scale=\"10.0\">-0.8</text>\n",
" <text x=\"14.59\" y=\"71.54\" text-anchor=\"end\" dy=\"0.35em\" visibility=\"hidden\" gadfly:scale=\"10.0\">-0.6</text>\n",
" <text x=\"14.59\" y=\"67.96\" text-anchor=\"end\" dy=\"0.35em\" visibility=\"hidden\" gadfly:scale=\"10.0\">-0.4</text>\n",
" <text x=\"14.59\" y=\"64.37\" text-anchor=\"end\" dy=\"0.35em\" visibility=\"hidden\" gadfly:scale=\"10.0\">-0.2</text>\n",
" <text x=\"14.59\" y=\"60.79\" text-anchor=\"end\" dy=\"0.35em\" visibility=\"hidden\" gadfly:scale=\"10.0\">0.0</text>\n",
" <text x=\"14.59\" y=\"57.2\" text-anchor=\"end\" dy=\"0.35em\" visibility=\"hidden\" gadfly:scale=\"10.0\">0.2</text>\n",
" <text x=\"14.59\" y=\"53.61\" text-anchor=\"end\" dy=\"0.35em\" visibility=\"hidden\" gadfly:scale=\"10.0\">0.4</text>\n",
" <text x=\"14.59\" y=\"50.03\" text-anchor=\"end\" dy=\"0.35em\" visibility=\"hidden\" gadfly:scale=\"10.0\">0.6</text>\n",
" <text x=\"14.59\" y=\"46.44\" text-anchor=\"end\" dy=\"0.35em\" visibility=\"hidden\" gadfly:scale=\"10.0\">0.8</text>\n",
" <text x=\"14.59\" y=\"42.86\" text-anchor=\"end\" dy=\"0.35em\" visibility=\"hidden\" gadfly:scale=\"10.0\">1.0</text>\n",
" <text x=\"14.59\" y=\"39.27\" text-anchor=\"end\" dy=\"0.35em\" visibility=\"hidden\" gadfly:scale=\"10.0\">1.2</text>\n",
" <text x=\"14.59\" y=\"35.69\" text-anchor=\"end\" dy=\"0.35em\" visibility=\"hidden\" gadfly:scale=\"10.0\">1.4</text>\n",
" <text x=\"14.59\" y=\"32.1\" text-anchor=\"end\" dy=\"0.35em\" visibility=\"hidden\" gadfly:scale=\"10.0\">1.6</text>\n",
" <text x=\"14.59\" y=\"28.51\" text-anchor=\"end\" dy=\"0.35em\" visibility=\"hidden\" gadfly:scale=\"10.0\">1.8</text>\n",
" <text x=\"14.59\" y=\"24.93\" text-anchor=\"end\" dy=\"0.35em\" visibility=\"hidden\" gadfly:scale=\"10.0\">2.0</text>\n",
" <text x=\"14.59\" y=\"21.34\" text-anchor=\"end\" dy=\"0.35em\" visibility=\"hidden\" gadfly:scale=\"10.0\">2.2</text>\n",
" <text x=\"14.59\" y=\"17.76\" text-anchor=\"end\" dy=\"0.35em\" visibility=\"hidden\" gadfly:scale=\"10.0\">2.4</text>\n",
" <text x=\"14.59\" y=\"14.17\" text-anchor=\"end\" dy=\"0.35em\" visibility=\"hidden\" gadfly:scale=\"10.0\">2.6</text>\n",
" <text x=\"14.59\" y=\"10.59\" text-anchor=\"end\" dy=\"0.35em\" visibility=\"hidden\" gadfly:scale=\"10.0\">2.8</text>\n",
" <text x=\"14.59\" y=\"7\" text-anchor=\"end\" dy=\"0.35em\" visibility=\"hidden\" gadfly:scale=\"10.0\">3.0</text>\n",
" <text x=\"14.59\" y=\"3.41\" text-anchor=\"end\" dy=\"0.35em\" visibility=\"hidden\" gadfly:scale=\"10.0\">3.2</text>\n",
" <text x=\"14.59\" y=\"-0.17\" text-anchor=\"end\" dy=\"0.35em\" visibility=\"hidden\" gadfly:scale=\"10.0\">3.4</text>\n",
" <text x=\"14.59\" y=\"-3.76\" text-anchor=\"end\" dy=\"0.35em\" visibility=\"hidden\" gadfly:scale=\"10.0\">3.6</text>\n",
" <text x=\"14.59\" y=\"-7.34\" text-anchor=\"end\" dy=\"0.35em\" visibility=\"hidden\" gadfly:scale=\"10.0\">3.8</text>\n",
" <text x=\"14.59\" y=\"-10.93\" text-anchor=\"end\" dy=\"0.35em\" visibility=\"hidden\" gadfly:scale=\"10.0\">4.0</text>\n",
" <text x=\"14.59\" y=\"-14.51\" text-anchor=\"end\" dy=\"0.35em\" visibility=\"hidden\" gadfly:scale=\"10.0\">4.2</text>\n",
" <text x=\"14.59\" y=\"-18.1\" text-anchor=\"end\" dy=\"0.35em\" visibility=\"hidden\" gadfly:scale=\"10.0\">4.4</text>\n",
" <text x=\"14.59\" y=\"-21.69\" text-anchor=\"end\" dy=\"0.35em\" visibility=\"hidden\" gadfly:scale=\"10.0\">4.6</text>\n",
" <text x=\"14.59\" y=\"-25.27\" text-anchor=\"end\" dy=\"0.35em\" visibility=\"hidden\" gadfly:scale=\"10.0\">4.8</text>\n",
" <text x=\"14.59\" y=\"-28.86\" text-anchor=\"end\" dy=\"0.35em\" visibility=\"hidden\" gadfly:scale=\"10.0\">5.0</text>\n",
" <text x=\"14.59\" y=\"-32.44\" text-anchor=\"end\" dy=\"0.35em\" visibility=\"hidden\" gadfly:scale=\"10.0\">5.2</text>\n",
" <text x=\"14.59\" y=\"-36.03\" text-anchor=\"end\" dy=\"0.35em\" visibility=\"hidden\" gadfly:scale=\"10.0\">5.4</text>\n",
" <text x=\"14.59\" y=\"-39.61\" text-anchor=\"end\" dy=\"0.35em\" visibility=\"hidden\" gadfly:scale=\"10.0\">5.6</text>\n",
" <text x=\"14.59\" y=\"-43.2\" text-anchor=\"end\" dy=\"0.35em\" visibility=\"hidden\" gadfly:scale=\"10.0\">5.8</text>\n",
" <text x=\"14.59\" y=\"-46.79\" text-anchor=\"end\" dy=\"0.35em\" visibility=\"hidden\" gadfly:scale=\"10.0\">6.0</text>\n",
" <text x=\"14.59\" y=\"-50.37\" text-anchor=\"end\" dy=\"0.35em\" visibility=\"hidden\" gadfly:scale=\"10.0\">6.2</text>\n",
" <text x=\"14.59\" y=\"-53.96\" text-anchor=\"end\" dy=\"0.35em\" visibility=\"hidden\" gadfly:scale=\"10.0\">6.4</text>\n",
" <text x=\"14.59\" y=\"-57.54\" text-anchor=\"end\" dy=\"0.35em\" visibility=\"hidden\" gadfly:scale=\"10.0\">6.6</text>\n",
" <text x=\"14.59\" y=\"-61.13\" text-anchor=\"end\" dy=\"0.35em\" visibility=\"hidden\" gadfly:scale=\"10.0\">6.8</text>\n",
" <text x=\"14.59\" y=\"-64.71\" text-anchor=\"end\" dy=\"0.35em\" visibility=\"hidden\" gadfly:scale=\"10.0\">7.0</text>\n",
" <text x=\"14.59\" y=\"150.43\" text-anchor=\"end\" dy=\"0.35em\" visibility=\"hidden\" gadfly:scale=\"0.5\">-5</text>\n",
" <text x=\"14.59\" y=\"60.79\" text-anchor=\"end\" dy=\"0.35em\" visibility=\"hidden\" gadfly:scale=\"0.5\">0</text>\n",
" <text x=\"14.59\" y=\"-28.86\" text-anchor=\"end\" dy=\"0.35em\" visibility=\"hidden\" gadfly:scale=\"0.5\">5</text>\n",
" <text x=\"14.59\" y=\"-118.5\" text-anchor=\"end\" dy=\"0.35em\" visibility=\"hidden\" gadfly:scale=\"0.5\">10</text>\n",
" <text x=\"14.59\" y=\"150.43\" text-anchor=\"end\" dy=\"0.35em\" visibility=\"hidden\" gadfly:scale=\"5.0\">-5.0</text>\n",
" <text x=\"14.59\" y=\"141.47\" text-anchor=\"end\" dy=\"0.35em\" visibility=\"hidden\" gadfly:scale=\"5.0\">-4.5</text>\n",
" <text x=\"14.59\" y=\"132.5\" text-anchor=\"end\" dy=\"0.35em\" visibility=\"hidden\" gadfly:scale=\"5.0\">-4.0</text>\n",
" <text x=\"14.59\" y=\"123.54\" text-anchor=\"end\" dy=\"0.35em\" visibility=\"hidden\" gadfly:scale=\"5.0\">-3.5</text>\n",
" <text x=\"14.59\" y=\"114.57\" text-anchor=\"end\" dy=\"0.35em\" visibility=\"hidden\" gadfly:scale=\"5.0\">-3.0</text>\n",
" <text x=\"14.59\" y=\"105.61\" text-anchor=\"end\" dy=\"0.35em\" visibility=\"hidden\" gadfly:scale=\"5.0\">-2.5</text>\n",
" <text x=\"14.59\" y=\"96.64\" text-anchor=\"end\" dy=\"0.35em\" visibility=\"hidden\" gadfly:scale=\"5.0\">-2.0</text>\n",
" <text x=\"14.59\" y=\"87.68\" text-anchor=\"end\" dy=\"0.35em\" visibility=\"hidden\" gadfly:scale=\"5.0\">-1.5</text>\n",
" <text x=\"14.59\" y=\"78.71\" text-anchor=\"end\" dy=\"0.35em\" visibility=\"hidden\" gadfly:scale=\"5.0\">-1.0</text>\n",
" <text x=\"14.59\" y=\"69.75\" text-anchor=\"end\" dy=\"0.35em\" visibility=\"hidden\" gadfly:scale=\"5.0\">-0.5</text>\n",
" <text x=\"14.59\" y=\"60.79\" text-anchor=\"end\" dy=\"0.35em\" visibility=\"hidden\" gadfly:scale=\"5.0\">0.0</text>\n",
" <text x=\"14.59\" y=\"51.82\" text-anchor=\"end\" dy=\"0.35em\" visibility=\"hidden\" gadfly:scale=\"5.0\">0.5</text>\n",
" <text x=\"14.59\" y=\"42.86\" text-anchor=\"end\" dy=\"0.35em\" visibility=\"hidden\" gadfly:scale=\"5.0\">1.0</text>\n",
" <text x=\"14.59\" y=\"33.89\" text-anchor=\"end\" dy=\"0.35em\" visibility=\"hidden\" gadfly:scale=\"5.0\">1.5</text>\n",
" <text x=\"14.59\" y=\"24.93\" text-anchor=\"end\" dy=\"0.35em\" visibility=\"hidden\" gadfly:scale=\"5.0\">2.0</text>\n",
" <text x=\"14.59\" y=\"15.96\" text-anchor=\"end\" dy=\"0.35em\" visibility=\"hidden\" gadfly:scale=\"5.0\">2.5</text>\n",
" <text x=\"14.59\" y=\"7\" text-anchor=\"end\" dy=\"0.35em\" visibility=\"hidden\" gadfly:scale=\"5.0\">3.0</text>\n",
" <text x=\"14.59\" y=\"-1.96\" text-anchor=\"end\" dy=\"0.35em\" visibility=\"hidden\" gadfly:scale=\"5.0\">3.5</text>\n",
" <text x=\"14.59\" y=\"-10.93\" text-anchor=\"end\" dy=\"0.35em\" visibility=\"hidden\" gadfly:scale=\"5.0\">4.0</text>\n",
" <text x=\"14.59\" y=\"-19.89\" text-anchor=\"end\" dy=\"0.35em\" visibility=\"hidden\" gadfly:scale=\"5.0\">4.5</text>\n",
" <text x=\"14.59\" y=\"-28.86\" text-anchor=\"end\" dy=\"0.35em\" visibility=\"hidden\" gadfly:scale=\"5.0\">5.0</text>\n",
" <text x=\"14.59\" y=\"-37.82\" text-anchor=\"end\" dy=\"0.35em\" visibility=\"hidden\" gadfly:scale=\"5.0\">5.5</text>\n",
" <text x=\"14.59\" y=\"-46.79\" text-anchor=\"end\" dy=\"0.35em\" visibility=\"hidden\" gadfly:scale=\"5.0\">6.0</text>\n",
" <text x=\"14.59\" y=\"-55.75\" text-anchor=\"end\" dy=\"0.35em\" visibility=\"hidden\" gadfly:scale=\"5.0\">6.5</text>\n",
" <text x=\"14.59\" y=\"-64.71\" text-anchor=\"end\" dy=\"0.35em\" visibility=\"hidden\" gadfly:scale=\"5.0\">7.0</text>\n",
" </g>\n",
" <g font-size=\"3.88\" font-family=\"'PT Sans','Helvetica Neue','Helvetica',sans-serif\" fill=\"#564A55\" stroke=\"#000000\" stroke-opacity=\"0.000\" id=\"fig-24ef7475d29e43d3b5056099cb85fa82-element-19\">\n",
" <text x=\"8.81\" y=\"42.86\" text-anchor=\"end\" dy=\"0.35em\">y</text>\n",
" </g>\n",
"</g>\n",
"<defs>\n",
"<clipPath id=\"fig-24ef7475d29e43d3b5056099cb85fa82-element-5\">\n",
" <path d=\"M15.59,5 L 136.42 5 136.42 80.72 15.59 80.72\" />\n",
"</clipPath\n",
"></defs>\n",
"<script> <![CDATA[\n",
"(function(N){var k=/[\\.\\/]/,L=/\\s*,\\s*/,C=function(a,d){return a-d},a,v,y={n:{}},M=function(){for(var a=0,d=this.length;a<d;a++)if(\"undefined\"!=typeof this[a])return this[a]},A=function(){for(var a=this.length;--a;)if(\"undefined\"!=typeof this[a])return this[a]},w=function(k,d){k=String(k);var f=v,n=Array.prototype.slice.call(arguments,2),u=w.listeners(k),p=0,b,q=[],e={},l=[],r=a;l.firstDefined=M;l.lastDefined=A;a=k;for(var s=v=0,x=u.length;s<x;s++)\"zIndex\"in u[s]&&(q.push(u[s].zIndex),0>u[s].zIndex&&\n",
"(e[u[s].zIndex]=u[s]));for(q.sort(C);0>q[p];)if(b=e[q[p++] ],l.push(b.apply(d,n)),v)return v=f,l;for(s=0;s<x;s++)if(b=u[s],\"zIndex\"in b)if(b.zIndex==q[p]){l.push(b.apply(d,n));if(v)break;do if(p++,(b=e[q[p] ])&&l.push(b.apply(d,n)),v)break;while(b)}else e[b.zIndex]=b;else if(l.push(b.apply(d,n)),v)break;v=f;a=r;return l};w._events=y;w.listeners=function(a){a=a.split(k);var d=y,f,n,u,p,b,q,e,l=[d],r=[];u=0;for(p=a.length;u<p;u++){e=[];b=0;for(q=l.length;b<q;b++)for(d=l[b].n,f=[d[a[u] ],d[\"*\"] ],n=2;n--;)if(d=\n",
"f[n])e.push(d),r=r.concat(d.f||[]);l=e}return r};w.on=function(a,d){a=String(a);if(\"function\"!=typeof d)return function(){};for(var f=a.split(L),n=0,u=f.length;n<u;n++)(function(a){a=a.split(k);for(var b=y,f,e=0,l=a.length;e<l;e++)b=b.n,b=b.hasOwnProperty(a[e])&&b[a[e] ]||(b[a[e] ]={n:{}});b.f=b.f||[];e=0;for(l=b.f.length;e<l;e++)if(b.f[e]==d){f=!0;break}!f&&b.f.push(d)})(f[n]);return function(a){+a==+a&&(d.zIndex=+a)}};w.f=function(a){var d=[].slice.call(arguments,1);return function(){w.apply(null,\n",
"[a,null].concat(d).concat([].slice.call(arguments,0)))}};w.stop=function(){v=1};w.nt=function(k){return k?(new RegExp(\"(?:\\\\.|\\\\/|^)\"+k+\"(?:\\\\.|\\\\/|$)\")).test(a):a};w.nts=function(){return a.split(k)};w.off=w.unbind=function(a,d){if(a){var f=a.split(L);if(1<f.length)for(var n=0,u=f.length;n<u;n++)w.off(f[n],d);else{for(var f=a.split(k),p,b,q,e,l=[y],n=0,u=f.length;n<u;n++)for(e=0;e<l.length;e+=q.length-2){q=[e,1];p=l[e].n;if(\"*\"!=f[n])p[f[n] ]&&q.push(p[f[n] ]);else for(b in p)p.hasOwnProperty(b)&&\n",
"q.push(p[b]);l.splice.apply(l,q)}n=0;for(u=l.length;n<u;n++)for(p=l[n];p.n;){if(d){if(p.f){e=0;for(f=p.f.length;e<f;e++)if(p.f[e]==d){p.f.splice(e,1);break}!p.f.length&&delete p.f}for(b in p.n)if(p.n.hasOwnProperty(b)&&p.n[b].f){q=p.n[b].f;e=0;for(f=q.length;e<f;e++)if(q[e]==d){q.splice(e,1);break}!q.length&&delete p.n[b].f}}else for(b in delete p.f,p.n)p.n.hasOwnProperty(b)&&p.n[b].f&&delete p.n[b].f;p=p.n}}}else w._events=y={n:{}}};w.once=function(a,d){var f=function(){w.unbind(a,f);return d.apply(this,\n",
"arguments)};return w.on(a,f)};w.version=\"0.4.2\";w.toString=function(){return\"You are running Eve 0.4.2\"};\"undefined\"!=typeof module&&module.exports?module.exports=w:\"function\"===typeof define&&define.amd?define(\"eve\",[],function(){return w}):N.eve=w})(this);\n",
"(function(N,k){\"function\"===typeof define&&define.amd?define(\"Snap.svg\",[\"eve\"],function(L){return k(N,L)}):k(N,N.eve)})(this,function(N,k){var L=function(a){var k={},y=N.requestAnimationFrame||N.webkitRequestAnimationFrame||N.mozRequestAnimationFrame||N.oRequestAnimationFrame||N.msRequestAnimationFrame||function(a){setTimeout(a,16)},M=Array.isArray||function(a){return a instanceof Array||\"[object Array]\"==Object.prototype.toString.call(a)},A=0,w=\"M\"+(+new Date).toString(36),z=function(a){if(null==\n",
"a)return this.s;var b=this.s-a;this.b+=this.dur*b;this.B+=this.dur*b;this.s=a},d=function(a){if(null==a)return this.spd;this.spd=a},f=function(a){if(null==a)return this.dur;this.s=this.s*a/this.dur;this.dur=a},n=function(){delete k[this.id];this.update();a(\"mina.stop.\"+this.id,this)},u=function(){this.pdif||(delete k[this.id],this.update(),this.pdif=this.get()-this.b)},p=function(){this.pdif&&(this.b=this.get()-this.pdif,delete this.pdif,k[this.id]=this)},b=function(){var a;if(M(this.start)){a=[];\n",
"for(var b=0,e=this.start.length;b<e;b++)a[b]=+this.start[b]+(this.end[b]-this.start[b])*this.easing(this.s)}else a=+this.start+(this.end-this.start)*this.easing(this.s);this.set(a)},q=function(){var l=0,b;for(b in k)if(k.hasOwnProperty(b)){var e=k[b],f=e.get();l++;e.s=(f-e.b)/(e.dur/e.spd);1<=e.s&&(delete k[b],e.s=1,l--,function(b){setTimeout(function(){a(\"mina.finish.\"+b.id,b)})}(e));e.update()}l&&y(q)},e=function(a,r,s,x,G,h,J){a={id:w+(A++).toString(36),start:a,end:r,b:s,s:0,dur:x-s,spd:1,get:G,\n",
"set:h,easing:J||e.linear,status:z,speed:d,duration:f,stop:n,pause:u,resume:p,update:b};k[a.id]=a;r=0;for(var K in k)if(k.hasOwnProperty(K)&&(r++,2==r))break;1==r&&y(q);return a};e.time=Date.now||function(){return+new Date};e.getById=function(a){return k[a]||null};e.linear=function(a){return a};e.easeout=function(a){return Math.pow(a,1.7)};e.easein=function(a){return Math.pow(a,0.48)};e.easeinout=function(a){if(1==a)return 1;if(0==a)return 0;var b=0.48-a/1.04,e=Math.sqrt(0.1734+b*b);a=e-b;a=Math.pow(Math.abs(a),\n",
"1/3)*(0>a?-1:1);b=-e-b;b=Math.pow(Math.abs(b),1/3)*(0>b?-1:1);a=a+b+0.5;return 3*(1-a)*a*a+a*a*a};e.backin=function(a){return 1==a?1:a*a*(2.70158*a-1.70158)};e.backout=function(a){if(0==a)return 0;a-=1;return a*a*(2.70158*a+1.70158)+1};e.elastic=function(a){return a==!!a?a:Math.pow(2,-10*a)*Math.sin(2*(a-0.075)*Math.PI/0.3)+1};e.bounce=function(a){a<1/2.75?a*=7.5625*a:a<2/2.75?(a-=1.5/2.75,a=7.5625*a*a+0.75):a<2.5/2.75?(a-=2.25/2.75,a=7.5625*a*a+0.9375):(a-=2.625/2.75,a=7.5625*a*a+0.984375);return a};\n",
"return N.mina=e}(\"undefined\"==typeof k?function(){}:k),C=function(){function a(c,t){if(c){if(c.tagName)return x(c);if(y(c,\"array\")&&a.set)return a.set.apply(a,c);if(c instanceof e)return c;if(null==t)return c=G.doc.querySelector(c),x(c)}return new s(null==c?\"100%\":c,null==t?\"100%\":t)}function v(c,a){if(a){\"#text\"==c&&(c=G.doc.createTextNode(a.text||\"\"));\"string\"==typeof c&&(c=v(c));if(\"string\"==typeof a)return\"xlink:\"==a.substring(0,6)?c.getAttributeNS(m,a.substring(6)):\"xml:\"==a.substring(0,4)?c.getAttributeNS(la,\n",
"a.substring(4)):c.getAttribute(a);for(var da in a)if(a[h](da)){var b=J(a[da]);b?\"xlink:\"==da.substring(0,6)?c.setAttributeNS(m,da.substring(6),b):\"xml:\"==da.substring(0,4)?c.setAttributeNS(la,da.substring(4),b):c.setAttribute(da,b):c.removeAttribute(da)}}else c=G.doc.createElementNS(la,c);return c}function y(c,a){a=J.prototype.toLowerCase.call(a);return\"finite\"==a?isFinite(c):\"array\"==a&&(c instanceof Array||Array.isArray&&Array.isArray(c))?!0:\"null\"==a&&null===c||a==typeof c&&null!==c||\"object\"==\n",
"a&&c===Object(c)||$.call(c).slice(8,-1).toLowerCase()==a}function M(c){if(\"function\"==typeof c||Object(c)!==c)return c;var a=new c.constructor,b;for(b in c)c[h](b)&&(a[b]=M(c[b]));return a}function A(c,a,b){function m(){var e=Array.prototype.slice.call(arguments,0),f=e.join(\"\\u2400\"),d=m.cache=m.cache||{},l=m.count=m.count||[];if(d[h](f)){a:for(var e=l,l=f,B=0,H=e.length;B<H;B++)if(e[B]===l){e.push(e.splice(B,1)[0]);break a}return b?b(d[f]):d[f]}1E3<=l.length&&delete d[l.shift()];l.push(f);d[f]=c.apply(a,\n",
"e);return b?b(d[f]):d[f]}return m}function w(c,a,b,m,e,f){return null==e?(c-=b,a-=m,c||a?(180*I.atan2(-a,-c)/C+540)%360:0):w(c,a,e,f)-w(b,m,e,f)}function z(c){return c%360*C/180}function d(c){var a=[];c=c.replace(/(?:^|\\s)(\\w+)\\(([^)]+)\\)/g,function(c,b,m){m=m.split(/\\s*,\\s*|\\s+/);\"rotate\"==b&&1==m.length&&m.push(0,0);\"scale\"==b&&(2<m.length?m=m.slice(0,2):2==m.length&&m.push(0,0),1==m.length&&m.push(m[0],0,0));\"skewX\"==b?a.push([\"m\",1,0,I.tan(z(m[0])),1,0,0]):\"skewY\"==b?a.push([\"m\",1,I.tan(z(m[0])),\n",
"0,1,0,0]):a.push([b.charAt(0)].concat(m));return c});return a}function f(c,t){var b=O(c),m=new a.Matrix;if(b)for(var e=0,f=b.length;e<f;e++){var h=b[e],d=h.length,B=J(h[0]).toLowerCase(),H=h[0]!=B,l=H?m.invert():0,E;\"t\"==B&&2==d?m.translate(h[1],0):\"t\"==B&&3==d?H?(d=l.x(0,0),B=l.y(0,0),H=l.x(h[1],h[2]),l=l.y(h[1],h[2]),m.translate(H-d,l-B)):m.translate(h[1],h[2]):\"r\"==B?2==d?(E=E||t,m.rotate(h[1],E.x+E.width/2,E.y+E.height/2)):4==d&&(H?(H=l.x(h[2],h[3]),l=l.y(h[2],h[3]),m.rotate(h[1],H,l)):m.rotate(h[1],\n",
"h[2],h[3])):\"s\"==B?2==d||3==d?(E=E||t,m.scale(h[1],h[d-1],E.x+E.width/2,E.y+E.height/2)):4==d?H?(H=l.x(h[2],h[3]),l=l.y(h[2],h[3]),m.scale(h[1],h[1],H,l)):m.scale(h[1],h[1],h[2],h[3]):5==d&&(H?(H=l.x(h[3],h[4]),l=l.y(h[3],h[4]),m.scale(h[1],h[2],H,l)):m.scale(h[1],h[2],h[3],h[4])):\"m\"==B&&7==d&&m.add(h[1],h[2],h[3],h[4],h[5],h[6])}return m}function n(c,t){if(null==t){var m=!0;t=\"linearGradient\"==c.type||\"radialGradient\"==c.type?c.node.getAttribute(\"gradientTransform\"):\"pattern\"==c.type?c.node.getAttribute(\"patternTransform\"):\n",
"c.node.getAttribute(\"transform\");if(!t)return new a.Matrix;t=d(t)}else t=a._.rgTransform.test(t)?J(t).replace(/\\.{3}|\\u2026/g,c._.transform||aa):d(t),y(t,\"array\")&&(t=a.path?a.path.toString.call(t):J(t)),c._.transform=t;var b=f(t,c.getBBox(1));if(m)return b;c.matrix=b}function u(c){c=c.node.ownerSVGElement&&x(c.node.ownerSVGElement)||c.node.parentNode&&x(c.node.parentNode)||a.select(\"svg\")||a(0,0);var t=c.select(\"defs\"),t=null==t?!1:t.node;t||(t=r(\"defs\",c.node).node);return t}function p(c){return c.node.ownerSVGElement&&\n",
"x(c.node.ownerSVGElement)||a.select(\"svg\")}function b(c,a,m){function b(c){if(null==c)return aa;if(c==+c)return c;v(B,{width:c});try{return B.getBBox().width}catch(a){return 0}}function h(c){if(null==c)return aa;if(c==+c)return c;v(B,{height:c});try{return B.getBBox().height}catch(a){return 0}}function e(b,B){null==a?d[b]=B(c.attr(b)||0):b==a&&(d=B(null==m?c.attr(b)||0:m))}var f=p(c).node,d={},B=f.querySelector(\".svg---mgr\");B||(B=v(\"rect\"),v(B,{x:-9E9,y:-9E9,width:10,height:10,\"class\":\"svg---mgr\",\n",
"fill:\"none\"}),f.appendChild(B));switch(c.type){case \"rect\":e(\"rx\",b),e(\"ry\",h);case \"image\":e(\"width\",b),e(\"height\",h);case \"text\":e(\"x\",b);e(\"y\",h);break;case \"circle\":e(\"cx\",b);e(\"cy\",h);e(\"r\",b);break;case \"ellipse\":e(\"cx\",b);e(\"cy\",h);e(\"rx\",b);e(\"ry\",h);break;case \"line\":e(\"x1\",b);e(\"x2\",b);e(\"y1\",h);e(\"y2\",h);break;case \"marker\":e(\"refX\",b);e(\"markerWidth\",b);e(\"refY\",h);e(\"markerHeight\",h);break;case \"radialGradient\":e(\"fx\",b);e(\"fy\",h);break;case \"tspan\":e(\"dx\",b);e(\"dy\",h);break;default:e(a,\n",
"b)}f.removeChild(B);return d}function q(c){y(c,\"array\")||(c=Array.prototype.slice.call(arguments,0));for(var a=0,b=0,m=this.node;this[a];)delete this[a++];for(a=0;a<c.length;a++)\"set\"==c[a].type?c[a].forEach(function(c){m.appendChild(c.node)}):m.appendChild(c[a].node);for(var h=m.childNodes,a=0;a<h.length;a++)this[b++]=x(h[a]);return this}function e(c){if(c.snap in E)return E[c.snap];var a=this.id=V(),b;try{b=c.ownerSVGElement}catch(m){}this.node=c;b&&(this.paper=new s(b));this.type=c.tagName;this.anims=\n",
"{};this._={transform:[]};c.snap=a;E[a]=this;\"g\"==this.type&&(this.add=q);if(this.type in{g:1,mask:1,pattern:1})for(var e in s.prototype)s.prototype[h](e)&&(this[e]=s.prototype[e])}function l(c){this.node=c}function r(c,a){var b=v(c);a.appendChild(b);return x(b)}function s(c,a){var b,m,f,d=s.prototype;if(c&&\"svg\"==c.tagName){if(c.snap in E)return E[c.snap];var l=c.ownerDocument;b=new e(c);m=c.getElementsByTagName(\"desc\")[0];f=c.getElementsByTagName(\"defs\")[0];m||(m=v(\"desc\"),m.appendChild(l.createTextNode(\"Created with Snap\")),\n",
"b.node.appendChild(m));f||(f=v(\"defs\"),b.node.appendChild(f));b.defs=f;for(var ca in d)d[h](ca)&&(b[ca]=d[ca]);b.paper=b.root=b}else b=r(\"svg\",G.doc.body),v(b.node,{height:a,version:1.1,width:c,xmlns:la});return b}function x(c){return!c||c instanceof e||c instanceof l?c:c.tagName&&\"svg\"==c.tagName.toLowerCase()?new s(c):c.tagName&&\"object\"==c.tagName.toLowerCase()&&\"image/svg+xml\"==c.type?new s(c.contentDocument.getElementsByTagName(\"svg\")[0]):new e(c)}a.version=\"0.3.0\";a.toString=function(){return\"Snap v\"+\n",
"this.version};a._={};var G={win:N,doc:N.document};a._.glob=G;var h=\"hasOwnProperty\",J=String,K=parseFloat,U=parseInt,I=Math,P=I.max,Q=I.min,Y=I.abs,C=I.PI,aa=\"\",$=Object.prototype.toString,F=/^\\s*((#[a-f\\d]{6})|(#[a-f\\d]{3})|rgba?\\(\\s*([\\d\\.]+%?\\s*,\\s*[\\d\\.]+%?\\s*,\\s*[\\d\\.]+%?(?:\\s*,\\s*[\\d\\.]+%?)?)\\s*\\)|hsba?\\(\\s*([\\d\\.]+(?:deg|\\xb0|%)?\\s*,\\s*[\\d\\.]+%?\\s*,\\s*[\\d\\.]+(?:%?\\s*,\\s*[\\d\\.]+)?%?)\\s*\\)|hsla?\\(\\s*([\\d\\.]+(?:deg|\\xb0|%)?\\s*,\\s*[\\d\\.]+%?\\s*,\\s*[\\d\\.]+(?:%?\\s*,\\s*[\\d\\.]+)?%?)\\s*\\))\\s*$/i;a._.separator=\n",
"RegExp(\"[,\\t\\n\\x0B\\f\\r \\u00a0\\u1680\\u180e\\u2000\\u2001\\u2002\\u2003\\u2004\\u2005\\u2006\\u2007\\u2008\\u2009\\u200a\\u202f\\u205f\\u3000\\u2028\\u2029]+\");var S=RegExp(\"[\\t\\n\\x0B\\f\\r \\u00a0\\u1680\\u180e\\u2000\\u2001\\u2002\\u2003\\u2004\\u2005\\u2006\\u2007\\u2008\\u2009\\u200a\\u202f\\u205f\\u3000\\u2028\\u2029]*,[\\t\\n\\x0B\\f\\r \\u00a0\\u1680\\u180e\\u2000\\u2001\\u2002\\u2003\\u2004\\u2005\\u2006\\u2007\\u2008\\u2009\\u200a\\u202f\\u205f\\u3000\\u2028\\u2029]*\"),X={hs:1,rg:1},W=RegExp(\"([a-z])[\\t\\n\\x0B\\f\\r \\u00a0\\u1680\\u180e\\u2000\\u2001\\u2002\\u2003\\u2004\\u2005\\u2006\\u2007\\u2008\\u2009\\u200a\\u202f\\u205f\\u3000\\u2028\\u2029,]*((-?\\\\d*\\\\.?\\\\d*(?:e[\\\\-+]?\\\\d+)?[\\t\\n\\x0B\\f\\r \\u00a0\\u1680\\u180e\\u2000\\u2001\\u2002\\u2003\\u2004\\u2005\\u2006\\u2007\\u2008\\u2009\\u200a\\u202f\\u205f\\u3000\\u2028\\u2029]*,?[\\t\\n\\x0B\\f\\r \\u00a0\\u1680\\u180e\\u2000\\u2001\\u2002\\u2003\\u2004\\u2005\\u2006\\u2007\\u2008\\u2009\\u200a\\u202f\\u205f\\u3000\\u2028\\u2029]*)+)\",\n",
"\"ig\"),ma=RegExp(\"([rstm])[\\t\\n\\x0B\\f\\r \\u00a0\\u1680\\u180e\\u2000\\u2001\\u2002\\u2003\\u2004\\u2005\\u2006\\u2007\\u2008\\u2009\\u200a\\u202f\\u205f\\u3000\\u2028\\u2029,]*((-?\\\\d*\\\\.?\\\\d*(?:e[\\\\-+]?\\\\d+)?[\\t\\n\\x0B\\f\\r \\u00a0\\u1680\\u180e\\u2000\\u2001\\u2002\\u2003\\u2004\\u2005\\u2006\\u2007\\u2008\\u2009\\u200a\\u202f\\u205f\\u3000\\u2028\\u2029]*,?[\\t\\n\\x0B\\f\\r \\u00a0\\u1680\\u180e\\u2000\\u2001\\u2002\\u2003\\u2004\\u2005\\u2006\\u2007\\u2008\\u2009\\u200a\\u202f\\u205f\\u3000\\u2028\\u2029]*)+)\",\"ig\"),Z=RegExp(\"(-?\\\\d*\\\\.?\\\\d*(?:e[\\\\-+]?\\\\d+)?)[\\t\\n\\x0B\\f\\r \\u00a0\\u1680\\u180e\\u2000\\u2001\\u2002\\u2003\\u2004\\u2005\\u2006\\u2007\\u2008\\u2009\\u200a\\u202f\\u205f\\u3000\\u2028\\u2029]*,?[\\t\\n\\x0B\\f\\r \\u00a0\\u1680\\u180e\\u2000\\u2001\\u2002\\u2003\\u2004\\u2005\\u2006\\u2007\\u2008\\u2009\\u200a\\u202f\\u205f\\u3000\\u2028\\u2029]*\",\n",
"\"ig\"),na=0,ba=\"S\"+(+new Date).toString(36),V=function(){return ba+(na++).toString(36)},m=\"http://www.w3.org/1999/xlink\",la=\"http://www.w3.org/2000/svg\",E={},ca=a.url=function(c){return\"url('#\"+c+\"')\"};a._.$=v;a._.id=V;a.format=function(){var c=/\\{([^\\}]+)\\}/g,a=/(?:(?:^|\\.)(.+?)(?=\\[|\\.|$|\\()|\\[('|\")(.+?)\\2\\])(\\(\\))?/g,b=function(c,b,m){var h=m;b.replace(a,function(c,a,b,m,t){a=a||m;h&&(a in h&&(h=h[a]),\"function\"==typeof h&&t&&(h=h()))});return h=(null==h||h==m?c:h)+\"\"};return function(a,m){return J(a).replace(c,\n",
"function(c,a){return b(c,a,m)})}}();a._.clone=M;a._.cacher=A;a.rad=z;a.deg=function(c){return 180*c/C%360};a.angle=w;a.is=y;a.snapTo=function(c,a,b){b=y(b,\"finite\")?b:10;if(y(c,\"array\"))for(var m=c.length;m--;){if(Y(c[m]-a)<=b)return c[m]}else{c=+c;m=a%c;if(m<b)return a-m;if(m>c-b)return a-m+c}return a};a.getRGB=A(function(c){if(!c||(c=J(c)).indexOf(\"-\")+1)return{r:-1,g:-1,b:-1,hex:\"none\",error:1,toString:ka};if(\"none\"==c)return{r:-1,g:-1,b:-1,hex:\"none\",toString:ka};!X[h](c.toLowerCase().substring(0,\n",
"2))&&\"#\"!=c.charAt()&&(c=T(c));if(!c)return{r:-1,g:-1,b:-1,hex:\"none\",error:1,toString:ka};var b,m,e,f,d;if(c=c.match(F)){c[2]&&(e=U(c[2].substring(5),16),m=U(c[2].substring(3,5),16),b=U(c[2].substring(1,3),16));c[3]&&(e=U((d=c[3].charAt(3))+d,16),m=U((d=c[3].charAt(2))+d,16),b=U((d=c[3].charAt(1))+d,16));c[4]&&(d=c[4].split(S),b=K(d[0]),\"%\"==d[0].slice(-1)&&(b*=2.55),m=K(d[1]),\"%\"==d[1].slice(-1)&&(m*=2.55),e=K(d[2]),\"%\"==d[2].slice(-1)&&(e*=2.55),\"rgba\"==c[1].toLowerCase().slice(0,4)&&(f=K(d[3])),\n",
"d[3]&&\"%\"==d[3].slice(-1)&&(f/=100));if(c[5])return d=c[5].split(S),b=K(d[0]),\"%\"==d[0].slice(-1)&&(b/=100),m=K(d[1]),\"%\"==d[1].slice(-1)&&(m/=100),e=K(d[2]),\"%\"==d[2].slice(-1)&&(e/=100),\"deg\"!=d[0].slice(-3)&&\"\\u00b0\"!=d[0].slice(-1)||(b/=360),\"hsba\"==c[1].toLowerCase().slice(0,4)&&(f=K(d[3])),d[3]&&\"%\"==d[3].slice(-1)&&(f/=100),a.hsb2rgb(b,m,e,f);if(c[6])return d=c[6].split(S),b=K(d[0]),\"%\"==d[0].slice(-1)&&(b/=100),m=K(d[1]),\"%\"==d[1].slice(-1)&&(m/=100),e=K(d[2]),\"%\"==d[2].slice(-1)&&(e/=100),\n",
"\"deg\"!=d[0].slice(-3)&&\"\\u00b0\"!=d[0].slice(-1)||(b/=360),\"hsla\"==c[1].toLowerCase().slice(0,4)&&(f=K(d[3])),d[3]&&\"%\"==d[3].slice(-1)&&(f/=100),a.hsl2rgb(b,m,e,f);b=Q(I.round(b),255);m=Q(I.round(m),255);e=Q(I.round(e),255);f=Q(P(f,0),1);c={r:b,g:m,b:e,toString:ka};c.hex=\"#\"+(16777216|e|m<<8|b<<16).toString(16).slice(1);c.opacity=y(f,\"finite\")?f:1;return c}return{r:-1,g:-1,b:-1,hex:\"none\",error:1,toString:ka}},a);a.hsb=A(function(c,b,m){return a.hsb2rgb(c,b,m).hex});a.hsl=A(function(c,b,m){return a.hsl2rgb(c,\n",
"b,m).hex});a.rgb=A(function(c,a,b,m){if(y(m,\"finite\")){var e=I.round;return\"rgba(\"+[e(c),e(a),e(b),+m.toFixed(2)]+\")\"}return\"#\"+(16777216|b|a<<8|c<<16).toString(16).slice(1)});var T=function(c){var a=G.doc.getElementsByTagName(\"head\")[0]||G.doc.getElementsByTagName(\"svg\")[0];T=A(function(c){if(\"red\"==c.toLowerCase())return\"rgb(255, 0, 0)\";a.style.color=\"rgb(255, 0, 0)\";a.style.color=c;c=G.doc.defaultView.getComputedStyle(a,aa).getPropertyValue(\"color\");return\"rgb(255, 0, 0)\"==c?null:c});return T(c)},\n",
"qa=function(){return\"hsb(\"+[this.h,this.s,this.b]+\")\"},ra=function(){return\"hsl(\"+[this.h,this.s,this.l]+\")\"},ka=function(){return 1==this.opacity||null==this.opacity?this.hex:\"rgba(\"+[this.r,this.g,this.b,this.opacity]+\")\"},D=function(c,b,m){null==b&&y(c,\"object\")&&\"r\"in c&&\"g\"in c&&\"b\"in c&&(m=c.b,b=c.g,c=c.r);null==b&&y(c,string)&&(m=a.getRGB(c),c=m.r,b=m.g,m=m.b);if(1<c||1<b||1<m)c/=255,b/=255,m/=255;return[c,b,m]},oa=function(c,b,m,e){c=I.round(255*c);b=I.round(255*b);m=I.round(255*m);c={r:c,\n",
"g:b,b:m,opacity:y(e,\"finite\")?e:1,hex:a.rgb(c,b,m),toString:ka};y(e,\"finite\")&&(c.opacity=e);return c};a.color=function(c){var b;y(c,\"object\")&&\"h\"in c&&\"s\"in c&&\"b\"in c?(b=a.hsb2rgb(c),c.r=b.r,c.g=b.g,c.b=b.b,c.opacity=1,c.hex=b.hex):y(c,\"object\")&&\"h\"in c&&\"s\"in c&&\"l\"in c?(b=a.hsl2rgb(c),c.r=b.r,c.g=b.g,c.b=b.b,c.opacity=1,c.hex=b.hex):(y(c,\"string\")&&(c=a.getRGB(c)),y(c,\"object\")&&\"r\"in c&&\"g\"in c&&\"b\"in c&&!(\"error\"in c)?(b=a.rgb2hsl(c),c.h=b.h,c.s=b.s,c.l=b.l,b=a.rgb2hsb(c),c.v=b.b):(c={hex:\"none\"},\n",
"c.r=c.g=c.b=c.h=c.s=c.v=c.l=-1,c.error=1));c.toString=ka;return c};a.hsb2rgb=function(c,a,b,m){y(c,\"object\")&&\"h\"in c&&\"s\"in c&&\"b\"in c&&(b=c.b,a=c.s,c=c.h,m=c.o);var e,h,d;c=360*c%360/60;d=b*a;a=d*(1-Y(c%2-1));b=e=h=b-d;c=~~c;b+=[d,a,0,0,a,d][c];e+=[a,d,d,a,0,0][c];h+=[0,0,a,d,d,a][c];return oa(b,e,h,m)};a.hsl2rgb=function(c,a,b,m){y(c,\"object\")&&\"h\"in c&&\"s\"in c&&\"l\"in c&&(b=c.l,a=c.s,c=c.h);if(1<c||1<a||1<b)c/=360,a/=100,b/=100;var e,h,d;c=360*c%360/60;d=2*a*(0.5>b?b:1-b);a=d*(1-Y(c%2-1));b=e=\n",
"h=b-d/2;c=~~c;b+=[d,a,0,0,a,d][c];e+=[a,d,d,a,0,0][c];h+=[0,0,a,d,d,a][c];return oa(b,e,h,m)};a.rgb2hsb=function(c,a,b){b=D(c,a,b);c=b[0];a=b[1];b=b[2];var m,e;m=P(c,a,b);e=m-Q(c,a,b);c=((0==e?0:m==c?(a-b)/e:m==a?(b-c)/e+2:(c-a)/e+4)+360)%6*60/360;return{h:c,s:0==e?0:e/m,b:m,toString:qa}};a.rgb2hsl=function(c,a,b){b=D(c,a,b);c=b[0];a=b[1];b=b[2];var m,e,h;m=P(c,a,b);e=Q(c,a,b);h=m-e;c=((0==h?0:m==c?(a-b)/h:m==a?(b-c)/h+2:(c-a)/h+4)+360)%6*60/360;m=(m+e)/2;return{h:c,s:0==h?0:0.5>m?h/(2*m):h/(2-2*\n",
"m),l:m,toString:ra}};a.parsePathString=function(c){if(!c)return null;var b=a.path(c);if(b.arr)return a.path.clone(b.arr);var m={a:7,c:6,o:2,h:1,l:2,m:2,r:4,q:4,s:4,t:2,v:1,u:3,z:0},e=[];y(c,\"array\")&&y(c[0],\"array\")&&(e=a.path.clone(c));e.length||J(c).replace(W,function(c,a,b){var h=[];c=a.toLowerCase();b.replace(Z,function(c,a){a&&h.push(+a)});\"m\"==c&&2<h.length&&(e.push([a].concat(h.splice(0,2))),c=\"l\",a=\"m\"==a?\"l\":\"L\");\"o\"==c&&1==h.length&&e.push([a,h[0] ]);if(\"r\"==c)e.push([a].concat(h));else for(;h.length>=\n",
"m[c]&&(e.push([a].concat(h.splice(0,m[c]))),m[c]););});e.toString=a.path.toString;b.arr=a.path.clone(e);return e};var O=a.parseTransformString=function(c){if(!c)return null;var b=[];y(c,\"array\")&&y(c[0],\"array\")&&(b=a.path.clone(c));b.length||J(c).replace(ma,function(c,a,m){var e=[];a.toLowerCase();m.replace(Z,function(c,a){a&&e.push(+a)});b.push([a].concat(e))});b.toString=a.path.toString;return b};a._.svgTransform2string=d;a._.rgTransform=RegExp(\"^[a-z][\\t\\n\\x0B\\f\\r \\u00a0\\u1680\\u180e\\u2000\\u2001\\u2002\\u2003\\u2004\\u2005\\u2006\\u2007\\u2008\\u2009\\u200a\\u202f\\u205f\\u3000\\u2028\\u2029]*-?\\\\.?\\\\d\",\n",
"\"i\");a._.transform2matrix=f;a._unit2px=b;a._.getSomeDefs=u;a._.getSomeSVG=p;a.select=function(c){return x(G.doc.querySelector(c))};a.selectAll=function(c){c=G.doc.querySelectorAll(c);for(var b=(a.set||Array)(),m=0;m<c.length;m++)b.push(x(c[m]));return b};setInterval(function(){for(var c in E)if(E[h](c)){var a=E[c],b=a.node;(\"svg\"!=a.type&&!b.ownerSVGElement||\"svg\"==a.type&&(!b.parentNode||\"ownerSVGElement\"in b.parentNode&&!b.ownerSVGElement))&&delete E[c]}},1E4);(function(c){function m(c){function a(c,\n",
"b){var m=v(c.node,b);(m=(m=m&&m.match(d))&&m[2])&&\"#\"==m.charAt()&&(m=m.substring(1))&&(f[m]=(f[m]||[]).concat(function(a){var m={};m[b]=ca(a);v(c.node,m)}))}function b(c){var a=v(c.node,\"xlink:href\");a&&\"#\"==a.charAt()&&(a=a.substring(1))&&(f[a]=(f[a]||[]).concat(function(a){c.attr(\"xlink:href\",\"#\"+a)}))}var e=c.selectAll(\"*\"),h,d=/^\\s*url\\((\"|'|)(.*)\\1\\)\\s*$/;c=[];for(var f={},l=0,E=e.length;l<E;l++){h=e[l];a(h,\"fill\");a(h,\"stroke\");a(h,\"filter\");a(h,\"mask\");a(h,\"clip-path\");b(h);var t=v(h.node,\n",
"\"id\");t&&(v(h.node,{id:h.id}),c.push({old:t,id:h.id}))}l=0;for(E=c.length;l<E;l++)if(e=f[c[l].old])for(h=0,t=e.length;h<t;h++)e[h](c[l].id)}function e(c,a,b){return function(m){m=m.slice(c,a);1==m.length&&(m=m[0]);return b?b(m):m}}function d(c){return function(){var a=c?\"<\"+this.type:\"\",b=this.node.attributes,m=this.node.childNodes;if(c)for(var e=0,h=b.length;e<h;e++)a+=\" \"+b[e].name+'=\"'+b[e].value.replace(/\"/g,'\\\\\"')+'\"';if(m.length){c&&(a+=\">\");e=0;for(h=m.length;e<h;e++)3==m[e].nodeType?a+=m[e].nodeValue:\n",
"1==m[e].nodeType&&(a+=x(m[e]).toString());c&&(a+=\"</\"+this.type+\">\")}else c&&(a+=\"/>\");return a}}c.attr=function(c,a){if(!c)return this;if(y(c,\"string\"))if(1<arguments.length){var b={};b[c]=a;c=b}else return k(\"snap.util.getattr.\"+c,this).firstDefined();for(var m in c)c[h](m)&&k(\"snap.util.attr.\"+m,this,c[m]);return this};c.getBBox=function(c){if(!a.Matrix||!a.path)return this.node.getBBox();var b=this,m=new a.Matrix;if(b.removed)return a._.box();for(;\"use\"==b.type;)if(c||(m=m.add(b.transform().localMatrix.translate(b.attr(\"x\")||\n",
"0,b.attr(\"y\")||0))),b.original)b=b.original;else var e=b.attr(\"xlink:href\"),b=b.original=b.node.ownerDocument.getElementById(e.substring(e.indexOf(\"#\")+1));var e=b._,h=a.path.get[b.type]||a.path.get.deflt;try{if(c)return e.bboxwt=h?a.path.getBBox(b.realPath=h(b)):a._.box(b.node.getBBox()),a._.box(e.bboxwt);b.realPath=h(b);b.matrix=b.transform().localMatrix;e.bbox=a.path.getBBox(a.path.map(b.realPath,m.add(b.matrix)));return a._.box(e.bbox)}catch(d){return a._.box()}};var f=function(){return this.string};\n",
"c.transform=function(c){var b=this._;if(null==c){var m=this;c=new a.Matrix(this.node.getCTM());for(var e=n(this),h=[e],d=new a.Matrix,l=e.toTransformString(),b=J(e)==J(this.matrix)?J(b.transform):l;\"svg\"!=m.type&&(m=m.parent());)h.push(n(m));for(m=h.length;m--;)d.add(h[m]);return{string:b,globalMatrix:c,totalMatrix:d,localMatrix:e,diffMatrix:c.clone().add(e.invert()),global:c.toTransformString(),total:d.toTransformString(),local:l,toString:f}}c instanceof a.Matrix?this.matrix=c:n(this,c);this.node&&\n",
"(\"linearGradient\"==this.type||\"radialGradient\"==this.type?v(this.node,{gradientTransform:this.matrix}):\"pattern\"==this.type?v(this.node,{patternTransform:this.matrix}):v(this.node,{transform:this.matrix}));return this};c.parent=function(){return x(this.node.parentNode)};c.append=c.add=function(c){if(c){if(\"set\"==c.type){var a=this;c.forEach(function(c){a.add(c)});return this}c=x(c);this.node.appendChild(c.node);c.paper=this.paper}return this};c.appendTo=function(c){c&&(c=x(c),c.append(this));return this};\n",
"c.prepend=function(c){if(c){if(\"set\"==c.type){var a=this,b;c.forEach(function(c){b?b.after(c):a.prepend(c);b=c});return this}c=x(c);var m=c.parent();this.node.insertBefore(c.node,this.node.firstChild);this.add&&this.add();c.paper=this.paper;this.parent()&&this.parent().add();m&&m.add()}return this};c.prependTo=function(c){c=x(c);c.prepend(this);return this};c.before=function(c){if(\"set\"==c.type){var a=this;c.forEach(function(c){var b=c.parent();a.node.parentNode.insertBefore(c.node,a.node);b&&b.add()});\n",
"this.parent().add();return this}c=x(c);var b=c.parent();this.node.parentNode.insertBefore(c.node,this.node);this.parent()&&this.parent().add();b&&b.add();c.paper=this.paper;return this};c.after=function(c){c=x(c);var a=c.parent();this.node.nextSibling?this.node.parentNode.insertBefore(c.node,this.node.nextSibling):this.node.parentNode.appendChild(c.node);this.parent()&&this.parent().add();a&&a.add();c.paper=this.paper;return this};c.insertBefore=function(c){c=x(c);var a=this.parent();c.node.parentNode.insertBefore(this.node,\n",
"c.node);this.paper=c.paper;a&&a.add();c.parent()&&c.parent().add();return this};c.insertAfter=function(c){c=x(c);var a=this.parent();c.node.parentNode.insertBefore(this.node,c.node.nextSibling);this.paper=c.paper;a&&a.add();c.parent()&&c.parent().add();return this};c.remove=function(){var c=this.parent();this.node.parentNode&&this.node.parentNode.removeChild(this.node);delete this.paper;this.removed=!0;c&&c.add();return this};c.select=function(c){return x(this.node.querySelector(c))};c.selectAll=\n",
"function(c){c=this.node.querySelectorAll(c);for(var b=(a.set||Array)(),m=0;m<c.length;m++)b.push(x(c[m]));return b};c.asPX=function(c,a){null==a&&(a=this.attr(c));return+b(this,c,a)};c.use=function(){var c,a=this.node.id;a||(a=this.id,v(this.node,{id:a}));c=\"linearGradient\"==this.type||\"radialGradient\"==this.type||\"pattern\"==this.type?r(this.type,this.node.parentNode):r(\"use\",this.node.parentNode);v(c.node,{\"xlink:href\":\"#\"+a});c.original=this;return c};var l=/\\S+/g;c.addClass=function(c){var a=(c||\n",
"\"\").match(l)||[];c=this.node;var b=c.className.baseVal,m=b.match(l)||[],e,h,d;if(a.length){for(e=0;d=a[e++];)h=m.indexOf(d),~h||m.push(d);a=m.join(\" \");b!=a&&(c.className.baseVal=a)}return this};c.removeClass=function(c){var a=(c||\"\").match(l)||[];c=this.node;var b=c.className.baseVal,m=b.match(l)||[],e,h;if(m.length){for(e=0;h=a[e++];)h=m.indexOf(h),~h&&m.splice(h,1);a=m.join(\" \");b!=a&&(c.className.baseVal=a)}return this};c.hasClass=function(c){return!!~(this.node.className.baseVal.match(l)||[]).indexOf(c)};\n",
"c.toggleClass=function(c,a){if(null!=a)return a?this.addClass(c):this.removeClass(c);var b=(c||\"\").match(l)||[],m=this.node,e=m.className.baseVal,h=e.match(l)||[],d,f,E;for(d=0;E=b[d++];)f=h.indexOf(E),~f?h.splice(f,1):h.push(E);b=h.join(\" \");e!=b&&(m.className.baseVal=b);return this};c.clone=function(){var c=x(this.node.cloneNode(!0));v(c.node,\"id\")&&v(c.node,{id:c.id});m(c);c.insertAfter(this);return c};c.toDefs=function(){u(this).appendChild(this.node);return this};c.pattern=c.toPattern=function(c,\n",
"a,b,m){var e=r(\"pattern\",u(this));null==c&&(c=this.getBBox());y(c,\"object\")&&\"x\"in c&&(a=c.y,b=c.width,m=c.height,c=c.x);v(e.node,{x:c,y:a,width:b,height:m,patternUnits:\"userSpaceOnUse\",id:e.id,viewBox:[c,a,b,m].join(\" \")});e.node.appendChild(this.node);return e};c.marker=function(c,a,b,m,e,h){var d=r(\"marker\",u(this));null==c&&(c=this.getBBox());y(c,\"object\")&&\"x\"in c&&(a=c.y,b=c.width,m=c.height,e=c.refX||c.cx,h=c.refY||c.cy,c=c.x);v(d.node,{viewBox:[c,a,b,m].join(\" \"),markerWidth:b,markerHeight:m,\n",
"orient:\"auto\",refX:e||0,refY:h||0,id:d.id});d.node.appendChild(this.node);return d};var E=function(c,a,b,m){\"function\"!=typeof b||b.length||(m=b,b=L.linear);this.attr=c;this.dur=a;b&&(this.easing=b);m&&(this.callback=m)};a._.Animation=E;a.animation=function(c,a,b,m){return new E(c,a,b,m)};c.inAnim=function(){var c=[],a;for(a in this.anims)this.anims[h](a)&&function(a){c.push({anim:new E(a._attrs,a.dur,a.easing,a._callback),mina:a,curStatus:a.status(),status:function(c){return a.status(c)},stop:function(){a.stop()}})}(this.anims[a]);\n",
"return c};a.animate=function(c,a,b,m,e,h){\"function\"!=typeof e||e.length||(h=e,e=L.linear);var d=L.time();c=L(c,a,d,d+m,L.time,b,e);h&&k.once(\"mina.finish.\"+c.id,h);return c};c.stop=function(){for(var c=this.inAnim(),a=0,b=c.length;a<b;a++)c[a].stop();return this};c.animate=function(c,a,b,m){\"function\"!=typeof b||b.length||(m=b,b=L.linear);c instanceof E&&(m=c.callback,b=c.easing,a=b.dur,c=c.attr);var d=[],f=[],l={},t,ca,n,T=this,q;for(q in c)if(c[h](q)){T.equal?(n=T.equal(q,J(c[q])),t=n.from,ca=\n",
"n.to,n=n.f):(t=+T.attr(q),ca=+c[q]);var la=y(t,\"array\")?t.length:1;l[q]=e(d.length,d.length+la,n);d=d.concat(t);f=f.concat(ca)}t=L.time();var p=L(d,f,t,t+a,L.time,function(c){var a={},b;for(b in l)l[h](b)&&(a[b]=l[b](c));T.attr(a)},b);T.anims[p.id]=p;p._attrs=c;p._callback=m;k(\"snap.animcreated.\"+T.id,p);k.once(\"mina.finish.\"+p.id,function(){delete T.anims[p.id];m&&m.call(T)});k.once(\"mina.stop.\"+p.id,function(){delete T.anims[p.id]});return T};var T={};c.data=function(c,b){var m=T[this.id]=T[this.id]||\n",
"{};if(0==arguments.length)return k(\"snap.data.get.\"+this.id,this,m,null),m;if(1==arguments.length){if(a.is(c,\"object\")){for(var e in c)c[h](e)&&this.data(e,c[e]);return this}k(\"snap.data.get.\"+this.id,this,m[c],c);return m[c]}m[c]=b;k(\"snap.data.set.\"+this.id,this,b,c);return this};c.removeData=function(c){null==c?T[this.id]={}:T[this.id]&&delete T[this.id][c];return this};c.outerSVG=c.toString=d(1);c.innerSVG=d()})(e.prototype);a.parse=function(c){var a=G.doc.createDocumentFragment(),b=!0,m=G.doc.createElement(\"div\");\n",
"c=J(c);c.match(/^\\s*<\\s*svg(?:\\s|>)/)||(c=\"<svg>\"+c+\"</svg>\",b=!1);m.innerHTML=c;if(c=m.getElementsByTagName(\"svg\")[0])if(b)a=c;else for(;c.firstChild;)a.appendChild(c.firstChild);m.innerHTML=aa;return new l(a)};l.prototype.select=e.prototype.select;l.prototype.selectAll=e.prototype.selectAll;a.fragment=function(){for(var c=Array.prototype.slice.call(arguments,0),b=G.doc.createDocumentFragment(),m=0,e=c.length;m<e;m++){var h=c[m];h.node&&h.node.nodeType&&b.appendChild(h.node);h.nodeType&&b.appendChild(h);\n",
"\"string\"==typeof h&&b.appendChild(a.parse(h).node)}return new l(b)};a._.make=r;a._.wrap=x;s.prototype.el=function(c,a){var b=r(c,this.node);a&&b.attr(a);return b};k.on(\"snap.util.getattr\",function(){var c=k.nt(),c=c.substring(c.lastIndexOf(\".\")+1),a=c.replace(/[A-Z]/g,function(c){return\"-\"+c.toLowerCase()});return pa[h](a)?this.node.ownerDocument.defaultView.getComputedStyle(this.node,null).getPropertyValue(a):v(this.node,c)});var pa={\"alignment-baseline\":0,\"baseline-shift\":0,clip:0,\"clip-path\":0,\n",
"\"clip-rule\":0,color:0,\"color-interpolation\":0,\"color-interpolation-filters\":0,\"color-profile\":0,\"color-rendering\":0,cursor:0,direction:0,display:0,\"dominant-baseline\":0,\"enable-background\":0,fill:0,\"fill-opacity\":0,\"fill-rule\":0,filter:0,\"flood-color\":0,\"flood-opacity\":0,font:0,\"font-family\":0,\"font-size\":0,\"font-size-adjust\":0,\"font-stretch\":0,\"font-style\":0,\"font-variant\":0,\"font-weight\":0,\"glyph-orientation-horizontal\":0,\"glyph-orientation-vertical\":0,\"image-rendering\":0,kerning:0,\"letter-spacing\":0,\n",
"\"lighting-color\":0,marker:0,\"marker-end\":0,\"marker-mid\":0,\"marker-start\":0,mask:0,opacity:0,overflow:0,\"pointer-events\":0,\"shape-rendering\":0,\"stop-color\":0,\"stop-opacity\":0,stroke:0,\"stroke-dasharray\":0,\"stroke-dashoffset\":0,\"stroke-linecap\":0,\"stroke-linejoin\":0,\"stroke-miterlimit\":0,\"stroke-opacity\":0,\"stroke-width\":0,\"text-anchor\":0,\"text-decoration\":0,\"text-rendering\":0,\"unicode-bidi\":0,visibility:0,\"word-spacing\":0,\"writing-mode\":0};k.on(\"snap.util.attr\",function(c){var a=k.nt(),b={},a=a.substring(a.lastIndexOf(\".\")+\n",
"1);b[a]=c;var m=a.replace(/-(\\w)/gi,function(c,a){return a.toUpperCase()}),a=a.replace(/[A-Z]/g,function(c){return\"-\"+c.toLowerCase()});pa[h](a)?this.node.style[m]=null==c?aa:c:v(this.node,b)});a.ajax=function(c,a,b,m){var e=new XMLHttpRequest,h=V();if(e){if(y(a,\"function\"))m=b,b=a,a=null;else if(y(a,\"object\")){var d=[],f;for(f in a)a.hasOwnProperty(f)&&d.push(encodeURIComponent(f)+\"=\"+encodeURIComponent(a[f]));a=d.join(\"&\")}e.open(a?\"POST\":\"GET\",c,!0);a&&(e.setRequestHeader(\"X-Requested-With\",\"XMLHttpRequest\"),\n",
"e.setRequestHeader(\"Content-type\",\"application/x-www-form-urlencoded\"));b&&(k.once(\"snap.ajax.\"+h+\".0\",b),k.once(\"snap.ajax.\"+h+\".200\",b),k.once(\"snap.ajax.\"+h+\".304\",b));e.onreadystatechange=function(){4==e.readyState&&k(\"snap.ajax.\"+h+\".\"+e.status,m,e)};if(4==e.readyState)return e;e.send(a);return e}};a.load=function(c,b,m){a.ajax(c,function(c){c=a.parse(c.responseText);m?b.call(m,c):b(c)})};a.getElementByPoint=function(c,a){var b,m,e=G.doc.elementFromPoint(c,a);if(G.win.opera&&\"svg\"==e.tagName){b=\n",
"e;m=b.getBoundingClientRect();b=b.ownerDocument;var h=b.body,d=b.documentElement;b=m.top+(g.win.pageYOffset||d.scrollTop||h.scrollTop)-(d.clientTop||h.clientTop||0);m=m.left+(g.win.pageXOffset||d.scrollLeft||h.scrollLeft)-(d.clientLeft||h.clientLeft||0);h=e.createSVGRect();h.x=c-m;h.y=a-b;h.width=h.height=1;b=e.getIntersectionList(h,null);b.length&&(e=b[b.length-1])}return e?x(e):null};a.plugin=function(c){c(a,e,s,G,l)};return G.win.Snap=a}();C.plugin(function(a,k,y,M,A){function w(a,d,f,b,q,e){null==\n",
"d&&\"[object SVGMatrix]\"==z.call(a)?(this.a=a.a,this.b=a.b,this.c=a.c,this.d=a.d,this.e=a.e,this.f=a.f):null!=a?(this.a=+a,this.b=+d,this.c=+f,this.d=+b,this.e=+q,this.f=+e):(this.a=1,this.c=this.b=0,this.d=1,this.f=this.e=0)}var z=Object.prototype.toString,d=String,f=Math;(function(n){function k(a){return a[0]*a[0]+a[1]*a[1]}function p(a){var d=f.sqrt(k(a));a[0]&&(a[0]/=d);a[1]&&(a[1]/=d)}n.add=function(a,d,e,f,n,p){var k=[[],[],[] ],u=[[this.a,this.c,this.e],[this.b,this.d,this.f],[0,0,1] ];d=[[a,\n",
"e,n],[d,f,p],[0,0,1] ];a&&a instanceof w&&(d=[[a.a,a.c,a.e],[a.b,a.d,a.f],[0,0,1] ]);for(a=0;3>a;a++)for(e=0;3>e;e++){for(f=n=0;3>f;f++)n+=u[a][f]*d[f][e];k[a][e]=n}this.a=k[0][0];this.b=k[1][0];this.c=k[0][1];this.d=k[1][1];this.e=k[0][2];this.f=k[1][2];return this};n.invert=function(){var a=this.a*this.d-this.b*this.c;return new w(this.d/a,-this.b/a,-this.c/a,this.a/a,(this.c*this.f-this.d*this.e)/a,(this.b*this.e-this.a*this.f)/a)};n.clone=function(){return new w(this.a,this.b,this.c,this.d,this.e,\n",
"this.f)};n.translate=function(a,d){return this.add(1,0,0,1,a,d)};n.scale=function(a,d,e,f){null==d&&(d=a);(e||f)&&this.add(1,0,0,1,e,f);this.add(a,0,0,d,0,0);(e||f)&&this.add(1,0,0,1,-e,-f);return this};n.rotate=function(b,d,e){b=a.rad(b);d=d||0;e=e||0;var l=+f.cos(b).toFixed(9);b=+f.sin(b).toFixed(9);this.add(l,b,-b,l,d,e);return this.add(1,0,0,1,-d,-e)};n.x=function(a,d){return a*this.a+d*this.c+this.e};n.y=function(a,d){return a*this.b+d*this.d+this.f};n.get=function(a){return+this[d.fromCharCode(97+\n",
"a)].toFixed(4)};n.toString=function(){return\"matrix(\"+[this.get(0),this.get(1),this.get(2),this.get(3),this.get(4),this.get(5)].join()+\")\"};n.offset=function(){return[this.e.toFixed(4),this.f.toFixed(4)]};n.determinant=function(){return this.a*this.d-this.b*this.c};n.split=function(){var b={};b.dx=this.e;b.dy=this.f;var d=[[this.a,this.c],[this.b,this.d] ];b.scalex=f.sqrt(k(d[0]));p(d[0]);b.shear=d[0][0]*d[1][0]+d[0][1]*d[1][1];d[1]=[d[1][0]-d[0][0]*b.shear,d[1][1]-d[0][1]*b.shear];b.scaley=f.sqrt(k(d[1]));\n",
"p(d[1]);b.shear/=b.scaley;0>this.determinant()&&(b.scalex=-b.scalex);var e=-d[0][1],d=d[1][1];0>d?(b.rotate=a.deg(f.acos(d)),0>e&&(b.rotate=360-b.rotate)):b.rotate=a.deg(f.asin(e));b.isSimple=!+b.shear.toFixed(9)&&(b.scalex.toFixed(9)==b.scaley.toFixed(9)||!b.rotate);b.isSuperSimple=!+b.shear.toFixed(9)&&b.scalex.toFixed(9)==b.scaley.toFixed(9)&&!b.rotate;b.noRotation=!+b.shear.toFixed(9)&&!b.rotate;return b};n.toTransformString=function(a){a=a||this.split();if(+a.shear.toFixed(9))return\"m\"+[this.get(0),\n",
"this.get(1),this.get(2),this.get(3),this.get(4),this.get(5)];a.scalex=+a.scalex.toFixed(4);a.scaley=+a.scaley.toFixed(4);a.rotate=+a.rotate.toFixed(4);return(a.dx||a.dy?\"t\"+[+a.dx.toFixed(4),+a.dy.toFixed(4)]:\"\")+(1!=a.scalex||1!=a.scaley?\"s\"+[a.scalex,a.scaley,0,0]:\"\")+(a.rotate?\"r\"+[+a.rotate.toFixed(4),0,0]:\"\")}})(w.prototype);a.Matrix=w;a.matrix=function(a,d,f,b,k,e){return new w(a,d,f,b,k,e)}});C.plugin(function(a,v,y,M,A){function w(h){return function(d){k.stop();d instanceof A&&1==d.node.childNodes.length&&\n",
"(\"radialGradient\"==d.node.firstChild.tagName||\"linearGradient\"==d.node.firstChild.tagName||\"pattern\"==d.node.firstChild.tagName)&&(d=d.node.firstChild,b(this).appendChild(d),d=u(d));if(d instanceof v)if(\"radialGradient\"==d.type||\"linearGradient\"==d.type||\"pattern\"==d.type){d.node.id||e(d.node,{id:d.id});var f=l(d.node.id)}else f=d.attr(h);else f=a.color(d),f.error?(f=a(b(this).ownerSVGElement).gradient(d))?(f.node.id||e(f.node,{id:f.id}),f=l(f.node.id)):f=d:f=r(f);d={};d[h]=f;e(this.node,d);this.node.style[h]=\n",
"x}}function z(a){k.stop();a==+a&&(a+=\"px\");this.node.style.fontSize=a}function d(a){var b=[];a=a.childNodes;for(var e=0,f=a.length;e<f;e++){var l=a[e];3==l.nodeType&&b.push(l.nodeValue);\"tspan\"==l.tagName&&(1==l.childNodes.length&&3==l.firstChild.nodeType?b.push(l.firstChild.nodeValue):b.push(d(l)))}return b}function f(){k.stop();return this.node.style.fontSize}var n=a._.make,u=a._.wrap,p=a.is,b=a._.getSomeDefs,q=/^url\\(#?([^)]+)\\)$/,e=a._.$,l=a.url,r=String,s=a._.separator,x=\"\";k.on(\"snap.util.attr.mask\",\n",
"function(a){if(a instanceof v||a instanceof A){k.stop();a instanceof A&&1==a.node.childNodes.length&&(a=a.node.firstChild,b(this).appendChild(a),a=u(a));if(\"mask\"==a.type)var d=a;else d=n(\"mask\",b(this)),d.node.appendChild(a.node);!d.node.id&&e(d.node,{id:d.id});e(this.node,{mask:l(d.id)})}});(function(a){k.on(\"snap.util.attr.clip\",a);k.on(\"snap.util.attr.clip-path\",a);k.on(\"snap.util.attr.clipPath\",a)})(function(a){if(a instanceof v||a instanceof A){k.stop();if(\"clipPath\"==a.type)var d=a;else d=\n",
"n(\"clipPath\",b(this)),d.node.appendChild(a.node),!d.node.id&&e(d.node,{id:d.id});e(this.node,{\"clip-path\":l(d.id)})}});k.on(\"snap.util.attr.fill\",w(\"fill\"));k.on(\"snap.util.attr.stroke\",w(\"stroke\"));var G=/^([lr])(?:\\(([^)]*)\\))?(.*)$/i;k.on(\"snap.util.grad.parse\",function(a){a=r(a);var b=a.match(G);if(!b)return null;a=b[1];var e=b[2],b=b[3],e=e.split(/\\s*,\\s*/).map(function(a){return+a==a?+a:a});1==e.length&&0==e[0]&&(e=[]);b=b.split(\"-\");b=b.map(function(a){a=a.split(\":\");var b={color:a[0]};a[1]&&\n",
"(b.offset=parseFloat(a[1]));return b});return{type:a,params:e,stops:b}});k.on(\"snap.util.attr.d\",function(b){k.stop();p(b,\"array\")&&p(b[0],\"array\")&&(b=a.path.toString.call(b));b=r(b);b.match(/[ruo]/i)&&(b=a.path.toAbsolute(b));e(this.node,{d:b})})(-1);k.on(\"snap.util.attr.#text\",function(a){k.stop();a=r(a);for(a=M.doc.createTextNode(a);this.node.firstChild;)this.node.removeChild(this.node.firstChild);this.node.appendChild(a)})(-1);k.on(\"snap.util.attr.path\",function(a){k.stop();this.attr({d:a})})(-1);\n",
"k.on(\"snap.util.attr.class\",function(a){k.stop();this.node.className.baseVal=a})(-1);k.on(\"snap.util.attr.viewBox\",function(a){a=p(a,\"object\")&&\"x\"in a?[a.x,a.y,a.width,a.height].join(\" \"):p(a,\"array\")?a.join(\" \"):a;e(this.node,{viewBox:a});k.stop()})(-1);k.on(\"snap.util.attr.transform\",function(a){this.transform(a);k.stop()})(-1);k.on(\"snap.util.attr.r\",function(a){\"rect\"==this.type&&(k.stop(),e(this.node,{rx:a,ry:a}))})(-1);k.on(\"snap.util.attr.textpath\",function(a){k.stop();if(\"text\"==this.type){var d,\n",
"f;if(!a&&this.textPath){for(a=this.textPath;a.node.firstChild;)this.node.appendChild(a.node.firstChild);a.remove();delete this.textPath}else if(p(a,\"string\")?(d=b(this),a=u(d.parentNode).path(a),d.appendChild(a.node),d=a.id,a.attr({id:d})):(a=u(a),a instanceof v&&(d=a.attr(\"id\"),d||(d=a.id,a.attr({id:d})))),d)if(a=this.textPath,f=this.node,a)a.attr({\"xlink:href\":\"#\"+d});else{for(a=e(\"textPath\",{\"xlink:href\":\"#\"+d});f.firstChild;)a.appendChild(f.firstChild);f.appendChild(a);this.textPath=u(a)}}})(-1);\n",
"k.on(\"snap.util.attr.text\",function(a){if(\"text\"==this.type){for(var b=this.node,d=function(a){var b=e(\"tspan\");if(p(a,\"array\"))for(var f=0;f<a.length;f++)b.appendChild(d(a[f]));else b.appendChild(M.doc.createTextNode(a));b.normalize&&b.normalize();return b};b.firstChild;)b.removeChild(b.firstChild);for(a=d(a);a.firstChild;)b.appendChild(a.firstChild)}k.stop()})(-1);k.on(\"snap.util.attr.fontSize\",z)(-1);k.on(\"snap.util.attr.font-size\",z)(-1);k.on(\"snap.util.getattr.transform\",function(){k.stop();\n",
"return this.transform()})(-1);k.on(\"snap.util.getattr.textpath\",function(){k.stop();return this.textPath})(-1);(function(){function b(d){return function(){k.stop();var b=M.doc.defaultView.getComputedStyle(this.node,null).getPropertyValue(\"marker-\"+d);return\"none\"==b?b:a(M.doc.getElementById(b.match(q)[1]))}}function d(a){return function(b){k.stop();var d=\"marker\"+a.charAt(0).toUpperCase()+a.substring(1);if(\"\"==b||!b)this.node.style[d]=\"none\";else if(\"marker\"==b.type){var f=b.node.id;f||e(b.node,{id:b.id});\n",
"this.node.style[d]=l(f)}}}k.on(\"snap.util.getattr.marker-end\",b(\"end\"))(-1);k.on(\"snap.util.getattr.markerEnd\",b(\"end\"))(-1);k.on(\"snap.util.getattr.marker-start\",b(\"start\"))(-1);k.on(\"snap.util.getattr.markerStart\",b(\"start\"))(-1);k.on(\"snap.util.getattr.marker-mid\",b(\"mid\"))(-1);k.on(\"snap.util.getattr.markerMid\",b(\"mid\"))(-1);k.on(\"snap.util.attr.marker-end\",d(\"end\"))(-1);k.on(\"snap.util.attr.markerEnd\",d(\"end\"))(-1);k.on(\"snap.util.attr.marker-start\",d(\"start\"))(-1);k.on(\"snap.util.attr.markerStart\",\n",
"d(\"start\"))(-1);k.on(\"snap.util.attr.marker-mid\",d(\"mid\"))(-1);k.on(\"snap.util.attr.markerMid\",d(\"mid\"))(-1)})();k.on(\"snap.util.getattr.r\",function(){if(\"rect\"==this.type&&e(this.node,\"rx\")==e(this.node,\"ry\"))return k.stop(),e(this.node,\"rx\")})(-1);k.on(\"snap.util.getattr.text\",function(){if(\"text\"==this.type||\"tspan\"==this.type){k.stop();var a=d(this.node);return 1==a.length?a[0]:a}})(-1);k.on(\"snap.util.getattr.#text\",function(){return this.node.textContent})(-1);k.on(\"snap.util.getattr.viewBox\",\n",
"function(){k.stop();var b=e(this.node,\"viewBox\");if(b)return b=b.split(s),a._.box(+b[0],+b[1],+b[2],+b[3])})(-1);k.on(\"snap.util.getattr.points\",function(){var a=e(this.node,\"points\");k.stop();if(a)return a.split(s)})(-1);k.on(\"snap.util.getattr.path\",function(){var a=e(this.node,\"d\");k.stop();return a})(-1);k.on(\"snap.util.getattr.class\",function(){return this.node.className.baseVal})(-1);k.on(\"snap.util.getattr.fontSize\",f)(-1);k.on(\"snap.util.getattr.font-size\",f)(-1)});C.plugin(function(a,v,y,\n",
"M,A){function w(a){return a}function z(a){return function(b){return+b.toFixed(3)+a}}var d={\"+\":function(a,b){return a+b},\"-\":function(a,b){return a-b},\"/\":function(a,b){return a/b},\"*\":function(a,b){return a*b}},f=String,n=/[a-z]+$/i,u=/^\\s*([+\\-\\/*])\\s*=\\s*([\\d.eE+\\-]+)\\s*([^\\d\\s]+)?\\s*$/;k.on(\"snap.util.attr\",function(a){if(a=f(a).match(u)){var b=k.nt(),b=b.substring(b.lastIndexOf(\".\")+1),q=this.attr(b),e={};k.stop();var l=a[3]||\"\",r=q.match(n),s=d[a[1] ];r&&r==l?a=s(parseFloat(q),+a[2]):(q=this.asPX(b),\n",
"a=s(this.asPX(b),this.asPX(b,a[2]+l)));isNaN(q)||isNaN(a)||(e[b]=a,this.attr(e))}})(-10);k.on(\"snap.util.equal\",function(a,b){var q=f(this.attr(a)||\"\"),e=f(b).match(u);if(e){k.stop();var l=e[3]||\"\",r=q.match(n),s=d[e[1] ];if(r&&r==l)return{from:parseFloat(q),to:s(parseFloat(q),+e[2]),f:z(r)};q=this.asPX(a);return{from:q,to:s(q,this.asPX(a,e[2]+l)),f:w}}})(-10)});C.plugin(function(a,v,y,M,A){var w=y.prototype,z=a.is;w.rect=function(a,d,k,p,b,q){var e;null==q&&(q=b);z(a,\"object\")&&\"[object Object]\"==\n",
"a?e=a:null!=a&&(e={x:a,y:d,width:k,height:p},null!=b&&(e.rx=b,e.ry=q));return this.el(\"rect\",e)};w.circle=function(a,d,k){var p;z(a,\"object\")&&\"[object Object]\"==a?p=a:null!=a&&(p={cx:a,cy:d,r:k});return this.el(\"circle\",p)};var d=function(){function a(){this.parentNode.removeChild(this)}return function(d,k){var p=M.doc.createElement(\"img\"),b=M.doc.body;p.style.cssText=\"position:absolute;left:-9999em;top:-9999em\";p.onload=function(){k.call(p);p.onload=p.onerror=null;b.removeChild(p)};p.onerror=a;\n",
"b.appendChild(p);p.src=d}}();w.image=function(f,n,k,p,b){var q=this.el(\"image\");if(z(f,\"object\")&&\"src\"in f)q.attr(f);else if(null!=f){var e={\"xlink:href\":f,preserveAspectRatio:\"none\"};null!=n&&null!=k&&(e.x=n,e.y=k);null!=p&&null!=b?(e.width=p,e.height=b):d(f,function(){a._.$(q.node,{width:this.offsetWidth,height:this.offsetHeight})});a._.$(q.node,e)}return q};w.ellipse=function(a,d,k,p){var b;z(a,\"object\")&&\"[object Object]\"==a?b=a:null!=a&&(b={cx:a,cy:d,rx:k,ry:p});return this.el(\"ellipse\",b)};\n",
"w.path=function(a){var d;z(a,\"object\")&&!z(a,\"array\")?d=a:a&&(d={d:a});return this.el(\"path\",d)};w.group=w.g=function(a){var d=this.el(\"g\");1==arguments.length&&a&&!a.type?d.attr(a):arguments.length&&d.add(Array.prototype.slice.call(arguments,0));return d};w.svg=function(a,d,k,p,b,q,e,l){var r={};z(a,\"object\")&&null==d?r=a:(null!=a&&(r.x=a),null!=d&&(r.y=d),null!=k&&(r.width=k),null!=p&&(r.height=p),null!=b&&null!=q&&null!=e&&null!=l&&(r.viewBox=[b,q,e,l]));return this.el(\"svg\",r)};w.mask=function(a){var d=\n",
"this.el(\"mask\");1==arguments.length&&a&&!a.type?d.attr(a):arguments.length&&d.add(Array.prototype.slice.call(arguments,0));return d};w.ptrn=function(a,d,k,p,b,q,e,l){if(z(a,\"object\"))var r=a;else arguments.length?(r={},null!=a&&(r.x=a),null!=d&&(r.y=d),null!=k&&(r.width=k),null!=p&&(r.height=p),null!=b&&null!=q&&null!=e&&null!=l&&(r.viewBox=[b,q,e,l])):r={patternUnits:\"userSpaceOnUse\"};return this.el(\"pattern\",r)};w.use=function(a){return null!=a?(make(\"use\",this.node),a instanceof v&&(a.attr(\"id\")||\n",
"a.attr({id:ID()}),a=a.attr(\"id\")),this.el(\"use\",{\"xlink:href\":a})):v.prototype.use.call(this)};w.text=function(a,d,k){var p={};z(a,\"object\")?p=a:null!=a&&(p={x:a,y:d,text:k||\"\"});return this.el(\"text\",p)};w.line=function(a,d,k,p){var b={};z(a,\"object\")?b=a:null!=a&&(b={x1:a,x2:k,y1:d,y2:p});return this.el(\"line\",b)};w.polyline=function(a){1<arguments.length&&(a=Array.prototype.slice.call(arguments,0));var d={};z(a,\"object\")&&!z(a,\"array\")?d=a:null!=a&&(d={points:a});return this.el(\"polyline\",d)};\n",
"w.polygon=function(a){1<arguments.length&&(a=Array.prototype.slice.call(arguments,0));var d={};z(a,\"object\")&&!z(a,\"array\")?d=a:null!=a&&(d={points:a});return this.el(\"polygon\",d)};(function(){function d(){return this.selectAll(\"stop\")}function n(b,d){var f=e(\"stop\"),k={offset:+d+\"%\"};b=a.color(b);k[\"stop-color\"]=b.hex;1>b.opacity&&(k[\"stop-opacity\"]=b.opacity);e(f,k);this.node.appendChild(f);return this}function u(){if(\"linearGradient\"==this.type){var b=e(this.node,\"x1\")||0,d=e(this.node,\"x2\")||\n",
"1,f=e(this.node,\"y1\")||0,k=e(this.node,\"y2\")||0;return a._.box(b,f,math.abs(d-b),math.abs(k-f))}b=this.node.r||0;return a._.box((this.node.cx||0.5)-b,(this.node.cy||0.5)-b,2*b,2*b)}function p(a,d){function f(a,b){for(var d=(b-u)/(a-w),e=w;e<a;e++)h[e].offset=+(+u+d*(e-w)).toFixed(2);w=a;u=b}var n=k(\"snap.util.grad.parse\",null,d).firstDefined(),p;if(!n)return null;n.params.unshift(a);p=\"l\"==n.type.toLowerCase()?b.apply(0,n.params):q.apply(0,n.params);n.type!=n.type.toLowerCase()&&e(p.node,{gradientUnits:\"userSpaceOnUse\"});\n",
"var h=n.stops,n=h.length,u=0,w=0;n--;for(var v=0;v<n;v++)\"offset\"in h[v]&&f(v,h[v].offset);h[n].offset=h[n].offset||100;f(n,h[n].offset);for(v=0;v<=n;v++){var y=h[v];p.addStop(y.color,y.offset)}return p}function b(b,k,p,q,w){b=a._.make(\"linearGradient\",b);b.stops=d;b.addStop=n;b.getBBox=u;null!=k&&e(b.node,{x1:k,y1:p,x2:q,y2:w});return b}function q(b,k,p,q,w,h){b=a._.make(\"radialGradient\",b);b.stops=d;b.addStop=n;b.getBBox=u;null!=k&&e(b.node,{cx:k,cy:p,r:q});null!=w&&null!=h&&e(b.node,{fx:w,fy:h});\n",
"return b}var e=a._.$;w.gradient=function(a){return p(this.defs,a)};w.gradientLinear=function(a,d,e,f){return b(this.defs,a,d,e,f)};w.gradientRadial=function(a,b,d,e,f){return q(this.defs,a,b,d,e,f)};w.toString=function(){var b=this.node.ownerDocument,d=b.createDocumentFragment(),b=b.createElement(\"div\"),e=this.node.cloneNode(!0);d.appendChild(b);b.appendChild(e);a._.$(e,{xmlns:\"http://www.w3.org/2000/svg\"});b=b.innerHTML;d.removeChild(d.firstChild);return b};w.clear=function(){for(var a=this.node.firstChild,\n",
"b;a;)b=a.nextSibling,\"defs\"!=a.tagName?a.parentNode.removeChild(a):w.clear.call({node:a}),a=b}})()});C.plugin(function(a,k,y,M){function A(a){var b=A.ps=A.ps||{};b[a]?b[a].sleep=100:b[a]={sleep:100};setTimeout(function(){for(var d in b)b[L](d)&&d!=a&&(b[d].sleep--,!b[d].sleep&&delete b[d])});return b[a]}function w(a,b,d,e){null==a&&(a=b=d=e=0);null==b&&(b=a.y,d=a.width,e=a.height,a=a.x);return{x:a,y:b,width:d,w:d,height:e,h:e,x2:a+d,y2:b+e,cx:a+d/2,cy:b+e/2,r1:F.min(d,e)/2,r2:F.max(d,e)/2,r0:F.sqrt(d*\n",
"d+e*e)/2,path:s(a,b,d,e),vb:[a,b,d,e].join(\" \")}}function z(){return this.join(\",\").replace(N,\"$1\")}function d(a){a=C(a);a.toString=z;return a}function f(a,b,d,h,f,k,l,n,p){if(null==p)return e(a,b,d,h,f,k,l,n);if(0>p||e(a,b,d,h,f,k,l,n)<p)p=void 0;else{var q=0.5,O=1-q,s;for(s=e(a,b,d,h,f,k,l,n,O);0.01<Z(s-p);)q/=2,O+=(s<p?1:-1)*q,s=e(a,b,d,h,f,k,l,n,O);p=O}return u(a,b,d,h,f,k,l,n,p)}function n(b,d){function e(a){return+(+a).toFixed(3)}return a._.cacher(function(a,h,l){a instanceof k&&(a=a.attr(\"d\"));\n",
"a=I(a);for(var n,p,D,q,O=\"\",s={},c=0,t=0,r=a.length;t<r;t++){D=a[t];if(\"M\"==D[0])n=+D[1],p=+D[2];else{q=f(n,p,D[1],D[2],D[3],D[4],D[5],D[6]);if(c+q>h){if(d&&!s.start){n=f(n,p,D[1],D[2],D[3],D[4],D[5],D[6],h-c);O+=[\"C\"+e(n.start.x),e(n.start.y),e(n.m.x),e(n.m.y),e(n.x),e(n.y)];if(l)return O;s.start=O;O=[\"M\"+e(n.x),e(n.y)+\"C\"+e(n.n.x),e(n.n.y),e(n.end.x),e(n.end.y),e(D[5]),e(D[6])].join();c+=q;n=+D[5];p=+D[6];continue}if(!b&&!d)return n=f(n,p,D[1],D[2],D[3],D[4],D[5],D[6],h-c)}c+=q;n=+D[5];p=+D[6]}O+=\n",
"D.shift()+D}s.end=O;return n=b?c:d?s:u(n,p,D[0],D[1],D[2],D[3],D[4],D[5],1)},null,a._.clone)}function u(a,b,d,e,h,f,k,l,n){var p=1-n,q=ma(p,3),s=ma(p,2),c=n*n,t=c*n,r=q*a+3*s*n*d+3*p*n*n*h+t*k,q=q*b+3*s*n*e+3*p*n*n*f+t*l,s=a+2*n*(d-a)+c*(h-2*d+a),t=b+2*n*(e-b)+c*(f-2*e+b),x=d+2*n*(h-d)+c*(k-2*h+d),c=e+2*n*(f-e)+c*(l-2*f+e);a=p*a+n*d;b=p*b+n*e;h=p*h+n*k;f=p*f+n*l;l=90-180*F.atan2(s-x,t-c)/S;return{x:r,y:q,m:{x:s,y:t},n:{x:x,y:c},start:{x:a,y:b},end:{x:h,y:f},alpha:l}}function p(b,d,e,h,f,n,k,l){a.is(b,\n",
"\"array\")||(b=[b,d,e,h,f,n,k,l]);b=U.apply(null,b);return w(b.min.x,b.min.y,b.max.x-b.min.x,b.max.y-b.min.y)}function b(a,b,d){return b>=a.x&&b<=a.x+a.width&&d>=a.y&&d<=a.y+a.height}function q(a,d){a=w(a);d=w(d);return b(d,a.x,a.y)||b(d,a.x2,a.y)||b(d,a.x,a.y2)||b(d,a.x2,a.y2)||b(a,d.x,d.y)||b(a,d.x2,d.y)||b(a,d.x,d.y2)||b(a,d.x2,d.y2)||(a.x<d.x2&&a.x>d.x||d.x<a.x2&&d.x>a.x)&&(a.y<d.y2&&a.y>d.y||d.y<a.y2&&d.y>a.y)}function e(a,b,d,e,h,f,n,k,l){null==l&&(l=1);l=(1<l?1:0>l?0:l)/2;for(var p=[-0.1252,\n",
"0.1252,-0.3678,0.3678,-0.5873,0.5873,-0.7699,0.7699,-0.9041,0.9041,-0.9816,0.9816],q=[0.2491,0.2491,0.2335,0.2335,0.2032,0.2032,0.1601,0.1601,0.1069,0.1069,0.0472,0.0472],s=0,c=0;12>c;c++)var t=l*p[c]+l,r=t*(t*(-3*a+9*d-9*h+3*n)+6*a-12*d+6*h)-3*a+3*d,t=t*(t*(-3*b+9*e-9*f+3*k)+6*b-12*e+6*f)-3*b+3*e,s=s+q[c]*F.sqrt(r*r+t*t);return l*s}function l(a,b,d){a=I(a);b=I(b);for(var h,f,l,n,k,s,r,O,x,c,t=d?0:[],w=0,v=a.length;w<v;w++)if(x=a[w],\"M\"==x[0])h=k=x[1],f=s=x[2];else{\"C\"==x[0]?(x=[h,f].concat(x.slice(1)),\n",
"h=x[6],f=x[7]):(x=[h,f,h,f,k,s,k,s],h=k,f=s);for(var G=0,y=b.length;G<y;G++)if(c=b[G],\"M\"==c[0])l=r=c[1],n=O=c[2];else{\"C\"==c[0]?(c=[l,n].concat(c.slice(1)),l=c[6],n=c[7]):(c=[l,n,l,n,r,O,r,O],l=r,n=O);var z;var K=x,B=c;z=d;var H=p(K),J=p(B);if(q(H,J)){for(var H=e.apply(0,K),J=e.apply(0,B),H=~~(H/8),J=~~(J/8),U=[],A=[],F={},M=z?0:[],P=0;P<H+1;P++){var C=u.apply(0,K.concat(P/H));U.push({x:C.x,y:C.y,t:P/H})}for(P=0;P<J+1;P++)C=u.apply(0,B.concat(P/J)),A.push({x:C.x,y:C.y,t:P/J});for(P=0;P<H;P++)for(K=\n",
"0;K<J;K++){var Q=U[P],L=U[P+1],B=A[K],C=A[K+1],N=0.001>Z(L.x-Q.x)?\"y\":\"x\",S=0.001>Z(C.x-B.x)?\"y\":\"x\",R;R=Q.x;var Y=Q.y,V=L.x,ea=L.y,fa=B.x,ga=B.y,ha=C.x,ia=C.y;if(W(R,V)<X(fa,ha)||X(R,V)>W(fa,ha)||W(Y,ea)<X(ga,ia)||X(Y,ea)>W(ga,ia))R=void 0;else{var $=(R*ea-Y*V)*(fa-ha)-(R-V)*(fa*ia-ga*ha),aa=(R*ea-Y*V)*(ga-ia)-(Y-ea)*(fa*ia-ga*ha),ja=(R-V)*(ga-ia)-(Y-ea)*(fa-ha);if(ja){var $=$/ja,aa=aa/ja,ja=+$.toFixed(2),ba=+aa.toFixed(2);R=ja<+X(R,V).toFixed(2)||ja>+W(R,V).toFixed(2)||ja<+X(fa,ha).toFixed(2)||\n",
"ja>+W(fa,ha).toFixed(2)||ba<+X(Y,ea).toFixed(2)||ba>+W(Y,ea).toFixed(2)||ba<+X(ga,ia).toFixed(2)||ba>+W(ga,ia).toFixed(2)?void 0:{x:$,y:aa}}else R=void 0}R&&F[R.x.toFixed(4)]!=R.y.toFixed(4)&&(F[R.x.toFixed(4)]=R.y.toFixed(4),Q=Q.t+Z((R[N]-Q[N])/(L[N]-Q[N]))*(L.t-Q.t),B=B.t+Z((R[S]-B[S])/(C[S]-B[S]))*(C.t-B.t),0<=Q&&1>=Q&&0<=B&&1>=B&&(z?M++:M.push({x:R.x,y:R.y,t1:Q,t2:B})))}z=M}else z=z?0:[];if(d)t+=z;else{H=0;for(J=z.length;H<J;H++)z[H].segment1=w,z[H].segment2=G,z[H].bez1=x,z[H].bez2=c;t=t.concat(z)}}}return t}\n",
"function r(a){var b=A(a);if(b.bbox)return C(b.bbox);if(!a)return w();a=I(a);for(var d=0,e=0,h=[],f=[],l,n=0,k=a.length;n<k;n++)l=a[n],\"M\"==l[0]?(d=l[1],e=l[2],h.push(d),f.push(e)):(d=U(d,e,l[1],l[2],l[3],l[4],l[5],l[6]),h=h.concat(d.min.x,d.max.x),f=f.concat(d.min.y,d.max.y),d=l[5],e=l[6]);a=X.apply(0,h);l=X.apply(0,f);h=W.apply(0,h);f=W.apply(0,f);f=w(a,l,h-a,f-l);b.bbox=C(f);return f}function s(a,b,d,e,h){if(h)return[[\"M\",+a+ +h,b],[\"l\",d-2*h,0],[\"a\",h,h,0,0,1,h,h],[\"l\",0,e-2*h],[\"a\",h,h,0,0,1,\n",
"-h,h],[\"l\",2*h-d,0],[\"a\",h,h,0,0,1,-h,-h],[\"l\",0,2*h-e],[\"a\",h,h,0,0,1,h,-h],[\"z\"] ];a=[[\"M\",a,b],[\"l\",d,0],[\"l\",0,e],[\"l\",-d,0],[\"z\"] ];a.toString=z;return a}function x(a,b,d,e,h){null==h&&null==e&&(e=d);a=+a;b=+b;d=+d;e=+e;if(null!=h){var f=Math.PI/180,l=a+d*Math.cos(-e*f);a+=d*Math.cos(-h*f);var n=b+d*Math.sin(-e*f);b+=d*Math.sin(-h*f);d=[[\"M\",l,n],[\"A\",d,d,0,+(180<h-e),0,a,b] ]}else d=[[\"M\",a,b],[\"m\",0,-e],[\"a\",d,e,0,1,1,0,2*e],[\"a\",d,e,0,1,1,0,-2*e],[\"z\"] ];d.toString=z;return d}function G(b){var e=\n",
"A(b);if(e.abs)return d(e.abs);Q(b,\"array\")&&Q(b&&b[0],\"array\")||(b=a.parsePathString(b));if(!b||!b.length)return[[\"M\",0,0] ];var h=[],f=0,l=0,n=0,k=0,p=0;\"M\"==b[0][0]&&(f=+b[0][1],l=+b[0][2],n=f,k=l,p++,h[0]=[\"M\",f,l]);for(var q=3==b.length&&\"M\"==b[0][0]&&\"R\"==b[1][0].toUpperCase()&&\"Z\"==b[2][0].toUpperCase(),s,r,w=p,c=b.length;w<c;w++){h.push(s=[]);r=b[w];p=r[0];if(p!=p.toUpperCase())switch(s[0]=p.toUpperCase(),s[0]){case \"A\":s[1]=r[1];s[2]=r[2];s[3]=r[3];s[4]=r[4];s[5]=r[5];s[6]=+r[6]+f;s[7]=+r[7]+\n",
"l;break;case \"V\":s[1]=+r[1]+l;break;case \"H\":s[1]=+r[1]+f;break;case \"R\":for(var t=[f,l].concat(r.slice(1)),u=2,v=t.length;u<v;u++)t[u]=+t[u]+f,t[++u]=+t[u]+l;h.pop();h=h.concat(P(t,q));break;case \"O\":h.pop();t=x(f,l,r[1],r[2]);t.push(t[0]);h=h.concat(t);break;case \"U\":h.pop();h=h.concat(x(f,l,r[1],r[2],r[3]));s=[\"U\"].concat(h[h.length-1].slice(-2));break;case \"M\":n=+r[1]+f,k=+r[2]+l;default:for(u=1,v=r.length;u<v;u++)s[u]=+r[u]+(u%2?f:l)}else if(\"R\"==p)t=[f,l].concat(r.slice(1)),h.pop(),h=h.concat(P(t,\n",
"q)),s=[\"R\"].concat(r.slice(-2));else if(\"O\"==p)h.pop(),t=x(f,l,r[1],r[2]),t.push(t[0]),h=h.concat(t);else if(\"U\"==p)h.pop(),h=h.concat(x(f,l,r[1],r[2],r[3])),s=[\"U\"].concat(h[h.length-1].slice(-2));else for(t=0,u=r.length;t<u;t++)s[t]=r[t];p=p.toUpperCase();if(\"O\"!=p)switch(s[0]){case \"Z\":f=+n;l=+k;break;case \"H\":f=s[1];break;case \"V\":l=s[1];break;case \"M\":n=s[s.length-2],k=s[s.length-1];default:f=s[s.length-2],l=s[s.length-1]}}h.toString=z;e.abs=d(h);return h}function h(a,b,d,e){return[a,b,d,e,d,\n",
"e]}function J(a,b,d,e,h,f){var l=1/3,n=2/3;return[l*a+n*d,l*b+n*e,l*h+n*d,l*f+n*e,h,f]}function K(b,d,e,h,f,l,n,k,p,s){var r=120*S/180,q=S/180*(+f||0),c=[],t,x=a._.cacher(function(a,b,c){var d=a*F.cos(c)-b*F.sin(c);a=a*F.sin(c)+b*F.cos(c);return{x:d,y:a}});if(s)v=s[0],t=s[1],l=s[2],u=s[3];else{t=x(b,d,-q);b=t.x;d=t.y;t=x(k,p,-q);k=t.x;p=t.y;F.cos(S/180*f);F.sin(S/180*f);t=(b-k)/2;v=(d-p)/2;u=t*t/(e*e)+v*v/(h*h);1<u&&(u=F.sqrt(u),e*=u,h*=u);var u=e*e,w=h*h,u=(l==n?-1:1)*F.sqrt(Z((u*w-u*v*v-w*t*t)/\n",
"(u*v*v+w*t*t)));l=u*e*v/h+(b+k)/2;var u=u*-h*t/e+(d+p)/2,v=F.asin(((d-u)/h).toFixed(9));t=F.asin(((p-u)/h).toFixed(9));v=b<l?S-v:v;t=k<l?S-t:t;0>v&&(v=2*S+v);0>t&&(t=2*S+t);n&&v>t&&(v-=2*S);!n&&t>v&&(t-=2*S)}if(Z(t-v)>r){var c=t,w=k,G=p;t=v+r*(n&&t>v?1:-1);k=l+e*F.cos(t);p=u+h*F.sin(t);c=K(k,p,e,h,f,0,n,w,G,[t,c,l,u])}l=t-v;f=F.cos(v);r=F.sin(v);n=F.cos(t);t=F.sin(t);l=F.tan(l/4);e=4/3*e*l;l*=4/3*h;h=[b,d];b=[b+e*r,d-l*f];d=[k+e*t,p-l*n];k=[k,p];b[0]=2*h[0]-b[0];b[1]=2*h[1]-b[1];if(s)return[b,d,k].concat(c);\n",
"c=[b,d,k].concat(c).join().split(\",\");s=[];k=0;for(p=c.length;k<p;k++)s[k]=k%2?x(c[k-1],c[k],q).y:x(c[k],c[k+1],q).x;return s}function U(a,b,d,e,h,f,l,k){for(var n=[],p=[[],[] ],s,r,c,t,q=0;2>q;++q)0==q?(r=6*a-12*d+6*h,s=-3*a+9*d-9*h+3*l,c=3*d-3*a):(r=6*b-12*e+6*f,s=-3*b+9*e-9*f+3*k,c=3*e-3*b),1E-12>Z(s)?1E-12>Z(r)||(s=-c/r,0<s&&1>s&&n.push(s)):(t=r*r-4*c*s,c=F.sqrt(t),0>t||(t=(-r+c)/(2*s),0<t&&1>t&&n.push(t),s=(-r-c)/(2*s),0<s&&1>s&&n.push(s)));for(r=q=n.length;q--;)s=n[q],c=1-s,p[0][q]=c*c*c*a+3*\n",
"c*c*s*d+3*c*s*s*h+s*s*s*l,p[1][q]=c*c*c*b+3*c*c*s*e+3*c*s*s*f+s*s*s*k;p[0][r]=a;p[1][r]=b;p[0][r+1]=l;p[1][r+1]=k;p[0].length=p[1].length=r+2;return{min:{x:X.apply(0,p[0]),y:X.apply(0,p[1])},max:{x:W.apply(0,p[0]),y:W.apply(0,p[1])}}}function I(a,b){var e=!b&&A(a);if(!b&&e.curve)return d(e.curve);var f=G(a),l=b&&G(b),n={x:0,y:0,bx:0,by:0,X:0,Y:0,qx:null,qy:null},k={x:0,y:0,bx:0,by:0,X:0,Y:0,qx:null,qy:null},p=function(a,b,c){if(!a)return[\"C\",b.x,b.y,b.x,b.y,b.x,b.y];a[0]in{T:1,Q:1}||(b.qx=b.qy=null);\n",
"switch(a[0]){case \"M\":b.X=a[1];b.Y=a[2];break;case \"A\":a=[\"C\"].concat(K.apply(0,[b.x,b.y].concat(a.slice(1))));break;case \"S\":\"C\"==c||\"S\"==c?(c=2*b.x-b.bx,b=2*b.y-b.by):(c=b.x,b=b.y);a=[\"C\",c,b].concat(a.slice(1));break;case \"T\":\"Q\"==c||\"T\"==c?(b.qx=2*b.x-b.qx,b.qy=2*b.y-b.qy):(b.qx=b.x,b.qy=b.y);a=[\"C\"].concat(J(b.x,b.y,b.qx,b.qy,a[1],a[2]));break;case \"Q\":b.qx=a[1];b.qy=a[2];a=[\"C\"].concat(J(b.x,b.y,a[1],a[2],a[3],a[4]));break;case \"L\":a=[\"C\"].concat(h(b.x,b.y,a[1],a[2]));break;case \"H\":a=[\"C\"].concat(h(b.x,\n",
"b.y,a[1],b.y));break;case \"V\":a=[\"C\"].concat(h(b.x,b.y,b.x,a[1]));break;case \"Z\":a=[\"C\"].concat(h(b.x,b.y,b.X,b.Y))}return a},s=function(a,b){if(7<a[b].length){a[b].shift();for(var c=a[b];c.length;)q[b]=\"A\",l&&(u[b]=\"A\"),a.splice(b++,0,[\"C\"].concat(c.splice(0,6)));a.splice(b,1);v=W(f.length,l&&l.length||0)}},r=function(a,b,c,d,e){a&&b&&\"M\"==a[e][0]&&\"M\"!=b[e][0]&&(b.splice(e,0,[\"M\",d.x,d.y]),c.bx=0,c.by=0,c.x=a[e][1],c.y=a[e][2],v=W(f.length,l&&l.length||0))},q=[],u=[],c=\"\",t=\"\",x=0,v=W(f.length,\n",
"l&&l.length||0);for(;x<v;x++){f[x]&&(c=f[x][0]);\"C\"!=c&&(q[x]=c,x&&(t=q[x-1]));f[x]=p(f[x],n,t);\"A\"!=q[x]&&\"C\"==c&&(q[x]=\"C\");s(f,x);l&&(l[x]&&(c=l[x][0]),\"C\"!=c&&(u[x]=c,x&&(t=u[x-1])),l[x]=p(l[x],k,t),\"A\"!=u[x]&&\"C\"==c&&(u[x]=\"C\"),s(l,x));r(f,l,n,k,x);r(l,f,k,n,x);var w=f[x],z=l&&l[x],y=w.length,U=l&&z.length;n.x=w[y-2];n.y=w[y-1];n.bx=$(w[y-4])||n.x;n.by=$(w[y-3])||n.y;k.bx=l&&($(z[U-4])||k.x);k.by=l&&($(z[U-3])||k.y);k.x=l&&z[U-2];k.y=l&&z[U-1]}l||(e.curve=d(f));return l?[f,l]:f}function P(a,\n",
"b){for(var d=[],e=0,h=a.length;h-2*!b>e;e+=2){var f=[{x:+a[e-2],y:+a[e-1]},{x:+a[e],y:+a[e+1]},{x:+a[e+2],y:+a[e+3]},{x:+a[e+4],y:+a[e+5]}];b?e?h-4==e?f[3]={x:+a[0],y:+a[1]}:h-2==e&&(f[2]={x:+a[0],y:+a[1]},f[3]={x:+a[2],y:+a[3]}):f[0]={x:+a[h-2],y:+a[h-1]}:h-4==e?f[3]=f[2]:e||(f[0]={x:+a[e],y:+a[e+1]});d.push([\"C\",(-f[0].x+6*f[1].x+f[2].x)/6,(-f[0].y+6*f[1].y+f[2].y)/6,(f[1].x+6*f[2].x-f[3].x)/6,(f[1].y+6*f[2].y-f[3].y)/6,f[2].x,f[2].y])}return d}y=k.prototype;var Q=a.is,C=a._.clone,L=\"hasOwnProperty\",\n",
"N=/,?([a-z]),?/gi,$=parseFloat,F=Math,S=F.PI,X=F.min,W=F.max,ma=F.pow,Z=F.abs;M=n(1);var na=n(),ba=n(0,1),V=a._unit2px;a.path=A;a.path.getTotalLength=M;a.path.getPointAtLength=na;a.path.getSubpath=function(a,b,d){if(1E-6>this.getTotalLength(a)-d)return ba(a,b).end;a=ba(a,d,1);return b?ba(a,b).end:a};y.getTotalLength=function(){if(this.node.getTotalLength)return this.node.getTotalLength()};y.getPointAtLength=function(a){return na(this.attr(\"d\"),a)};y.getSubpath=function(b,d){return a.path.getSubpath(this.attr(\"d\"),\n",
"b,d)};a._.box=w;a.path.findDotsAtSegment=u;a.path.bezierBBox=p;a.path.isPointInsideBBox=b;a.path.isBBoxIntersect=q;a.path.intersection=function(a,b){return l(a,b)};a.path.intersectionNumber=function(a,b){return l(a,b,1)};a.path.isPointInside=function(a,d,e){var h=r(a);return b(h,d,e)&&1==l(a,[[\"M\",d,e],[\"H\",h.x2+10] ],1)%2};a.path.getBBox=r;a.path.get={path:function(a){return a.attr(\"path\")},circle:function(a){a=V(a);return x(a.cx,a.cy,a.r)},ellipse:function(a){a=V(a);return x(a.cx||0,a.cy||0,a.rx,\n",
"a.ry)},rect:function(a){a=V(a);return s(a.x||0,a.y||0,a.width,a.height,a.rx,a.ry)},image:function(a){a=V(a);return s(a.x||0,a.y||0,a.width,a.height)},line:function(a){return\"M\"+[a.attr(\"x1\")||0,a.attr(\"y1\")||0,a.attr(\"x2\"),a.attr(\"y2\")]},polyline:function(a){return\"M\"+a.attr(\"points\")},polygon:function(a){return\"M\"+a.attr(\"points\")+\"z\"},deflt:function(a){a=a.node.getBBox();return s(a.x,a.y,a.width,a.height)}};a.path.toRelative=function(b){var e=A(b),h=String.prototype.toLowerCase;if(e.rel)return d(e.rel);\n",
"a.is(b,\"array\")&&a.is(b&&b[0],\"array\")||(b=a.parsePathString(b));var f=[],l=0,n=0,k=0,p=0,s=0;\"M\"==b[0][0]&&(l=b[0][1],n=b[0][2],k=l,p=n,s++,f.push([\"M\",l,n]));for(var r=b.length;s<r;s++){var q=f[s]=[],x=b[s];if(x[0]!=h.call(x[0]))switch(q[0]=h.call(x[0]),q[0]){case \"a\":q[1]=x[1];q[2]=x[2];q[3]=x[3];q[4]=x[4];q[5]=x[5];q[6]=+(x[6]-l).toFixed(3);q[7]=+(x[7]-n).toFixed(3);break;case \"v\":q[1]=+(x[1]-n).toFixed(3);break;case \"m\":k=x[1],p=x[2];default:for(var c=1,t=x.length;c<t;c++)q[c]=+(x[c]-(c%2?l:\n",
"n)).toFixed(3)}else for(f[s]=[],\"m\"==x[0]&&(k=x[1]+l,p=x[2]+n),q=0,c=x.length;q<c;q++)f[s][q]=x[q];x=f[s].length;switch(f[s][0]){case \"z\":l=k;n=p;break;case \"h\":l+=+f[s][x-1];break;case \"v\":n+=+f[s][x-1];break;default:l+=+f[s][x-2],n+=+f[s][x-1]}}f.toString=z;e.rel=d(f);return f};a.path.toAbsolute=G;a.path.toCubic=I;a.path.map=function(a,b){if(!b)return a;var d,e,h,f,l,n,k;a=I(a);h=0;for(l=a.length;h<l;h++)for(k=a[h],f=1,n=k.length;f<n;f+=2)d=b.x(k[f],k[f+1]),e=b.y(k[f],k[f+1]),k[f]=d,k[f+1]=e;return a};\n",
"a.path.toString=z;a.path.clone=d});C.plugin(function(a,v,y,C){var A=Math.max,w=Math.min,z=function(a){this.items=[];this.bindings={};this.length=0;this.type=\"set\";if(a)for(var f=0,n=a.length;f<n;f++)a[f]&&(this[this.items.length]=this.items[this.items.length]=a[f],this.length++)};v=z.prototype;v.push=function(){for(var a,f,n=0,k=arguments.length;n<k;n++)if(a=arguments[n])f=this.items.length,this[f]=this.items[f]=a,this.length++;return this};v.pop=function(){this.length&&delete this[this.length--];\n",
"return this.items.pop()};v.forEach=function(a,f){for(var n=0,k=this.items.length;n<k&&!1!==a.call(f,this.items[n],n);n++);return this};v.animate=function(d,f,n,u){\"function\"!=typeof n||n.length||(u=n,n=L.linear);d instanceof a._.Animation&&(u=d.callback,n=d.easing,f=n.dur,d=d.attr);var p=arguments;if(a.is(d,\"array\")&&a.is(p[p.length-1],\"array\"))var b=!0;var q,e=function(){q?this.b=q:q=this.b},l=0,r=u&&function(){l++==this.length&&u.call(this)};return this.forEach(function(a,l){k.once(\"snap.animcreated.\"+\n",
"a.id,e);b?p[l]&&a.animate.apply(a,p[l]):a.animate(d,f,n,r)})};v.remove=function(){for(;this.length;)this.pop().remove();return this};v.bind=function(a,f,k){var u={};if(\"function\"==typeof f)this.bindings[a]=f;else{var p=k||a;this.bindings[a]=function(a){u[p]=a;f.attr(u)}}return this};v.attr=function(a){var f={},k;for(k in a)if(this.bindings[k])this.bindings[k](a[k]);else f[k]=a[k];a=0;for(k=this.items.length;a<k;a++)this.items[a].attr(f);return this};v.clear=function(){for(;this.length;)this.pop()};\n",
"v.splice=function(a,f,k){a=0>a?A(this.length+a,0):a;f=A(0,w(this.length-a,f));var u=[],p=[],b=[],q;for(q=2;q<arguments.length;q++)b.push(arguments[q]);for(q=0;q<f;q++)p.push(this[a+q]);for(;q<this.length-a;q++)u.push(this[a+q]);var e=b.length;for(q=0;q<e+u.length;q++)this.items[a+q]=this[a+q]=q<e?b[q]:u[q-e];for(q=this.items.length=this.length-=f-e;this[q];)delete this[q++];return new z(p)};v.exclude=function(a){for(var f=0,k=this.length;f<k;f++)if(this[f]==a)return this.splice(f,1),!0;return!1};\n",
"v.insertAfter=function(a){for(var f=this.items.length;f--;)this.items[f].insertAfter(a);return this};v.getBBox=function(){for(var a=[],f=[],k=[],u=[],p=this.items.length;p--;)if(!this.items[p].removed){var b=this.items[p].getBBox();a.push(b.x);f.push(b.y);k.push(b.x+b.width);u.push(b.y+b.height)}a=w.apply(0,a);f=w.apply(0,f);k=A.apply(0,k);u=A.apply(0,u);return{x:a,y:f,x2:k,y2:u,width:k-a,height:u-f,cx:a+(k-a)/2,cy:f+(u-f)/2}};v.clone=function(a){a=new z;for(var f=0,k=this.items.length;f<k;f++)a.push(this.items[f].clone());\n",
"return a};v.toString=function(){return\"Snap\\u2018s set\"};v.type=\"set\";a.set=function(){var a=new z;arguments.length&&a.push.apply(a,Array.prototype.slice.call(arguments,0));return a}});C.plugin(function(a,v,y,C){function A(a){var b=a[0];switch(b.toLowerCase()){case \"t\":return[b,0,0];case \"m\":return[b,1,0,0,1,0,0];case \"r\":return 4==a.length?[b,0,a[2],a[3] ]:[b,0];case \"s\":return 5==a.length?[b,1,1,a[3],a[4] ]:3==a.length?[b,1,1]:[b,1]}}function w(b,d,f){d=q(d).replace(/\\.{3}|\\u2026/g,b);b=a.parseTransformString(b)||\n",
"[];d=a.parseTransformString(d)||[];for(var k=Math.max(b.length,d.length),p=[],v=[],h=0,w,z,y,I;h<k;h++){y=b[h]||A(d[h]);I=d[h]||A(y);if(y[0]!=I[0]||\"r\"==y[0].toLowerCase()&&(y[2]!=I[2]||y[3]!=I[3])||\"s\"==y[0].toLowerCase()&&(y[3]!=I[3]||y[4]!=I[4])){b=a._.transform2matrix(b,f());d=a._.transform2matrix(d,f());p=[[\"m\",b.a,b.b,b.c,b.d,b.e,b.f] ];v=[[\"m\",d.a,d.b,d.c,d.d,d.e,d.f] ];break}p[h]=[];v[h]=[];w=0;for(z=Math.max(y.length,I.length);w<z;w++)w in y&&(p[h][w]=y[w]),w in I&&(v[h][w]=I[w])}return{from:u(p),\n",
"to:u(v),f:n(p)}}function z(a){return a}function d(a){return function(b){return+b.toFixed(3)+a}}function f(b){return a.rgb(b[0],b[1],b[2])}function n(a){var b=0,d,f,k,n,h,p,q=[];d=0;for(f=a.length;d<f;d++){h=\"[\";p=['\"'+a[d][0]+'\"'];k=1;for(n=a[d].length;k<n;k++)p[k]=\"val[\"+b++ +\"]\";h+=p+\"]\";q[d]=h}return Function(\"val\",\"return Snap.path.toString.call([\"+q+\"])\")}function u(a){for(var b=[],d=0,f=a.length;d<f;d++)for(var k=1,n=a[d].length;k<n;k++)b.push(a[d][k]);return b}var p={},b=/[a-z]+$/i,q=String;\n",
"p.stroke=p.fill=\"colour\";v.prototype.equal=function(a,b){return k(\"snap.util.equal\",this,a,b).firstDefined()};k.on(\"snap.util.equal\",function(e,k){var r,s;r=q(this.attr(e)||\"\");var x=this;if(r==+r&&k==+k)return{from:+r,to:+k,f:z};if(\"colour\"==p[e])return r=a.color(r),s=a.color(k),{from:[r.r,r.g,r.b,r.opacity],to:[s.r,s.g,s.b,s.opacity],f:f};if(\"transform\"==e||\"gradientTransform\"==e||\"patternTransform\"==e)return k instanceof a.Matrix&&(k=k.toTransformString()),a._.rgTransform.test(k)||(k=a._.svgTransform2string(k)),\n",
"w(r,k,function(){return x.getBBox(1)});if(\"d\"==e||\"path\"==e)return r=a.path.toCubic(r,k),{from:u(r[0]),to:u(r[1]),f:n(r[0])};if(\"points\"==e)return r=q(r).split(a._.separator),s=q(k).split(a._.separator),{from:r,to:s,f:function(a){return a}};aUnit=r.match(b);s=q(k).match(b);return aUnit&&aUnit==s?{from:parseFloat(r),to:parseFloat(k),f:d(aUnit)}:{from:this.asPX(e),to:this.asPX(e,k),f:z}})});C.plugin(function(a,v,y,C){var A=v.prototype,w=\"createTouch\"in C.doc;v=\"click dblclick mousedown mousemove mouseout mouseover mouseup touchstart touchmove touchend touchcancel\".split(\" \");\n",
"var z={mousedown:\"touchstart\",mousemove:\"touchmove\",mouseup:\"touchend\"},d=function(a,b){var d=\"y\"==a?\"scrollTop\":\"scrollLeft\",e=b&&b.node?b.node.ownerDocument:C.doc;return e[d in e.documentElement?\"documentElement\":\"body\"][d]},f=function(){this.returnValue=!1},n=function(){return this.originalEvent.preventDefault()},u=function(){this.cancelBubble=!0},p=function(){return this.originalEvent.stopPropagation()},b=function(){if(C.doc.addEventListener)return function(a,b,e,f){var k=w&&z[b]?z[b]:b,l=function(k){var l=\n",
"d(\"y\",f),q=d(\"x\",f);if(w&&z.hasOwnProperty(b))for(var r=0,u=k.targetTouches&&k.targetTouches.length;r<u;r++)if(k.targetTouches[r].target==a||a.contains(k.targetTouches[r].target)){u=k;k=k.targetTouches[r];k.originalEvent=u;k.preventDefault=n;k.stopPropagation=p;break}return e.call(f,k,k.clientX+q,k.clientY+l)};b!==k&&a.addEventListener(b,l,!1);a.addEventListener(k,l,!1);return function(){b!==k&&a.removeEventListener(b,l,!1);a.removeEventListener(k,l,!1);return!0}};if(C.doc.attachEvent)return function(a,\n",
"b,e,h){var k=function(a){a=a||h.node.ownerDocument.window.event;var b=d(\"y\",h),k=d(\"x\",h),k=a.clientX+k,b=a.clientY+b;a.preventDefault=a.preventDefault||f;a.stopPropagation=a.stopPropagation||u;return e.call(h,a,k,b)};a.attachEvent(\"on\"+b,k);return function(){a.detachEvent(\"on\"+b,k);return!0}}}(),q=[],e=function(a){for(var b=a.clientX,e=a.clientY,f=d(\"y\"),l=d(\"x\"),n,p=q.length;p--;){n=q[p];if(w)for(var r=a.touches&&a.touches.length,u;r--;){if(u=a.touches[r],u.identifier==n.el._drag.id||n.el.node.contains(u.target)){b=\n",
"u.clientX;e=u.clientY;(a.originalEvent?a.originalEvent:a).preventDefault();break}}else a.preventDefault();b+=l;e+=f;k(\"snap.drag.move.\"+n.el.id,n.move_scope||n.el,b-n.el._drag.x,e-n.el._drag.y,b,e,a)}},l=function(b){a.unmousemove(e).unmouseup(l);for(var d=q.length,f;d--;)f=q[d],f.el._drag={},k(\"snap.drag.end.\"+f.el.id,f.end_scope||f.start_scope||f.move_scope||f.el,b);q=[]};for(y=v.length;y--;)(function(d){a[d]=A[d]=function(e,f){a.is(e,\"function\")&&(this.events=this.events||[],this.events.push({name:d,\n",
"f:e,unbind:b(this.node||document,d,e,f||this)}));return this};a[\"un\"+d]=A[\"un\"+d]=function(a){for(var b=this.events||[],e=b.length;e--;)if(b[e].name==d&&(b[e].f==a||!a)){b[e].unbind();b.splice(e,1);!b.length&&delete this.events;break}return this}})(v[y]);A.hover=function(a,b,d,e){return this.mouseover(a,d).mouseout(b,e||d)};A.unhover=function(a,b){return this.unmouseover(a).unmouseout(b)};var r=[];A.drag=function(b,d,f,h,n,p){function u(r,v,w){(r.originalEvent||r).preventDefault();this._drag.x=v;\n",
"this._drag.y=w;this._drag.id=r.identifier;!q.length&&a.mousemove(e).mouseup(l);q.push({el:this,move_scope:h,start_scope:n,end_scope:p});d&&k.on(\"snap.drag.start.\"+this.id,d);b&&k.on(\"snap.drag.move.\"+this.id,b);f&&k.on(\"snap.drag.end.\"+this.id,f);k(\"snap.drag.start.\"+this.id,n||h||this,v,w,r)}if(!arguments.length){var v;return this.drag(function(a,b){this.attr({transform:v+(v?\"T\":\"t\")+[a,b]})},function(){v=this.transform().local})}this._drag={};r.push({el:this,start:u});this.mousedown(u);return this};\n",
"A.undrag=function(){for(var b=r.length;b--;)r[b].el==this&&(this.unmousedown(r[b].start),r.splice(b,1),k.unbind(\"snap.drag.*.\"+this.id));!r.length&&a.unmousemove(e).unmouseup(l);return this}});C.plugin(function(a,v,y,C){y=y.prototype;var A=/^\\s*url\\((.+)\\)/,w=String,z=a._.$;a.filter={};y.filter=function(d){var f=this;\"svg\"!=f.type&&(f=f.paper);d=a.parse(w(d));var k=a._.id(),u=z(\"filter\");z(u,{id:k,filterUnits:\"userSpaceOnUse\"});u.appendChild(d.node);f.defs.appendChild(u);return new v(u)};k.on(\"snap.util.getattr.filter\",\n",
"function(){k.stop();var d=z(this.node,\"filter\");if(d)return(d=w(d).match(A))&&a.select(d[1])});k.on(\"snap.util.attr.filter\",function(d){if(d instanceof v&&\"filter\"==d.type){k.stop();var f=d.node.id;f||(z(d.node,{id:d.id}),f=d.id);z(this.node,{filter:a.url(f)})}d&&\"none\"!=d||(k.stop(),this.node.removeAttribute(\"filter\"))});a.filter.blur=function(d,f){null==d&&(d=2);return a.format('<feGaussianBlur stdDeviation=\"{def}\"/>',{def:null==f?d:[d,f]})};a.filter.blur.toString=function(){return this()};a.filter.shadow=\n",
"function(d,f,k,u,p){\"string\"==typeof k&&(p=u=k,k=4);\"string\"!=typeof u&&(p=u,u=\"#000\");null==k&&(k=4);null==p&&(p=1);null==d&&(d=0,f=2);null==f&&(f=d);u=a.color(u||\"#000\");return a.format('<feGaussianBlur in=\"SourceAlpha\" stdDeviation=\"{blur}\"/><feOffset dx=\"{dx}\" dy=\"{dy}\" result=\"offsetblur\"/><feFlood flood-color=\"{color}\"/><feComposite in2=\"offsetblur\" operator=\"in\"/><feComponentTransfer><feFuncA type=\"linear\" slope=\"{opacity}\"/></feComponentTransfer><feMerge><feMergeNode/><feMergeNode in=\"SourceGraphic\"/></feMerge>',\n",
"{color:u,dx:d,dy:f,blur:k,opacity:p})};a.filter.shadow.toString=function(){return this()};a.filter.grayscale=function(d){null==d&&(d=1);return a.format('<feColorMatrix type=\"matrix\" values=\"{a} {b} {c} 0 0 {d} {e} {f} 0 0 {g} {b} {h} 0 0 0 0 0 1 0\"/>',{a:0.2126+0.7874*(1-d),b:0.7152-0.7152*(1-d),c:0.0722-0.0722*(1-d),d:0.2126-0.2126*(1-d),e:0.7152+0.2848*(1-d),f:0.0722-0.0722*(1-d),g:0.2126-0.2126*(1-d),h:0.0722+0.9278*(1-d)})};a.filter.grayscale.toString=function(){return this()};a.filter.sepia=\n",
"function(d){null==d&&(d=1);return a.format('<feColorMatrix type=\"matrix\" values=\"{a} {b} {c} 0 0 {d} {e} {f} 0 0 {g} {h} {i} 0 0 0 0 0 1 0\"/>',{a:0.393+0.607*(1-d),b:0.769-0.769*(1-d),c:0.189-0.189*(1-d),d:0.349-0.349*(1-d),e:0.686+0.314*(1-d),f:0.168-0.168*(1-d),g:0.272-0.272*(1-d),h:0.534-0.534*(1-d),i:0.131+0.869*(1-d)})};a.filter.sepia.toString=function(){return this()};a.filter.saturate=function(d){null==d&&(d=1);return a.format('<feColorMatrix type=\"saturate\" values=\"{amount}\"/>',{amount:1-\n",
"d})};a.filter.saturate.toString=function(){return this()};a.filter.hueRotate=function(d){return a.format('<feColorMatrix type=\"hueRotate\" values=\"{angle}\"/>',{angle:d||0})};a.filter.hueRotate.toString=function(){return this()};a.filter.invert=function(d){null==d&&(d=1);return a.format('<feComponentTransfer><feFuncR type=\"table\" tableValues=\"{amount} {amount2}\"/><feFuncG type=\"table\" tableValues=\"{amount} {amount2}\"/><feFuncB type=\"table\" tableValues=\"{amount} {amount2}\"/></feComponentTransfer>',{amount:d,\n",
"amount2:1-d})};a.filter.invert.toString=function(){return this()};a.filter.brightness=function(d){null==d&&(d=1);return a.format('<feComponentTransfer><feFuncR type=\"linear\" slope=\"{amount}\"/><feFuncG type=\"linear\" slope=\"{amount}\"/><feFuncB type=\"linear\" slope=\"{amount}\"/></feComponentTransfer>',{amount:d})};a.filter.brightness.toString=function(){return this()};a.filter.contrast=function(d){null==d&&(d=1);return a.format('<feComponentTransfer><feFuncR type=\"linear\" slope=\"{amount}\" intercept=\"{amount2}\"/><feFuncG type=\"linear\" slope=\"{amount}\" intercept=\"{amount2}\"/><feFuncB type=\"linear\" slope=\"{amount}\" intercept=\"{amount2}\"/></feComponentTransfer>',\n",
"{amount:d,amount2:0.5-d/2})};a.filter.contrast.toString=function(){return this()}});return C});\n",
"\n",
"]]> </script>\n",
"<script> <![CDATA[\n",
"\n",
"(function (glob, factory) {\n",
" // AMD support\n",
" if (typeof define === \"function\" && define.amd) {\n",
" // Define as an anonymous module\n",
" define(\"Gadfly\", [\"Snap.svg\"], function (Snap) {\n",
" return factory(Snap);\n",
" });\n",
" } else {\n",
" // Browser globals (glob is window)\n",
" // Snap adds itself to window\n",
" glob.Gadfly = factory(glob.Snap);\n",
" }\n",
"}(this, function (Snap) {\n",
"\n",
"var Gadfly = {};\n",
"\n",
"// Get an x/y coordinate value in pixels\n",
"var xPX = function(fig, x) {\n",
" var client_box = fig.node.getBoundingClientRect();\n",
" return x * fig.node.viewBox.baseVal.width / client_box.width;\n",
"};\n",
"\n",
"var yPX = function(fig, y) {\n",
" var client_box = fig.node.getBoundingClientRect();\n",
" return y * fig.node.viewBox.baseVal.height / client_box.height;\n",
"};\n",
"\n",
"\n",
"Snap.plugin(function (Snap, Element, Paper, global) {\n",
" // Traverse upwards from a snap element to find and return the first\n",
" // note with the \"plotroot\" class.\n",
" Element.prototype.plotroot = function () {\n",
" var element = this;\n",
" while (!element.hasClass(\"plotroot\") && element.parent() != null) {\n",
" element = element.parent();\n",
" }\n",
" return element;\n",
" };\n",
"\n",
" Element.prototype.svgroot = function () {\n",
" var element = this;\n",
" while (element.node.nodeName != \"svg\" && element.parent() != null) {\n",
" element = element.parent();\n",
" }\n",
" return element;\n",
" };\n",
"\n",
" Element.prototype.plotbounds = function () {\n",
" var root = this.plotroot()\n",
" var bbox = root.select(\".guide.background\").node.getBBox();\n",
" return {\n",
" x0: bbox.x,\n",
" x1: bbox.x + bbox.width,\n",
" y0: bbox.y,\n",
" y1: bbox.y + bbox.height\n",
" };\n",
" };\n",
"\n",
" Element.prototype.plotcenter = function () {\n",
" var root = this.plotroot()\n",
" var bbox = root.select(\".guide.background\").node.getBBox();\n",
" return {\n",
" x: bbox.x + bbox.width / 2,\n",
" y: bbox.y + bbox.height / 2\n",
" };\n",
" };\n",
"\n",
" // Emulate IE style mouseenter/mouseleave events, since Microsoft always\n",
" // does everything right.\n",
" // See: http://www.dynamic-tools.net/toolbox/isMouseLeaveOrEnter/\n",
" var events = [\"mouseenter\", \"mouseleave\"];\n",
"\n",
" for (i in events) {\n",
" (function (event_name) {\n",
" var event_name = events[i];\n",
" Element.prototype[event_name] = function (fn, scope) {\n",
" if (Snap.is(fn, \"function\")) {\n",
" var fn2 = function (event) {\n",
" if (event.type != \"mouseover\" && event.type != \"mouseout\") {\n",
" return;\n",
" }\n",
"\n",
" var reltg = event.relatedTarget ? event.relatedTarget :\n",
" event.type == \"mouseout\" ? event.toElement : event.fromElement;\n",
" while (reltg && reltg != this.node) reltg = reltg.parentNode;\n",
"\n",
" if (reltg != this.node) {\n",
" return fn.apply(this, event);\n",
" }\n",
" };\n",
"\n",
" if (event_name == \"mouseenter\") {\n",
" this.mouseover(fn2, scope);\n",
" } else {\n",
" this.mouseout(fn2, scope);\n",
" }\n",
" }\n",
" return this;\n",
" };\n",
" })(events[i]);\n",
" }\n",
"\n",
"\n",
" Element.prototype.mousewheel = function (fn, scope) {\n",
" if (Snap.is(fn, \"function\")) {\n",
" var el = this;\n",
" var fn2 = function (event) {\n",
" fn.apply(el, [event]);\n",
" };\n",
" }\n",
"\n",
" this.node.addEventListener(\n",
" /Firefox/i.test(navigator.userAgent) ? \"DOMMouseScroll\" : \"mousewheel\",\n",
" fn2);\n",
"\n",
" return this;\n",
" };\n",
"\n",
"\n",
" // Snap's attr function can be too slow for things like panning/zooming.\n",
" // This is a function to directly update element attributes without going\n",
" // through eve.\n",
" Element.prototype.attribute = function(key, val) {\n",
" if (val === undefined) {\n",
" return this.node.getAttribute(key);\n",
" } else {\n",
" this.node.setAttribute(key, val);\n",
" return this;\n",
" }\n",
" };\n",
"\n",
" Element.prototype.init_gadfly = function() {\n",
" this.mouseenter(Gadfly.plot_mouseover)\n",
" .mouseleave(Gadfly.plot_mouseout)\n",
" .dblclick(Gadfly.plot_dblclick)\n",
" .mousewheel(Gadfly.guide_background_scroll)\n",
" .drag(Gadfly.guide_background_drag_onmove,\n",
" Gadfly.guide_background_drag_onstart,\n",
" Gadfly.guide_background_drag_onend);\n",
" this.mouseenter(function (event) {\n",
" init_pan_zoom(this.plotroot());\n",
" });\n",
" return this;\n",
" };\n",
"});\n",
"\n",
"\n",
"// When the plot is moused over, emphasize the grid lines.\n",
"Gadfly.plot_mouseover = function(event) {\n",
" var root = this.plotroot();\n",
"\n",
" var keyboard_zoom = function(event) {\n",
" if (event.which == 187) { // plus\n",
" set_zoom(root, root.data(\"scale\") * 1.5, true);\n",
" } else if (event.which == 189) { // minus\n",
" set_zoom(root, root.data(\"scale\") / 1.5, true);\n",
" }\n",
" };\n",
" root.data(\"keyboard_zoom\", keyboard_zoom);\n",
" window.addEventListener(\"keyup\", keyboard_zoom);\n",
"\n",
" var xgridlines = root.select(\".xgridlines\"),\n",
" ygridlines = root.select(\".ygridlines\");\n",
"\n",
" xgridlines.data(\"unfocused_strokedash\",\n",
" xgridlines.attribute(\"stroke-dasharray\").replace(/(\\d)(,|$)/g, \"$1mm$2\"));\n",
" ygridlines.data(\"unfocused_strokedash\",\n",
" ygridlines.attribute(\"stroke-dasharray\").replace(/(\\d)(,|$)/g, \"$1mm$2\"));\n",
"\n",
" // emphasize grid lines\n",
" var destcolor = root.data(\"focused_xgrid_color\");\n",
" xgridlines.attribute(\"stroke-dasharray\", \"none\")\n",
" .selectAll(\"path\")\n",
" .animate({stroke: destcolor}, 250);\n",
"\n",
" destcolor = root.data(\"focused_ygrid_color\");\n",
" ygridlines.attribute(\"stroke-dasharray\", \"none\")\n",
" .selectAll(\"path\")\n",
" .animate({stroke: destcolor}, 250);\n",
"\n",
" // reveal zoom slider\n",
" root.select(\".zoomslider\")\n",
" .animate({opacity: 1.0}, 250);\n",
"};\n",
"\n",
"// Reset pan and zoom on double click\n",
"Gadfly.plot_dblclick = function(event) {\n",
" set_plot_pan_zoom(this.plotroot(), 0.0, 0.0, 1.0);\n",
"};\n",
"\n",
"// Unemphasize grid lines on mouse out.\n",
"Gadfly.plot_mouseout = function(event) {\n",
" var root = this.plotroot();\n",
"\n",
" window.removeEventListener(\"keyup\", root.data(\"keyboard_zoom\"));\n",
" root.data(\"keyboard_zoom\", undefined);\n",
"\n",
" var xgridlines = root.select(\".xgridlines\"),\n",
" ygridlines = root.select(\".ygridlines\");\n",
"\n",
" var destcolor = root.data(\"unfocused_xgrid_color\");\n",
"\n",
" xgridlines.attribute(\"stroke-dasharray\", xgridlines.data(\"unfocused_strokedash\"))\n",
" .selectAll(\"path\")\n",
" .animate({stroke: destcolor}, 250);\n",
"\n",
" destcolor = root.data(\"unfocused_ygrid_color\");\n",
" ygridlines.attribute(\"stroke-dasharray\", ygridlines.data(\"unfocused_strokedash\"))\n",
" .selectAll(\"path\")\n",
" .animate({stroke: destcolor}, 250);\n",
"\n",
" // hide zoom slider\n",
" root.select(\".zoomslider\")\n",
" .animate({opacity: 0.0}, 250);\n",
"};\n",
"\n",
"\n",
"var set_geometry_transform = function(root, tx, ty, scale) {\n",
" var xscalable = root.hasClass(\"xscalable\"),\n",
" yscalable = root.hasClass(\"yscalable\");\n",
"\n",
" var old_scale = root.data(\"scale\");\n",
"\n",
" var xscale = xscalable ? scale : 1.0,\n",
" yscale = yscalable ? scale : 1.0;\n",
"\n",
" tx = xscalable ? tx : 0.0;\n",
" ty = yscalable ? ty : 0.0;\n",
"\n",
" var t = new Snap.Matrix().translate(tx, ty).scale(xscale, yscale);\n",
"\n",
" root.selectAll(\".geometry, image\")\n",
" .forEach(function (element, i) {\n",
" element.transform(t);\n",
" });\n",
"\n",
" bounds = root.plotbounds();\n",
"\n",
" if (yscalable) {\n",
" var xfixed_t = new Snap.Matrix().translate(0, ty).scale(1.0, yscale);\n",
" root.selectAll(\".xfixed\")\n",
" .forEach(function (element, i) {\n",
" element.transform(xfixed_t);\n",
" });\n",
"\n",
" root.select(\".ylabels\")\n",
" .transform(xfixed_t)\n",
" .selectAll(\"text\")\n",
" .forEach(function (element, i) {\n",
" if (element.attribute(\"gadfly:inscale\") == \"true\") {\n",
" var cx = element.asPX(\"x\"),\n",
" cy = element.asPX(\"y\");\n",
" var st = element.data(\"static_transform\");\n",
" unscale_t = new Snap.Matrix();\n",
" unscale_t.scale(1, 1/scale, cx, cy).add(st);\n",
" element.transform(unscale_t);\n",
"\n",
" var y = cy * scale + ty;\n",
" element.attr(\"visibility\",\n",
" bounds.y0 <= y && y <= bounds.y1 ? \"visible\" : \"hidden\");\n",
" }\n",
" });\n",
" }\n",
"\n",
" if (xscalable) {\n",
" var yfixed_t = new Snap.Matrix().translate(tx, 0).scale(xscale, 1.0);\n",
" var xtrans = new Snap.Matrix().translate(tx, 0);\n",
" root.selectAll(\".yfixed\")\n",
" .forEach(function (element, i) {\n",
" element.transform(yfixed_t);\n",
" });\n",
"\n",
" root.select(\".xlabels\")\n",
" .transform(yfixed_t)\n",
" .selectAll(\"text\")\n",
" .forEach(function (element, i) {\n",
" if (element.attribute(\"gadfly:inscale\") == \"true\") {\n",
" var cx = element.asPX(\"x\"),\n",
" cy = element.asPX(\"y\");\n",
" var st = element.data(\"static_transform\");\n",
" unscale_t = new Snap.Matrix();\n",
" unscale_t.scale(1/scale, 1, cx, cy).add(st);\n",
"\n",
" element.transform(unscale_t);\n",
"\n",
" var x = cx * scale + tx;\n",
" element.attr(\"visibility\",\n",
" bounds.x0 <= x && x <= bounds.x1 ? \"visible\" : \"hidden\");\n",
" }\n",
" });\n",
" }\n",
"\n",
" // we must unscale anything that is scale invariance: widths, raiduses, etc.\n",
" var size_attribs = [\"font-size\"];\n",
" var unscaled_selection = \".geometry, .geometry *\";\n",
" if (xscalable) {\n",
" size_attribs.push(\"rx\");\n",
" unscaled_selection += \", .xgridlines\";\n",
" }\n",
" if (yscalable) {\n",
" size_attribs.push(\"ry\");\n",
" unscaled_selection += \", .ygridlines\";\n",
" }\n",
"\n",
" root.selectAll(unscaled_selection)\n",
" .forEach(function (element, i) {\n",
" // circle need special help\n",
" if (element.node.nodeName == \"circle\") {\n",
" var cx = element.attribute(\"cx\"),\n",
" cy = element.attribute(\"cy\");\n",
" unscale_t = new Snap.Matrix().scale(1/xscale, 1/yscale,\n",
" cx, cy);\n",
" element.transform(unscale_t);\n",
" return;\n",
" }\n",
"\n",
" for (i in size_attribs) {\n",
" var key = size_attribs[i];\n",
" var val = parseFloat(element.attribute(key));\n",
" if (val !== undefined && val != 0 && !isNaN(val)) {\n",
" element.attribute(key, val * old_scale / scale);\n",
" }\n",
" }\n",
" });\n",
"};\n",
"\n",
"\n",
"// Find the most appropriate tick scale and update label visibility.\n",
"var update_tickscale = function(root, scale, axis) {\n",
" if (!root.hasClass(axis + \"scalable\")) return;\n",
"\n",
" var tickscales = root.data(axis + \"tickscales\");\n",
" var best_tickscale = 1.0;\n",
" var best_tickscale_dist = Infinity;\n",
" for (tickscale in tickscales) {\n",
" var dist = Math.abs(Math.log(tickscale) - Math.log(scale));\n",
" if (dist < best_tickscale_dist) {\n",
" best_tickscale_dist = dist;\n",
" best_tickscale = tickscale;\n",
" }\n",
" }\n",
"\n",
" if (best_tickscale != root.data(axis + \"tickscale\")) {\n",
" root.data(axis + \"tickscale\", best_tickscale);\n",
" var mark_inscale_gridlines = function (element, i) {\n",
" var inscale = element.attr(\"gadfly:scale\") == best_tickscale;\n",
" element.attribute(\"gadfly:inscale\", inscale);\n",
" element.attr(\"visibility\", inscale ? \"visible\" : \"hidden\");\n",
" };\n",
"\n",
" var mark_inscale_labels = function (element, i) {\n",
" var inscale = element.attr(\"gadfly:scale\") == best_tickscale;\n",
" element.attribute(\"gadfly:inscale\", inscale);\n",
" element.attr(\"visibility\", inscale ? \"visible\" : \"hidden\");\n",
" };\n",
"\n",
" root.select(\".\" + axis + \"gridlines\").selectAll(\"path\").forEach(mark_inscale_gridlines);\n",
" root.select(\".\" + axis + \"labels\").selectAll(\"text\").forEach(mark_inscale_labels);\n",
" }\n",
"};\n",
"\n",
"\n",
"var set_plot_pan_zoom = function(root, tx, ty, scale) {\n",
" var old_scale = root.data(\"scale\");\n",
" var bounds = root.plotbounds();\n",
"\n",
" var width = bounds.x1 - bounds.x0,\n",
" height = bounds.y1 - bounds.y0;\n",
"\n",
" // compute the viewport derived from tx, ty, and scale\n",
" var x_min = -width * scale - (scale * width - width),\n",
" x_max = width * scale,\n",
" y_min = -height * scale - (scale * height - height),\n",
" y_max = height * scale;\n",
"\n",
" var x0 = bounds.x0 - scale * bounds.x0,\n",
" y0 = bounds.y0 - scale * bounds.y0;\n",
"\n",
" var tx = Math.max(Math.min(tx - x0, x_max), x_min),\n",
" ty = Math.max(Math.min(ty - y0, y_max), y_min);\n",
"\n",
" tx += x0;\n",
" ty += y0;\n",
"\n",
" // when the scale change, we may need to alter which set of\n",
" // ticks is being displayed\n",
" if (scale != old_scale) {\n",
" update_tickscale(root, scale, \"x\");\n",
" update_tickscale(root, scale, \"y\");\n",
" }\n",
"\n",
" set_geometry_transform(root, tx, ty, scale);\n",
"\n",
" root.data(\"scale\", scale);\n",
" root.data(\"tx\", tx);\n",
" root.data(\"ty\", ty);\n",
"};\n",
"\n",
"\n",
"var scale_centered_translation = function(root, scale) {\n",
" var bounds = root.plotbounds();\n",
"\n",
" var width = bounds.x1 - bounds.x0,\n",
" height = bounds.y1 - bounds.y0;\n",
"\n",
" var tx0 = root.data(\"tx\"),\n",
" ty0 = root.data(\"ty\");\n",
"\n",
" var scale0 = root.data(\"scale\");\n",
"\n",
" // how off from center the current view is\n",
" var xoff = tx0 - (bounds.x0 * (1 - scale0) + (width * (1 - scale0)) / 2),\n",
" yoff = ty0 - (bounds.y0 * (1 - scale0) + (height * (1 - scale0)) / 2);\n",
"\n",
" // rescale offsets\n",
" xoff = xoff * scale / scale0;\n",
" yoff = yoff * scale / scale0;\n",
"\n",
" // adjust for the panel position being scaled\n",
" var x_edge_adjust = bounds.x0 * (1 - scale),\n",
" y_edge_adjust = bounds.y0 * (1 - scale);\n",
"\n",
" return {\n",
" x: xoff + x_edge_adjust + (width - width * scale) / 2,\n",
" y: yoff + y_edge_adjust + (height - height * scale) / 2\n",
" };\n",
"};\n",
"\n",
"\n",
"// Initialize data for panning zooming if it isn't already.\n",
"var init_pan_zoom = function(root) {\n",
" if (root.data(\"zoompan-ready\")) {\n",
" return;\n",
" }\n",
"\n",
" // The non-scaling-stroke trick. Rather than try to correct for the\n",
" // stroke-width when zooming, we force it to a fixed value.\n",
" var px_per_mm = root.node.getCTM().a;\n",
"\n",
" // Drag events report deltas in pixels, which we'd like to convert to\n",
" // millimeters.\n",
" root.data(\"px_per_mm\", px_per_mm);\n",
"\n",
" root.selectAll(\"path\")\n",
" .forEach(function (element, i) {\n",
" sw = element.asPX(\"stroke-width\") * px_per_mm;\n",
" if (sw > 0) {\n",
" element.attribute(\"stroke-width\", sw);\n",
" element.attribute(\"vector-effect\", \"non-scaling-stroke\");\n",
" }\n",
" });\n",
"\n",
" // Store ticks labels original tranformation\n",
" root.selectAll(\".xlabels > text, .ylabels > text\")\n",
" .forEach(function (element, i) {\n",
" var lm = element.transform().localMatrix;\n",
" element.data(\"static_transform\",\n",
" new Snap.Matrix(lm.a, lm.b, lm.c, lm.d, lm.e, lm.f));\n",
" });\n",
"\n",
" var xgridlines = root.select(\".xgridlines\");\n",
" var ygridlines = root.select(\".ygridlines\");\n",
" var xlabels = root.select(\".xlabels\");\n",
" var ylabels = root.select(\".ylabels\");\n",
"\n",
" if (root.data(\"tx\") === undefined) root.data(\"tx\", 0);\n",
" if (root.data(\"ty\") === undefined) root.data(\"ty\", 0);\n",
" if (root.data(\"scale\") === undefined) root.data(\"scale\", 1.0);\n",
" if (root.data(\"xtickscales\") === undefined) {\n",
"\n",
" // index all the tick scales that are listed\n",
" var xtickscales = {};\n",
" var ytickscales = {};\n",
" var add_x_tick_scales = function (element, i) {\n",
" xtickscales[element.attribute(\"gadfly:scale\")] = true;\n",
" };\n",
" var add_y_tick_scales = function (element, i) {\n",
" ytickscales[element.attribute(\"gadfly:scale\")] = true;\n",
" };\n",
"\n",
" if (xgridlines) xgridlines.selectAll(\"path\").forEach(add_x_tick_scales);\n",
" if (ygridlines) ygridlines.selectAll(\"path\").forEach(add_y_tick_scales);\n",
" if (xlabels) xlabels.selectAll(\"text\").forEach(add_x_tick_scales);\n",
" if (ylabels) ylabels.selectAll(\"text\").forEach(add_y_tick_scales);\n",
"\n",
" root.data(\"xtickscales\", xtickscales);\n",
" root.data(\"ytickscales\", ytickscales);\n",
" root.data(\"xtickscale\", 1.0);\n",
" }\n",
"\n",
" var min_scale = 1.0, max_scale = 1.0;\n",
" for (scale in xtickscales) {\n",
" min_scale = Math.min(min_scale, scale);\n",
" max_scale = Math.max(max_scale, scale);\n",
" }\n",
" for (scale in ytickscales) {\n",
" min_scale = Math.min(min_scale, scale);\n",
" max_scale = Math.max(max_scale, scale);\n",
" }\n",
" root.data(\"min_scale\", min_scale);\n",
" root.data(\"max_scale\", max_scale);\n",
"\n",
" // store the original positions of labels\n",
" if (xlabels) {\n",
" xlabels.selectAll(\"text\")\n",
" .forEach(function (element, i) {\n",
" element.data(\"x\", element.asPX(\"x\"));\n",
" });\n",
" }\n",
"\n",
" if (ylabels) {\n",
" ylabels.selectAll(\"text\")\n",
" .forEach(function (element, i) {\n",
" element.data(\"y\", element.asPX(\"y\"));\n",
" });\n",
" }\n",
"\n",
" // mark grid lines and ticks as in or out of scale.\n",
" var mark_inscale = function (element, i) {\n",
" element.attribute(\"gadfly:inscale\", element.attribute(\"gadfly:scale\") == 1.0);\n",
" };\n",
"\n",
" if (xgridlines) xgridlines.selectAll(\"path\").forEach(mark_inscale);\n",
" if (ygridlines) ygridlines.selectAll(\"path\").forEach(mark_inscale);\n",
" if (xlabels) xlabels.selectAll(\"text\").forEach(mark_inscale);\n",
" if (ylabels) ylabels.selectAll(\"text\").forEach(mark_inscale);\n",
"\n",
" // figure out the upper ond lower bounds on panning using the maximum\n",
" // and minum grid lines\n",
" var bounds = root.plotbounds();\n",
" var pan_bounds = {\n",
" x0: 0.0,\n",
" y0: 0.0,\n",
" x1: 0.0,\n",
" y1: 0.0\n",
" };\n",
"\n",
" if (xgridlines) {\n",
" xgridlines\n",
" .selectAll(\"path\")\n",
" .forEach(function (element, i) {\n",
" if (element.attribute(\"gadfly:inscale\") == \"true\") {\n",
" var bbox = element.node.getBBox();\n",
" if (bounds.x1 - bbox.x < pan_bounds.x0) {\n",
" pan_bounds.x0 = bounds.x1 - bbox.x;\n",
" }\n",
" if (bounds.x0 - bbox.x > pan_bounds.x1) {\n",
" pan_bounds.x1 = bounds.x0 - bbox.x;\n",
" }\n",
" element.attr(\"visibility\", \"visible\");\n",
" }\n",
" });\n",
" }\n",
"\n",
" if (ygridlines) {\n",
" ygridlines\n",
" .selectAll(\"path\")\n",
" .forEach(function (element, i) {\n",
" if (element.attribute(\"gadfly:inscale\") == \"true\") {\n",
" var bbox = element.node.getBBox();\n",
" if (bounds.y1 - bbox.y < pan_bounds.y0) {\n",
" pan_bounds.y0 = bounds.y1 - bbox.y;\n",
" }\n",
" if (bounds.y0 - bbox.y > pan_bounds.y1) {\n",
" pan_bounds.y1 = bounds.y0 - bbox.y;\n",
" }\n",
" element.attr(\"visibility\", \"visible\");\n",
" }\n",
" });\n",
" }\n",
"\n",
" // nudge these values a little\n",
" pan_bounds.x0 -= 5;\n",
" pan_bounds.x1 += 5;\n",
" pan_bounds.y0 -= 5;\n",
" pan_bounds.y1 += 5;\n",
" root.data(\"pan_bounds\", pan_bounds);\n",
"\n",
" root.data(\"zoompan-ready\", true)\n",
"};\n",
"\n",
"\n",
"// drag actions, i.e. zooming and panning\n",
"var pan_action = {\n",
" start: function(root, x, y, event) {\n",
" root.data(\"dx\", 0);\n",
" root.data(\"dy\", 0);\n",
" root.data(\"tx0\", root.data(\"tx\"));\n",
" root.data(\"ty0\", root.data(\"ty\"));\n",
" },\n",
" update: function(root, dx, dy, x, y, event) {\n",
" var px_per_mm = root.data(\"px_per_mm\");\n",
" dx /= px_per_mm;\n",
" dy /= px_per_mm;\n",
"\n",
" var tx0 = root.data(\"tx\"),\n",
" ty0 = root.data(\"ty\");\n",
"\n",
" var dx0 = root.data(\"dx\"),\n",
" dy0 = root.data(\"dy\");\n",
"\n",
" root.data(\"dx\", dx);\n",
" root.data(\"dy\", dy);\n",
"\n",
" dx = dx - dx0;\n",
" dy = dy - dy0;\n",
"\n",
" var tx = tx0 + dx,\n",
" ty = ty0 + dy;\n",
"\n",
" set_plot_pan_zoom(root, tx, ty, root.data(\"scale\"));\n",
" },\n",
" end: function(root, event) {\n",
"\n",
" },\n",
" cancel: function(root) {\n",
" set_plot_pan_zoom(root, root.data(\"tx0\"), root.data(\"ty0\"), root.data(\"scale\"));\n",
" }\n",
"};\n",
"\n",
"var zoom_box;\n",
"var zoom_action = {\n",
" start: function(root, x, y, event) {\n",
" var bounds = root.plotbounds();\n",
" var width = bounds.x1 - bounds.x0,\n",
" height = bounds.y1 - bounds.y0;\n",
" var ratio = width / height;\n",
" var xscalable = root.hasClass(\"xscalable\"),\n",
" yscalable = root.hasClass(\"yscalable\");\n",
" var px_per_mm = root.data(\"px_per_mm\");\n",
" x = xscalable ? x / px_per_mm : bounds.x0;\n",
" y = yscalable ? y / px_per_mm : bounds.y0;\n",
" var w = xscalable ? 0 : width;\n",
" var h = yscalable ? 0 : height;\n",
" zoom_box = root.rect(x, y, w, h).attr({\n",
" \"fill\": \"#000\",\n",
" \"opacity\": 0.25\n",
" });\n",
" zoom_box.data(\"ratio\", ratio);\n",
" },\n",
" update: function(root, dx, dy, x, y, event) {\n",
" var xscalable = root.hasClass(\"xscalable\"),\n",
" yscalable = root.hasClass(\"yscalable\");\n",
" var px_per_mm = root.data(\"px_per_mm\");\n",
" var bounds = root.plotbounds();\n",
" if (yscalable) {\n",
" y /= px_per_mm;\n",
" y = Math.max(bounds.y0, y);\n",
" y = Math.min(bounds.y1, y);\n",
" } else {\n",
" y = bounds.y1;\n",
" }\n",
" if (xscalable) {\n",
" x /= px_per_mm;\n",
" x = Math.max(bounds.x0, x);\n",
" x = Math.min(bounds.x1, x);\n",
" } else {\n",
" x = bounds.x1;\n",
" }\n",
"\n",
" dx = x - zoom_box.attr(\"x\");\n",
" dy = y - zoom_box.attr(\"y\");\n",
" if (xscalable && yscalable) {\n",
" var ratio = zoom_box.data(\"ratio\");\n",
" var width = Math.min(Math.abs(dx), ratio * Math.abs(dy));\n",
" var height = Math.min(Math.abs(dy), Math.abs(dx) / ratio);\n",
" dx = width * dx / Math.abs(dx);\n",
" dy = height * dy / Math.abs(dy);\n",
" }\n",
" var xoffset = 0,\n",
" yoffset = 0;\n",
" if (dx < 0) {\n",
" xoffset = dx;\n",
" dx = -1 * dx;\n",
" }\n",
" if (dy < 0) {\n",
" yoffset = dy;\n",
" dy = -1 * dy;\n",
" }\n",
" if (isNaN(dy)) {\n",
" dy = 0.0;\n",
" }\n",
" if (isNaN(dx)) {\n",
" dx = 0.0;\n",
" }\n",
" zoom_box.transform(\"T\" + xoffset + \",\" + yoffset);\n",
" zoom_box.attr(\"width\", dx);\n",
" zoom_box.attr(\"height\", dy);\n",
" },\n",
" end: function(root, event) {\n",
" var xscalable = root.hasClass(\"xscalable\"),\n",
" yscalable = root.hasClass(\"yscalable\");\n",
" var zoom_bounds = zoom_box.getBBox();\n",
" if (zoom_bounds.width * zoom_bounds.height <= 0) {\n",
" return;\n",
" }\n",
" var plot_bounds = root.plotbounds();\n",
" var zoom_factor = 1.0;\n",
" if (yscalable) {\n",
" zoom_factor = (plot_bounds.y1 - plot_bounds.y0) / zoom_bounds.height;\n",
" } else {\n",
" zoom_factor = (plot_bounds.x1 - plot_bounds.x0) / zoom_bounds.width;\n",
" }\n",
" var tx = (root.data(\"tx\") - zoom_bounds.x) * zoom_factor + plot_bounds.x0,\n",
" ty = (root.data(\"ty\") - zoom_bounds.y) * zoom_factor + plot_bounds.y0;\n",
" set_plot_pan_zoom(root, tx, ty, root.data(\"scale\") * zoom_factor);\n",
" zoom_box.remove();\n",
" },\n",
" cancel: function(root) {\n",
" zoom_box.remove();\n",
" }\n",
"};\n",
"\n",
"\n",
"Gadfly.guide_background_drag_onstart = function(x, y, event) {\n",
" var root = this.plotroot();\n",
" var scalable = root.hasClass(\"xscalable\") || root.hasClass(\"yscalable\");\n",
" var zoomable = !event.altKey && !event.ctrlKey && event.shiftKey && scalable;\n",
" var panable = !event.altKey && !event.ctrlKey && !event.shiftKey && scalable;\n",
" var drag_action = zoomable ? zoom_action :\n",
" panable ? pan_action :\n",
" undefined;\n",
" root.data(\"drag_action\", drag_action);\n",
" if (drag_action) {\n",
" var cancel_drag_action = function(event) {\n",
" if (event.which == 27) { // esc key\n",
" drag_action.cancel(root);\n",
" root.data(\"drag_action\", undefined);\n",
" }\n",
" };\n",
" window.addEventListener(\"keyup\", cancel_drag_action);\n",
" root.data(\"cancel_drag_action\", cancel_drag_action);\n",
" drag_action.start(root, x, y, event);\n",
" }\n",
"};\n",
"\n",
"\n",
"Gadfly.guide_background_drag_onmove = function(dx, dy, x, y, event) {\n",
" var root = this.plotroot();\n",
" var drag_action = root.data(\"drag_action\");\n",
" if (drag_action) {\n",
" drag_action.update(root, dx, dy, x, y, event);\n",
" }\n",
"};\n",
"\n",
"\n",
"Gadfly.guide_background_drag_onend = function(event) {\n",
" var root = this.plotroot();\n",
" window.removeEventListener(\"keyup\", root.data(\"cancel_drag_action\"));\n",
" root.data(\"cancel_drag_action\", undefined);\n",
" var drag_action = root.data(\"drag_action\");\n",
" if (drag_action) {\n",
" drag_action.end(root, event);\n",
" }\n",
" root.data(\"drag_action\", undefined);\n",
"};\n",
"\n",
"\n",
"Gadfly.guide_background_scroll = function(event) {\n",
" if (event.shiftKey) {\n",
" var root = this.plotroot();\n",
" var new_scale = root.data(\"scale\") * Math.pow(2, 0.002 * event.wheelDelta);\n",
" set_zoom(root, new_scale);\n",
" event.preventDefault();\n",
" }\n",
"};\n",
"\n",
"\n",
"Gadfly.zoomslider_button_mouseover = function(event) {\n",
" this.select(\".button_logo\")\n",
" .animate({fill: this.data(\"mouseover_color\")}, 100);\n",
"};\n",
"\n",
"\n",
"Gadfly.zoomslider_button_mouseout = function(event) {\n",
" this.select(\".button_logo\")\n",
" .animate({fill: this.data(\"mouseout_color\")}, 100);\n",
"};\n",
"\n",
"\n",
"Gadfly.zoomslider_zoomout_click = function(event) {\n",
" var root = this.plotroot();\n",
" set_zoom(root, root.data(\"scale\") / 1.5, true);\n",
"};\n",
"\n",
"\n",
"Gadfly.zoomslider_zoomin_click = function(event) {\n",
" var root = this.plotroot();\n",
" set_zoom(root, root.data(\"scale\") * 1.5, true);\n",
"};\n",
"\n",
"\n",
"Gadfly.zoomslider_track_click = function(event) {\n",
" // TODO\n",
"};\n",
"\n",
"\n",
"Gadfly.zoomslider_thumb_mousedown = function(event) {\n",
" this.animate({fill: this.data(\"mouseover_color\")}, 100);\n",
"};\n",
"\n",
"\n",
"Gadfly.zoomslider_thumb_mouseup = function(event) {\n",
" this.animate({fill: this.data(\"mouseout_color\")}, 100);\n",
"};\n",
"\n",
"\n",
"// compute the position in [0, 1] of the zoom slider thumb from the current scale\n",
"var slider_position_from_scale = function(scale, min_scale, max_scale) {\n",
" if (scale >= 1.0) {\n",
" return 0.5 + 0.5 * (Math.log(scale) / Math.log(max_scale));\n",
" }\n",
" else {\n",
" return 0.5 * (Math.log(scale) - Math.log(min_scale)) / (0 - Math.log(min_scale));\n",
" }\n",
"}\n",
"\n",
"\n",
"var set_zoom = function(root, scale, animate) {\n",
" var min_scale = root.data(\"min_scale\"),\n",
" max_scale = root.data(\"max_scale\"),\n",
" old_scale = root.data(\"scale\");\n",
" var new_scale = Math.max(min_scale, Math.min(scale, max_scale));\n",
" if (animate) {\n",
" Snap.animate(\n",
" old_scale,\n",
" new_scale,\n",
" function (new_scale) {\n",
" update_plot_scale(root, new_scale);\n",
" },\n",
" 200);\n",
" } else {\n",
" update_plot_scale(root, new_scale);\n",
" }\n",
"}\n",
"\n",
"\n",
"var update_plot_scale = function(root, new_scale) {\n",
" var trans = scale_centered_translation(root, new_scale);\n",
" set_plot_pan_zoom(root, trans.x, trans.y, new_scale);\n",
"\n",
" root.selectAll(\".zoomslider_thumb\")\n",
" .forEach(function (element, i) {\n",
" var min_pos = element.data(\"min_pos\"),\n",
" max_pos = element.data(\"max_pos\"),\n",
" min_scale = root.data(\"min_scale\"),\n",
" max_scale = root.data(\"max_scale\");\n",
" var xmid = (min_pos + max_pos) / 2;\n",
" var xpos = slider_position_from_scale(new_scale, min_scale, max_scale);\n",
" element.transform(new Snap.Matrix().translate(\n",
" Math.max(min_pos, Math.min(\n",
" max_pos, min_pos + (max_pos - min_pos) * xpos)) - xmid, 0));\n",
" });\n",
"};\n",
"\n",
"\n",
"Gadfly.zoomslider_thumb_dragmove = function(dx, dy, x, y, event) {\n",
" var root = this.plotroot();\n",
" var min_pos = this.data(\"min_pos\"),\n",
" max_pos = this.data(\"max_pos\"),\n",
" min_scale = root.data(\"min_scale\"),\n",
" max_scale = root.data(\"max_scale\"),\n",
" old_scale = root.data(\"old_scale\");\n",
"\n",
" var px_per_mm = root.data(\"px_per_mm\");\n",
" dx /= px_per_mm;\n",
" dy /= px_per_mm;\n",
"\n",
" var xmid = (min_pos + max_pos) / 2;\n",
" var xpos = slider_position_from_scale(old_scale, min_scale, max_scale) +\n",
" dx / (max_pos - min_pos);\n",
"\n",
" // compute the new scale\n",
" var new_scale;\n",
" if (xpos >= 0.5) {\n",
" new_scale = Math.exp(2.0 * (xpos - 0.5) * Math.log(max_scale));\n",
" }\n",
" else {\n",
" new_scale = Math.exp(2.0 * xpos * (0 - Math.log(min_scale)) +\n",
" Math.log(min_scale));\n",
" }\n",
" new_scale = Math.min(max_scale, Math.max(min_scale, new_scale));\n",
"\n",
" update_plot_scale(root, new_scale);\n",
" event.stopPropagation();\n",
"};\n",
"\n",
"\n",
"Gadfly.zoomslider_thumb_dragstart = function(x, y, event) {\n",
" var root = this.plotroot();\n",
"\n",
" // keep track of what the scale was when we started dragging\n",
" root.data(\"old_scale\", root.data(\"scale\"));\n",
" event.stopPropagation();\n",
"};\n",
"\n",
"\n",
"Gadfly.zoomslider_thumb_dragend = function(event) {\n",
" event.stopPropagation();\n",
"};\n",
"\n",
"\n",
"var toggle_color_class = function(root, color_class, ison) {\n",
" var guides = root.selectAll(\".guide.\" + color_class + \",.guide .\" + color_class);\n",
" var geoms = root.selectAll(\".geometry.\" + color_class + \",.geometry .\" + color_class);\n",
" if (ison) {\n",
" guides.animate({opacity: 0.5}, 250);\n",
" geoms.animate({opacity: 0.0}, 250);\n",
" } else {\n",
" guides.animate({opacity: 1.0}, 250);\n",
" geoms.animate({opacity: 1.0}, 250);\n",
" }\n",
"};\n",
"\n",
"\n",
"Gadfly.colorkey_swatch_click = function(event) {\n",
" var root = this.plotroot();\n",
" var color_class = this.data(\"color_class\");\n",
"\n",
" if (event.shiftKey) {\n",
" root.selectAll(\".colorkey text\")\n",
" .forEach(function (element) {\n",
" var other_color_class = element.data(\"color_class\");\n",
" if (other_color_class != color_class) {\n",
" toggle_color_class(root, other_color_class,\n",
" element.attr(\"opacity\") == 1.0);\n",
" }\n",
" });\n",
" } else {\n",
" toggle_color_class(root, color_class, this.attr(\"opacity\") == 1.0);\n",
" }\n",
"};\n",
"\n",
"\n",
"return Gadfly;\n",
"\n",
"}));\n",
"\n",
"\n",
"//@ sourceURL=gadfly.js\n",
"\n",
"(function (glob, factory) {\n",
" // AMD support\n",
" if (typeof require === \"function\" && typeof define === \"function\" && define.amd) {\n",
" require([\"Snap.svg\", \"Gadfly\"], function (Snap, Gadfly) {\n",
" factory(Snap, Gadfly);\n",
" });\n",
" } else {\n",
" factory(glob.Snap, glob.Gadfly);\n",
" }\n",
"})(window, function (Snap, Gadfly) {\n",
" var fig = Snap(\"#fig-24ef7475d29e43d3b5056099cb85fa82\");\n",
"fig.select(\"#fig-24ef7475d29e43d3b5056099cb85fa82-element-4\")\n",
" .init_gadfly();\n",
"fig.select(\"#fig-24ef7475d29e43d3b5056099cb85fa82-element-7\")\n",
" .plotroot().data(\"unfocused_ygrid_color\", \"#D0D0E0\")\n",
";\n",
"fig.select(\"#fig-24ef7475d29e43d3b5056099cb85fa82-element-7\")\n",
" .plotroot().data(\"focused_ygrid_color\", \"#A0A0A0\")\n",
";\n",
"fig.select(\"#fig-24ef7475d29e43d3b5056099cb85fa82-element-8\")\n",
" .plotroot().data(\"unfocused_xgrid_color\", \"#D0D0E0\")\n",
";\n",
"fig.select(\"#fig-24ef7475d29e43d3b5056099cb85fa82-element-8\")\n",
" .plotroot().data(\"focused_xgrid_color\", \"#A0A0A0\")\n",
";\n",
"fig.select(\"#fig-24ef7475d29e43d3b5056099cb85fa82-element-12\")\n",
" .data(\"mouseover_color\", \"#CD5C5C\")\n",
";\n",
"fig.select(\"#fig-24ef7475d29e43d3b5056099cb85fa82-element-12\")\n",
" .data(\"mouseout_color\", \"#6A6A6A\")\n",
";\n",
"fig.select(\"#fig-24ef7475d29e43d3b5056099cb85fa82-element-12\")\n",
" .click(Gadfly.zoomslider_zoomin_click)\n",
".mouseenter(Gadfly.zoomslider_button_mouseover)\n",
".mouseleave(Gadfly.zoomslider_button_mouseout)\n",
";\n",
"fig.select(\"#fig-24ef7475d29e43d3b5056099cb85fa82-element-14\")\n",
" .data(\"max_pos\", 120.42)\n",
";\n",
"fig.select(\"#fig-24ef7475d29e43d3b5056099cb85fa82-element-14\")\n",
" .data(\"min_pos\", 103.42)\n",
";\n",
"fig.select(\"#fig-24ef7475d29e43d3b5056099cb85fa82-element-14\")\n",
" .click(Gadfly.zoomslider_track_click);\n",
"fig.select(\"#fig-24ef7475d29e43d3b5056099cb85fa82-element-15\")\n",
" .data(\"max_pos\", 120.42)\n",
";\n",
"fig.select(\"#fig-24ef7475d29e43d3b5056099cb85fa82-element-15\")\n",
" .data(\"min_pos\", 103.42)\n",
";\n",
"fig.select(\"#fig-24ef7475d29e43d3b5056099cb85fa82-element-15\")\n",
" .data(\"mouseover_color\", \"#CD5C5C\")\n",
";\n",
"fig.select(\"#fig-24ef7475d29e43d3b5056099cb85fa82-element-15\")\n",
" .data(\"mouseout_color\", \"#6A6A6A\")\n",
";\n",
"fig.select(\"#fig-24ef7475d29e43d3b5056099cb85fa82-element-15\")\n",
" .drag(Gadfly.zoomslider_thumb_dragmove,\n",
" Gadfly.zoomslider_thumb_dragstart,\n",
" Gadfly.zoomslider_thumb_dragend)\n",
".mousedown(Gadfly.zoomslider_thumb_mousedown)\n",
".mouseup(Gadfly.zoomslider_thumb_mouseup)\n",
";\n",
"fig.select(\"#fig-24ef7475d29e43d3b5056099cb85fa82-element-16\")\n",
" .data(\"mouseover_color\", \"#CD5C5C\")\n",
";\n",
"fig.select(\"#fig-24ef7475d29e43d3b5056099cb85fa82-element-16\")\n",
" .data(\"mouseout_color\", \"#6A6A6A\")\n",
";\n",
"fig.select(\"#fig-24ef7475d29e43d3b5056099cb85fa82-element-16\")\n",
" .click(Gadfly.zoomslider_zoomout_click)\n",
".mouseenter(Gadfly.zoomslider_button_mouseover)\n",
".mouseleave(Gadfly.zoomslider_button_mouseout)\n",
";\n",
" });\n",
"]]> </script>\n",
"</svg>\n"
],
"text/plain": [
"Plot(...)"
]
},
"execution_count": 3,
"metadata": {
"comm_id": "7b3c6eb6-d34f-4366-ac37-bd90d64a3982",
"reactive": true
},
"output_type": "execute_result"
}
],
"source": [
"u0=Fun(x->2exp(-x^2),[-10.,10.])\n",
"d=domain(u0);D=Derivative(d)\n",
"g(y)=-6.y*y'\n",
"x=Input(u0);lift(y->ApproxFun.plot(y;axis=[-1.,3.]),x)"
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"u0=BDF2([neumann(d);rdirichlet(d)],-D^3,g,zeros(3),u0,0.004,300,x,10E-7);"
]
}
],
"metadata": {
"kernelspec": {
"display_name": "Julia 0.4.0",
"language": "julia",
"name": "julia-0.4"
},
"language_info": {
"file_extension": ".jl",
"mimetype": "application/julia",
"name": "julia",
"version": "0.4.0"
}
},
"nbformat": 4,
"nbformat_minor": 0
}
| bsd-3-clause |
pligor/predicting-future-product-prices | 04_time_series_prediction/.ipynb_checkpoints/30_price_history_dataset_per_mobile_phone-arima-checkpoint.ipynb | 1 | 142958 | {
"cells": [
{
"cell_type": "code",
"execution_count": 1,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"# -*- coding: UTF-8 -*-\n",
"#%load_ext autoreload\n",
"%reload_ext autoreload\n",
"%autoreload 2"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"/home/studenthp/anaconda2/envs/dis/lib/python2.7/site-packages/statsmodels/compat/pandas.py:56: FutureWarning: The pandas.core.datetools module is deprecated and will be removed in a future version. Please use the pandas.tseries module instead.\n",
" from pandas.core import datetools\n"
]
}
],
"source": [
"from __future__ import division\n",
"import tensorflow as tf\n",
"from os import path, remove\n",
"import numpy as np\n",
"import pandas as pd\n",
"import csv\n",
"from sklearn.model_selection import StratifiedShuffleSplit\n",
"from time import time\n",
"from matplotlib import pyplot as plt\n",
"import seaborn as sns\n",
"from mylibs.jupyter_notebook_helper import show_graph, renderStatsList, renderStatsCollection, \\\n",
" renderStatsListWithLabels, renderStatsCollectionOfCrossValids\n",
"from tensorflow.contrib import rnn\n",
"from tensorflow.contrib import learn\n",
"import shutil\n",
"from tensorflow.contrib.learn.python.learn import learn_runner\n",
"from mylibs.tf_helper import getDefaultGPUconfig\n",
"from sklearn.metrics import r2_score\n",
"from mylibs.py_helper import factors\n",
"from fastdtw import fastdtw\n",
"from collections import OrderedDict\n",
"from scipy.spatial.distance import euclidean\n",
"from statsmodels.tsa.stattools import coint\n",
"from common import get_or_run_nn\n",
"from data_providers.price_history_seq2seq_data_provider import PriceHistorySeq2SeqDataProvider\n",
"from skopt.space.space import Integer, Real\n",
"from skopt import gp_minimize\n",
"from skopt.plots import plot_convergence\n",
"import pickle\n",
"import inspect\n",
"import dill\n",
"import sys\n",
"#from models.price_history_21_seq2seq_dyn_dec_ins import PriceHistorySeq2SeqDynDecIns\n",
"from data_providers.PriceHistoryMobileAttrsCombinator import PriceHistoryMobileAttrsCombinator\n",
"from sklearn.neighbors import NearestNeighbors\n",
"from datetime import datetime\n",
"from data_providers.price_hist_with_relevant_deals import PriceHistWithRelevantDeals\n",
"from data_providers.price_history_29_dataset_per_mobile_phone import PriceHistoryDatasetPerMobilePhone\n",
"from arima.arima_estimator import ArimaEstimator\n",
"import warnings\n",
"from collections import OrderedDict\n",
"from mylibs.py_helper import cartesian_coord\n",
"from arima.arima_cv import ArimaCV"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"dtype = tf.float32\n",
"seed = 16011984\n",
"random_state = np.random.RandomState(seed=seed)\n",
"config = getDefaultGPUconfig()\n",
"n_jobs = 1\n",
"%matplotlib inline"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Step 0 - hyperparams"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"vocab_size is all the potential words you could have (classification for translation case)\n",
"and max sequence length are the SAME thing\n",
"\n",
"decoder RNN hidden units are usually same size as encoder RNN hidden units in translation but for our case it does not seem really to be a relationship there but we can experiment and find out later, not a priority thing right now"
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"input_len = 60\n",
"target_len = 30\n",
"batch_size = 50\n",
"with_EOS = False"
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"csv_in = '../price_history_03_seq_start_suddens_trimmed.csv'"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Actual Run"
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"data_path = '../../../../Dropbox/data'\n",
"ph_data_path = data_path + '/price_history'\n",
"assert path.isdir(ph_data_path)"
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"npz_full = ph_data_path + '/price_history_per_mobile_phone.npz'"
]
},
{
"cell_type": "code",
"execution_count": 8,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"#dataset_gen = PriceHistoryDatasetPerMobilePhone(random_state=random_state)"
]
},
{
"cell_type": "code",
"execution_count": 9,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"['9820435',\n",
" '8332719',\n",
" '7357394',\n",
" '9351583',\n",
" '7655259',\n",
" '6253594',\n",
" '8138004',\n",
" '10576161',\n",
" '7408246',\n",
" '7967487']"
]
},
"execution_count": 9,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"dic = np.load(npz_full)\n",
"dic.keys()[:10]"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Arima"
]
},
{
"cell_type": "code",
"execution_count": 10,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"parameters = OrderedDict([\n",
" ('p_auto_regression_order', range(6)), #0-5\n",
" ('d_integration_level', range(3)), #0-2\n",
" ('q_moving_average', range(6)), #0-5\n",
"])"
]
},
{
"cell_type": "code",
"execution_count": 11,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"(108, 3)"
]
},
"execution_count": 11,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"cart = cartesian_coord(*parameters.values())\n",
"cart.shape"
]
},
{
"cell_type": "code",
"execution_count": 12,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"'9820435'"
]
},
"execution_count": 12,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"cur_key = dic.keys()[0]\n",
"cur_key"
]
},
{
"cell_type": "code",
"execution_count": 13,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"['test', 'train', 'train_dates']"
]
},
"execution_count": 13,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"cur_sku = dic[cur_key][()]\n",
"cur_sku.keys()"
]
},
{
"cell_type": "code",
"execution_count": 14,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"(31, 90)"
]
},
"execution_count": 14,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"train_mat = cur_sku['train']\n",
"train_mat.shape"
]
},
{
"cell_type": "code",
"execution_count": 15,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"30"
]
},
"execution_count": 15,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"target_len"
]
},
{
"cell_type": "code",
"execution_count": 16,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"(31, 60)"
]
},
"execution_count": 16,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"inputs = train_mat[:, :-target_len]\n",
"inputs.shape"
]
},
{
"cell_type": "code",
"execution_count": 17,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"(31, 30)"
]
},
"execution_count": 17,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"targets = train_mat[:, -target_len:]\n",
"targets.shape"
]
},
{
"cell_type": "code",
"execution_count": 18,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"easy_mode = False"
]
},
{
"cell_type": "code",
"execution_count": 19,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"'/home/studenthp/Dropbox/data/arima/scoredic_easy_mode_False_9820435.npy'"
]
},
"execution_count": 19,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"score_dic_filepath = data_path + \"/arima/scoredic_easy_mode_{}_{}.npy\".format(easy_mode, cur_key)\n",
"path.abspath(score_dic_filepath)"
]
},
{
"cell_type": "code",
"execution_count": 20,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"CPU times: user 4 ms, sys: 0 ns, total: 4 ms\n",
"Wall time: 2.79 ms\n"
]
}
],
"source": [
"%%time\n",
"with warnings.catch_warnings():\n",
" warnings.filterwarnings(\"ignore\")\n",
" scoredic = ArimaCV.cross_validate(inputs=inputs, targets=targets, cartesian_combinations=cart,\n",
" score_dic_filepath=score_dic_filepath, easy_mode=easy_mode)"
]
},
{
"cell_type": "code",
"execution_count": 21,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"#4h 4min 51s / 108 cases => ~= 136 seconds per case !"
]
},
{
"cell_type": "code",
"execution_count": 22,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"(108, 2)"
]
},
"execution_count": 22,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"arr = np.array(list(scoredic.iteritems()))\n",
"arr.shape"
]
},
{
"cell_type": "code",
"execution_count": 23,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"(102, 2)"
]
},
"execution_count": 23,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"#np.isnan()\n",
"filtered_arr = arr[ np.logical_not(arr[:, 1] != arr[:, 1]) ]\n",
"filtered_arr.shape"
]
},
{
"cell_type": "code",
"execution_count": 24,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"[<matplotlib.lines.Line2D at 0x7fe2d219e110>]"
]
},
"execution_count": 24,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"image/png": "iVBORw0KGgoAAAANSUhEUgAAAXUAAAD4CAYAAAATpHZ6AAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x7fe2d229ed90>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"plt.plot(filtered_arr[:, 1])"
]
},
{
"cell_type": "code",
"execution_count": 25,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"62"
]
},
"execution_count": 25,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"minarg = np.argmin(filtered_arr[:, 1])\n",
"minarg"
]
},
{
"cell_type": "code",
"execution_count": 26,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"(4, 1, 3)"
]
},
"execution_count": 26,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"best_params = filtered_arr[minarg, 0]\n",
"best_params"
]
},
{
"cell_type": "code",
"execution_count": 27,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"(60,)"
]
},
"execution_count": 27,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"test_mat = cur_sku['test']\n",
"test_ins = test_mat[:-target_len]\n",
"test_ins.shape"
]
},
{
"cell_type": "code",
"execution_count": 28,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"(30,)"
]
},
"execution_count": 28,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"test_tars = test_mat[-target_len:]\n",
"test_tars.shape"
]
},
{
"cell_type": "code",
"execution_count": 29,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"(1, 60)"
]
},
"execution_count": 29,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"test_ins_vals = test_ins.values.reshape(1, -1)\n",
"test_ins_vals.shape"
]
},
{
"cell_type": "code",
"execution_count": 30,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"(1, 30)"
]
},
"execution_count": 30,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"test_tars_vals = test_tars.values.reshape(1, -1)\n",
"test_tars_vals.shape"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Testing with easy mode on"
]
},
{
"cell_type": "code",
"execution_count": 31,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"CPU times: user 6.86 s, sys: 7.2 s, total: 14.1 s\n",
"Wall time: 6.02 s\n"
]
}
],
"source": [
"%%time\n",
"with warnings.catch_warnings():\n",
" warnings.filterwarnings(\"ignore\")\n",
" ae = ArimaEstimator(p_auto_regression_order=best_params[0],\n",
" d_integration_level=best_params[1],\n",
" q_moving_average=best_params[2],\n",
" easy_mode=True)\n",
" score = ae.fit(test_ins_vals, test_tars_vals).score(test_ins_vals, test_tars_vals)"
]
},
{
"cell_type": "code",
"execution_count": 32,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"6.567110444670174"
]
},
"execution_count": 32,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"score"
]
},
{
"cell_type": "code",
"execution_count": 33,
"metadata": {},
"outputs": [
{
"data": {
"image/png": "iVBORw0KGgoAAAANSUhEUgAAA3MAAAGbCAYAAABuwcm8AAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nrkJggg==\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x7fe2d229eed0>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"plt.figure(figsize=(15,7))\n",
"plt.plot(ae.preds.flatten(), label='preds')\n",
"test_tars.plot(label='real')\n",
"plt.legend()\n",
"plt.show()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Testing with easy mode off"
]
},
{
"cell_type": "code",
"execution_count": 34,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"CPU times: user 6.75 s, sys: 7.15 s, total: 13.9 s\n",
"Wall time: 5.81 s\n"
]
}
],
"source": [
"%%time\n",
"with warnings.catch_warnings():\n",
" warnings.filterwarnings(\"ignore\")\n",
" ae = ArimaEstimator(p_auto_regression_order=best_params[0],\n",
" d_integration_level=best_params[1],\n",
" q_moving_average=best_params[2],\n",
" easy_mode=False)\n",
" score = ae.fit(test_ins_vals, test_tars_vals).score(test_ins_vals, test_tars_vals)"
]
},
{
"cell_type": "code",
"execution_count": 35,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"72.308500086941933"
]
},
"execution_count": 35,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"score"
]
},
{
"cell_type": "code",
"execution_count": 36,
"metadata": {},
"outputs": [
{
"data": {
"image/png": "iVBORw0KGgoAAAANSUhEUgAAA20AAAGbCAYAAABXpnjnAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x7fe2d2241790>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"plt.figure(figsize=(15,7))\n",
"plt.plot(ae.preds.flatten(), label='preds')\n",
"test_tars.plot(label='real')\n",
"plt.legend()\n",
"plt.show()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"#### Conclusion\n",
"If you are training in easy mode then what you get at the end is that the model only cares for the previous value in order to do its predictions and this makes it much easier for everybody but in reality we might not have advantage"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Trying"
]
},
{
"cell_type": "code",
"execution_count": 37,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"array([ 62, 63, 69, 5, 39, 41, 67, 40, 20, 26, 23, 4, 17,\n",
" 3, 16, 2, 14, 27, 8, 29, 38, 22, 11, 37, 54, 10,\n",
" 21, 42, 48, 9, 36, 61, 59, 28, 34, 68, 6, 12, 7,\n",
" 56, 18, 13, 65, 24, 1, 60, 55, 19, 25, 15, 0, 30,\n",
" 66, 43, 31, 32, 35, 58, 46, 53, 47, 33, 64, 45, 49,\n",
" 50, 52, 57, 44, 51, 86, 78, 94, 87, 85, 88, 80, 101,\n",
" 95, 77, 100, 74, 93, 83, 98, 75, 71, 81, 99, 92, 97,\n",
" 90, 84, 72, 91, 96, 79, 73, 70, 89, 82, 76])"
]
},
"execution_count": 37,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"args = np.argsort(filtered_arr[:, 1])\n",
"args"
]
},
{
"cell_type": "code",
"execution_count": 38,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"array([(4, 1, 3), (4, 1, 4), (5, 1, 4), (0, 0, 5), (0, 1, 3), (0, 1, 5),\n",
" (5, 1, 2), (0, 1, 4), (3, 0, 2), (4, 0, 2)], dtype=object)"
]
},
"execution_count": 38,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"filtered_arr[args[:10], 0]"
]
},
{
"cell_type": "code",
"execution_count": 39,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"72.3085000869\n"
]
},
{
"data": {
"image/png": "iVBORw0KGgoAAAANSUhEUgAAA20AAAGbCAYAAABXpnjnAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x7fe2ce8e3dd0>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"CPU times: user 6.85 s, sys: 7.51 s, total: 14.4 s\n",
"Wall time: 5.87 s\n"
]
}
],
"source": [
"%%time\n",
"with warnings.catch_warnings():\n",
" warnings.filterwarnings(\"ignore\")\n",
" ae = ArimaEstimator(p_auto_regression_order=4,\n",
" d_integration_level=1,\n",
" q_moving_average=3,\n",
" easy_mode=False)\n",
" print ae.fit(test_ins_vals, test_tars_vals).score(test_ins_vals, test_tars_vals)\n",
"\n",
"plt.figure(figsize=(15,7))\n",
"plt.plot(ae.preds.flatten(), label='preds')\n",
"test_tars.plot(label='real')\n",
"plt.legend()\n",
"plt.show()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# All tests"
]
},
{
"cell_type": "code",
"execution_count": 44,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"from arima.arima_testing import ArimaTesting"
]
},
{
"cell_type": "code",
"execution_count": 49,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"((4, 1, 3),\n",
" 30,\n",
" '../../../../Dropbox/data/price_history/price_history_per_mobile_phone.npz')"
]
},
"execution_count": 49,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"best_params, target_len, npz_full"
]
},
{
"cell_type": "code",
"execution_count": 53,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"0 , 9820435\n",
"1 , 8332719\n",
"2 , 7357394\n",
"3 , 9351583\n",
"4 , 7655259\n",
"5 , 6253594\n",
"6 , 8138004\n",
"7 , 10576161\n",
"8 , 7408246\n",
"9 , 7967487\n",
"10 , 9130370\n",
"11 , 8779166\n",
"12 , 7653378\n",
"13 , 10536998\n",
"14 , 8669008\n",
"15 , 9896674\n",
"16 , 9941958\n",
"17 , 7780575\n",
"18 , 10499793\n",
"19 , 9259167\n",
"20 , 9409718\n",
"21 , 9986194\n",
"22 , 9898913\n",
"23 , 10129132\n",
"24 , 9055991\n",
"25 , 8515393\n",
"26 , 7364333\n",
"27 , 8176770\n",
"28 , 7507905\n",
"29 , 3656048\n",
"30 , 10112367\n",
"31 , 8695009\n",
"32 , 8735993\n",
"33 , 10242128\n",
"34 , 8414311\n",
"35 , 9547257\n",
"36 , 7508833\n",
"37 , 9426447\n",
"38 , 6261140\n",
"39 , 7294741\n",
"40 , 9674179\n",
"41 , 9064268\n",
"42 , 9672674\n",
"43 , 10409332\n",
"44 , 7697920\n",
"45 , 10020902\n",
"46 , 10002261\n",
"47 , 6870822\n",
"48 , 7992726\n",
"49 , 10620877\n",
"50 , 7620866\n",
"51 , 9301596\n",
"52 , 9981614\n",
"53 , 8087014\n",
"54 , 9941623\n",
"55 , 5308163\n",
"56 , 9469401\n",
"57 , 8938578\n",
"58 , 10046764\n",
"59 , 10468270\n",
"60 , 9035623\n",
"61 , 9558425\n",
"62 , 10340158\n",
"63 , 7514433\n",
"64 , 9758209\n",
"65 , 9757914\n",
"66 , 9028851\n",
"67 , 10339856\n",
"68 , 7509017\n",
"69 , 6989466\n",
"70 , 6999080\n",
"71 , 10340038\n",
"72 , 10242193\n",
"73 , 9306016\n",
"74 , 7401406\n",
"75 , 8379645\n",
"76 , 8379646\n",
"77 , 9783218\n",
"78 , 9783213\n",
"79 , 9956199\n",
"80 , 9268867\n",
"81 , 9783217\n",
"82 , 9783216\n",
"83 , 9783215\n",
"84 , 6998933\n",
"85 , 7634031\n",
"86 , 8999919\n",
"87 , 6756290\n",
"88 , 6536691\n",
"89 , 7448041\n",
"90 , 9017595\n",
"91 , 8864711\n",
"92 , 10550085\n",
"93 , 7351792\n",
"94 , 8438203\n",
"95 , 10430833\n",
"96 , 7504732\n",
"97 , 10620558\n",
"98 , 10456126\n",
"99 , 8130418\n",
"100 , 9549977\n",
"101 , 9562308\n",
"102 , 10430368\n",
"103 , 7811257\n",
"104 , 8820025\n",
"105 , 6808160\n",
"106 , 9730380\n",
"107 , 8095786\n",
"108 , 8770390\n",
"109 , 10513102\n",
"110 , 8340504\n",
"111 , 8797638\n",
"112 , 8873832\n",
"113 , 9272109\n",
"114 , 8909629\n",
"115 , 6044652\n",
"116 , 7723431\n",
"117 , 9287995\n",
"118 , 9395784\n",
"119 , 9561760\n",
"120 , 9098838\n",
"121 , 10598138\n",
"122 , 7129068\n",
"123 , 9333571\n",
"124 , 7321695\n",
"125 , 8972925\n",
"126 , 10117891\n",
"127 , 9454972\n",
"128 , 10620346\n",
"129 , 10251789\n",
"130 , 8684161\n",
"131 , 10263538\n",
"132 , 10327727\n",
"133 , 9669402\n",
"134 , 7541851\n",
"135 , 8783760\n",
"136 , 10021828\n",
"137 , 7946058\n",
"138 , 10646927\n",
"139 , 8735524\n",
"140 , 9039085\n",
"141 , 10372691\n",
"142 , 3136502\n",
"143 , 9536994\n",
"144 , 7599025\n",
"145 , 7621172\n",
"146 , 6317061\n",
"147 , 9338643\n",
"148 , 10529304\n",
"149 , 7621093\n",
"150 , 8414880\n",
"151 , 7335154\n",
"152 , 8515183\n",
"153 , 9192579\n",
"154 , 10537404\n",
"155 , 10019886\n",
"156 , 9524580\n",
"157 , 9383253\n",
"158 , 8628040\n",
"159 , 7282995\n",
"160 , 9473245\n",
"161 , 8082430\n",
"162 , 6487622\n",
"163 , 10000553\n",
"164 , 10455691\n",
"165 , 7957675\n",
"166 , 9555755\n",
"167 , 9654138\n",
"168 , 8136245\n",
"169 , 8116861\n",
"170 , 10000323\n",
"171 , 8791213\n",
"172 , 8909064\n",
"173 , 9524579\n",
"174 , 9524578\n",
"175 , 8239538\n",
"176 , 9759446\n",
"177 , 9815040\n",
"178 , 10620599\n",
"179 , 10339621\n",
"180 , 8153953\n",
"181 , 7753482\n",
"182 , 8130646\n",
"183 , 9845360\n",
"184 , 9034797\n",
"185 , 9374090\n",
"186 , 10327645\n",
"187 , 9889976\n",
"188 , 9633962\n",
"189 , 10603019\n",
"190 , 8913842\n",
"191 , 9618789\n",
"192 , 8693332\n",
"193 , 10093039\n",
"194 , 8784094\n",
"195 , 9685830\n",
"196 , 10446143\n",
"197 , 8435610\n",
"198 , 5983545\n",
"199 , 6918686\n",
"200 , 8317727\n",
"201 , 9624747\n",
"202 , 9216668\n",
"203 , 10106328\n",
"204 , 9844865\n",
"205 , 10250011\n",
"206 , 4763156\n",
"207 , 10455448\n",
"208 , 7333160\n",
"209 , 10351727\n",
"210 , 10550505\n",
"211 , 7988857\n",
"212 , 7050290\n",
"213 , 10512952\n",
"214 , 8864706\n",
"215 , 8934572\n",
"216 , 9232277\n",
"217 , 8286967\n",
"218 , 9658075\n",
"219 , 9416983\n",
"220 , 9468414\n",
"221 , 5535970\n",
"222 , 9517024\n",
"223 , 8742945\n",
"224 , 10081282\n",
"225 , 7219275\n",
"226 , 10327862\n",
"227 , 9875453\n",
"228 , 6696391\n",
"229 , 8597734\n",
"230 , 8842715\n",
"231 , 9668098\n",
"232 , 7751495\n",
"233 , 9259567\n",
"234 , 9668091\n",
"235 , 7288733\n",
"236 , 5450546\n",
"237 , 8863487\n",
"238 , 9132174\n",
"239 , 10550986\n",
"240 , 9615391\n",
"241 , 9925594\n",
"242 , 7562927\n",
"243 , 7257310\n",
"244 , 10455301\n",
"245 , 10549703\n",
"246 , 9192081\n",
"247 , 9535232\n",
"248 , 7310752\n",
"249 , 6744233\n",
"250 , 9898380\n",
"251 , 6733376\n",
"252 , 9195103\n",
"253 , 10351487\n",
"254 , 9535485\n",
"255 , 9410037\n",
"256 , 7180618\n",
"257 , 10263525\n",
"258 , 9304030\n",
"259 , 9995201\n",
"260 , 10445742\n",
"261 , 9615498\n",
"262 , 9615369\n",
"263 , 8444503\n",
"264 , 10296471\n",
"265 , 8349172\n",
"266 , 8284007\n",
"267 , 10513434\n",
"268 , 10445832\n",
"269 , 7264929\n",
"270 , 6957383\n",
"271 , 6724273\n",
"272 , 9561798\n",
"273 , 7321837\n",
"274 , 7630281\n",
"275 , 7945834\n",
"276 , 9445080\n",
"277 , 8384618\n",
"278 , 9468830\n",
"279 , 10597333\n",
"280 , 10569851\n",
"281 , 8984512\n",
"282 , 8333136\n",
"283 , 10430269\n",
"284 , 9724038\n",
"285 , 10297324\n",
"286 , 8515689\n",
"287 , 7487368\n",
"288 , 10631640\n",
"289 , 7423345\n",
"290 , 6808527\n",
"291 , 7904356\n",
"292 , 10020336\n",
"293 , 10551073\n",
"294 , 9188134\n",
"295 , 8913485\n",
"296 , 8437937\n",
"297 , 8153619\n",
"298 , 9001596\n",
"299 , 9608073\n",
"300 , 8874019\n",
"301 , 8294272\n",
"302 , 10468510\n",
"303 , 7259273\n",
"304 , 9542492\n",
"305 , 8348233\n",
"306 , 6666214\n",
"307 , 9402788\n",
"308 , 7314178\n",
"309 , 8460398\n",
"310 , 10199444\n",
"311 , 7344391\n",
"312 , 6871862\n",
"313 , 6601918\n",
"314 , 8720352\n",
"315 , 10328199\n",
"316 , 9189255\n",
"317 , 9970245\n",
"318 , 6871261\n",
"319 , 9043554\n",
"320 , 9198786\n",
"321 , 10057571\n",
"322 , 9489172\n",
"323 , 9192250\n",
"324 , 8880414\n",
"325 , 7750303\n",
"326 , 10643104\n",
"327 , 7505653\n",
"328 , 8281639\n",
"329 , 10617623\n",
"330 , 7750919\n",
"331 , 9668397\n",
"332 , 9664652\n",
"333 , 8107566\n",
"334 , 9329087\n",
"335 , 10350760\n",
"336 , 8864604\n",
"337 , 3783654\n",
"338 , 9520852\n",
"339 , 8864161\n",
"340 , 7335955\n",
"341 , 9501836\n",
"342 , 10000278\n",
"343 , 7356761\n",
"344 , 9595109\n",
"345 , 10282598\n",
"346 , 10619619\n",
"347 , 9360800\n",
"348 , 9669553\n",
"349 , 8244331\n",
"350 , 9409925\n",
"351 , 8617989\n",
"352 , 9824249\n",
"353 , 9332994\n",
"354 , 10644470\n",
"355 , 8645920\n",
"356 , 8758228\n",
"357 , 10619076\n",
"358 , 10315304\n",
"359 , 8874945\n",
"360 , 8046417\n",
"361 , 8648639\n",
"362 , 8221632\n",
"363 , 8221631\n",
"364 , 9079935\n",
"365 , 9107905\n",
"366 , 5898447\n",
"367 , 9542574\n",
"368 , 9898535\n",
"369 , 9042908\n",
"370 , 6592480\n",
"371 , 8311334\n",
"372 , 8145112\n",
"373 , 10315447\n",
"374 , 9815037\n",
"375 , 9352876\n",
"376 , 10373479\n",
"377 , 10603203\n",
"378 , 9815039\n",
"379 , 8435811\n",
"380 , 8880028\n",
"381 , 9655436\n",
"382 , 9500739\n",
"383 , 9550139\n",
"384 , 6264653\n",
"385 , 8864337\n",
"386 , 8256116\n",
"387 , 9572084\n",
"388 , 6798407\n",
"389 , 7311062\n",
"390 , 10084353\n",
"391 , 10001441\n",
"392 , 7945421\n",
"393 , 9445259\n",
"394 , 10603460\n",
"395 , 8669043\n",
"396 , 9757586\n",
"397 , 5804541\n",
"398 , 8995944\n",
"399 , 5558359\n",
"400 , 10529171\n",
"401 , 8395434\n",
"402 , 9561935\n",
"403 , 7360931\n",
"404 , 10036784\n",
"405 , 9969844\n",
"406 , 9877558\n",
"407 , 6677566\n",
"408 , 8012281\n",
"409 , 8436601\n",
"410 , 9672740\n",
"411 , 10085889\n",
"412 , 10252434\n",
"413 , 10019997\n",
"414 , 8436609\n",
"415 , 10283639\n",
"416 , 9981788\n",
"417 , 9412950\n",
"418 , 7426369\n",
"419 , 6317107\n",
"420 , 8873827\n",
"421 , 8874195\n",
"422 , 8988217\n",
"423 , 8490920\n",
"424 , 9419455\n",
"425 , 9105758\n",
"426 , 6487975\n",
"427 , 9177791\n",
"428 , 10315407\n",
"429 , 6933062\n",
"430 , 9173758\n",
"431 , 7868248\n",
"432 , 10065912\n",
"433 , 9517102\n",
"434 , 7860520\n",
"435 , 6260915\n",
"436 , 8985553\n",
"CPU times: user 34min 12s, sys: 44min 9s, total: 1h 18min 22s\n",
"Wall time: 28min 34s\n"
]
}
],
"source": [
"%%time\n",
"keys, scores, preds = ArimaTesting.full_testing(best_params=best_params, target_len=target_len,\n",
" npz_full=npz_full)"
]
},
{
"cell_type": "code",
"execution_count": 52,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"# render graphs here"
]
},
{
"cell_type": "code",
"execution_count": 56,
"metadata": {},
"outputs": [],
"source": [
"score_arr = np.array(scores)"
]
},
{
"cell_type": "code",
"execution_count": 59,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"21.673050084413859"
]
},
"execution_count": 59,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"np.mean(score_arr[np.logical_not(score_arr != score_arr)])"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": []
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 2",
"language": "python",
"name": "python2"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 2
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython2",
"version": "2.7.13"
}
},
"nbformat": 4,
"nbformat_minor": 2
}
| agpl-3.0 |
cavestruz/StrongCNN | notebooks/Bootstrap_Analysis.ipynb | 1 | 27548 | {
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"(1) average the scores between the _0,_1,_2,_3 directions to get average score per image in each HOG configuration. \n",
"\n",
"(2) In each HOG configuration, calculate the Precision and Recall values. \n",
"\n",
"(3) \"Bootstrap\" or \"jacknife\" to get an error on the AUC for each HOG configuration, describe how you bootstrapped it in words.\n",
"\n",
"(4) Output should look like: \n",
"\n",
"HOG config | Precision | Recall | AUC | AUCerr"
]
},
{
"cell_type": "code",
"execution_count": 1,
"metadata": {},
"outputs": [],
"source": [
"import glob\n",
"import numpy as np\n",
"import pandas as pd\n",
"from sklearn.metrics import precision_score, recall_score, roc_auc_score"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": []
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {},
"outputs": [],
"source": [
"def get_data(datadir):\n",
" \"\"\"\n",
" Read the data files from different subdirectories of datadir corresponding\n",
" to different HOG configurations.\n",
" \n",
" Inputs\n",
" \n",
" datadir: top level directory in which there are subdirectories corresponding\n",
" to different HOG configurations\n",
" \n",
" Output\n",
" \n",
" data: {hogname: list(pd.DataFrame)} where each key corresponds to a\n",
" different subdirectory (HOG configuration) and the value is\n",
" a list of dataframes read from each of the files in that\n",
" subdirectory\n",
" \"\"\"\n",
" hognames = [s.split('/')[-1] for s in glob.glob(datadir + '/*')]\n",
" return {hogname: [pd.read_csv(filename, sep=None)\n",
" for filename in glob.glob('{}/{}/filenames_*.txt'.format(datadir, hogname))]\n",
" for hogname in hognames}"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {},
"outputs": [],
"source": [
"def get_average_scores(dataframes):\n",
" \"\"\"\n",
" Average the scores from several different rotations.\n",
" \n",
" Inputs\n",
" \n",
" dataframes: list(pd.DataFrame['filename', 'score', 'label'])\n",
" \n",
" Output\n",
" \n",
" df_out: pd.DataFrame['filename', 'score', 'label'] where 'score'\n",
" is the average over all of the input dataframes and\n",
" 'label' is taken arbitrarily from the first input dataframe\n",
" \"\"\"\n",
" dataframes = [df.rename(columns={'score': 'score_{}'.format(idx),\n",
" 'label': 'label_{}'.format(idx)})\n",
" for idx, df in enumerate(dataframes)]\n",
" merged_df = reduce(lambda df1, df2: pd.merge(df1, df2, on='filename'), dataframes)\n",
" assert all(df.shape[0] == merged_df.shape[0] for df in dataframes), \\\n",
" 'Not all keys are the same in the data sets'\n",
" \n",
" merged_df['score'] = sum(merged_df['score_{}'.format(idx)] for idx, _ in enumerate(dataframes))\n",
" merged_df['label'] = merged_df['label_0']\n",
" return merged_df[['filename', 'score', 'label']]"
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {},
"outputs": [],
"source": [
"def bootstrap(df, func, num_samples, sample_size_frac=1):\n",
" \"\"\"\n",
" Returns the bootstrap average and standard deviation when applying\n",
" func to df. It is assumed that applying func to df returns a scalar.\n",
" \n",
" In each iteration, sample_size_frac*N rows are drawn from df at\n",
" random with replacement, where N is the number of rows in df.\n",
" In this way a DataFrame df_sample is created of the same type\n",
" as df, with possible a different number of rows. The calculation\n",
" of interest is done on df_sample by applying func and returning\n",
" a number. This number is collected into an array, and this\n",
" process is repeated for num_samples iterations. Finally, the\n",
" mean and standard deviation of the array of length num_samples\n",
" is returned. The standard deviation is an estimate of the error\n",
" (due to finite sample size) that you would get when applying\n",
" func to the full DataFrame df to get a number.\n",
" \n",
" Inputs\n",
" \n",
" df: pd.DataFrame of any type\n",
" func: function that takes in df and returns a scalar\n",
" num_samples: number of bootstrap samples/iterations,\n",
" see description above\n",
" sample_size_frac: in each bootstrap sample, the number\n",
" of rows sampled is this fraction of\n",
" the actual number of rows in df\n",
" \n",
" Outputs\n",
" \n",
" mean: mean of the bootstrap values. Should be close to\n",
" func(df) if num_samples is large enough.\n",
" std: standard deviation of the bootstrap values. This is\n",
" an estimate of the error (due to finite sample size)\n",
" of func(df).\n",
" \"\"\"\n",
" N = df.shape[0]\n",
" sample_size = int(N*sample_size_frac)\n",
" bootstrap_values = [func(df.iloc[np.random.randint(N, size=sample_size)])\n",
" for _ in range(num_samples)]\n",
" return np.mean(bootstrap_values), np.std(bootstrap_values)"
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {},
"outputs": [],
"source": [
"def main(datadir, num_boot_samples, bands=None):\n",
" \"\"\"\n",
" For each HOG configuration, average scores from different rotations and\n",
" output metrics: precision, recall, AUC, and standard deviation of the AUC\n",
" from the bootstrap analysis. Details of the bootstrap analysis described\n",
" in the bootstrap function.\n",
" \n",
" Inputs\n",
" \n",
" datadir: directory name in which there are subdirectories corresponding\n",
" to different HOG configurations\n",
" num_boot_samples: number of bootstrap samples to create in the bootstrap\n",
" analysis (see bootstrap function)\n",
" bands: list of bands to analyze separately. If None, don't separate out\n",
" bands.\n",
" \n",
" Output\n",
" \n",
" pd.DataFrame['HOG_config', 'Precision', 'Recall', 'AUC',\n",
" 'AUC_boot_avg', 'AUC_boot_std']\n",
" \n",
" OR\n",
" \n",
" pd.DataFrame['HOG_config', 'Band', 'Precision', 'Recall', 'AUC',\n",
" 'AUC_boot_avg', 'AUC_boot_std']\n",
" \"\"\"\n",
" data = get_data(datadir)\n",
" columns = ['HOG_config',\n",
" 'Precision',\n",
" 'Recall',\n",
" 'AUC',\n",
" 'AUC_boot_avg',\n",
" 'AUC_boot_std']\n",
" if bands is not None:\n",
" columns = columns[:1] + ['Band'] + columns[1:]\n",
" output = {k: [] for k in columns}\n",
"\n",
" for hogname, dataframes in data.iteritems():\n",
" scores_all_bands = get_average_scores(dataframes)\n",
" if bands is not None:\n",
" scores_all_bands['band'] = scores_all_bands['filename'].apply(lambda s: s.split('_')[2])\n",
" # filter filenames further here if needed\n",
" for band in (bands if bands is not None else ['']):\n",
" if bands is not None:\n",
" scores = scores_all_bands[scores_all_bands['band'] == band]\n",
" output['Band'].append(band)\n",
" else:\n",
" scores = scores_all_bands\n",
" output['HOG_config'].append(hogname)\n",
" output['Precision'].append(precision_score(scores['label'], scores['score'] > 0.5))\n",
" output['Recall'].append(recall_score(scores['label'], scores['score'] > 0.5))\n",
" output['AUC'].append(roc_auc_score(scores['label'], scores['score']))\n",
" boot_avg, boot_std = bootstrap(scores, lambda sc: roc_auc_score(sc['label'], sc['score']),\n",
" num_boot_samples)\n",
" output['AUC_boot_avg'].append(boot_avg)\n",
" output['AUC_boot_std'].append(boot_std)\n",
" \n",
" return pd.DataFrame(output)[columns]"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": []
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Test on Mock"
]
},
{
"cell_type": "code",
"execution_count": 182,
"metadata": {},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"/Users/f566998/anaconda/lib/python2.7/site-packages/ipykernel_launcher.py:5: ParserWarning: Falling back to the 'python' engine because the 'c' engine does not support sep=None with delim_whitespace=False; you can avoid this warning by specifying engine='python'.\n",
" \"\"\"\n"
]
},
{
"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>HOG_config</th>\n",
" <th>Precision</th>\n",
" <th>Recall</th>\n",
" <th>AUC</th>\n",
" <th>AUC_boot_avg</th>\n",
" <th>AUC_boot_std</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>ppc16cpb1</td>\n",
" <td>0.580585</td>\n",
" <td>0.933</td>\n",
" <td>0.837152</td>\n",
" <td>0.837301</td>\n",
" <td>0.008851</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>ppc16cpb3</td>\n",
" <td>0.698332</td>\n",
" <td>0.963</td>\n",
" <td>0.947882</td>\n",
" <td>0.947874</td>\n",
" <td>0.004986</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>ppc10cpb3</td>\n",
" <td>0.729282</td>\n",
" <td>0.924</td>\n",
" <td>0.925003</td>\n",
" <td>0.925028</td>\n",
" <td>0.005971</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>ppc6cpb3</td>\n",
" <td>0.713396</td>\n",
" <td>0.916</td>\n",
" <td>0.901193</td>\n",
" <td>0.901253</td>\n",
" <td>0.006918</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>ppc8cpb4</td>\n",
" <td>0.731240</td>\n",
" <td>0.955</td>\n",
" <td>0.942853</td>\n",
" <td>0.942810</td>\n",
" <td>0.005231</td>\n",
" </tr>\n",
" <tr>\n",
" <th>5</th>\n",
" <td>ppc12cpb3</td>\n",
" <td>0.723715</td>\n",
" <td>0.943</td>\n",
" <td>0.932294</td>\n",
" <td>0.932276</td>\n",
" <td>0.005629</td>\n",
" </tr>\n",
" <tr>\n",
" <th>6</th>\n",
" <td>ppc8cpb2</td>\n",
" <td>0.670968</td>\n",
" <td>0.936</td>\n",
" <td>0.907659</td>\n",
" <td>0.907594</td>\n",
" <td>0.006544</td>\n",
" </tr>\n",
" <tr>\n",
" <th>7</th>\n",
" <td>ppc8cpb3</td>\n",
" <td>0.711940</td>\n",
" <td>0.954</td>\n",
" <td>0.932361</td>\n",
" <td>0.932293</td>\n",
" <td>0.005674</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" HOG_config Precision Recall AUC AUC_boot_avg AUC_boot_std\n",
"0 ppc16cpb1 0.580585 0.933 0.837152 0.837301 0.008851\n",
"1 ppc16cpb3 0.698332 0.963 0.947882 0.947874 0.004986\n",
"2 ppc10cpb3 0.729282 0.924 0.925003 0.925028 0.005971\n",
"3 ppc6cpb3 0.713396 0.916 0.901193 0.901253 0.006918\n",
"4 ppc8cpb4 0.731240 0.955 0.942853 0.942810 0.005231\n",
"5 ppc12cpb3 0.723715 0.943 0.932294 0.932276 0.005629\n",
"6 ppc8cpb2 0.670968 0.936 0.907659 0.907594 0.006544\n",
"7 ppc8cpb3 0.711940 0.954 0.932361 0.932293 0.005674"
]
},
"execution_count": 182,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"main('/path/to/data/directory', 10000)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Test on SLACS"
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"/Users/f566998/anaconda/lib/python2.7/site-packages/ipykernel_launcher.py:21: ParserWarning: Falling back to the 'python' engine because the 'c' engine does not support sep=None with delim_whitespace=False; you can avoid this warning by specifying engine='python'.\n"
]
},
{
"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>HOG_config</th>\n",
" <th>Precision</th>\n",
" <th>Recall</th>\n",
" <th>AUC</th>\n",
" <th>AUC_boot_avg</th>\n",
" <th>AUC_boot_std</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>ppc16cpb1</td>\n",
" <td>0.781818</td>\n",
" <td>0.710744</td>\n",
" <td>0.508855</td>\n",
" <td>0.509372</td>\n",
" <td>0.055635</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>ppc16cpb3</td>\n",
" <td>0.797872</td>\n",
" <td>0.619835</td>\n",
" <td>0.579693</td>\n",
" <td>0.579594</td>\n",
" <td>0.051548</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>ppc10cpb3</td>\n",
" <td>0.787234</td>\n",
" <td>0.611570</td>\n",
" <td>0.565998</td>\n",
" <td>0.566449</td>\n",
" <td>0.052739</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>ppc6cpb3</td>\n",
" <td>0.740741</td>\n",
" <td>0.495868</td>\n",
" <td>0.469658</td>\n",
" <td>0.469583</td>\n",
" <td>0.052430</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>ppc8cpb4</td>\n",
" <td>0.795181</td>\n",
" <td>0.545455</td>\n",
" <td>0.573554</td>\n",
" <td>0.574829</td>\n",
" <td>0.051678</td>\n",
" </tr>\n",
" <tr>\n",
" <th>5</th>\n",
" <td>ppc12cpb3</td>\n",
" <td>0.817073</td>\n",
" <td>0.553719</td>\n",
" <td>0.611806</td>\n",
" <td>0.611472</td>\n",
" <td>0.051830</td>\n",
" </tr>\n",
" <tr>\n",
" <th>6</th>\n",
" <td>ppc8cpb2</td>\n",
" <td>0.780952</td>\n",
" <td>0.677686</td>\n",
" <td>0.540260</td>\n",
" <td>0.540837</td>\n",
" <td>0.053210</td>\n",
" </tr>\n",
" <tr>\n",
" <th>7</th>\n",
" <td>ppc8cpb3</td>\n",
" <td>0.776596</td>\n",
" <td>0.603306</td>\n",
" <td>0.566234</td>\n",
" <td>0.566288</td>\n",
" <td>0.052566</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" HOG_config Precision Recall AUC AUC_boot_avg AUC_boot_std\n",
"0 ppc16cpb1 0.781818 0.710744 0.508855 0.509372 0.055635\n",
"1 ppc16cpb3 0.797872 0.619835 0.579693 0.579594 0.051548\n",
"2 ppc10cpb3 0.787234 0.611570 0.565998 0.566449 0.052739\n",
"3 ppc6cpb3 0.740741 0.495868 0.469658 0.469583 0.052430\n",
"4 ppc8cpb4 0.795181 0.545455 0.573554 0.574829 0.051678\n",
"5 ppc12cpb3 0.817073 0.553719 0.611806 0.611472 0.051830\n",
"6 ppc8cpb2 0.780952 0.677686 0.540260 0.540837 0.053210\n",
"7 ppc8cpb3 0.776596 0.603306 0.566234 0.566288 0.052566"
]
},
"execution_count": 6,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"main('/path/to/data/directory', 10000)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Test on SLACS separating out different bands"
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"/Users/f566998/anaconda/lib/python2.7/site-packages/ipykernel_launcher.py:21: ParserWarning: Falling back to the 'python' engine because the 'c' engine does not support sep=None with delim_whitespace=False; you can avoid this warning by specifying engine='python'.\n"
]
},
{
"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>HOG_config</th>\n",
" <th>Band</th>\n",
" <th>Precision</th>\n",
" <th>Recall</th>\n",
" <th>AUC</th>\n",
" <th>AUC_boot_avg</th>\n",
" <th>AUC_boot_std</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>ppc16cpb1</td>\n",
" <td>435</td>\n",
" <td>0.666667</td>\n",
" <td>0.500000</td>\n",
" <td>0.474359</td>\n",
" <td>0.475056</td>\n",
" <td>0.108194</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>ppc16cpb1</td>\n",
" <td>814</td>\n",
" <td>0.769231</td>\n",
" <td>0.750000</td>\n",
" <td>0.480682</td>\n",
" <td>0.480990</td>\n",
" <td>0.066764</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>ppc16cpb3</td>\n",
" <td>435</td>\n",
" <td>0.400000</td>\n",
" <td>0.083333</td>\n",
" <td>0.439103</td>\n",
" <td>0.437226</td>\n",
" <td>0.115580</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>ppc16cpb3</td>\n",
" <td>814</td>\n",
" <td>0.786667</td>\n",
" <td>0.737500</td>\n",
" <td>0.575568</td>\n",
" <td>0.574227</td>\n",
" <td>0.063030</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>ppc10cpb3</td>\n",
" <td>435</td>\n",
" <td>0.500000</td>\n",
" <td>0.208333</td>\n",
" <td>0.432692</td>\n",
" <td>0.432912</td>\n",
" <td>0.111085</td>\n",
" </tr>\n",
" <tr>\n",
" <th>5</th>\n",
" <td>ppc10cpb3</td>\n",
" <td>814</td>\n",
" <td>0.788732</td>\n",
" <td>0.700000</td>\n",
" <td>0.548295</td>\n",
" <td>0.547827</td>\n",
" <td>0.067872</td>\n",
" </tr>\n",
" <tr>\n",
" <th>6</th>\n",
" <td>ppc6cpb3</td>\n",
" <td>435</td>\n",
" <td>0.333333</td>\n",
" <td>0.125000</td>\n",
" <td>0.310897</td>\n",
" <td>0.312192</td>\n",
" <td>0.098854</td>\n",
" </tr>\n",
" <tr>\n",
" <th>7</th>\n",
" <td>ppc6cpb3</td>\n",
" <td>814</td>\n",
" <td>0.758065</td>\n",
" <td>0.587500</td>\n",
" <td>0.470455</td>\n",
" <td>0.471318</td>\n",
" <td>0.066270</td>\n",
" </tr>\n",
" <tr>\n",
" <th>8</th>\n",
" <td>ppc8cpb4</td>\n",
" <td>435</td>\n",
" <td>0.333333</td>\n",
" <td>0.083333</td>\n",
" <td>0.423077</td>\n",
" <td>0.422859</td>\n",
" <td>0.117460</td>\n",
" </tr>\n",
" <tr>\n",
" <th>9</th>\n",
" <td>ppc8cpb4</td>\n",
" <td>814</td>\n",
" <td>0.800000</td>\n",
" <td>0.650000</td>\n",
" <td>0.578409</td>\n",
" <td>0.578828</td>\n",
" <td>0.063724</td>\n",
" </tr>\n",
" <tr>\n",
" <th>10</th>\n",
" <td>ppc12cpb3</td>\n",
" <td>435</td>\n",
" <td>0.333333</td>\n",
" <td>0.041667</td>\n",
" <td>0.503205</td>\n",
" <td>0.505153</td>\n",
" <td>0.106680</td>\n",
" </tr>\n",
" <tr>\n",
" <th>11</th>\n",
" <td>ppc12cpb3</td>\n",
" <td>814</td>\n",
" <td>0.808824</td>\n",
" <td>0.687500</td>\n",
" <td>0.606818</td>\n",
" <td>0.607309</td>\n",
" <td>0.065499</td>\n",
" </tr>\n",
" <tr>\n",
" <th>12</th>\n",
" <td>ppc8cpb2</td>\n",
" <td>435</td>\n",
" <td>0.562500</td>\n",
" <td>0.375000</td>\n",
" <td>0.416667</td>\n",
" <td>0.417233</td>\n",
" <td>0.107811</td>\n",
" </tr>\n",
" <tr>\n",
" <th>13</th>\n",
" <td>ppc8cpb2</td>\n",
" <td>814</td>\n",
" <td>0.789474</td>\n",
" <td>0.750000</td>\n",
" <td>0.543182</td>\n",
" <td>0.542849</td>\n",
" <td>0.065817</td>\n",
" </tr>\n",
" <tr>\n",
" <th>14</th>\n",
" <td>ppc8cpb3</td>\n",
" <td>435</td>\n",
" <td>0.538462</td>\n",
" <td>0.291667</td>\n",
" <td>0.426282</td>\n",
" <td>0.425760</td>\n",
" <td>0.113388</td>\n",
" </tr>\n",
" <tr>\n",
" <th>15</th>\n",
" <td>ppc8cpb3</td>\n",
" <td>814</td>\n",
" <td>0.779412</td>\n",
" <td>0.662500</td>\n",
" <td>0.572727</td>\n",
" <td>0.572643</td>\n",
" <td>0.065960</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" HOG_config Band Precision Recall AUC AUC_boot_avg AUC_boot_std\n",
"0 ppc16cpb1 435 0.666667 0.500000 0.474359 0.475056 0.108194\n",
"1 ppc16cpb1 814 0.769231 0.750000 0.480682 0.480990 0.066764\n",
"2 ppc16cpb3 435 0.400000 0.083333 0.439103 0.437226 0.115580\n",
"3 ppc16cpb3 814 0.786667 0.737500 0.575568 0.574227 0.063030\n",
"4 ppc10cpb3 435 0.500000 0.208333 0.432692 0.432912 0.111085\n",
"5 ppc10cpb3 814 0.788732 0.700000 0.548295 0.547827 0.067872\n",
"6 ppc6cpb3 435 0.333333 0.125000 0.310897 0.312192 0.098854\n",
"7 ppc6cpb3 814 0.758065 0.587500 0.470455 0.471318 0.066270\n",
"8 ppc8cpb4 435 0.333333 0.083333 0.423077 0.422859 0.117460\n",
"9 ppc8cpb4 814 0.800000 0.650000 0.578409 0.578828 0.063724\n",
"10 ppc12cpb3 435 0.333333 0.041667 0.503205 0.505153 0.106680\n",
"11 ppc12cpb3 814 0.808824 0.687500 0.606818 0.607309 0.065499\n",
"12 ppc8cpb2 435 0.562500 0.375000 0.416667 0.417233 0.107811\n",
"13 ppc8cpb2 814 0.789474 0.750000 0.543182 0.542849 0.065817\n",
"14 ppc8cpb3 435 0.538462 0.291667 0.426282 0.425760 0.113388\n",
"15 ppc8cpb3 814 0.779412 0.662500 0.572727 0.572643 0.065960"
]
},
"execution_count": 7,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"main('/path/to/data/directory', 10000, bands=['435', '814'])"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": []
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 2",
"language": "python",
"name": "python2"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 2
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython2",
"version": "2.7.15"
}
},
"nbformat": 4,
"nbformat_minor": 2
}
| mit |
Lstyle1/Deep_learning_projects | autoencoder/Simple_Autoencoder_Solution.ipynb | 1 | 41276 | {
"cells": [
{
"cell_type": "markdown",
"metadata": {
"deletable": true,
"editable": true
},
"source": [
"# A Simple Autoencoder\n",
"\n",
"We'll start off by building a simple autoencoder to compress the MNIST dataset. With autoencoders, we pass input data through an encoder that makes a compressed representation of the input. Then, this representation is passed through a decoder to reconstruct the input data. Generally the encoder and decoder will be built with neural networks, then trained on example data.\n",
"\n",
"\n",
"\n",
"In this notebook, we'll be build a simple network architecture for the encoder and decoder. Let's get started by importing our libraries and getting the dataset."
]
},
{
"cell_type": "code",
"execution_count": 1,
"metadata": {
"collapsed": true,
"deletable": true,
"editable": true
},
"outputs": [],
"source": [
"%matplotlib inline\n",
"\n",
"import numpy as np\n",
"import tensorflow as tf\n",
"import matplotlib.pyplot as plt"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {
"collapsed": false,
"deletable": true,
"editable": true
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Successfully downloaded train-images-idx3-ubyte.gz 9912422 bytes.\n",
"Extracting MNIST_data/train-images-idx3-ubyte.gz\n",
"Successfully downloaded train-labels-idx1-ubyte.gz 28881 bytes.\n",
"Extracting MNIST_data/train-labels-idx1-ubyte.gz\n",
"Successfully downloaded t10k-images-idx3-ubyte.gz 1648877 bytes.\n",
"Extracting MNIST_data/t10k-images-idx3-ubyte.gz\n",
"Successfully downloaded t10k-labels-idx1-ubyte.gz 4542 bytes.\n",
"Extracting MNIST_data/t10k-labels-idx1-ubyte.gz\n"
]
}
],
"source": [
"from tensorflow.examples.tutorials.mnist import input_data\n",
"mnist = input_data.read_data_sets('MNIST_data', validation_size=0)"
]
},
{
"cell_type": "markdown",
"metadata": {
"deletable": true,
"editable": true
},
"source": [
"Below I'm plotting an example image from the MNIST dataset. These are 28x28 grayscale images of handwritten digits."
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {
"collapsed": false,
"deletable": true,
"editable": true
},
"outputs": [
{
"data": {
"text/plain": [
"<matplotlib.image.AxesImage at 0x11abae4a8>"
]
},
"execution_count": 3,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"image/png": "iVBORw0KGgoAAAANSUhEUgAAAP8AAAD8CAYAAAC4nHJkAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\ngg==\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x11aa66da0>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"img = mnist.train.images[2]\n",
"plt.imshow(img.reshape((28, 28)), cmap='Greys_r')"
]
},
{
"cell_type": "markdown",
"metadata": {
"deletable": true,
"editable": true
},
"source": [
"We'll train an autoencoder with these images by flattening them into 784 length vectors. The images from this dataset are already normalized such that the values are between 0 and 1. Let's start by building basically the simplest autoencoder with a **single ReLU hidden layer**. This layer will be used as the compressed representation. Then, the encoder is the input layer and the hidden layer. The decoder is the hidden layer and the output layer. Since the images are normalized between 0 and 1, we need to use a **sigmoid activation on the output layer** to get values matching the input.\n",
"\n",
"\n",
"\n",
"\n",
"> **Exercise:** Build the graph for the autoencoder in the cell below. The input images will be flattened into 784 length vectors. The targets are the same as the inputs. And there should be one hidden layer with a ReLU activation and an output layer with a sigmoid activation. Feel free to use TensorFlow's higher level API, `tf.layers`. For instance, you would use [`tf.layers.dense(inputs, units, activation=tf.nn.relu)`](https://www.tensorflow.org/api_docs/python/tf/layers/dense) to create a fully connected layer with a ReLU activation. The loss should be calculated with the cross-entropy loss, there is a convenient TensorFlow function for this `tf.nn.sigmoid_cross_entropy_with_logits` ([documentation](https://www.tensorflow.org/api_docs/python/tf/nn/sigmoid_cross_entropy_with_logits)). You should note that `tf.nn.sigmoid_cross_entropy_with_logits` takes the logits, but to get the reconstructed images you'll need to pass the logits through the sigmoid function."
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {
"collapsed": true,
"deletable": true,
"editable": true
},
"outputs": [],
"source": [
"# Size of the encoding layer (the hidden layer)\n",
"encoding_dim = 32\n",
"\n",
"image_size = mnist.train.images.shape[1]\n",
"\n",
"inputs_ = tf.placeholder(tf.float32, (None, image_size), name='inputs')\n",
"targets_ = tf.placeholder(tf.float32, (None, image_size), name='targets')\n",
"\n",
"# Output of hidden layer\n",
"encoded = tf.layers.dense(inputs_, encoding_dim, activation=tf.nn.relu)\n",
"\n",
"# Output layer logits\n",
"logits = tf.layers.dense(encoded, image_size, activation=None)\n",
"# Sigmoid output from\n",
"decoded = tf.nn.sigmoid(logits, name='output')\n",
"\n",
"loss = tf.nn.sigmoid_cross_entropy_with_logits(labels=targets_, logits=logits)\n",
"cost = tf.reduce_mean(loss)\n",
"opt = tf.train.AdamOptimizer(0.001).minimize(cost)"
]
},
{
"cell_type": "markdown",
"metadata": {
"deletable": true,
"editable": true
},
"source": [
"## Training"
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {
"collapsed": true,
"deletable": true,
"editable": true
},
"outputs": [],
"source": [
"# Create the session\n",
"sess = tf.Session()"
]
},
{
"cell_type": "markdown",
"metadata": {
"deletable": true,
"editable": true
},
"source": [
"Here I'll write a bit of code to train the network. I'm not too interested in validation here, so I'll just monitor the training loss and the test loss afterwards. \n",
"\n",
"Calling `mnist.train.next_batch(batch_size)` will return a tuple of `(images, labels)`. We're not concerned with the labels here, we just need the images. Otherwise this is pretty straightfoward training with TensorFlow. We initialize the variables with `sess.run(tf.global_variables_initializer())`. Then, run the optimizer and get the loss with `batch_cost, _ = sess.run([cost, opt], feed_dict=feed)`."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true,
"deletable": true,
"editable": true
},
"outputs": [],
"source": [
"epochs = 20\n",
"batch_size = 200\n",
"sess.run(tf.global_variables_initializer())\n",
"for e in range(epochs):\n",
" for ii in range(mnist.train.num_examples//batch_size):\n",
" batch = mnist.train.next_batch(batch_size)\n",
" feed = {inputs_: batch[0], targets_: batch[0]}\n",
" batch_cost, _ = sess.run([cost, opt], feed_dict=feed)\n",
"\n",
" print(\"Epoch: {}/{}...\".format(e+1, epochs),\n",
" \"Training loss: {:.4f}\".format(batch_cost))"
]
},
{
"cell_type": "markdown",
"metadata": {
"deletable": true,
"editable": true
},
"source": [
"## Checking out the results\n",
"\n",
"Below I've plotted some of the test images along with their reconstructions. For the most part these look pretty good except for some blurriness in some parts."
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {
"collapsed": false,
"deletable": true,
"editable": true
},
"outputs": [
{
"data": {
"image/png": "iVBORw0KGgoAAAANSUhEUgAABawAAAEsCAYAAAAvofT2AAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x125d34908>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"fig, axes = plt.subplots(nrows=2, ncols=10, sharex=True, sharey=True, figsize=(20,4))\n",
"in_imgs = mnist.test.images[:10]\n",
"reconstructed, compressed = sess.run([decoded, encoded], feed_dict={inputs_: in_imgs})\n",
"\n",
"for images, row in zip([in_imgs, reconstructed], axes):\n",
" for img, ax in zip(images, row):\n",
" ax.imshow(img.reshape((28, 28)), cmap='Greys_r')\n",
" ax.get_xaxis().set_visible(False)\n",
" ax.get_yaxis().set_visible(False)\n",
"\n",
"fig.tight_layout(pad=0.1)"
]
},
{
"cell_type": "code",
"execution_count": 9,
"metadata": {
"collapsed": true,
"deletable": true,
"editable": true
},
"outputs": [],
"source": [
"sess.close()"
]
},
{
"cell_type": "markdown",
"metadata": {
"collapsed": true,
"deletable": true,
"editable": true
},
"source": [
"## Up Next\n",
"\n",
"We're dealing with images here, so we can (usually) get better performance using convolution layers. So, next we'll build a better autoencoder with convolutional layers.\n",
"\n",
"In practice, autoencoders aren't actually better at compression compared to typical methods like JPEGs and MP3s. But, they are being used for noise reduction, which you'll also build."
]
}
],
"metadata": {
"kernelspec": {
"display_name": "Python [default]",
"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": "3.5.3"
}
},
"nbformat": 4,
"nbformat_minor": 2
}
| mit |
4DGenome/Chromosomal-Conformation-Course | Participants/eralda/Compartments-TADs.ipynb | 1 | 476367 | {
"cells": [
{
"cell_type": "code",
"execution_count": 1,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"from pytadbit import load_hic_data_from_reads"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"reso=300000"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"hic_data1 = load_hic_data_from_reads('results/HindIII/03_filtering/valid_reads12_HindIII.tsv', resolution = reso) "
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"iterative correction\n",
" - copying matrix\n",
" - computing baises\n",
"rescaling to factor 1\n",
" - getting the sum of the matrix\n",
" => 11709.348\n",
" - rescaling biases\n"
]
}
],
"source": [
"hic_data1 .normalize_hic() "
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"hic_data2 = load_hic_data_from_reads('results/HindIII/03_filtering/valid_reads12_HindIII.tsv', resolution = reso)\n"
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"iterative correction\n",
" - copying matrix\n",
" - computing baises\n",
"rescaling to factor 1\n",
" - getting the sum of the matrix\n",
" => 11709.348\n",
" - rescaling biases\n"
]
}
],
"source": [
"hic_data2 .normalize_hic() "
]
},
{
"cell_type": "code",
"execution_count": 8,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"hic_data = load_hic_data_from_reads ('results/both/03_filtering/valid_reads12.tsv', resolution = 300000) "
]
},
{
"cell_type": "code",
"execution_count": 9,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"iterative correction\n",
" - copying matrix\n",
" - computing baises\n",
"rescaling to factor 1\n",
" - getting the sum of the matrix\n",
" => 11770.466\n",
" - rescaling biases\n"
]
}
],
"source": [
"hic_data .normalize_hic() "
]
},
{
"cell_type": "code",
"execution_count": 14,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"image/png": "iVBORw0KGgoAAAANSUhEUgAAA4UAAAN7CAYAAADySD/JAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nDWH9PwYx7KBkAHuWAAAAAElFTkSuQmCC\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x7fd727ee9bd0>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"ename": "ValueError",
"evalue": "list.remove(x): x not in list",
"output_type": "error",
"traceback": [
"\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
"\u001b[0;31mValueError\u001b[0m Traceback (most recent call last)",
"\u001b[0;32m/home/student/.miniconda2/lib/python2.7/site-packages/IPython/core/formatters.pyc\u001b[0m in \u001b[0;36m__call__\u001b[0;34m(self, obj)\u001b[0m\n\u001b[1;32m 305\u001b[0m \u001b[0;32mpass\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 306\u001b[0m \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 307\u001b[0;31m \u001b[0;32mreturn\u001b[0m \u001b[0mprinter\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mobj\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 308\u001b[0m \u001b[0;31m# Finally look for special method names\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 309\u001b[0m \u001b[0mmethod\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mget_real_method\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mobj\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mprint_method\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;32m/home/student/.miniconda2/lib/python2.7/site-packages/IPython/core/pylabtools.pyc\u001b[0m in \u001b[0;36m<lambda>\u001b[0;34m(fig)\u001b[0m\n\u001b[1;32m 238\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 239\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0;34m'png'\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mformats\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 240\u001b[0;31m \u001b[0mpng_formatter\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfor_type\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mFigure\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;32mlambda\u001b[0m \u001b[0mfig\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mprint_figure\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfig\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m'png'\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[0m\u001b[1;32m 241\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0;34m'retina'\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mformats\u001b[0m \u001b[0;32mor\u001b[0m \u001b[0;34m'png2x'\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mformats\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 242\u001b[0m \u001b[0mpng_formatter\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfor_type\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mFigure\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;32mlambda\u001b[0m \u001b[0mfig\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mretina_figure\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfig\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/home/student/.miniconda2/lib/python2.7/site-packages/IPython/core/pylabtools.pyc\u001b[0m in \u001b[0;36mprint_figure\u001b[0;34m(fig, fmt, bbox_inches, **kwargs)\u001b[0m\n\u001b[1;32m 122\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 123\u001b[0m \u001b[0mbytes_io\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mBytesIO\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 124\u001b[0;31m \u001b[0mfig\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcanvas\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mprint_figure\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mbytes_io\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkw\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 125\u001b[0m \u001b[0mdata\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mbytes_io\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mgetvalue\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 126\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mfmt\u001b[0m \u001b[0;34m==\u001b[0m \u001b[0;34m'svg'\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;32m/home/student/.miniconda2/lib/python2.7/site-packages/matplotlib/backend_bases.pyc\u001b[0m in \u001b[0;36mprint_figure\u001b[0;34m(self, filename, dpi, facecolor, edgecolor, orientation, format, **kwargs)\u001b[0m\n\u001b[1;32m 2196\u001b[0m \u001b[0mbbox_artists\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mkwargs\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mpop\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"bbox_extra_artists\"\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mNone\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 2197\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mbbox_artists\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 2198\u001b[0;31m \u001b[0mbbox_artists\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfigure\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget_default_bbox_extra_artists\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 2199\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 2200\u001b[0m \u001b[0mbbox_filtered\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/home/student/.miniconda2/lib/python2.7/site-packages/matplotlib/figure.pyc\u001b[0m in \u001b[0;36mget_default_bbox_extra_artists\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m 1690\u001b[0m \u001b[0mbbox_artists\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mextend\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0max\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget_default_bbox_extra_artists\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 1691\u001b[0m \u001b[0;31m# we don't want the figure's patch to influence the bbox calculation\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1692\u001b[0;31m \u001b[0mbbox_artists\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mremove\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mpatch\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 1693\u001b[0m \u001b[0;32mreturn\u001b[0m \u001b[0mbbox_artists\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 1694\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;31mValueError\u001b[0m: list.remove(x): x not in list"
]
},
{
"data": {
"text/plain": [
"<matplotlib.figure.Figure at 0x7fd7282c1ed0>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"corr = hic_data.find_compartments(crms=['chr18'], vmin=-0.2, vmax=0.2, show=True, \n",
" savedata='results/both/compartments.tsv')\n"
]
},
{
"cell_type": "code",
"execution_count": 15,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"## CHR chr18\tEigenvector: 1\r\n",
"#start\tend\tdensity\ttype\r\n",
"chr18\t0\t2\t0.05\t\r\n",
"chr18\t3\t7\t0.14\t\r\n",
"chr18\t8\t12\t0.12\t\r\n",
"chr18\t13\t22\t0.57\t\r\n",
"chr18\t23\t23\t0.00\t\r\n",
"chr18\t24\t24\t0.01\t\r\n",
"chr18\t25\t25\t0.01\t\r\n",
"chr18\t26\t26\t0.01\t\r\n"
]
}
],
"source": [
"! head results/both/compartments.tsv"
]
},
{
"cell_type": "code",
"execution_count": 16,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"head: cannot open ‘results/both/correlation.tsv/chr18_corr-matrix.tsv’ for reading: No such file or directory\r\n"
]
}
],
"source": [
"! head results/both/correlation.tsv/chr18_corr-matrix.tsv "
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": []
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 2",
"language": "python",
"name": "python2"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 2
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython2",
"version": "2.7.13"
}
},
"nbformat": 4,
"nbformat_minor": 2
}
| gpl-3.0 |
crystalzhaizhai/cs207_yi_zhai | homeworks/HW10/HW10.ipynb | 1 | 29987 | {
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Homework 10: `SQL`\n",
"\n",
"## Due Date: Thursday, November 16th at 11:59 PM\n",
"\n",
"You will create a database of the NASA polynomial coefficients for each specie.\n",
"\n",
"**Please turn in your database with your `Jupyter` notebook!**"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Question 1: Convert XML to a SQL database"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Create two tables named `LOW` and `HIGH`, each corresponding to data given for the low and high temperature range.\n",
"Each should have the following column names:\n",
"\n",
"- `SPECIES_NAME`\n",
"- `TLOW`\n",
"- `THIGH`\n",
"- `COEFF_1`\n",
"- `COEFF_2`\n",
"- `COEFF_3`\n",
"- `COEFF_4`\n",
"- `COEFF_5`\n",
"- `COEFF_6`\n",
"- `COEFF_7`\n",
"\n",
"Populate the tables using the XML file you created in last assignment. If you did not complete the last assignment, you may also use the `example_thermo.xml` file.\n",
"\n",
"`TLOW` should refer to the temperature at the low range and `THIGH` should refer to the temperature at the high range. For example, in the `LOW` table, $H$ would have `TLOW` at $200$ and `THIGH` at $1000$ and in the `HIGH` table, $H$ would have `TLOW` at $1000$ and `THIGH` at $3500$.\n",
"\n",
"For both tables, `COEFF_1` through `COEFF_7` should be populated with the corresponding coefficients for the low temperature data and high temperature data."
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"import x as ET\n",
"tree=ET.parse(\"example_thermo.xml\")\n",
"elementroot=tree.getroot()\n",
"import sqlite3\n",
"import pandas as pd\n",
"db = sqlite3.connect('thermo.sqlite')\n",
"cursor = db.cursor()"
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"<sqlite3.Cursor at 0x1169bab20>"
]
},
"execution_count": 4,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"cursor.execute(\"DROP TABLE IF EXISTS low\")\n",
"cursor.execute(\"DROP TABLE IF EXISTS high\")\n",
"cursor.execute(\"PRAGMA foreign_keys=1\")\n",
"cursor.execute('''CREATE TABLE low (\n",
" species_name TEXT PRIMARY KEY NOT NULL, \n",
" tlow INT, \n",
" thigh INT, \n",
" coeff_1 FLOAT,\n",
" coeff_2 FLOAT,\n",
" coeff_3 FLOAT,\n",
" coeff_4 FLOAT,\n",
" coeff_5 FLOAT,\n",
" coeff_6 FLOAT,\n",
" coeff_7 FLOAT)''')\n",
"\n",
"cursor.execute('''CREATE TABLE high (\n",
" species_name TEXT PRIMARY KEY NOT NULL, \n",
" tlow INT, \n",
" thigh INT, \n",
" coeff_1 FLOAT,\n",
" coeff_2 FLOAT,\n",
" coeff_3 FLOAT,\n",
" coeff_4 FLOAT,\n",
" coeff_5 FLOAT,\n",
" coeff_6 FLOAT,\n",
" coeff_7 FLOAT)''')"
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"['H', 'O', 'OH', 'H2', 'O2', 'H2O', 'HO2', 'H2O2', 'N2', 'Hp', 'Op']\n"
]
}
],
"source": [
"elements=elementroot.find('phase').find(\"speciesArray\").text.strip().split(\" \")\n"
]
},
{
"cell_type": "code",
"execution_count": 13,
"metadata": {},
"outputs": [],
"source": [
"speciesroot=elementroot.find(\"speciesData\")\n",
"for specie in speciesroot:\n",
" species_name=specie.attrib['name']\n",
" C=specie.find(\"thermo\").findall(\"NASA\")\n",
" for i,cc in enumerate(C):\n",
" coefficients=cc.find(\"floatArray\").text.strip().split(\" \")\n",
" thigh=cc.attrib['Tmax']\n",
" tlow=cc.attrib['Tmin']\n",
" coeff_1=coefficients[0]\n",
" coeff_2=coefficients[1] \n",
" coeff_3=coefficients[2]\n",
" coeff_4=coefficients[3]\n",
" coeff_5=coefficients[4]\n",
" coeff_6=coefficients[5]\n",
" coeff_7=coefficients[6]\n",
" \n",
" vals_to_insert = (species_name, tlow, thigh, \n",
" coeff_1, coeff_2, coeff_3, coeff_4, coeff_5, coeff_6, coeff_7)\n",
"\n",
" if i==0:\n",
" cursor.execute('''INSERT INTO low (species_name, tlow, thigh, \n",
" coeff_1, coeff_2, coeff_3, coeff_4, coeff_5, coeff_6, coeff_7) \n",
" VALUES (?, ?, ?, ?, ?, ?, ?, ?, ?, ?)''', vals_to_insert)\n",
" else:\n",
" cursor.execute('''INSERT INTO high (species_name, tlow, thigh, \n",
" coeff_1, coeff_2, coeff_3, coeff_4, coeff_5, coeff_6, coeff_7) \n",
" VALUES (?, ?, ?, ?, ?, ?, ?, ?, ?, ?)''', vals_to_insert)\n",
" "
]
},
{
"cell_type": "code",
"execution_count": 16,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"db.commit()"
]
},
{
"cell_type": "code",
"execution_count": 71,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<style>\n",
" .dataframe thead tr:only-child th {\n",
" text-align: right;\n",
" }\n",
"\n",
" .dataframe thead th {\n",
" text-align: left;\n",
" }\n",
"\n",
" .dataframe tbody tr th {\n",
" vertical-align: top;\n",
" }\n",
"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>species_name</th>\n",
" <th>tlow</th>\n",
" <th>thigh</th>\n",
" <th>coeff_1</th>\n",
" <th>coeff_2</th>\n",
" <th>coeff_3</th>\n",
" <th>coeff_4</th>\n",
" <th>coeff_5</th>\n",
" <th>coeff_6</th>\n",
" <th>coeff_7</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>H</td>\n",
" <td>1000</td>\n",
" <td>5000</td>\n",
" <td>2.50000000E+00,</td>\n",
" <td>0.00000000E+00,</td>\n",
" <td>0.00000000E+00,</td>\n",
" <td>0.00000000E+00,</td>\n",
" <td>0.00000000E+00,</td>\n",
" <td>2.54716270E+04,</td>\n",
" <td>-0.460118</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>O</td>\n",
" <td>1000</td>\n",
" <td>5000</td>\n",
" <td>2.54205966E+00,</td>\n",
" <td>-2.75506191E-05,</td>\n",
" <td>-3.10280335E-09,</td>\n",
" <td>4.55106742E-12,</td>\n",
" <td>-4.36805150E-16,</td>\n",
" <td>2.92308027E+04,</td>\n",
" <td>4.920308</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>OH</td>\n",
" <td>1000</td>\n",
" <td>6000</td>\n",
" <td>2.86472886E+00,</td>\n",
" <td>1.05650448E-03,</td>\n",
" <td>-2.59082758E-07,</td>\n",
" <td>3.05218674E-11,</td>\n",
" <td>-1.33195876E-15,</td>\n",
" <td>3.68362875E+03,</td>\n",
" <td>5.701641</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>H2</td>\n",
" <td>1000</td>\n",
" <td>5000</td>\n",
" <td>2.99142337E+00,</td>\n",
" <td>7.00064411E-04,</td>\n",
" <td>-5.63382869E-08,</td>\n",
" <td>-9.23157818E-12,</td>\n",
" <td>1.58275179E-15,</td>\n",
" <td>-8.35033997E+02,</td>\n",
" <td>-1.355110</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>O2</td>\n",
" <td>1000</td>\n",
" <td>5000</td>\n",
" <td>3.69757819E+00,</td>\n",
" <td>6.13519689E-04,</td>\n",
" <td>-1.25884199E-07,</td>\n",
" <td>1.77528148E-11,</td>\n",
" <td>-1.13643531E-15,</td>\n",
" <td>-1.23393018E+03,</td>\n",
" <td>3.189166</td>\n",
" </tr>\n",
" <tr>\n",
" <th>5</th>\n",
" <td>H2O</td>\n",
" <td>1000</td>\n",
" <td>5000</td>\n",
" <td>2.67214561E+00,</td>\n",
" <td>3.05629289E-03,</td>\n",
" <td>-8.73026011E-07,</td>\n",
" <td>1.20099639E-10,</td>\n",
" <td>-6.39161787E-15,</td>\n",
" <td>-2.98992090E+04,</td>\n",
" <td>6.862817</td>\n",
" </tr>\n",
" <tr>\n",
" <th>6</th>\n",
" <td>HO2</td>\n",
" <td>1000</td>\n",
" <td>3500</td>\n",
" <td>4.01721090E+00,</td>\n",
" <td>2.23982013E-03,</td>\n",
" <td>-6.33658150E-07,</td>\n",
" <td>1.14246370E-10,</td>\n",
" <td>-1.07908535E-14,</td>\n",
" <td>1.11856713E+02,</td>\n",
" <td>3.785102</td>\n",
" </tr>\n",
" <tr>\n",
" <th>7</th>\n",
" <td>H2O2</td>\n",
" <td>1000</td>\n",
" <td>5000</td>\n",
" <td>4.57316685E+00,</td>\n",
" <td>4.33613639E-03,</td>\n",
" <td>-1.47468882E-06,</td>\n",
" <td>2.34890357E-10,</td>\n",
" <td>-1.43165356E-14,</td>\n",
" <td>-1.80069609E+04,</td>\n",
" <td>0.501137</td>\n",
" </tr>\n",
" <tr>\n",
" <th>8</th>\n",
" <td>N2</td>\n",
" <td>1000</td>\n",
" <td>5000</td>\n",
" <td>0.02926640E+02,</td>\n",
" <td>0.01487977E-01,</td>\n",
" <td>-0.05684761E-05,</td>\n",
" <td>0.01009704E-08,</td>\n",
" <td>-0.06753351E-13,</td>\n",
" <td>-0.09227977E+04,</td>\n",
" <td>5.980528</td>\n",
" </tr>\n",
" <tr>\n",
" <th>9</th>\n",
" <td>Hp</td>\n",
" <td>1000</td>\n",
" <td>5000</td>\n",
" <td>1.64243522,</td>\n",
" <td>2.89759059E-04,</td>\n",
" <td>0.00000000E+00,</td>\n",
" <td>0.00000000E+00,</td>\n",
" <td>0.00000000E+00,</td>\n",
" <td>-4.69510297E+00,</td>\n",
" <td>-11.148334</td>\n",
" </tr>\n",
" <tr>\n",
" <th>10</th>\n",
" <td>Op</td>\n",
" <td>1000</td>\n",
" <td>5000</td>\n",
" <td>1.64243522,</td>\n",
" <td>2.89759059E-04,</td>\n",
" <td>0.00000000E+00,</td>\n",
" <td>0.00000000E+00,</td>\n",
" <td>0.00000000E+00,</td>\n",
" <td>-4.69510297E+00,</td>\n",
" <td>-14.467683</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" species_name tlow thigh coeff_1 coeff_2 \\\n",
"0 H 1000 5000 2.50000000E+00, 0.00000000E+00, \n",
"1 O 1000 5000 2.54205966E+00, -2.75506191E-05, \n",
"2 OH 1000 6000 2.86472886E+00, 1.05650448E-03, \n",
"3 H2 1000 5000 2.99142337E+00, 7.00064411E-04, \n",
"4 O2 1000 5000 3.69757819E+00, 6.13519689E-04, \n",
"5 H2O 1000 5000 2.67214561E+00, 3.05629289E-03, \n",
"6 HO2 1000 3500 4.01721090E+00, 2.23982013E-03, \n",
"7 H2O2 1000 5000 4.57316685E+00, 4.33613639E-03, \n",
"8 N2 1000 5000 0.02926640E+02, 0.01487977E-01, \n",
"9 Hp 1000 5000 1.64243522, 2.89759059E-04, \n",
"10 Op 1000 5000 1.64243522, 2.89759059E-04, \n",
"\n",
" coeff_3 coeff_4 coeff_5 coeff_6 \\\n",
"0 0.00000000E+00, 0.00000000E+00, 0.00000000E+00, 2.54716270E+04, \n",
"1 -3.10280335E-09, 4.55106742E-12, -4.36805150E-16, 2.92308027E+04, \n",
"2 -2.59082758E-07, 3.05218674E-11, -1.33195876E-15, 3.68362875E+03, \n",
"3 -5.63382869E-08, -9.23157818E-12, 1.58275179E-15, -8.35033997E+02, \n",
"4 -1.25884199E-07, 1.77528148E-11, -1.13643531E-15, -1.23393018E+03, \n",
"5 -8.73026011E-07, 1.20099639E-10, -6.39161787E-15, -2.98992090E+04, \n",
"6 -6.33658150E-07, 1.14246370E-10, -1.07908535E-14, 1.11856713E+02, \n",
"7 -1.47468882E-06, 2.34890357E-10, -1.43165356E-14, -1.80069609E+04, \n",
"8 -0.05684761E-05, 0.01009704E-08, -0.06753351E-13, -0.09227977E+04, \n",
"9 0.00000000E+00, 0.00000000E+00, 0.00000000E+00, -4.69510297E+00, \n",
"10 0.00000000E+00, 0.00000000E+00, 0.00000000E+00, -4.69510297E+00, \n",
"\n",
" coeff_7 \n",
"0 -0.460118 \n",
"1 4.920308 \n",
"2 5.701641 \n",
"3 -1.355110 \n",
"4 3.189166 \n",
"5 6.862817 \n",
"6 3.785102 \n",
"7 0.501137 \n",
"8 5.980528 \n",
"9 -11.148334 \n",
"10 -14.467683 "
]
},
"execution_count": 71,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"high_cols = [col[1] for col in cursor.execute(\"PRAGMA table_info(HIGH)\")]\n",
"query = '''SELECT * FROM HIGH'''\n",
"viz_tables(high_cols, query)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Question 2: `WHERE` Statements"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"1. Write a `Python` function `get_coeffs` that returns an array of 7 coefficients. \n",
" \n",
" The function should take in two parameters: 1.) `species_name` and 2.) `temp_range`, an indicator variable ('low' or 'high') to indicate whether the coefficients should come from the low or high temperature range. \n",
" The function should use `SQL` commands and `WHERE` statements on the table you just created in Question 1 (rather than taking data from the XML directly).\n",
"```python\n",
"def get_coeffs(species_name, temp_range):\n",
" ''' Fill in here'''\n",
" return coeffs\n",
"```\n",
"\n",
"2. Write a python function `get_species` that returns all species that have a temperature range above or below a given value. The function should take in two parameters: 1.) `temp` and 2.) `temp_range`, an indicator variable ('low' or 'high').\n",
"\n",
" When temp_range is 'low', we are looking for species with a temperature range lower than the given temperature, and for a 'high' temp_range, we want species with a temperature range higher than the given temperature.\n",
"\n",
" This exercise may be useful if different species have different `LOW` and `HIGH` ranges.\n",
"\n",
" And as before, you should accomplish this through `SQL` queries and where statements.\n",
"\n",
"```python\n",
"def get_species(temp, temp_range):\n",
" ''' Fill in here'''\n",
" return coeffs\n",
"```"
]
},
{
"cell_type": "code",
"execution_count": 72,
"metadata": {},
"outputs": [],
"source": [
"def get_coeffs(species_name, temp_range):\n",
" if temp_range==\"low\":\n",
" function = '''SELECT coeff_1,coeff_2,coeff_3,coeff_4,coeff_5,coeff_6,coeff_7 FROM low WHERE species_name=?'''\n",
" coeffs=cursor.execute(function,(species_name)).fetchone()\n",
" \n",
" else:\n",
" function = '''SELECT coeff_1,coeff_2,coeff_3,coeff_4,coeff_5,coeff_6,coeff_7 FROM high WHERE species_name=?'''\n",
" coeffs=cursor.execute(function,(species_name)).fetchone()\n",
"\n",
" return coeffs"
]
},
{
"cell_type": "code",
"execution_count": 74,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"('2.50000000E+00,',\n",
" '0.00000000E+00,',\n",
" '0.00000000E+00,',\n",
" '0.00000000E+00,',\n",
" '0.00000000E+00,',\n",
" '2.54716270E+04,',\n",
" -0.460117638)"
]
},
"execution_count": 74,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"get_coeffs(\"H\",\"high\")"
]
},
{
"cell_type": "code",
"execution_count": 84,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"def get_species(temp, temp_range):\n",
" ''' Fill in here'''\n",
" if temp_range==\"low\":\n",
" \n",
" function = '''SELECT species_name FROM low WHERE tlow<?'''\n",
" species=cursor.execute(function,(temp_range,)).fetchall()\n",
"\n",
" else:\n",
" function = '''SELECT species_name FROM high WHERE thigh>?'''\n",
" species=cursor.execute(function,(temp_range,)).fetchall()\n",
" return species"
]
},
{
"cell_type": "code",
"execution_count": 88,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"[('H',),\n",
" ('O',),\n",
" ('OH',),\n",
" ('H2',),\n",
" ('O2',),\n",
" ('H2O',),\n",
" ('HO2',),\n",
" ('H2O2',),\n",
" ('N2',),\n",
" ('Hp',),\n",
" ('Op',)]"
]
},
"execution_count": 88,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"get_species(\"low\",1)"
]
},
{
"cell_type": "code",
"execution_count": 99,
"metadata": {},
"outputs": [
{
"ename": "ValueError",
"evalue": "operation parameter must be str",
"output_type": "error",
"traceback": [
"\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
"\u001b[0;31mValueError\u001b[0m Traceback (most recent call last)",
"\u001b[0;32m<ipython-input-99-d008008da568>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m 1\u001b[0m \u001b[0mall_cols\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0mcol\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0mcol\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mcursor\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mexecute\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"PRAGMA table_info(ALL_TEMPS)\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 2\u001b[0m \u001b[0mquery\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m'''SELECT species_name FROM low WHERE tlow<?'''\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 3\u001b[0;31m \u001b[0mviz_tables\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mall_cols\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mquery\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m,\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[0m",
"\u001b[0;32m<ipython-input-14-82a1f34bfc69>\u001b[0m in \u001b[0;36mviz_tables\u001b[0;34m(cols, query)\u001b[0m\n\u001b[1;32m 1\u001b[0m \u001b[0;32mdef\u001b[0m \u001b[0mviz_tables\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mcols\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mquery\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 2\u001b[0;31m \u001b[0mq\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mcursor\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mexecute\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mquery\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfetchall\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 3\u001b[0m \u001b[0mframelist\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 4\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0mi\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcol_name\u001b[0m \u001b[0;32min\u001b[0m \u001b[0menumerate\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mcols\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 5\u001b[0m \u001b[0mframelist\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mappend\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mcol_name\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0mcol\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mi\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0mcol\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mq\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;31mValueError\u001b[0m: operation parameter must be str"
]
}
],
"source": [
"all_cols = [col[1] for col in cursor.execute(\"PRAGMA table_info(ALL_TEMPS)\")]\n",
"query = '''SELECT species_name FROM low WHERE tlow<?'''\n",
"viz_tables(all_cols,(query,(1,)))"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Question 3: `JOIN` STATEMENTS"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Create a table named `ALL_TEMPS` that has the following columns:\n",
"\n",
"- `SPECIES_NAME`\n",
"- `TEMP_LOW`\n",
"- `TEMP_HIGH`\n",
"\n",
"This table should be created by joining the tables `LOW` and `HIGH` on the value `SPECIES_NAME`."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"1. Write a `Python` function `get_range` that returns the range of temperatures for a given species_name.\n",
"\n",
"The range should be computed within the `SQL` query (i.e. you should subtract within the `SELECT` statement in the `SQL` query).\n",
"```python\n",
"def get_range(species_name):\n",
" '''Fill in here'''\n",
" return range\n",
"```\n",
"\n",
"Note that `TEMP_LOW` is the lowest temperature in the `LOW` range and `TEMP_HIGH` is the highest temperature in the `HIGH` range."
]
},
{
"cell_type": "code",
"execution_count": 90,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"def get_range(species_name):\n",
" function = '''SELECT tlow FROM low WHERE species_name=?'''\n",
" temp_low=cursor.execute(function, (species_name,)).fetchall()[0]\n",
"\n",
" function = '''SELECT thigh FROM high WHERE species_name=?'''\n",
" temp_high=cursor.execute(function,(species_name,)).fetchall()[0]\n",
" \n",
" return (temp_low[0],temp_high[0])"
]
},
{
"cell_type": "code",
"execution_count": 91,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"(200, 3500)"
]
},
"execution_count": 91,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"get_range(\"HO2\")"
]
},
{
"cell_type": "code",
"execution_count": 96,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"('H',) 300 5000\n",
"('H2',) 300 5000\n",
"('H2O',) 300 5000\n",
"('H2O2',) 300 5000\n",
"('HO2',) 200 3500\n",
"('Hp',) 300 5000\n",
"('N2',) 300 5000\n",
"('O',) 300 5000\n",
"('O2',) 300 5000\n",
"('OH',) 200 6000\n",
"('Op',) 300 5000\n"
]
}
],
"source": [
"cursor.execute(\"DROP TABLE IF EXISTS all_temps\")\n",
"cursor.execute('''CREATE TABLE all_temps(\n",
" species_name TEXT PRIMARY KEY NOT NULL, \n",
" temp_low INT, \n",
" temp_high INT)''')\n",
"\n",
"function = '''SELECT species_name FROM low'''\n",
"species=cursor.execute(function).fetchall()\n",
"for specie in species:\n",
" temp_low,temp_high=get_range(specie[0])\n",
" print(specie,temp_low,temp_high)\n",
" cursor.execute('''INSERT INTO all_temps(species_name, temp_low, temp_high) \n",
" VALUES(?, ?, ?)''',(specie[0],temp_low,temp_high))\n",
" \n",
" "
]
},
{
"cell_type": "code",
"execution_count": 97,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"cursor.execute(\"DROP TABLE IF EXISTS ALL_TEMPS\")\n",
"cursor.execute('''\n",
"CREATE TABLE ALL_TEMPS AS\n",
" SELECT HIGH.SPECIES_NAME, HIGH.THIGH AS TEMP_HIGH, LOW.TLOW AS TEMP_LOW\n",
" FROM HIGH\n",
" JOIN LOW\n",
" ON HIGH.SPECIES_NAME = LOW.SPECIES_NAME''')\n",
"db.commit()"
]
},
{
"cell_type": "code",
"execution_count": 98,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<style>\n",
" .dataframe thead tr:only-child th {\n",
" text-align: right;\n",
" }\n",
"\n",
" .dataframe thead th {\n",
" text-align: left;\n",
" }\n",
"\n",
" .dataframe tbody tr th {\n",
" vertical-align: top;\n",
" }\n",
"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>species_name</th>\n",
" <th>TEMP_HIGH</th>\n",
" <th>TEMP_LOW</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>H</td>\n",
" <td>5000</td>\n",
" <td>300</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>O</td>\n",
" <td>5000</td>\n",
" <td>300</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>OH</td>\n",
" <td>6000</td>\n",
" <td>200</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>H2</td>\n",
" <td>5000</td>\n",
" <td>300</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>O2</td>\n",
" <td>5000</td>\n",
" <td>300</td>\n",
" </tr>\n",
" <tr>\n",
" <th>5</th>\n",
" <td>H2O</td>\n",
" <td>5000</td>\n",
" <td>300</td>\n",
" </tr>\n",
" <tr>\n",
" <th>6</th>\n",
" <td>HO2</td>\n",
" <td>3500</td>\n",
" <td>200</td>\n",
" </tr>\n",
" <tr>\n",
" <th>7</th>\n",
" <td>H2O2</td>\n",
" <td>5000</td>\n",
" <td>300</td>\n",
" </tr>\n",
" <tr>\n",
" <th>8</th>\n",
" <td>N2</td>\n",
" <td>5000</td>\n",
" <td>300</td>\n",
" </tr>\n",
" <tr>\n",
" <th>9</th>\n",
" <td>Hp</td>\n",
" <td>5000</td>\n",
" <td>300</td>\n",
" </tr>\n",
" <tr>\n",
" <th>10</th>\n",
" <td>Op</td>\n",
" <td>5000</td>\n",
" <td>300</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" species_name TEMP_HIGH TEMP_LOW\n",
"0 H 5000 300\n",
"1 O 5000 300\n",
"2 OH 6000 200\n",
"3 H2 5000 300\n",
"4 O2 5000 300\n",
"5 H2O 5000 300\n",
"6 HO2 3500 200\n",
"7 H2O2 5000 300\n",
"8 N2 5000 300\n",
"9 Hp 5000 300\n",
"10 Op 5000 300"
]
},
"execution_count": 98,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"all_cols = [col[1] for col in cursor.execute(\"PRAGMA table_info(ALL_TEMPS)\")]\n",
"query = '''SELECT * FROM ALL_TEMPS'''\n",
"viz_tables(all_cols, query)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"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
}
| mit |
zomansud/coursera | ml-classification/week-6/module-9-precision-recall-assignment-blank.ipynb | 1 | 107880 | {
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Exploring precision and recall\n",
"\n",
"The goal of this second notebook is to understand precision-recall in the context of classifiers.\n",
"\n",
" * Use Amazon review data in its entirety.\n",
" * Train a logistic regression model.\n",
" * Explore various evaluation metrics: accuracy, confusion matrix, precision, recall.\n",
" * Explore how various metrics can be combined to produce a cost of making an error.\n",
" * Explore precision and recall curves.\n",
" \n",
"Because we are using the full Amazon review dataset (not a subset of words or reviews), in this assignment we return to using GraphLab Create for its efficiency. As usual, let's start by **firing up GraphLab Create**.\n",
"\n",
"Make sure you have the latest version of GraphLab Create (1.8.3 or later). If you don't find the decision tree module, then you would need to upgrade graphlab-create using\n",
"\n",
"```\n",
" pip install graphlab-create --upgrade\n",
"```\n",
"See [this page](https://dato.com/download/) for detailed instructions on upgrading."
]
},
{
"cell_type": "code",
"execution_count": 48,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"import graphlab\n",
"from __future__ import division\n",
"import numpy as np\n",
"graphlab.canvas.set_target('ipynb')"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Load amazon review dataset"
]
},
{
"cell_type": "code",
"execution_count": 49,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"products = graphlab.SFrame('amazon_baby.gl/')"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Extract word counts and sentiments"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"As in the first assignment of this course, we compute the word counts for individual words and extract positive and negative sentiments from ratings. To summarize, we perform the following:\n",
"\n",
"1. Remove punctuation.\n",
"2. Remove reviews with \"neutral\" sentiment (rating 3).\n",
"3. Set reviews with rating 4 or more to be positive and those with 2 or less to be negative."
]
},
{
"cell_type": "code",
"execution_count": 50,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"def remove_punctuation(text):\n",
" import string\n",
" return text.translate(None, string.punctuation) \n",
"\n",
"# Remove punctuation.\n",
"review_clean = products['review'].apply(remove_punctuation)\n",
"\n",
"# Count words\n",
"products['word_count'] = graphlab.text_analytics.count_words(review_clean)\n",
"\n",
"# Drop neutral sentiment reviews.\n",
"products = products[products['rating'] != 3]\n",
"\n",
"# Positive sentiment to +1 and negative sentiment to -1\n",
"products['sentiment'] = products['rating'].apply(lambda rating : +1 if rating > 3 else -1)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Now, let's remember what the dataset looks like by taking a quick peek:"
]
},
{
"cell_type": "code",
"execution_count": 51,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/html": [
"<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\"><table frame=\"box\" rules=\"cols\">\n",
" <tr>\n",
" <th style=\"padding-left: 1em; padding-right: 1em; text-align: center\">name</th>\n",
" <th style=\"padding-left: 1em; padding-right: 1em; text-align: center\">review</th>\n",
" <th style=\"padding-left: 1em; padding-right: 1em; text-align: center\">rating</th>\n",
" <th style=\"padding-left: 1em; padding-right: 1em; text-align: center\">word_count</th>\n",
" <th style=\"padding-left: 1em; padding-right: 1em; text-align: center\">sentiment</th>\n",
" </tr>\n",
" <tr>\n",
" <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">Planetwise Wipe Pouch</td>\n",
" <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">it came early and was not<br>disappointed. i love ...</td>\n",
" <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">5.0</td>\n",
" <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">{'and': 3, 'love': 1,<br>'it': 3, 'highly': 1, ...</td>\n",
" <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">1</td>\n",
" </tr>\n",
" <tr>\n",
" <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">Annas Dream Full Quilt<br>with 2 Shams ...</td>\n",
" <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">Very soft and comfortable<br>and warmer than it ...</td>\n",
" <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">5.0</td>\n",
" <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">{'and': 2, 'quilt': 1,<br>'it': 1, 'comfortable': ...</td>\n",
" <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">1</td>\n",
" </tr>\n",
" <tr>\n",
" <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">Stop Pacifier Sucking<br>without tears with ...</td>\n",
" <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">This is a product well<br>worth the purchase. I ...</td>\n",
" <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">5.0</td>\n",
" <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">{'and': 3, 'ingenious':<br>1, 'love': 2, 'what': 1, ...</td>\n",
" <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">1</td>\n",
" </tr>\n",
" <tr>\n",
" <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">Stop Pacifier Sucking<br>without tears with ...</td>\n",
" <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">All of my kids have cried<br>non-stop when I tried to ...</td>\n",
" <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">5.0</td>\n",
" <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">{'and': 2, 'all': 2,<br>'help': 1, 'cried': 1, ...</td>\n",
" <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">1</td>\n",
" </tr>\n",
" <tr>\n",
" <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">Stop Pacifier Sucking<br>without tears with ...</td>\n",
" <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">When the Binky Fairy came<br>to our house, we didn't ...</td>\n",
" <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">5.0</td>\n",
" <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">{'and': 2, 'this': 2,<br>'her': 1, 'help': 2, ...</td>\n",
" <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">1</td>\n",
" </tr>\n",
" <tr>\n",
" <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">A Tale of Baby's Days<br>with Peter Rabbit ...</td>\n",
" <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">Lovely book, it's bound<br>tightly so you may no ...</td>\n",
" <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">4.0</td>\n",
" <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">{'shop': 1, 'noble': 1,<br>'is': 1, 'it': 1, 'as': ...</td>\n",
" <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">1</td>\n",
" </tr>\n",
" <tr>\n",
" <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">Baby Tracker&reg; - Daily<br>Childcare Journal, ...</td>\n",
" <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">Perfect for new parents.<br>We were able to keep ...</td>\n",
" <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">5.0</td>\n",
" <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">{'and': 2, 'all': 1,<br>'right': 1, 'had': 1, ...</td>\n",
" <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">1</td>\n",
" </tr>\n",
" <tr>\n",
" <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">Baby Tracker&reg; - Daily<br>Childcare Journal, ...</td>\n",
" <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">A friend of mine pinned<br>this product on Pinte ...</td>\n",
" <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">5.0</td>\n",
" <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">{'and': 1, 'fantastic':<br>1, 'help': 1, 'give': 1, ...</td>\n",
" <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">1</td>\n",
" </tr>\n",
" <tr>\n",
" <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">Baby Tracker&reg; - Daily<br>Childcare Journal, ...</td>\n",
" <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">This has been an easy way<br>for my nanny to record ...</td>\n",
" <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">4.0</td>\n",
" <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">{'all': 1, 'standarad':<br>1, 'another': 1, 'when': ...</td>\n",
" <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">1</td>\n",
" </tr>\n",
" <tr>\n",
" <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">Baby Tracker&reg; - Daily<br>Childcare Journal, ...</td>\n",
" <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">I love this journal and<br>our nanny uses it ...</td>\n",
" <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">4.0</td>\n",
" <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">{'all': 2, 'nannys': 1,<br>'just': 1, 'food': 1, ...</td>\n",
" <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">1</td>\n",
" </tr>\n",
"</table>\n",
"[166752 rows x 5 columns]<br/>Note: Only the head of the SFrame is printed.<br/>You can use print_rows(num_rows=m, num_columns=n) to print more rows and columns.\n",
"</div>"
],
"text/plain": [
"Columns:\n",
"\tname\tstr\n",
"\treview\tstr\n",
"\trating\tfloat\n",
"\tword_count\tdict\n",
"\tsentiment\tint\n",
"\n",
"Rows: 166752\n",
"\n",
"Data:\n",
"+-------------------------------+-------------------------------+--------+\n",
"| name | review | rating |\n",
"+-------------------------------+-------------------------------+--------+\n",
"| Planetwise Wipe Pouch | it came early and was not ... | 5.0 |\n",
"| Annas Dream Full Quilt wit... | Very soft and comfortable ... | 5.0 |\n",
"| Stop Pacifier Sucking with... | This is a product well wor... | 5.0 |\n",
"| Stop Pacifier Sucking with... | All of my kids have cried ... | 5.0 |\n",
"| Stop Pacifier Sucking with... | When the Binky Fairy came ... | 5.0 |\n",
"| A Tale of Baby's Days with... | Lovely book, it's bound ti... | 4.0 |\n",
"| Baby Tracker® - Daily ... | Perfect for new parents. W... | 5.0 |\n",
"| Baby Tracker® - Daily ... | A friend of mine pinned th... | 5.0 |\n",
"| Baby Tracker® - Daily ... | This has been an easy way ... | 4.0 |\n",
"| Baby Tracker® - Daily ... | I love this journal and ou... | 4.0 |\n",
"+-------------------------------+-------------------------------+--------+\n",
"+-------------------------------+-----------+\n",
"| word_count | sentiment |\n",
"+-------------------------------+-----------+\n",
"| {'and': 3, 'love': 1, 'it'... | 1 |\n",
"| {'and': 2, 'quilt': 1, 'it... | 1 |\n",
"| {'and': 3, 'ingenious': 1,... | 1 |\n",
"| {'and': 2, 'all': 2, 'help... | 1 |\n",
"| {'and': 2, 'this': 2, 'her... | 1 |\n",
"| {'shop': 1, 'noble': 1, 'i... | 1 |\n",
"| {'and': 2, 'all': 1, 'righ... | 1 |\n",
"| {'and': 1, 'fantastic': 1,... | 1 |\n",
"| {'all': 1, 'standarad': 1,... | 1 |\n",
"| {'all': 2, 'nannys': 1, 'j... | 1 |\n",
"+-------------------------------+-----------+\n",
"[166752 rows x 5 columns]\n",
"Note: Only the head of the SFrame is printed.\n",
"You can use print_rows(num_rows=m, num_columns=n) to print more rows and columns."
]
},
"execution_count": 51,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"products"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Split data into training and test sets\n",
"\n",
"We split the data into a 80-20 split where 80% is in the training set and 20% is in the test set."
]
},
{
"cell_type": "code",
"execution_count": 52,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"train_data, test_data = products.random_split(.8, seed=1)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Train a logistic regression classifier\n",
"\n",
"We will now train a logistic regression classifier with **sentiment** as the target and **word_count** as the features. We will set `validation_set=None` to make sure everyone gets exactly the same results. \n",
"\n",
"Remember, even though we now know how to implement logistic regression, we will use GraphLab Create for its efficiency at processing this Amazon dataset in its entirety. The focus of this assignment is instead on the topic of precision and recall."
]
},
{
"cell_type": "code",
"execution_count": 53,
"metadata": {
"collapsed": false,
"scrolled": true
},
"outputs": [
{
"data": {
"text/html": [
"<pre>Logistic regression:</pre>"
],
"text/plain": [
"Logistic regression:"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"text/html": [
"<pre>--------------------------------------------------------</pre>"
],
"text/plain": [
"--------------------------------------------------------"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"text/html": [
"<pre>Number of examples : 133416</pre>"
],
"text/plain": [
"Number of examples : 133416"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"text/html": [
"<pre>Number of classes : 2</pre>"
],
"text/plain": [
"Number of classes : 2"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"text/html": [
"<pre>Number of feature columns : 1</pre>"
],
"text/plain": [
"Number of feature columns : 1"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"text/html": [
"<pre>Number of unpacked features : 121712</pre>"
],
"text/plain": [
"Number of unpacked features : 121712"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"text/html": [
"<pre>Number of coefficients : 121713</pre>"
],
"text/plain": [
"Number of coefficients : 121713"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"text/html": [
"<pre>Starting L-BFGS</pre>"
],
"text/plain": [
"Starting L-BFGS"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"text/html": [
"<pre>--------------------------------------------------------</pre>"
],
"text/plain": [
"--------------------------------------------------------"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"text/html": [
"<pre>+-----------+----------+-----------+--------------+-------------------+</pre>"
],
"text/plain": [
"+-----------+----------+-----------+--------------+-------------------+"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"text/html": [
"<pre>| Iteration | Passes | Step size | Elapsed Time | Training-accuracy |</pre>"
],
"text/plain": [
"| Iteration | Passes | Step size | Elapsed Time | Training-accuracy |"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"text/html": [
"<pre>+-----------+----------+-----------+--------------+-------------------+</pre>"
],
"text/plain": [
"+-----------+----------+-----------+--------------+-------------------+"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"text/html": [
"<pre>| 1 | 5 | 0.000002 | 1.039824 | 0.840754 |</pre>"
],
"text/plain": [
"| 1 | 5 | 0.000002 | 1.039824 | 0.840754 |"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"text/html": [
"<pre>| 2 | 9 | 3.000000 | 2.086944 | 0.931350 |</pre>"
],
"text/plain": [
"| 2 | 9 | 3.000000 | 2.086944 | 0.931350 |"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"text/html": [
"<pre>| 3 | 10 | 3.000000 | 2.475899 | 0.882046 |</pre>"
],
"text/plain": [
"| 3 | 10 | 3.000000 | 2.475899 | 0.882046 |"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"text/html": [
"<pre>| 4 | 11 | 3.000000 | 2.834446 | 0.954076 |</pre>"
],
"text/plain": [
"| 4 | 11 | 3.000000 | 2.834446 | 0.954076 |"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"text/html": [
"<pre>| 5 | 12 | 3.000000 | 3.187388 | 0.960964 |</pre>"
],
"text/plain": [
"| 5 | 12 | 3.000000 | 3.187388 | 0.960964 |"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"text/html": [
"<pre>| 6 | 13 | 3.000000 | 3.542964 | 0.975033 |</pre>"
],
"text/plain": [
"| 6 | 13 | 3.000000 | 3.542964 | 0.975033 |"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"text/html": [
"<pre>+-----------+----------+-----------+--------------+-------------------+</pre>"
],
"text/plain": [
"+-----------+----------+-----------+--------------+-------------------+"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"text/html": [
"<pre>TERMINATED: Terminated due to numerical difficulties.</pre>"
],
"text/plain": [
"TERMINATED: Terminated due to numerical difficulties."
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"text/html": [
"<pre>This model may not be ideal. To improve it, consider doing one of the following:\n",
"(a) Increasing the regularization.\n",
"(b) Standardizing the input data.\n",
"(c) Removing highly correlated features.\n",
"(d) Removing `inf` and `NaN` values in the training data.</pre>"
],
"text/plain": [
"This model may not be ideal. To improve it, consider doing one of the following:\n",
"(a) Increasing the regularization.\n",
"(b) Standardizing the input data.\n",
"(c) Removing highly correlated features.\n",
"(d) Removing `inf` and `NaN` values in the training data."
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"model = graphlab.logistic_classifier.create(train_data, target='sentiment',\n",
" features=['word_count'],\n",
" validation_set=None)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Model Evaluation"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"We will explore the advanced model evaluation concepts that were discussed in the lectures.\n",
"\n",
"## Accuracy\n",
"\n",
"One performance metric we will use for our more advanced exploration is accuracy, which we have seen many times in past assignments. Recall that the accuracy is given by\n",
"\n",
"$$\n",
"\\mbox{accuracy} = \\frac{\\mbox{# correctly classified data points}}{\\mbox{# total data points}}\n",
"$$\n",
"\n",
"To obtain the accuracy of our trained models using GraphLab Create, simply pass the option `metric='accuracy'` to the `evaluate` function. We compute the **accuracy** of our logistic regression model on the **test_data** as follows:"
]
},
{
"cell_type": "code",
"execution_count": 54,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Test Accuracy: 0.914536837053\n"
]
}
],
"source": [
"accuracy= model.evaluate(test_data, metric='accuracy')['accuracy']\n",
"print \"Test Accuracy: %s\" % accuracy"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Baseline: Majority class prediction\n",
"\n",
"Recall from an earlier assignment that we used the **majority class classifier** as a baseline (i.e reference) model for a point of comparison with a more sophisticated classifier. The majority classifier model predicts the majority class for all data points. \n",
"\n",
"Typically, a good model should beat the majority class classifier. Since the majority class in this dataset is the positive class (i.e., there are more positive than negative reviews), the accuracy of the majority class classifier can be computed as follows:"
]
},
{
"cell_type": "code",
"execution_count": 55,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Baseline accuracy (majority class classifier): 0.842782577394\n"
]
}
],
"source": [
"baseline = len(test_data[test_data['sentiment'] == 1])/len(test_data)\n",
"print \"Baseline accuracy (majority class classifier): %s\" % baseline"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"** Quiz Question:** Using accuracy as the evaluation metric, was our **logistic regression model** better than the baseline (majority class classifier)?"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Confusion Matrix\n",
"\n",
"The accuracy, while convenient, does not tell the whole story. For a fuller picture, we turn to the **confusion matrix**. In the case of binary classification, the confusion matrix is a 2-by-2 matrix laying out correct and incorrect predictions made in each label as follows:\n",
"```\n",
" +---------------------------------------------+\n",
" | Predicted label |\n",
" +----------------------+----------------------+\n",
" | (+1) | (-1) |\n",
"+-------+-----+----------------------+----------------------+\n",
"| True |(+1) | # of true positives | # of false negatives |\n",
"| label +-----+----------------------+----------------------+\n",
"| |(-1) | # of false positives | # of true negatives |\n",
"+-------+-----+----------------------+----------------------+\n",
"```\n",
"To print out the confusion matrix for a classifier, use `metric='confusion_matrix'`:"
]
},
{
"cell_type": "code",
"execution_count": 56,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/html": [
"<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\"><table frame=\"box\" rules=\"cols\">\n",
" <tr>\n",
" <th style=\"padding-left: 1em; padding-right: 1em; text-align: center\">target_label</th>\n",
" <th style=\"padding-left: 1em; padding-right: 1em; text-align: center\">predicted_label</th>\n",
" <th style=\"padding-left: 1em; padding-right: 1em; text-align: center\">count</th>\n",
" </tr>\n",
" <tr>\n",
" <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">1</td>\n",
" <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">-1</td>\n",
" <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">1406</td>\n",
" </tr>\n",
" <tr>\n",
" <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">-1</td>\n",
" <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">-1</td>\n",
" <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">3798</td>\n",
" </tr>\n",
" <tr>\n",
" <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">-1</td>\n",
" <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">1</td>\n",
" <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">1443</td>\n",
" </tr>\n",
" <tr>\n",
" <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">1</td>\n",
" <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">1</td>\n",
" <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">26689</td>\n",
" </tr>\n",
"</table>\n",
"[4 rows x 3 columns]<br/>\n",
"</div>"
],
"text/plain": [
"Columns:\n",
"\ttarget_label\tint\n",
"\tpredicted_label\tint\n",
"\tcount\tint\n",
"\n",
"Rows: 4\n",
"\n",
"Data:\n",
"+--------------+-----------------+-------+\n",
"| target_label | predicted_label | count |\n",
"+--------------+-----------------+-------+\n",
"| 1 | -1 | 1406 |\n",
"| -1 | -1 | 3798 |\n",
"| -1 | 1 | 1443 |\n",
"| 1 | 1 | 26689 |\n",
"+--------------+-----------------+-------+\n",
"[4 rows x 3 columns]"
]
},
"execution_count": 56,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"confusion_matrix = model.evaluate(test_data, metric='confusion_matrix')['confusion_matrix']\n",
"confusion_matrix"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"**Quiz Question**: How many predicted values in the **test set** are **false positives**?"
]
},
{
"cell_type": "code",
"execution_count": 57,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"0.05"
]
},
"execution_count": 57,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"round(1443 / (26689 + 1443 ), 2)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Computing the cost of mistakes\n",
"\n",
"\n",
"Put yourself in the shoes of a manufacturer that sells a baby product on Amazon.com and you want to monitor your product's reviews in order to respond to complaints. Even a few negative reviews may generate a lot of bad publicity about the product. So you don't want to miss any reviews with negative sentiments --- you'd rather put up with false alarms about potentially negative reviews instead of missing negative reviews entirely. In other words, **false positives cost more than false negatives**. (It may be the other way around for other scenarios, but let's stick with the manufacturer's scenario for now.)\n",
"\n",
"Suppose you know the costs involved in each kind of mistake: \n",
"1. \\$100 for each false positive.\n",
"2. \\$1 for each false negative.\n",
"3. Correctly classified reviews incur no cost.\n",
"\n",
"**Quiz Question**: Given the stipulation, what is the cost associated with the logistic regression classifier's performance on the **test set**?"
]
},
{
"cell_type": "code",
"execution_count": 58,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"145706"
]
},
"execution_count": 58,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"100*1443 + 1*1406"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Precision and Recall"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"You may not have exact dollar amounts for each kind of mistake. Instead, you may simply prefer to reduce the percentage of false positives to be less than, say, 3.5% of all positive predictions. This is where **precision** comes in:\n",
"\n",
"$$\n",
"[\\text{precision}] = \\frac{[\\text{# positive data points with positive predicitions}]}{\\text{[# all data points with positive predictions]}} = \\frac{[\\text{# true positives}]}{[\\text{# true positives}] + [\\text{# false positives}]}\n",
"$$"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"So to keep the percentage of false positives below 3.5% of positive predictions, we must raise the precision to 96.5% or higher. \n",
"\n",
"**First**, let us compute the precision of the logistic regression classifier on the **test_data**."
]
},
{
"cell_type": "code",
"execution_count": 59,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Precision on test data: 0.948706099815\n"
]
}
],
"source": [
"precision = model.evaluate(test_data, metric='precision')['precision']\n",
"print \"Precision on test data: %s\" % precision"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"**Quiz Question**: Out of all reviews in the **test set** that are predicted to be positive, what fraction of them are **false positives**? (Round to the second decimal place e.g. 0.25)"
]
},
{
"cell_type": "code",
"execution_count": 60,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"0.05"
]
},
"execution_count": 60,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"round(1 - precision, 2)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"**Quiz Question:** Based on what we learned in lecture, if we wanted to reduce this fraction of false positives to be below 3.5%, we would: (see the quiz)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"A complementary metric is **recall**, which measures the ratio between the number of true positives and that of (ground-truth) positive reviews:\n",
"\n",
"$$\n",
"[\\text{recall}] = \\frac{[\\text{# positive data points with positive predicitions}]}{\\text{[# all positive data points]}} = \\frac{[\\text{# true positives}]}{[\\text{# true positives}] + [\\text{# false negatives}]}\n",
"$$\n",
"\n",
"Let us compute the recall on the **test_data**."
]
},
{
"cell_type": "code",
"execution_count": 61,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Recall on test data: 0.949955508098\n"
]
}
],
"source": [
"recall = model.evaluate(test_data, metric='recall')['recall']\n",
"print \"Recall on test data: %s\" % recall"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"**Quiz Question**: What fraction of the positive reviews in the **test_set** were correctly predicted as positive by the classifier?\n",
"\n",
"**Quiz Question**: What is the recall value for a classifier that predicts **+1** for all data points in the **test_data**?"
]
},
{
"cell_type": "markdown",
"metadata": {
"collapsed": true
},
"source": [
"# Precision-recall tradeoff\n",
"\n",
"In this part, we will explore the trade-off between precision and recall discussed in the lecture. We first examine what happens when we use a different threshold value for making class predictions. We then explore a range of threshold values and plot the associated precision-recall curve. \n"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Varying the threshold\n",
"\n",
"False positives are costly in our example, so we may want to be more conservative about making positive predictions. To achieve this, instead of thresholding class probabilities at 0.5, we can choose a higher threshold. \n",
"\n",
"Write a function called `apply_threshold` that accepts two things\n",
"* `probabilities` (an SArray of probability values)\n",
"* `threshold` (a float between 0 and 1).\n",
"\n",
"The function should return an SArray, where each element is set to +1 or -1 depending whether the corresponding probability exceeds `threshold`."
]
},
{
"cell_type": "code",
"execution_count": 62,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"def apply_threshold(probabilities, threshold):\n",
" ### YOUR CODE GOES HERE\n",
" # +1 if >= threshold and -1 otherwise.\n",
" return probabilities.apply(lambda x: +1 if x >= threshold else -1)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Run prediction with `output_type='probability'` to get the list of probability values. Then use thresholds set at 0.5 (default) and 0.9 to make predictions from these probability values."
]
},
{
"cell_type": "code",
"execution_count": 63,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"probabilities = model.predict(test_data, output_type='probability')\n",
"predictions_with_default_threshold = apply_threshold(probabilities, 0.5)\n",
"predictions_with_high_threshold = apply_threshold(probabilities, 0.9)"
]
},
{
"cell_type": "code",
"execution_count": 64,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Number of positive predicted reviews (threshold = 0.5): 28132\n"
]
}
],
"source": [
"print \"Number of positive predicted reviews (threshold = 0.5): %s\" % (predictions_with_default_threshold == 1).sum()"
]
},
{
"cell_type": "code",
"execution_count": 65,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Number of positive predicted reviews (threshold = 0.9): 25630\n"
]
}
],
"source": [
"print \"Number of positive predicted reviews (threshold = 0.9): %s\" % (predictions_with_high_threshold == 1).sum()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"**Quiz Question**: What happens to the number of positive predicted reviews as the threshold increased from 0.5 to 0.9?"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Exploring the associated precision and recall as the threshold varies"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"By changing the probability threshold, it is possible to influence precision and recall. We can explore this as follows:"
]
},
{
"cell_type": "code",
"execution_count": 66,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"# Threshold = 0.5\n",
"precision_with_default_threshold = graphlab.evaluation.precision(test_data['sentiment'],\n",
" predictions_with_default_threshold)\n",
"\n",
"recall_with_default_threshold = graphlab.evaluation.recall(test_data['sentiment'],\n",
" predictions_with_default_threshold)\n",
"\n",
"# Threshold = 0.9\n",
"precision_with_high_threshold = graphlab.evaluation.precision(test_data['sentiment'],\n",
" predictions_with_high_threshold)\n",
"recall_with_high_threshold = graphlab.evaluation.recall(test_data['sentiment'],\n",
" predictions_with_high_threshold)"
]
},
{
"cell_type": "code",
"execution_count": 67,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Precision (threshold = 0.5): 0.948706099815\n",
"Recall (threshold = 0.5) : 0.949955508098\n"
]
}
],
"source": [
"print \"Precision (threshold = 0.5): %s\" % precision_with_default_threshold\n",
"print \"Recall (threshold = 0.5) : %s\" % recall_with_default_threshold"
]
},
{
"cell_type": "code",
"execution_count": 68,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Precision (threshold = 0.9): 0.969527896996\n",
"Recall (threshold = 0.9) : 0.884463427656\n"
]
}
],
"source": [
"print \"Precision (threshold = 0.9): %s\" % precision_with_high_threshold\n",
"print \"Recall (threshold = 0.9) : %s\" % recall_with_high_threshold"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"**Quiz Question (variant 1)**: Does the **precision** increase with a higher threshold?\n",
"\n",
"**Quiz Question (variant 2)**: Does the **recall** increase with a higher threshold?"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Precision-recall curve\n",
"\n",
"Now, we will explore various different values of tresholds, compute the precision and recall scores, and then plot the precision-recall curve."
]
},
{
"cell_type": "code",
"execution_count": 69,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"[ 0.5 0.50505051 0.51010101 0.51515152 0.52020202 0.52525253\n",
" 0.53030303 0.53535354 0.54040404 0.54545455 0.55050505 0.55555556\n",
" 0.56060606 0.56565657 0.57070707 0.57575758 0.58080808 0.58585859\n",
" 0.59090909 0.5959596 0.6010101 0.60606061 0.61111111 0.61616162\n",
" 0.62121212 0.62626263 0.63131313 0.63636364 0.64141414 0.64646465\n",
" 0.65151515 0.65656566 0.66161616 0.66666667 0.67171717 0.67676768\n",
" 0.68181818 0.68686869 0.69191919 0.6969697 0.7020202 0.70707071\n",
" 0.71212121 0.71717172 0.72222222 0.72727273 0.73232323 0.73737374\n",
" 0.74242424 0.74747475 0.75252525 0.75757576 0.76262626 0.76767677\n",
" 0.77272727 0.77777778 0.78282828 0.78787879 0.79292929 0.7979798\n",
" 0.8030303 0.80808081 0.81313131 0.81818182 0.82323232 0.82828283\n",
" 0.83333333 0.83838384 0.84343434 0.84848485 0.85353535 0.85858586\n",
" 0.86363636 0.86868687 0.87373737 0.87878788 0.88383838 0.88888889\n",
" 0.89393939 0.8989899 0.9040404 0.90909091 0.91414141 0.91919192\n",
" 0.92424242 0.92929293 0.93434343 0.93939394 0.94444444 0.94949495\n",
" 0.95454545 0.95959596 0.96464646 0.96969697 0.97474747 0.97979798\n",
" 0.98484848 0.98989899 0.99494949 1. ]\n"
]
}
],
"source": [
"threshold_values = np.linspace(0.5, 1, num=100)\n",
"print threshold_values"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"For each of the values of threshold, we compute the precision and recall scores."
]
},
{
"cell_type": "code",
"execution_count": 70,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"precision_all = []\n",
"recall_all = []\n",
"\n",
"probabilities = model.predict(test_data, output_type='probability')\n",
"for threshold in threshold_values:\n",
" predictions = apply_threshold(probabilities, threshold)\n",
" \n",
" precision = graphlab.evaluation.precision(test_data['sentiment'], predictions)\n",
" recall = graphlab.evaluation.recall(test_data['sentiment'], predictions)\n",
" \n",
" precision_all.append(precision)\n",
" recall_all.append(recall)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Now, let's plot the precision-recall curve to visualize the precision-recall tradeoff as we vary the threshold."
]
},
{
"cell_type": "code",
"execution_count": 71,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"image/png": "iVBORw0KGgoAAAANSUhEUgAAAdgAAAFgCAYAAAAYQGiBAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x11db49c10>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"import matplotlib.pyplot as plt\n",
"%matplotlib inline\n",
"\n",
"def plot_pr_curve(precision, recall, title):\n",
" plt.rcParams['figure.figsize'] = 7, 5\n",
" plt.locator_params(axis = 'x', nbins = 5)\n",
" plt.plot(precision, recall, 'b-', linewidth=4.0, color = '#B0017F')\n",
" plt.title(title)\n",
" plt.xlabel('Precision')\n",
" plt.ylabel('Recall')\n",
" plt.rcParams.update({'font.size': 16})\n",
" \n",
"plot_pr_curve(precision_all, recall_all, 'Precision recall curve (all)')"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"**Quiz Question**: Among all the threshold values tried, what is the **smallest** threshold value that achieves a precision of 96.5% or better? Round your answer to 3 decimal places."
]
},
{
"cell_type": "code",
"execution_count": 72,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"0 -> 0.948706099815\n",
"1 -> 0.94905908719\n",
"2 -> 0.949288256228\n",
"3 -> 0.949506819072\n",
"4 -> 0.949624140511\n",
"5 -> 0.949805711026\n",
"6 -> 0.950203324534\n",
"7 -> 0.950417648319\n",
"8 -> 0.950696677385\n",
"9 -> 0.950877694755\n",
"10 -> 0.951062459755\n",
"11 -> 0.951424684994\n",
"12 -> 0.951534907046\n",
"13 -> 0.951761459341\n",
"14 -> 0.952177656598\n",
"15 -> 0.952541642734\n",
"16 -> 0.952825782345\n",
"17 -> 0.952950902164\n",
"18 -> 0.953033408854\n",
"19 -> 0.953081711222\n",
"20 -> 0.953231323132\n",
"21 -> 0.953525236877\n",
"22 -> 0.953680340278\n",
"23 -> 0.953691347784\n",
"24 -> 0.954012200845\n",
"25 -> 0.95415959253\n",
"26 -> 0.954481362305\n",
"27 -> 0.954630969609\n",
"28 -> 0.954956912159\n",
"29 -> 0.955217391304\n",
"30 -> 0.955425794284\n",
"31 -> 0.955603150978\n",
"32 -> 0.955716205907\n",
"33 -> 0.955933682373\n",
"34 -> 0.95600756859\n",
"35 -> 0.956162388494\n",
"36 -> 0.956453611253\n",
"37 -> 0.956670800204\n",
"38 -> 0.956951949759\n",
"39 -> 0.957200292398\n",
"40 -> 0.95730904302\n",
"41 -> 0.957558224696\n",
"42 -> 0.957740800469\n",
"43 -> 0.958172812328\n",
"44 -> 0.958434310054\n",
"45 -> 0.958762128786\n",
"46 -> 0.959152130713\n",
"47 -> 0.959266352387\n",
"48 -> 0.95958553044\n",
"49 -> 0.959906966441\n",
"50 -> 0.959957149717\n",
"51 -> 0.960170118343\n",
"52 -> 0.96034655115\n",
"53 -> 0.960716006374\n",
"54 -> 0.960870855278\n",
"55 -> 0.961087182534\n",
"56 -> 0.961366847624\n",
"57 -> 0.962202659674\n",
"58 -> 0.962415603901\n",
"59 -> 0.9624873268\n",
"60 -> 0.962727546261\n",
"61 -> 0.963204278397\n",
"62 -> 0.963492362814\n",
"63 -> 0.963922783423\n",
"64 -> 0.964218170815\n",
"65 -> 0.964581991742\n",
"66 -> 0.964945559391\n",
"67 -> 0.965311550152\n",
"68 -> 0.965662948723\n",
"69 -> 0.965982762566\n",
"70 -> 0.966381418093\n",
"71 -> 0.966780205901\n",
"72 -> 0.966996320147\n",
"73 -> 0.96737626806\n",
"74 -> 0.96765996766\n",
"75 -> 0.967978395062\n",
"76 -> 0.968586792526\n",
"77 -> 0.968960968418\n",
"78 -> 0.969313939017\n",
"79 -> 0.969468923029\n",
"80 -> 0.969731336279\n",
"81 -> 0.969926286073\n",
"82 -> 0.970296640176\n",
"83 -> 0.970813586098\n",
"84 -> 0.971404775125\n",
"85 -> 0.97203187251\n",
"86 -> 0.972883121045\n",
"87 -> 0.973425672411\n",
"88 -> 0.974041226258\n",
"89 -> 0.974463571837\n",
"90 -> 0.974766393611\n",
"91 -> 0.97549325026\n",
"92 -> 0.976197472818\n",
"93 -> 0.976871731644\n",
"94 -> 0.977337354589\n",
"95 -> 0.978530031612\n",
"96 -> 0.980131852253\n",
"97 -> 0.981307971185\n",
"98 -> 0.984238628196\n",
"99 -> 0.991666666667\n"
]
}
],
"source": [
"for i, p in enumerate(precision_all):\n",
" print str(i) + \" -> \" + str(p)"
]
},
{
"cell_type": "code",
"execution_count": 73,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"0.838"
]
},
"execution_count": 73,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"round(threshold_values[67], 3)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"**Quiz Question**: Using `threshold` = 0.98, how many **false negatives** do we get on the **test_data**? (**Hint**: You may use the `graphlab.evaluation.confusion_matrix` function implemented in GraphLab Create.)"
]
},
{
"cell_type": "code",
"execution_count": 74,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/html": [
"<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\"><table frame=\"box\" rules=\"cols\">\n",
" <tr>\n",
" <th style=\"padding-left: 1em; padding-right: 1em; text-align: center\">target_label</th>\n",
" <th style=\"padding-left: 1em; padding-right: 1em; text-align: center\">predicted_label</th>\n",
" <th style=\"padding-left: 1em; padding-right: 1em; text-align: center\">count</th>\n",
" </tr>\n",
" <tr>\n",
" <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">-1</td>\n",
" <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">1</td>\n",
" <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">487</td>\n",
" </tr>\n",
" <tr>\n",
" <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">1</td>\n",
" <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">1</td>\n",
" <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">22269</td>\n",
" </tr>\n",
" <tr>\n",
" <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">1</td>\n",
" <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">-1</td>\n",
" <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">5826</td>\n",
" </tr>\n",
" <tr>\n",
" <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">-1</td>\n",
" <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">-1</td>\n",
" <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">4754</td>\n",
" </tr>\n",
"</table>\n",
"[4 rows x 3 columns]<br/>\n",
"</div>"
],
"text/plain": [
"Columns:\n",
"\ttarget_label\tint\n",
"\tpredicted_label\tint\n",
"\tcount\tint\n",
"\n",
"Rows: 4\n",
"\n",
"Data:\n",
"+--------------+-----------------+-------+\n",
"| target_label | predicted_label | count |\n",
"+--------------+-----------------+-------+\n",
"| -1 | 1 | 487 |\n",
"| 1 | 1 | 22269 |\n",
"| 1 | -1 | 5826 |\n",
"| -1 | -1 | 4754 |\n",
"+--------------+-----------------+-------+\n",
"[4 rows x 3 columns]"
]
},
"execution_count": 74,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"predictions_with_98_threshold = apply_threshold(probabilities, 0.98)\n",
"cm = graphlab.evaluation.confusion_matrix(test_data['sentiment'],\n",
" predictions_with_98_threshold)\n",
"cm"
]
},
{
"cell_type": "markdown",
"metadata": {
"collapsed": false
},
"source": [
"This is the number of false negatives (i.e the number of reviews to look at when not needed) that we have to deal with using this classifier."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Evaluating specific search terms"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"So far, we looked at the number of false positives for the **entire test set**. In this section, let's select reviews using a specific search term and optimize the precision on these reviews only. After all, a manufacturer would be interested in tuning the false positive rate just for their products (the reviews they want to read) rather than that of the entire set of products on Amazon.\n",
"\n",
"## Precision-Recall on all baby related items\n",
"\n",
"From the **test set**, select all the reviews for all products with the word 'baby' in them."
]
},
{
"cell_type": "code",
"execution_count": 36,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"baby_reviews = test_data[test_data['name'].apply(lambda x: 'baby' in x.lower())]"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Now, let's predict the probability of classifying these reviews as positive:"
]
},
{
"cell_type": "code",
"execution_count": 37,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"probabilities = model.predict(baby_reviews, output_type='probability')"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Let's plot the precision-recall curve for the **baby_reviews** dataset.\n",
"\n",
"**First**, let's consider the following `threshold_values` ranging from 0.5 to 1:"
]
},
{
"cell_type": "code",
"execution_count": 38,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"threshold_values = np.linspace(0.5, 1, num=100)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"**Second**, as we did above, let's compute precision and recall for each value in `threshold_values` on the **baby_reviews** dataset. Complete the code block below."
]
},
{
"cell_type": "code",
"execution_count": 39,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"precision_all = []\n",
"recall_all = []\n",
"\n",
"for threshold in threshold_values:\n",
" \n",
" # Make predictions. Use the `apply_threshold` function \n",
" ## YOUR CODE HERE \n",
" predictions = apply_threshold(probabilities, threshold)\n",
"\n",
" # Calculate the precision.\n",
" # YOUR CODE HERE\n",
" precision = graphlab.evaluation.precision(baby_reviews['sentiment'], predictions)\n",
" \n",
" # YOUR CODE HERE\n",
" recall = graphlab.evaluation.recall(baby_reviews['sentiment'], predictions)\n",
" \n",
" # Append the precision and recall scores.\n",
" precision_all.append(precision)\n",
" recall_all.append(recall)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"**Quiz Question**: Among all the threshold values tried, what is the **smallest** threshold value that achieves a precision of 96.5% or better for the reviews of data in **baby_reviews**? Round your answer to 3 decimal places."
]
},
{
"cell_type": "code",
"execution_count": 43,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"0.864"
]
},
"execution_count": 43,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"round(threshold_values[72], 3)"
]
},
{
"cell_type": "code",
"execution_count": 44,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"0 -> 0.947656392486\n",
"1 -> 0.948165723672\n",
"2 -> 0.948319941563\n",
"3 -> 0.948474328522\n",
"4 -> 0.948638274538\n",
"5 -> 0.948792977323\n",
"6 -> 0.949487554905\n",
"7 -> 0.949459805896\n",
"8 -> 0.94998167827\n",
"9 -> 0.949954170486\n",
"10 -> 0.95011920044\n",
"11 -> 0.950816663608\n",
"12 -> 0.95080763583\n",
"13 -> 0.950964187328\n",
"14 -> 0.951793928243\n",
"15 -> 0.951951399116\n",
"16 -> 0.952082565426\n",
"17 -> 0.952407304925\n",
"18 -> 0.952363367799\n",
"19 -> 0.952345770225\n",
"20 -> 0.952336966562\n",
"21 -> 0.952856350527\n",
"22 -> 0.95282146161\n",
"23 -> 0.952795261014\n",
"24 -> 0.952901909883\n",
"25 -> 0.953035084463\n",
"26 -> 0.953212031192\n",
"27 -> 0.953354395094\n",
"28 -> 0.953683035714\n",
"29 -> 0.954020848846\n",
"30 -> 0.954172876304\n",
"31 -> 0.954164337619\n",
"32 -> 0.954291044776\n",
"33 -> 0.954248366013\n",
"34 -> 0.954214165577\n",
"35 -> 0.95436693473\n",
"36 -> 0.954715568862\n",
"37 -> 0.954852004496\n",
"38 -> 0.954997187324\n",
"39 -> 0.955355468017\n",
"40 -> 0.955492957746\n",
"41 -> 0.955622414442\n",
"42 -> 0.955768868812\n",
"43 -> 0.95627591406\n",
"44 -> 0.956259426848\n",
"45 -> 0.956579195771\n",
"46 -> 0.956924239562\n",
"47 -> 0.956883509834\n",
"48 -> 0.957188861527\n",
"49 -> 0.957321699545\n",
"50 -> 0.957471046136\n",
"51 -> 0.957596501236\n",
"52 -> 0.957912778518\n",
"53 -> 0.958245948522\n",
"54 -> 0.958778625954\n",
"55 -> 0.959105675521\n",
"56 -> 0.959586286152\n",
"57 -> 0.960655737705\n",
"58 -> 0.96098126328\n",
"59 -> 0.961121856867\n",
"60 -> 0.961084220716\n",
"61 -> 0.961419154711\n",
"62 -> 0.961545931249\n",
"63 -> 0.962062256809\n",
"64 -> 0.962378167641\n",
"65 -> 0.962724434036\n",
"66 -> 0.963064295486\n",
"67 -> 0.963194988254\n",
"68 -> 0.963711259317\n",
"69 -> 0.964194373402\n",
"70 -> 0.964722112732\n",
"71 -> 0.964863797868\n",
"72 -> 0.965019762846\n",
"73 -> 0.965510406343\n",
"74 -> 0.966037735849\n",
"75 -> 0.966155683854\n",
"76 -> 0.966839792249\n",
"77 -> 0.967935871743\n",
"78 -> 0.968072289157\n",
"79 -> 0.968014484007\n",
"80 -> 0.967911200807\n",
"81 -> 0.967859308672\n",
"82 -> 0.968154158215\n",
"83 -> 0.968470301058\n",
"84 -> 0.969139587165\n",
"85 -> 0.969771745836\n",
"86 -> 0.971050454921\n",
"87 -> 0.971226021685\n",
"88 -> 0.971476510067\n",
"89 -> 0.972245762712\n",
"90 -> 0.972418216806\n",
"91 -> 0.973411154345\n",
"92 -> 0.974202011369\n",
"93 -> 0.97500552975\n",
"94 -> 0.975100942127\n",
"95 -> 0.976659038902\n",
"96 -> 0.979048964218\n",
"97 -> 0.980103168755\n",
"98 -> 0.984425349087\n",
"99 -> 1.0\n"
]
}
],
"source": [
"for i, p in enumerate(precision_all):\n",
" print str(i) + \" -> \" + str(p)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"**Quiz Question:** Is this threshold value smaller or larger than the threshold used for the entire dataset to achieve the same specified precision of 96.5%?\n",
"\n",
"**Finally**, let's plot the precision recall curve."
]
},
{
"cell_type": "code",
"execution_count": 45,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"image/png": "iVBORw0KGgoAAAANSUhEUgAAAdgAAAFgCAYAAAAYQGiBAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nkqQKDFhJkiowYCVJqsCAlSSpgm/oUf4LOoajnwAAAABJRU5ErkJggg==\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x12ccafa50>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"plot_pr_curve(precision_all, recall_all, \"Precision-Recall (Baby)\")"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": []
}
],
"metadata": {
"kernelspec": {
"display_name": "Python [gl-env]",
"language": "python",
"name": "Python [gl-env]"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 2
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython2",
"version": "2.7.11"
}
},
"nbformat": 4,
"nbformat_minor": 0
}
| mit |
SheffieldML/GPyOpt | manual/GPyOpt_context.ipynb | 1 | 5599 | {
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# GPyOpt: using context variables\n",
"\n",
"### Javier Gonzalez and Rodolphe Jenatton, Amazon.com\n",
"\n",
"\n",
"*Last updated Monday, July 2017*"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"In this notebook we are going to see how to used GPyOpt to solve optimizaiton problems in which certain varaibles are fixed during the optimization phase. These are called context variables. For details see:\n",
"\n",
"\n",
"*Krause, A. & Ong, C. S. Contextual gaussian process bandit optimization Advances in Neural Information Processing Systems (NIPS), 2011, 2447-2455*"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"%pylab inline\n",
"import GPyOpt\n",
"from numpy.random import seed"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"func = GPyOpt.objective_examples.experimentsNd.alpine1(input_dim=5) "
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Now we define the domain of the function to optimize as usual."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"mixed_domain =[{'name': 'var1', 'type': 'continuous', 'domain': (-5,5),'dimensionality': 3},\n",
" {'name': 'var2', 'type': 'discrete', 'domain': (3,8,10)},\n",
" {'name': 'var3', 'type': 'categorical', 'domain': (0,1,2)},\n",
" {'name': 'var4', 'type': 'continuous', 'domain': (-1,2)}]"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"myBopt = GPyOpt.methods.BayesianOptimization(f=func.f, # Objective function \n",
" domain=mixed_domain, # Box-constraints of the problem\n",
" initial_design_numdata = 5, # Number data initial design\n",
" acquisition_type='EI', # Expected Improvement\n",
" exact_feval = True,\n",
" evaluator_type = 'local_penalization',\n",
" batch_size = 5\n",
" ) # True evaluations, no sample noise"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Now, we run the optimization for 20 iterations or a maximum of 60 seconds and we show the convergence plots."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false,
"scrolled": false
},
"outputs": [],
"source": [
"max_iter = 2 ## maximum number of iterations\n",
"max_time = 60 ## maximum allowed time\n",
"eps = 0 ## tolerance, max distance between consicutive evaluations."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"To set a context, we just need to create a dicctionary with the variables to fix and pass it to the Bayesian ottimization object when running the optimization. Note that, everytime we run new iterations we can set other variables to be the context. Note that for variables in which the dimaensionality has been specified in the domain, a subindex is internally asigned. For instance if the variables is called 'var1' and has dimensionality 3, the first three positions in the internal representation of the domain will be occupied by variables 'var1_1', 'var1_2' and 'var1_3'. If no dimensionality is added, the internal naming remains the same. For instance, in the example above 'var3' should be fixed its original name. See below for details."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"myBopt.run_optimization(max_iter,eps=eps)\n",
"myBopt.run_optimization(max_iter,eps=eps,context = {'var1_1':.3, 'var1_2':0.4})\n",
"myBopt.run_optimization(max_iter,eps=eps,context = {'var1_1':0, 'var3':2})\n",
"myBopt.run_optimization(max_iter,eps=eps,context = {'var1_1':0, 'var2':3},)\n",
"myBopt.run_optimization(max_iter,eps=eps,context = {'var1_1':0.3, 'var3':1, 'var4':-.4})\n",
"myBopt.run_optimization(max_iter,eps=eps)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"We can now visualize the results"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"np.round(myBopt.X,2)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": []
}
],
"metadata": {
"anaconda-cloud": {},
"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": "3.5.2"
}
},
"nbformat": 4,
"nbformat_minor": 0
}
| bsd-3-clause |
youssef-emad/shogun | doc/ipython-notebooks/classification/HashedDocDotFeatures.ipynb | 21 | 28534 | {
"metadata": {
"name": "",
"signature": "sha256:2e92f9472f083b2c902ee25ca9b903b5119ede7fd33944de6e74df7e4bc849f8"
},
"nbformat": 3,
"nbformat_minor": 0,
"worksheets": [
{
"cells": [
{
"cell_type": "heading",
"level": 1,
"metadata": {},
"source": [
"Large scale document classification with the Shogun Machine Learning Toolbox"
]
},
{
"cell_type": "heading",
"level": 4,
"metadata": {},
"source": [
"by Evangelos Anagnostopoulos (GitHub ID: <a href=\"http://github.com/van51\">van51</a>).<br>\n",
"Special thanks to my mentors for this project on GSoC 2013 : Soeren Sonnenburg, Olivier Chapelle and Benoit Rostykus</br>"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"This notebook is about <a href=\"http://en.wikipedia.org/wiki/Document_classification\">Document classification</a> in Shogun. After providing a semi-formal introduction to the <a href=\"http://en.wikipedia.org/wiki/Bag-of-words_model\">Bag of Words</a> model and its limitations, we illustrate the <a href=\"http://en.wikipedia.org/wiki/Feature_hashing\">hashing trick</a>. This is consolidated by performing experiments on the large-scale webspam data set."
]
},
{
"cell_type": "heading",
"level": 2,
"metadata": {},
"source": [
"Document Classification"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"<h3>Background</h3>\n",
"Document classification consists of assigning documents to specific predefined categories. This usually works by transforming the documents into a vector space that is acceptable by common classifiers, like SVMs, and then learning a model on that new representation.\n",
"\n",
"The most common and the most widely used representation of document collections is the <a href=\"http://en.wikipedia.org/wiki/Bag-of-words_model\">Bag of Words</a> model.The BoW representation considers each document as a collection of tokens. A token is defined as the minimum arbitrary sequence of characters that can be considered as atomic. Tokens depending on the choice of the user can be either whole words or n-grams, etc. A simple approach is to consider every possible token in your document collection as a different dimension in a feature space and then vectorize each document by assigning 1 to every dimension that corresponds to a token contained in that document and 0 to every other. This is the simpler approach and is known as the Boolean Vector Space Model. Another approach is, instead of using {1,0}, to use a weight for every term, like their frequencies, but this is out of our scope.<br>\n",
"\n",
"Let's now consider a document collection consisting of the following documents:<br>\n",
" <b>Document 1</b> = \"This is the first document\"<br>\n",
" <b>Document 2</b> = \"Document classification: Introduction\"<br>\n",
" <b>Document 3</b> = \"A third document about classification\"<br>\n",
"\n",
"and suppose that we consider as tokens, every word that appears in the collection.<br>\n",
"Then we would arrive to the following representation:\n",
"<table><tr><td></td><td><b>Document 1</b></td><td><b>Document 2</b></td><td><b>Document 3</b></td></tr>\n",
" <tr><td>[0] this</td><td>1</td><td>0</td><td>0</td></tr>\n",
" <tr><td>[1] is</td><td>1</td><td>0</td><td>0</td></tr>\n",
" <tr><td>[2] the</td><td>1</td><td>0</td><td>0</td></tr>\n",
" <tr><td>[3] first</td><td>1</td><td>0</td><td>0</td></tr>\n",
" <tr><td>[4] document</td><td>1</td><td>1</td><td>1</td></tr>\n",
" <tr><td>[5] classification</td><td>0</td><td>1</td><td>1</td></tr>\n",
" <tr><td>[6] introduction</td><td>0</td><td>1</td><td>0</td></tr>\n",
" <tr><td>[7] a</td><td>0</td><td>0</td><td>1</td></tr>\n",
" <tr><td>[8] third</td><td>0</td><td>0</td><td>1</td></tr>\n",
" <tr><td>[9] about</td><td>0</td><td>0</td><td>1</td></tr>\n",
"</table>\n",
"The above matrix is called the <a href=\"http://en.wikipedia.org/wiki/Document-term_matrix\">document-term matrix</a> and is widely used in Information Retrieval applications. In our case since every document is now represented by a numerical vector, we can just pass this as a dataset to common classifiers, including kernel machines. <br>\n",
"<h3>Limitations on large scale collections</h3>\n",
"Although the aformentioned procedure is pretty intuitive and straight-forward it has some drawbacks when considered on large collections of documents.<br>\n",
"<ul><li>First of all, the dimensionality of the feature space (or else, the number of distinct tokens in the collection) is usually <b>not known beforehand</b> and requires a pass over the entire dataset to be calculated. This can make the creation of the document-term matrix trickier, especially in online learning scenarios.</li>\n",
" <li>Secondly, this dimensionality can grow to be <b>very large</b>; imagine considering as tokens the set of every possible word, or every possible 8-gram.</li>\n",
" <li>Thirdly, this approach requires an <b>additional dictionary</b> in order to work that maps every token to its appropriate dimension, for instance every appearance of 'this' above should always be mapped to dimension 0, and this mapping data structure will also grow to be <b>very large.</b></li></ul><br>\n",
"<h3>The hashing response</h3>\n",
"A convenient approach that eliminates all the problems that the BoW representation introduced is to use a hash function to transform all the tokens into numerical entities and then restrict those values into a range [0, n-1] using the modulo function. The values in the [0, n-1] range will correspond to indices on a n-dimensional document-term matrix.<br>This way: <ul>\n",
"<li>the dimensionality of the target feature space is now <b>known beforehand</b>, as it's simply the number of all possible outcomes of the hash functions, restricted to a specific range [0, n-1] by using the modulo function. No extra passes over the dataset are needed this way.</li>\n",
"<li>In addition, the dimension can now be <b>restricted to a certain size</b> that is convenient depending on the limitations of the available hardware</li>\n",
"<li>The dictionary is also <b>not needed</b> anymore since each token is directly mapped to the appropriate dimension through the outcome of the hash function.</li></ul>\n",
"\n",
"The above approach will result in a hashed document-term matrix, similar to the one shown above, of a pre-defined dimension size (number of rows).<br>\n",
"Each token will now be assigned to a random dimension, although always the same for the same token, that is determined from the hash function.<br> This will probably incure some collisions, but if the hash functions is good enough and our choice for the dimension size is sufficiently large it will not be a problem. Especially if we are considering large collections. <br>\n",
"\n",
"After the creation of the hashed document-term matrix, the intuition is to pass it as a dataset to a linear classifier that will take care of the rest!<br>\n",
"<h3>On-the-fly Hashing</h3>\n",
"A naive way to implement the hashing procedure is to pre-hash every token of the collection and store it to the disk for later access. However, this approach is not very convenient since it will require space analogous to the size of the original dimension. It will also make the experimentation on different parameters, like the size of the hash function or the choice of tokens, a bit trickier since one would have to re-convert the entire collection based on the choice of those parameters.<br>\n",
"The response to that is to read our collection as it is and compute the hash of every token only when it's required, on-the-fly.<br>\n",
"<h3>On-the-fly Hashing with Shogun</h3>\n",
"We will now have a look at how the above idea is represented in the Shogun Toolbox. That is we will see how we can load our document collection in memory and consider a hashed document-term matrix with the hashing of every document (or token more specifically) happening on-the-fly, only when it's required to be computed. Altough it may sound a bit tricky, it's actually pretty straightforward and here is how.<br><br>\n",
"First of all we import the required components from the modshogun library."
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"%matplotlib inline\n",
"from modshogun import StringCharFeatures, RAWBYTE, HashedDocDotFeatures, NGramTokenizer"
],
"language": "python",
"metadata": {},
"outputs": []
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"The <a href=\"http://shogun-toolbox.org/doc/en/current/classshogun_1_1CStringFeatures.html\">StringCharFeatures</a> are nothing more than a collection of char vectors, where each one represents a single document.<br>\n",
"<a href=\"http://shogun-toolbox.org/doc/en/current/namespaceshogun.html#a7fffbfce3d76cf49bde3916d2\">RAWBYTE</a> is simply an enum that specifies that every possible character can be found in the collection.<br>\n",
"The HashedDocDotFeatures is where all the magic happens! This class is responsible for encapsulating the document collection and providing the calculation of the hashed representation of each document whenever it is required. <br>\n",
"The NGramTokenizer is the tokenizer we will use on our collection. Its job is to parse a document and create the tokens. As its name suggests, it creates a sequence of ngrams. Another option would be the DelimiterTokenizer, which allows the user to specify the delimiter that will be used in order to create the tokens. <br><br><br>\n",
"Suppose now that we have the following documents:"
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"doc_1 = \"this is the first document\"\n",
"doc_2 = \"document classification introduction\"\n",
"doc_3 = \"a third document about classification\"\n",
"document_collection = [doc_1, doc_2, doc_3]"
],
"language": "python",
"metadata": {},
"outputs": []
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"We will take some time off now to assign each document to a category, to ease our work later on. Since the two last documents refer to classification we will label them as \"Relevant\" and the first document as \"Not relevant\". Since we only have two categories, this makes it a binary problem and we will represent \"Relevant\" as 1 and \"Not relevant\" as -1."
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"from modshogun import BinaryLabels\n",
"from numpy import array\n",
"\n",
"labels = BinaryLabels(array([-1, 1, 1]))"
],
"language": "python",
"metadata": {},
"outputs": []
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"We now create our document collection:"
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"string_features = StringCharFeatures(document_collection, RAWBYTE)"
],
"language": "python",
"metadata": {},
"outputs": []
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Now we will create the object responsible for the hashed BoW representation. We are going to specify that we want a hash size of 8 bits, which will be translated to a dimension of size 2^8 = 256 (powers of 2 are considered to speed up computatins) and a tokenizer that creates 5-grams. We will also specify that we want to\n",
"normalize each hashed vector with regards to the square root of the document length, which is a common approach:"
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"hash_size = 8\n",
"tokenizer = NGramTokenizer(5)\n",
"normalize = True\n",
"\n",
"hashed_feats = HashedDocDotFeatures(hash_size, string_features, tokenizer, normalize)"
],
"language": "python",
"metadata": {},
"outputs": []
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"And that was it!<br>\n",
"The hashed_feats object now has all the required parameters and knows how to communicate and provide the hashed representation <b>only</b> when it is needed by the various algorithms of Shogun. <br><br>\n",
"So how do we proceed to actually learn a model that can classify documents?<br>\n",
"We said before that the idea after we have taken care of the hashing is to use a linear classifier.<br>\n",
"Shogun has many <a href=\"http://shogun-toolbox.org/doc/en/current/classshogun_1_1CLinearMachine.html#a13f46585aefbfa0376c051817d6f5c46\">linear</a> classifiers available, but for the moment we will consider <a href=\"http://shogun-toolbox.org/doc/en/current/classshogun_1_1CSVMOcas.html\">SVMOcas</a> and we will see how to use it:"
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"from modshogun import SVMOcas\n",
"\n",
"C = 0.1\n",
"epsilon = 0.01\n",
"svm = SVMOcas(C, hashed_feats, labels)\n",
"svm.set_epsilon(epsilon)"
],
"language": "python",
"metadata": {},
"outputs": []
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"We have now created our svm. The parameter C specifies the regularization constant. The best choice for this parameter will usually be selected after a model selection process.<br>\n",
"Now, let's train our svm:"
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"_=svm.train()"
],
"language": "python",
"metadata": {},
"outputs": []
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"When the execution finishes, we will have learned our so desired linear model! Mind that for large collections the above call can take hours.<br>\n",
"Now that we have a model ready, we would like to see how well it does. Let's see what it predicts for our training data:"
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"predicted_labels = svm.apply()\n",
"print (predicted_labels.get_labels())"
],
"language": "python",
"metadata": {},
"outputs": []
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"We can see that it misclassified the first document. This has to do with the nature of our overly-simplified toy dataset which doesn't provide enough information. However, another option of the HashedDocDotFeatures class will allow us to extract some more information from the same dataset!<br>\n",
"<br>\n",
"<h3>Quadratic Features</h3>\n",
"Generally, the quadratic features refer to the production of new features from our current dataset, by multiplying currently existing features.\n",
"<br>For example, if we have a dataset consisting of the following example vector:\n",
"<table><tr><td></td><td>A</td><td>B</td><td>C</td></tr>\n",
" <tr><td>x:</td><td>2</td><td>3</td><td>1</td></tr></table>\n",
"We can create the following extra quadratic features:\n",
"<table><tr><td></td><td>A</td><td>B</td><td>C</td><td>AA</td><td>AB</td><td>AC</td><td>BB</td><td>BC</td><td>CC</td></tr>\n",
" <tr><td>x:</td><td>2</td><td>3</td><td>1</td><td>2*2=4</td><td>2*3=6</td><td>2*1=2</td><td>3*3=9</td><td>3*1=3</td><td>1*1=1</td></tr></table>\n",
"The creation of these features can be very useful, however it should be used with caution since it will also increase the training time and the size requirements. <br>\n",
"The idea behind the quadratic features is very simple and can be easily implemented for numerical features. However in the case of text collections, where we have tokens instead of numbers, things are not so straightforward.<br><br>\n",
"The approach we have selected in Shogun for the text collections does not compute all the quadratic features, but only a subset of them defined using a k-skip n-grams rule. N-grams in this context refer to a collection of up to n consecutive tokens and should not be confused with our choice for what consists a token, where it can be whole words or ngrams themselves. The k-skip in front allows us to skip up to k tokens when we group them in up-to n sized groups. Although this is a simple idea it can be a bit confusing when introduced through a definition, so let's have a look at an example:\n",
"Suppose we have the following <b>consecutive</b> tokens:<br>\n",
"Tokens : [\"a\", \"b\", \"c\", \"d\"]<br>\n",
"and that we want to consider a 2-skip 2-grams approach. This means that we can combine up to 2 tokens (that is a single token or two tokens) while skipping up to 2 tokens between them (that means we can skip 0,1 and 2 tokens). Therefore, we obtain the following combinations: <br>\n",
"[\"a\" (0-skips, 1-gram), \"ab\" (0-skips, 2-gram), \"ac\" (1-skip 2-gram), \"ad\" (2-skips, 2-gram), \"b\" (0-skips, 1-gram), \"bc\" (0-skips, 2-gram),\n",
"\"bd\" (1-skip, 2-gram), \"c\" (0-skip, 1-gram), \"cd\" (0-skip, 2-gram), \"d\" (0-skip, 1-gram)] <br>\n",
"<br>These newly created tokens are then treated like our regular ones and hashed to find their appropriate dimension.\n",
"<br>As discussed above this idea may enhance our information on the dataset, but it also requires more time to be computed. So if used, the choice of k and n should be kept to small numbers.<br>\n",
"<h3>Quadratic Features with HashedDocDotFeatures</h3>\n",
"\n",
"In order to use the k-skip n-gram approach explained above, one only has to specify his choice for k and n to the HashedDocDotFeatures object he is creating.<br>\n",
"So, by keeping our previous choice of parameters intact and introducing the k and n we obtaing the following piece of code :"
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"k = 3 # number of tokens, up to which we allow it to skip\n",
"n = 3 # number of tokens, up to which we allow it to combine\n",
"\n",
"hashed_feats_quad = HashedDocDotFeatures(hash_size, string_features, tokenizer, normalize, n, k)"
],
"language": "python",
"metadata": {},
"outputs": []
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"If we do not specify these numbers, as we did not do before, then, (you maybe have guessed it!) they are set by default to the following values, n=1, k=0!\n",
"<br>\n",
"Let's see if there was any improvement in our predicting capabilities.<br>\n",
"By keeping the same svm settings, we will train on the new HashedDocDotFeatures object we have just created. Although we have the same document collection underneath, we are now considering a different representation model because of the quadratic features."
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"svm.set_features(hashed_feats_quad)\n",
"svm.train()\n",
"predicted_labels = svm.apply()\n",
"print(predicted_labels.get_labels())"
],
"language": "python",
"metadata": {},
"outputs": []
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Better!<br>\n",
"\n",
"<b>Attention!</b> Some clarifications now will follow on the Quadratic Features to clear any misunderstandings:\n",
"<ul><li>The term \"ngram\" can come up on two different levels. <br>The first one is in the tokenization process of the documents, where we have a raw stream of characters and our job there is to split the document on sequences of characters that are considered atomic. For instance, we may decide that sequences of three characters are the basic units which make up a document and therefore we are considering 3-grams (for tokens).<br>\n",
"The second level, is when we are combining the tokens generated in the previous phase to create quadratic features, by following the aforementioned k-skip n-grams rule. Now we are not considering anymore the document as a sequence of characters, but rather as a sequence of tokens. And it is on those tokens that we are now applying the rule. Therefore, if we decide to apply a 0-skip 3-grams rule then we may combine up to 3 tokens (not characters).\n",
" In conclusion, the way the quadratic features work is completely independent of the Tokenizer used on the documents. We could have chosen a DelimiterTokenizer and still consider ngrams (of tokens) on the quadratic level.</li>\n",
" <li>The \"quadratic\" in front of \"quadratic features\" also refers to the way the computing time increases when we select this approach! So it should only be used when it is necessary.</li></ul>"
]
},
{
"cell_type": "heading",
"level": 2,
"metadata": {},
"source": [
"Experiments on real datasets"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"The example that was demonstrated before was very small and simple and was manipulated for demonstration purposes, to show the capabilities of the HashedDocDotFeatures class. <br> <br><h3>Webspam</h3>Now let's have a look at the results the HashedDocDotFeatures class together with SVMOcas produced when faced against the <a href=\"ftp://largescale.ml.tu-berlin.de/largescale/webspam/\">webspam</a> dataset, which consists of 350000 documents labelled either as \"Spam\" or as \"Not spam\".<br>We demonstrate results when compared against SVMLight, using the Spectrum Kernel. <br>\n",
"We consider the following settings: <br>number of bits in hash = 16, 8-grams for tokens, SVM epsilon = 0.01."
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"from pylab import *\n",
"\n",
"# HashedDocDotFeatures results\n",
"hashed_training_examples = [5000, 10000, 15000, 20000, 25000, 30000, 50000, 100000]\n",
"\n",
"# For C=1\n",
"hashed_C_1_sec = [2682.750000,5202.690000,8120.460000,10846.410000,13944.200000,17016.840000,30496.720000,66302.950000]\n",
"hashed_C_1_roc = [0.980730,0.986382,0.988894,0.990666,0.991602,0.991957,0.993680,0.995184]\n",
"\n",
"# For C=0.1\n",
"hashed_C_01_sec = [1074.130000,2142.390000,3434.710000,4641.380000,5984.530000,7206.040000,12864.270000,28393.540000]\n",
"hashed_C_01_roc = [0.976560,0.982660,0.985251,0.987380,0.988368,0.989022,0.990950,0.993197]\n",
"\n",
"# Spectrum kernel results\n",
"kernel_training_examples = [5000, 10000, 15000, 20000, 25000]\n",
"\n",
"# For C=1\n",
"kernel_C_1_sec = [2912.410000,6543.220000,10840.550000,16108.360000,19899.610000]\n",
"kernel_C_1_roc = [0.971284,0.976628,0.979715,0.982084,0.984355]\n",
"\n",
"# For C=0.1\n",
"kernel_C_01_sec = [1441.380000,3261.870000,5071.040000,7568.130000,10436.430000]\n",
"kernel_C_01_roc = [0.946308,0.955245,0.961576,0.965204,0.968264]\n",
"\n",
"figure(figsize=(12,6))\n",
"subplot(1,2,1)\n",
"plot(hashed_training_examples, hashed_C_1_sec, 'b')\n",
"plot(kernel_training_examples, kernel_C_1_sec, 'r')\n",
"title(\"Time comparison for C=1\")\n",
"xlabel(\"Number of examples\")\n",
"ylabel(\"Time in seconds\")\n",
"legend([\"HashedDocDotFeatures\", \"Spectrum Kernel\"], loc=2)\n",
"\n",
"subplot(1,2,2)\n",
"plot(hashed_training_examples, hashed_C_1_roc, 'b')\n",
"plot(kernel_training_examples, kernel_C_1_roc, 'r')\n",
"title(\"Area under ROC comparison for C=1\")\n",
"xlabel(\"Number of examples\")\n",
"ylabel(\"auROC\")\n",
"_=legend([\"HashedDocDotFeatures\", \"Spectrum Kernel\"], loc=4)"
],
"language": "python",
"metadata": {},
"outputs": []
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"clf\n",
"figure(figsize=(12,6))\n",
"subplot(1,2,1)\n",
"plot(hashed_training_examples, hashed_C_01_sec, 'b')\n",
"plot(kernel_training_examples, kernel_C_01_sec, 'r')\n",
"title(\"Time comparison for C=0.1\")\n",
"xlabel(\"Number of examples\")\n",
"ylabel(\"Time in seconds\")\n",
"ylim((0,70000))\n",
"legend([\"HashedDocDotFeatures\", \"Spectrum Kernel\"], loc=2)\n",
"\n",
"subplot(1,2,2)\n",
"plot(hashed_training_examples, hashed_C_01_roc, 'b')\n",
"plot(kernel_training_examples, kernel_C_01_roc, 'r')\n",
"title(\"Area under ROC comparison for C=0.1\")\n",
"xlabel(\"Number of examples\")\n",
"ylabel(\"auROC\")\n",
"_=legend([\"HashedDocDotFeatures\", \"Spectrum Kernel\"], loc=4)"
],
"language": "python",
"metadata": {},
"outputs": []
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"<h3>Language detection</h3><br>\n",
"Another experiment we conducted was to create a language detection application. For that purpose we created a dataset consisting of 10,000 documents for 5 different languages; English, Greek, German, Italian and Spanish. <br>The demo uses underneath it a svm model trained on 30,000 documents with Ngrams of size 4, bits of hash size = 18, quadratic features with options: 2-skips 3-grams and svm C = 0.1. The demo can be found here and you can test it on your own how well it does!"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"<h3>Extra stuff</h3>\n",
"\n",
"The hashing trick has also been implemented in Shogun for the DenseFeatures and SparseFeatures classes, as HashedDenseFeatures and HashedSparseFeatures classes. These classes also support quadratic features and provide the option to maintain or drop the linear terms. <br>\n",
"For online algorithms or for cases when the data do not fit in memory there exist similar classes with the prefix \"Streaming\" that support reading examples from the disk and providing them to some algorithms one at a time. The classes specifically are StreamingHashedDocDotFeatures, StreamingHashedDenseFeatures and StreamingHashedSparseFeatures. If one has mixed features, that are not just numerical or not just text, then he can use the CombinedDotFeatures class to combine objects of the aforementioned classes!<br>Another option is to use the Vw* algorithms and the VwExample class that require the input to be in vw format and are a bit trickier to use.<br>\n",
"In addition, a HashedDocConverter class exists that allows the user to get the hashed BoW representation of a document collection as a <a href=\"http://shogun-toolbox.org/doc/en/current/classshogun_1_1CSparseFeatures.html\">CSparseFeatures</a> object."
]
},
{
"cell_type": "heading",
"level": 2,
"metadata": {},
"source": [
"References"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Kilian Weinberger, Anirban Dasgupta, Josh Attenberg, John Langford, Alex Smola : <a href=\"http://arxiv.org/pdf/0902.2206.pdf\">Feature Hashing for Large Scale Multitask Learning</a><br>\n",
"Olivier Chapelle, Eren Manavoglu, Romer Rosales : <a href=\"http://people.csail.mit.edu/romer/papers/TISTRespPredAds.pdf\">Simple and scalable response prediction for display advertising</a><br>\n",
"S\u0308oren Sonnenburg, Gunnar R\u0308atsch, Konrad Rieck : <a href=\"http://sonnenburgs.de/soeren/publications/SonRaeRie07.pdf\">Large Scale Learning with String Kernels</a>\n",
"<a href=\"https://github.com/JohnLangford/vowpal_wabbit/wiki\">Vowpal Wabbit</a><br>\n",
"A <a href=\"http://metaoptimize.com/qa/questions/6943/what-is-the-hashing-trick<\">post</a> in metaoptimize<br>"
]
}
],
"metadata": {}
}
]
}
| gpl-3.0 |
jomavera/Work | South_china_webscrape.ipynb | 1 | 49661 | {
"cells": [
{
"cell_type": "markdown",
"metadata": {
"collapsed": false
},
"source": [
"## South china morning post-Web scraping"
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Collecting beautifulsoup3\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
" Could not find a version that satisfies the requirement beautifulsoup3 (from versions: )\n",
"No matching distribution found for beautifulsoup3\n",
"You are using pip version 8.1.2, however version 9.0.1 is available.\n",
"You should consider upgrading via the 'python -m pip install --upgrade pip' command.\n"
]
}
],
"source": [
"! pip install beautifulsoup4"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Import libraries"
]
},
{
"cell_type": "code",
"execution_count": 1,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"from urllib.request import urlopen\n",
"from bs4 import BeautifulSoup\n",
"import csv\n",
"import numpy as np"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Load the URL and store it"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"#Open URL and read it\n",
"url = urlopen(\"http://www.scmp.com/content/search/canada?f[0]=im_field_label_tag%3A544&f[1]=ds_created%3A%5B2008-03-01T00%3A00%3A00Z%20TO%202017-02-29T23%3A59%3A59Z%5D\")\n",
"content = url.read()"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"C:\\Users\\Jose Manuel\\Anaconda3\\lib\\site-packages\\bs4\\__init__.py:181: UserWarning: No parser was explicitly specified, so I'm using the best available HTML parser for this system (\"lxml\"). This usually isn't a problem, but if you run this code on another system, or in a different virtual environment, it may use a different parser and behave differently.\n",
"\n",
"The code that caused this warning is on line 184 of the file C:\\Users\\Jose Manuel\\Anaconda3\\lib\\runpy.py. To get rid of this warning, change code that looks like this:\n",
"\n",
" BeautifulSoup([your markup])\n",
"\n",
"to this:\n",
"\n",
" BeautifulSoup([your markup], \"lxml\")\n",
"\n",
" markup_type=markup_type))\n"
]
}
],
"source": [
"soup = BeautifulSoup(content)"
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {
"collapsed": false,
"scrolled": true
},
"outputs": [],
"source": [
"# The are 12 results webpages with 10 news articles per webpage.\n",
"links=[] #create a list of links to the 5 pages\n",
"wbp=\"http://www.scmp.com\"\n",
"for divTag in soup.find_all( \"div\", {\"class\" : \"item-list\"}):\n",
" for ulTag in divTag.find_all( \"li\", {\"class\" : \"pager-item\"}):\n",
" links.append(wbp+ulTag.a[\"href\"])"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Extract data from each hyperlink and store it in the dictionary"
]
},
{
"cell_type": "code",
"execution_count": 12,
"metadata": {
"collapsed": false,
"scrolled": false
},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"C:\\Users\\Jose Manuel\\Anaconda3\\lib\\site-packages\\bs4\\__init__.py:181: UserWarning: No parser was explicitly specified, so I'm using the best available HTML parser for this system (\"lxml\"). This usually isn't a problem, but if you run this code on another system, or in a different virtual environment, it may use a different parser and behave differently.\n",
"\n",
"The code that caused this warning is on line 184 of the file C:\\Users\\Jose Manuel\\Anaconda3\\lib\\runpy.py. To get rid of this warning, change code that looks like this:\n",
"\n",
" BeautifulSoup([your markup])\n",
"\n",
"to this:\n",
"\n",
" BeautifulSoup([your markup], \"lxml\")\n",
"\n",
" markup_type=markup_type))\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"Link: 1 Page: 1\n",
"http://www.scmp.com/property/international/article/2066734/bc-government-plans-lift-15-cent-foreign-buyer-tax-those-work\n",
"Link: 1 Page: 2\n",
"http://www.scmp.com/news/world/united-states-canada/article/2065300/canadian-property-owner-sues-over-denial-moby-dick\n",
"Link: 1 Page: 3\n",
"http://www.scmp.com/news/world/united-states-canada/article/2063520/vancouver-airport-reveals-us42-billion-expansion\n",
"Link: 1 Page: 4\n",
"http://www.scmp.com/property/international/article/2061199/bc-housing-starts-hit-highest-level-decades-last-year\n",
"Link: 1 Page: 5\n",
"http://www.scmp.com/tech/article/2056856/canada-declares-broadband-internet-access-basic-service\n",
"Link: 1 Page: 6\n",
"http://www.scmp.com/property/international/article/2062776/bc-has-record-year-home-sales-2016\n",
"Link: 1 Page: 7\n",
"http://www.scmp.com/news/world/united-states-canada/article/2059523/canadian-winter-weather-pushes-bc-power-consumption\n",
"Link: 1 Page: 8\n",
"http://www.scmp.com/tech/start-ups/article/2058930/kick-starting-drive-hydrogen-fuel-cell-20\n",
"Link: 1 Page: 9\n",
"http://www.scmp.com/property/international/article/2056519/vancouver-home-affordability-hits-record-low-may-be-turning\n",
"Link: 1 Page: 10\n",
"http://www.scmp.com/news/world/united-states-canada/article/2048890/british-columbia-home-20-canadas-top-100-most\n",
"Link: 2 Page: 1\n",
"http://www.scmp.com/news/world/united-states-canada/article/2056266/canadian-environmentalists-fight-pipeline-expansion\n",
"Link: 2 Page: 2\n",
"http://www.scmp.com/news/world/united-states-canada/article/2046183/trump-win-could-strain-us-trade-relations-canada\n",
"Link: 2 Page: 3\n",
"http://www.scmp.com/news/world/united-states-canada/article/2048540/retail-sales-strong-across-bc-year-over-year\n",
"Link: 2 Page: 4\n",
"http://www.scmp.com/tech/article/2038822/satellite-phones-key-government-operations-when-big-one-hits-canada\n",
"Link: 2 Page: 5\n",
"http://www.scmp.com/property/international/article/2038546/bank-canada-maintains-overnight-rate-downgrades-outlook\n",
"Link: 2 Page: 6\n",
"http://www.scmp.com/tech/science-research/article/2028744/victoria-best-place-canada-be-woman\n",
"Link: 2 Page: 7\n",
"http://www.scmp.com/property/international/article/2028067/vancouver-once-again-has-canadas-biggest-jump-repeat-home\n",
"Link: 2 Page: 8\n",
"http://www.scmp.com/property/international/article/2027673/canadas-foreign-real-estate-buyers-tax-wont-be-long-term\n",
"Link: 2 Page: 9\n",
"http://www.scmp.com/news/world/united-states-canada/article/2026643/bc-growth-decelerating-canada-cranks-out-67000-jobs\n",
"Link: 2 Page: 10\n",
"http://www.scmp.com/news/world/united-states-canada/article/2026641/legalised-pot-set-spark-new-joint-ventures-canada\n",
"Link: 3 Page: 1\n",
"http://www.scmp.com/news/world/united-states-canada/article/2048189/provincial-candian-premiers-landlord-linked-soccer\n",
"Link: 3 Page: 2\n",
"http://www.scmp.com/property/international/article/2046074/vancouver-continue-lead-canadian-economic-growth-next-year\n",
"Link: 3 Page: 3\n",
"http://www.scmp.com/news/world/united-states-canada/article/2025975/bc-expected-lead-canadas-economic-growth-2017\n",
"Link: 3 Page: 4\n",
"http://www.scmp.com/news/world/united-states-canada/article/2024874/federal-government-sets-low-bar-carbon-pricing\n",
"Link: 3 Page: 5\n",
"http://www.scmp.com/news/world/united-states-canada/article/2024574/food-prices-rise-canada-sink-united-states\n",
"Link: 3 Page: 6\n",
"http://www.scmp.com/property/international/article/2047274/vancouver-outlines-exemptions-new-empty-homes-tax\n",
"Link: 3 Page: 7\n",
"http://www.scmp.com/news/world/united-states-canada/article/2023936/canadas-competition-bureau-takes-action-against\n",
"Link: 3 Page: 8\n",
"http://www.scmp.com/tech/start-ups/article/2042331/vancouvers-burgeoning-latin-american-community-means-business\n",
"Link: 3 Page: 9\n",
"http://www.scmp.com/news/world/united-states-canada/article/2042269/ottawa-spend-us60-billion-over-next-decade-capital\n",
"Link: 3 Page: 10\n",
"http://www.scmp.com/news/world/united-states-canada/article/2023290/canada-approves-petronas-lng-project-catch\n",
"Link: 4 Page: 1\n",
"http://www.scmp.com/news/world/united-states-canada/article/2022855/canadas-businesses-need-be-more-courageous-stimulate\n",
"Link: 4 Page: 2\n",
"http://www.scmp.com/news/china/policies-politics/article/2021977/china-canada-sign-treaty-return-assets-stolen-fraud\n",
"Link: 4 Page: 3\n",
"http://www.scmp.com/news/china/diplomacy-defence/article/2021917/china-canada-mull-free-trade-talks-deepen-commercial\n",
"Link: 4 Page: 4\n",
"http://www.scmp.com/news/world/united-states-canada/article/2039821/vancouvers-airport-boss-voices-disapproval-federal\n",
"Link: 4 Page: 5\n",
"http://www.scmp.com/property/international/article/2020874/canada-announces-c500-million-affordable-housing\n",
"Link: 4 Page: 6\n",
"http://www.scmp.com/news/world/united-states-canada/article/2039482/inflation-rate-cools-canadian-province-august\n",
"Link: 4 Page: 7\n",
"http://www.scmp.com/news/world/united-states-canada/article/2042593/bc-minister-forges-closer-ties-between-canadian\n",
"Link: 4 Page: 8\n",
"http://www.scmp.com/tech/start-ups/article/2042601/bcs-c100m-tech-fund-drops-c15m-vancouver-firm\n",
"Link: 4 Page: 9\n",
"http://www.scmp.com/news/world/united-states-canada/article/2041879/missed-revenue-opportunities-and-too-much-politics\n",
"Link: 4 Page: 10\n",
"http://www.scmp.com/news/world/united-states-canada/article/2018223/more-mexico-vancouver-flights-when-canada-lifts\n",
"Link: 5 Page: 1\n",
"http://www.scmp.com/property/international/article/2066734/bc-government-plans-lift-15-cent-foreign-buyer-tax-those-work\n",
"Link: 5 Page: 2\n",
"http://www.scmp.com/news/world/united-states-canada/article/2065300/canadian-property-owner-sues-over-denial-moby-dick\n",
"Link: 5 Page: 3\n",
"http://www.scmp.com/news/world/united-states-canada/article/2063520/vancouver-airport-reveals-us42-billion-expansion\n",
"Link: 5 Page: 4\n",
"http://www.scmp.com/property/international/article/2061199/bc-housing-starts-hit-highest-level-decades-last-year\n",
"Link: 5 Page: 5\n",
"http://www.scmp.com/tech/article/2056856/canada-declares-broadband-internet-access-basic-service\n",
"Link: 5 Page: 6\n",
"http://www.scmp.com/property/international/article/2062776/bc-has-record-year-home-sales-2016\n",
"Link: 5 Page: 7\n",
"http://www.scmp.com/news/world/united-states-canada/article/2059523/canadian-winter-weather-pushes-bc-power-consumption\n",
"Link: 5 Page: 8\n",
"http://www.scmp.com/tech/start-ups/article/2058930/kick-starting-drive-hydrogen-fuel-cell-20\n",
"Link: 5 Page: 9\n",
"http://www.scmp.com/property/international/article/2056519/vancouver-home-affordability-hits-record-low-may-be-turning\n",
"Link: 5 Page: 10\n",
"http://www.scmp.com/news/world/united-states-canada/article/2048890/british-columbia-home-20-canadas-top-100-most\n",
"Link: 6 Page: 1\n",
"http://www.scmp.com/news/world/united-states-canada/article/2056266/canadian-environmentalists-fight-pipeline-expansion\n",
"Link: 6 Page: 2\n",
"http://www.scmp.com/news/world/united-states-canada/article/2046183/trump-win-could-strain-us-trade-relations-canada\n",
"Link: 6 Page: 3\n",
"http://www.scmp.com/news/world/united-states-canada/article/2048540/retail-sales-strong-across-bc-year-over-year\n",
"Link: 6 Page: 4\n",
"http://www.scmp.com/tech/article/2038822/satellite-phones-key-government-operations-when-big-one-hits-canada\n",
"Link: 6 Page: 5\n",
"http://www.scmp.com/property/international/article/2038546/bank-canada-maintains-overnight-rate-downgrades-outlook\n",
"Link: 6 Page: 6\n",
"http://www.scmp.com/tech/science-research/article/2028744/victoria-best-place-canada-be-woman\n",
"Link: 6 Page: 7\n",
"http://www.scmp.com/property/international/article/2028067/vancouver-once-again-has-canadas-biggest-jump-repeat-home\n",
"Link: 6 Page: 8\n",
"http://www.scmp.com/property/international/article/2027673/canadas-foreign-real-estate-buyers-tax-wont-be-long-term\n",
"Link: 6 Page: 9\n",
"http://www.scmp.com/news/world/united-states-canada/article/2026643/bc-growth-decelerating-canada-cranks-out-67000-jobs\n",
"Link: 6 Page: 10\n",
"http://www.scmp.com/news/world/united-states-canada/article/2026641/legalised-pot-set-spark-new-joint-ventures-canada\n",
"Link: 7 Page: 1\n",
"http://www.scmp.com/news/world/united-states-canada/article/2048189/provincial-candian-premiers-landlord-linked-soccer\n",
"Link: 7 Page: 2\n",
"http://www.scmp.com/property/international/article/2046074/vancouver-continue-lead-canadian-economic-growth-next-year\n",
"Link: 7 Page: 3\n",
"http://www.scmp.com/news/world/united-states-canada/article/2025975/bc-expected-lead-canadas-economic-growth-2017\n",
"Link: 7 Page: 4\n",
"http://www.scmp.com/news/world/united-states-canada/article/2024874/federal-government-sets-low-bar-carbon-pricing\n",
"Link: 7 Page: 5\n",
"http://www.scmp.com/news/world/united-states-canada/article/2024574/food-prices-rise-canada-sink-united-states\n",
"Link: 7 Page: 6\n",
"http://www.scmp.com/property/international/article/2047274/vancouver-outlines-exemptions-new-empty-homes-tax\n",
"Link: 7 Page: 7\n",
"http://www.scmp.com/news/world/united-states-canada/article/2023936/canadas-competition-bureau-takes-action-against\n",
"Link: 7 Page: 8\n",
"http://www.scmp.com/tech/start-ups/article/2042331/vancouvers-burgeoning-latin-american-community-means-business\n",
"Link: 7 Page: 9\n",
"http://www.scmp.com/news/world/united-states-canada/article/2042269/ottawa-spend-us60-billion-over-next-decade-capital\n",
"Link: 7 Page: 10\n",
"http://www.scmp.com/news/world/united-states-canada/article/2023290/canada-approves-petronas-lng-project-catch\n",
"Link: 8 Page: 1\n",
"http://www.scmp.com/news/world/united-states-canada/article/2022855/canadas-businesses-need-be-more-courageous-stimulate\n",
"Link: 8 Page: 2\n",
"http://www.scmp.com/news/china/policies-politics/article/2021977/china-canada-sign-treaty-return-assets-stolen-fraud\n",
"Link: 8 Page: 3\n",
"http://www.scmp.com/news/china/diplomacy-defence/article/2021917/china-canada-mull-free-trade-talks-deepen-commercial\n",
"Link: 8 Page: 4\n",
"http://www.scmp.com/news/world/united-states-canada/article/2039821/vancouvers-airport-boss-voices-disapproval-federal\n",
"Link: 8 Page: 5\n",
"http://www.scmp.com/property/international/article/2020874/canada-announces-c500-million-affordable-housing\n",
"Link: 8 Page: 6\n",
"http://www.scmp.com/news/world/united-states-canada/article/2039482/inflation-rate-cools-canadian-province-august\n",
"Link: 8 Page: 7\n",
"http://www.scmp.com/news/world/united-states-canada/article/2042593/bc-minister-forges-closer-ties-between-canadian\n",
"Link: 8 Page: 8\n",
"http://www.scmp.com/tech/start-ups/article/2042601/bcs-c100m-tech-fund-drops-c15m-vancouver-firm\n",
"Link: 8 Page: 9\n",
"http://www.scmp.com/news/world/united-states-canada/article/2041879/missed-revenue-opportunities-and-too-much-politics\n",
"Link: 8 Page: 10\n",
"http://www.scmp.com/news/world/united-states-canada/article/2018223/more-mexico-vancouver-flights-when-canada-lifts\n",
"Link: 9 Page: 1\n",
"http://www.scmp.com/news/world/united-states-canada/article/2018127/bc-loses-jobs-august-unemployment-rate-remains\n",
"Link: 9 Page: 2\n",
"http://www.scmp.com/tech/science-research/article/2029001/new-us500m-biotech-venture-fund-be-based-vancouver\n",
"Link: 9 Page: 3\n",
"http://www.scmp.com/news/world/united-states-canada/article/2038823/bc-consumer-spending-more-7-cent-over-past-year\n",
"Link: 9 Page: 4\n",
"http://www.scmp.com/news/world/united-states-canada/article/2040528/bc-liberal-fundraising-challenged-court\n",
"Link: 9 Page: 5\n",
"http://www.scmp.com/tech/apps-gaming/article/2040217/vancouver-video-game-studio-retools-gears-war-saga\n",
"Link: 9 Page: 6\n",
"http://www.scmp.com/news/world/united-states-canada/article/2009209/canada-post-workers-union-issues-72-hour-notice-job\n",
"Link: 9 Page: 7\n",
"http://www.scmp.com/tech/start-ups/article/2025641/why-financial-institutions-vancouver-start-ups-are-aiming\n",
"Link: 9 Page: 8\n",
"http://www.scmp.com/property/international/article/2029370/class-action-against-canadian-city-vancouver-takes-aim\n",
"Link: 9 Page: 9\n",
"http://www.scmp.com/tech/e-commerce/article/2029070/canadian-e-commerce-firm-survival-requires-cutting-edge-technology\n",
"Link: 9 Page: 10\n",
"http://www.scmp.com/news/world/united-states-canada/article/2024894/canadian-turkey-consumption-shifts-more-processed\n",
"Link: 10 Page: 1\n",
"http://www.scmp.com/news/world/united-states-canada/article/2020878/bc-canadian-employers-forecast-modest-salary\n",
"Link: 10 Page: 2\n",
"http://www.scmp.com/news/world/united-states-canada/article/2025249/auditor-general-says-better-monitoring-needed-bc\n",
"Link: 10 Page: 3\n",
"http://www.scmp.com/news/china/diplomacy-defence/article/2010926/seven-things-you-need-know-about-canadian-prime\n",
"Link: 10 Page: 4\n",
"http://www.scmp.com/news/world/united-states-canada/article/2024873/canadian-liberals-nix-fall-legislature-sitting\n",
"Link: 10 Page: 5\n",
"http://www.scmp.com/property/international/article/2025246/metro-vancouver-home-sales-continue-plunge-september\n",
"Link: 10 Page: 6\n",
"http://www.scmp.com/news/world/united-states-canada/article/2023932/emissions-cap-petronas-could-be-boon-bc-hydro\n",
"Link: 10 Page: 7\n",
"http://www.scmp.com/tech/leaders-founders/article/2021233/microsofts-bill-gates-satya-nadella-champion-vancouvers\n",
"Link: 10 Page: 8\n",
"http://www.scmp.com/news/world/united-states-canada/article/2010965/bc-canadians-have-mixed-feelings-about-economic-ties\n",
"Link: 10 Page: 9\n",
"http://www.scmp.com/news/world/united-states-canada/article/2021905/august-was-busiest-ever-month-vancouver\n",
"Link: 10 Page: 10\n",
"http://www.scmp.com/tech/leaders-founders/article/2021641/google-canadian-company-global-e-commerce-wrangle\n",
"Link: 11 Page: 1\n",
"http://www.scmp.com/news/world/united-states-canada/article/2023241/canadian-utilities-commission-nixes-creative-energy\n",
"Link: 11 Page: 2\n",
"http://www.scmp.com/news/world/united-states-canada/article/2023232/vancouver-resident-accused-defrauding-investors\n",
"Link: 11 Page: 3\n",
"http://www.scmp.com/property/international/article/2023598/vancouver-real-estate-has-highest-bubble-risk-world-ubs\n",
"Link: 11 Page: 4\n",
"http://www.scmp.com/tech/article/2022552/vancouver-fintech-named-criminal-organisation-us\n",
"Link: 11 Page: 5\n",
"http://www.scmp.com/property/international/article/2021904/home-data-shows-provinces-15-cent-tax-foreign-buyers\n",
"Link: 11 Page: 6\n",
"http://www.scmp.com/tech/enterprises/article/2020884/tech-experts-call-dedicated-lanes-self-driving-cars-between\n",
"Link: 11 Page: 7\n",
"http://www.scmp.com/news/china/diplomacy-defence/article/2009681/what-expect-justin-trudeaus-trip-china\n",
"Link: 11 Page: 8\n",
"http://www.scmp.com/tech/start-ups/article/2011511/routific-putting-itself-tech-map-logistics-software\n",
"Link: 11 Page: 9\n",
"http://www.scmp.com/news/world/united-states-canada/article/2011211/canadian-cancer-agency-spinoff-faces-fraud\n",
"Link: 11 Page: 10\n",
"http://www.scmp.com/news/world/united-states-canada/article/2011512/canadian-citys-business-owners-fear-housing-costs\n",
"Link: 12 Page: 1\n",
"http://www.scmp.com/news/world/united-states-canada/article/2010963/airbnb-signing-bonus-could-eat-vancouvers-rental\n",
"Link: 12 Page: 2\n",
"http://www.scmp.com/tech/innovation/article/2011207/canadian-company-puts-touchless-tool-doctors-hands\n",
"Link: 12 Page: 3\n",
"http://www.scmp.com/news/world/united-states-canada/article/2009680/deep-fried-dishes-dominate-annual-vancouver-summer\n",
"Link: 12 Page: 4\n",
"http://www.scmp.com/property/international/article/2009180/prices-cut-canadian-property-markets-sales-plunge\n",
"Link: 12 Page: 5\n",
"http://www.scmp.com/news/world/united-states-canada/article/1994773/foreign-home-buyers-vancouver-hit-hk-style-15pc-tax\n",
"Link: 12 Page: 6\n",
"http://www.scmp.com/news/world/united-states-canada/article/1977384/wild-mama-bear-saves-woman-stalking-wolf\n",
"Link: 12 Page: 7\n",
"http://www.scmp.com/news/world/article/1907124/more-money-pot-canada-poised-reap-billions-dollars-tax-revenue-legalising\n",
"Link: 12 Page: 8\n",
"http://www.scmp.com/news/world/united-states-canada/article/1941623/thousands-be-airlifted-safety-canadian-wildfires\n",
"Link: 12 Page: 9\n",
"http://www.scmp.com/news/world/united-states-canada/article/1944701/canadians-are-standing-you-pm-justin-trudeau-vows\n",
"Link: 12 Page: 10\n",
"http://www.scmp.com/news/world/americas/article/1937011/watch-crazy-hat-lady-jumps-toronto-zoo-tiger-enclosure-retrieve\n"
]
}
],
"source": [
"#Initialize empty dictionary to fill with data \n",
"my_dict={}\n",
"wbp=\"http://www.scmp.com\"\n",
"count_1=0\n",
"for link in links: \n",
" count_1=count_1+1\n",
" #Open link\n",
" url = urlopen(link) #Open webpage of results\n",
" content = url.read()\n",
" soup = BeautifulSoup(content)\n",
" count_2=0\n",
" for divTag in soup.findAll(\"div\", { \"class\" : \"search-result-wrapper\"}): #Get the links of the pages of news\n",
" count_2=count_2+1\n",
" page=wbp+divTag.h3.a['href'] \n",
" try:\n",
" print('Link:',count_1,'Page:',count_2)\n",
" print(page)\n",
" url = urlopen(page) #Open page of the news article\n",
" content = url.read()\n",
" soup = BeautifulSoup(content)\n",
" content_temp=''\n",
" my_dict['Page-'+str((count_1-1)*10+count_2)]={}\n",
" for divTag_2 in soup.findAll(\"h1\", { \"class\" : \"title\"}): #Get headline\n",
" my_dict['Page-'+str((count_1-1)*10+count_2)]['headline']=divTag_2.text\n",
" for divTag_3 in soup.findAll(\"div\", { \"class\" : \"pane-content\"}): #Get content\n",
" for divTag_4 in divTag_3.findAll(\"p\"):\n",
" try:\n",
" content_temp=content_temp+' '+divTag_4.text\n",
" except:\n",
" continue\n",
" my_dict['Page-'+str((count_1-1)*10+count_2)]['content']= content_temp\n",
" except:\n",
" continue"
]
},
{
"cell_type": "code",
"execution_count": 20,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"#encode weird characters to ascii\n",
"my_dict_1={}\n",
"import unicodedata\n",
"for x in range(1,121):\n",
" my_dict_1['Page-'+str(x)]={}\n",
" my_dict_1['Page-'+str(x)]['content']=unicodedata.normalize('NFKD',my_dict['Page-'+str(x)]['content']).encode('ascii','ignore')"
]
},
{
"cell_type": "code",
"execution_count": 21,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"#encode content to utf-8\n",
"my_dict_fin={}\n",
"for x in range(1,121):\n",
" my_dict_fin['Page-'+str(x)]={}\n",
" my_dict_fin['Page-'+str(x)]['content'] = str(my_dict_1['Page-'+str(x)]['content'], \"utf-8\")"
]
},
{
"cell_type": "code",
"execution_count": 24,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"#Write CSV file\n",
"with open(\"scmp_news.csv\",\"w\") as toWrite:\n",
" writer=csv.writer(toWrite,delimiter=\",\")\n",
" writer.writerow([ \"Headline\",\"Content\"])\n",
" for x in range(1,121):\n",
" x=str(x)\n",
" writer.writerow([my_dict['Page-'+x]['headline'], my_dict_fin['Page-'+x]['content']])"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Import CSV File and open"
]
},
{
"cell_type": "code",
"execution_count": 16,
"metadata": {
"collapsed": false,
"scrolled": true
},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>Headline</th>\n",
" <th>Content</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>B.C. government plans to lift 15 per cent fore...</td>\n",
" <td>Business in Vancouver A timeline for the c...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>Canadian property owner sues over denial of Mo...</td>\n",
" <td>Business in Vancouver Owners councils ref...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>Vancouver airport reveals US$4.2 billion expan...</td>\n",
" <td>Business in Vancouver Expansions to five t...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>BC housing starts hit highest level in decades...</td>\n",
" <td>Business in Vancouver Provinces yearly to...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>Canada declares broadband Internet access a ba...</td>\n",
" <td>Business in Vancouver The regulator has se...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>5</th>\n",
" <td>BC has record year for home sales in 2016</td>\n",
" <td>Business in Vancouver Canadian provinces ...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>6</th>\n",
" <td>Canadian winter weather pushes BC power consum...</td>\n",
" <td>Business in Vancouver Monday saw the most ...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>7</th>\n",
" <td>Kick-starting the drive for hydrogen fuel cel...</td>\n",
" <td>Business in Vancouver Vancouver company Lo...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>8</th>\n",
" <td>Vancouver home affordability hits record low b...</td>\n",
" <td>Business in Vancouver The city is stillby...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>9</th>\n",
" <td>British Columbia home to 20 of Canadas top 10...</td>\n",
" <td>Business in Vancouver Womens Executive Ne...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>10</th>\n",
" <td>Canadian environmentalists fight pipeline expa...</td>\n",
" <td>Business in Vancouver Increased tanker tra...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>11</th>\n",
" <td>Trump win could strain US trade relations with...</td>\n",
" <td>Business in Vancouver Deal-making likely t...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>12</th>\n",
" <td>Retail sales strong across BC year-over-year i...</td>\n",
" <td>Business in Vancouver Top spending Canadia...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>13</th>\n",
" <td>Satellite phones key in government operations ...</td>\n",
" <td>Business in Vancouver Busy signals, power ...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>14</th>\n",
" <td>Bank of Canada maintains overnight rate but do...</td>\n",
" <td>Business in Vancouver Forecasted growth no...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>15</th>\n",
" <td>Victoria the best place in Canada to be a woman</td>\n",
" <td>Business in Vancouver Report says city is ...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>16</th>\n",
" <td>Vancouver once again has Canadas biggest jump...</td>\n",
" <td>Business in Vancouver 24 per cent increase...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>17</th>\n",
" <td>Canadas foreign real estate buyers tax wont ...</td>\n",
" <td>Business in Vancouver Royal Bank of Canada...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>18</th>\n",
" <td>BC growth decelerating as Canada cranks out ...</td>\n",
" <td>Business in Vancouver Unemployment rate st...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>19</th>\n",
" <td>Legalised pot set to spark new joint ventures ...</td>\n",
" <td>Business in Vancouver Prospect of legalisa...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>20</th>\n",
" <td>Provincial Canadian premiers landlord linked ...</td>\n",
" <td>Business in Vancouver Spokesman says Chris...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>21</th>\n",
" <td>Vancouver to continue to lead Canadian economi...</td>\n",
" <td>Business in Vancouver City is on track to ...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>22</th>\n",
" <td>BC expected to lead in Canadas economic growt...</td>\n",
" <td>Business in Vancouver Bank of Montreal exp...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>23</th>\n",
" <td>Canada sets a low bar for carbon pricing</td>\n",
" <td>Business in Vancouver Under new federal be...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>24</th>\n",
" <td>Food prices rise in Canada, sink in the United...</td>\n",
" <td>Business in Vancouver Walmart expansion sp...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>25</th>\n",
" <td>Vancouver outlines exemptions on new empty hom...</td>\n",
" <td>Business in Vancouver Owners who are under...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>26</th>\n",
" <td>Canadas Competition Bureau takes action again...</td>\n",
" <td>Business in Vancouver Application to Compe...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>27</th>\n",
" <td>Vancouvers burgeoning Latin American communit...</td>\n",
" <td>Business in Vancouver Trading bloc clearin...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>28</th>\n",
" <td>Ottawa to spend US$60 billion over next decade...</td>\n",
" <td>Business in Vancouver Liberal government t...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>29</th>\n",
" <td>Canada approves Petronas LNG project, but wit...</td>\n",
" <td>Business in Vancouver New LNG project will...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>...</th>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>90</th>\n",
" <td>BC Canadian employers forecast modest salary i...</td>\n",
" <td>Business in Vancouver Increase is up 0.3 p...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>91</th>\n",
" <td>Auditor General says better monitoring needed ...</td>\n",
" <td>Business in Vancouver Eight major projects...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>92</th>\n",
" <td>Seven things you need to know about Canadian P...</td>\n",
" <td>Comments: Canadian Prime Minister Just...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>93</th>\n",
" <td>Canadian Liberals nix fall Legislature sitting</td>\n",
" <td>Business in Vancouver BC Legislature will ...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>94</th>\n",
" <td>Metro Vancouver home sales continue to plunge ...</td>\n",
" <td>Business in Vancouver This is the first ti...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>95</th>\n",
" <td>Emissions cap for Petronas could be a boon for...</td>\n",
" <td>Business in Vancouver Using hydro power mo...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>96</th>\n",
" <td>Microsofts Bill Gates, Satya Nadella champion...</td>\n",
" <td>Business in Vancouver Microsoft CEO says t...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>97</th>\n",
" <td>BC Canadians have mixed feelings about economi...</td>\n",
" <td>Business in Vancouver Survey finds support...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>98</th>\n",
" <td>August was busiest-ever month at Vancouver Int...</td>\n",
" <td>Business in Vancouver Airport set to get b...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>99</th>\n",
" <td>Google, Canadian company in global e-commerce ...</td>\n",
" <td>Business Insider Internet censorship, corp...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>100</th>\n",
" <td>Canadian utilities commission nixes Creative E...</td>\n",
" <td>Business in Vancouver Provincial regulator...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>101</th>\n",
" <td>Vancouver resident accused of defrauding inves...</td>\n",
" <td>Business in Vancouver Provincial securitie...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>102</th>\n",
" <td>Vancouver real estate has highest bubble risk ...</td>\n",
" <td>Business in Vancouver Report says the Cana...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>103</th>\n",
" <td>Vancouver fintech named criminal organisation ...</td>\n",
" <td>Business in Vancouver Businesswoman implic...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>104</th>\n",
" <td>Home data shows provinces 15 per cent tax on ...</td>\n",
" <td>Business in Vancouver Percentage of Metro ...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>105</th>\n",
" <td>Tech experts call for dedicated lanes for self...</td>\n",
" <td>Business in Vancouver Report says the move...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>106</th>\n",
" <td>What to expect from Justin Trudeaus trip to C...</td>\n",
" <td>Business in Vancouver Climate change decla...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>107</th>\n",
" <td>Routific putting itself on tech map with logis...</td>\n",
" <td>Business in Vancouver Vancouver start-up o...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>108</th>\n",
" <td>Canadian Cancer agency spinoff faces fraud all...</td>\n",
" <td>Business in Vancouver Perceptronix Medical...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>109</th>\n",
" <td>Canadian city's business owners fear housing c...</td>\n",
" <td>Business in Vancouver 'This isnt just a h...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>110</th>\n",
" <td>Airbnb signing bonus could eat into Vancouver'...</td>\n",
" <td>Business in Vancouver Short-term rental co...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>111</th>\n",
" <td>Canadian company puts touchless tool in doctor...</td>\n",
" <td>Business in Vancouver Interactive device i...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>112</th>\n",
" <td>Deep-fried dishes dominate at annual Vancouver...</td>\n",
" <td>Business in Vancouver Ten-pound burgers an...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>113</th>\n",
" <td>Prices cut in Canadian property markets as sal...</td>\n",
" <td>Business in Vancouver Higher-end markets i...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>114</th>\n",
" <td>Foreign home buyers in Vancouver hit with HK-s...</td>\n",
" <td>Real estate board seeks exception for tran...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>115</th>\n",
" <td>Into the wild: mama bear saves woman from stal...</td>\n",
" <td>Comments: A Canadian woman has told of...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>116</th>\n",
" <td>More money in the pot: Canada poised to reap b...</td>\n",
" <td>If marijuana is taxed at the same rate as ...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>117</th>\n",
" <td>Thousands to be airlifted to safety as Canadia...</td>\n",
" <td>Comments: Canada prepared on Thursday ...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>118</th>\n",
" <td>Canadians are standing with you: PM Justin T...</td>\n",
" <td>The blaze could cost insurers as much as C...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>119</th>\n",
" <td>WATCH: Crazy hat lady jumps into Toronto Zoo t...</td>\n",
" <td>Comments: The Toronto Zoo said on Mond...</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"<p>120 rows × 2 columns</p>\n",
"</div>"
],
"text/plain": [
" Headline \\\n",
"0 B.C. government plans to lift 15 per cent fore... \n",
"1 Canadian property owner sues over denial of Mo... \n",
"2 Vancouver airport reveals US$4.2 billion expan... \n",
"3 BC housing starts hit highest level in decades... \n",
"4 Canada declares broadband Internet access a ba... \n",
"5 BC has record year for home sales in 2016 \n",
"6 Canadian winter weather pushes BC power consum... \n",
"7 Kick-starting the drive for hydrogen fuel cel... \n",
"8 Vancouver home affordability hits record low b... \n",
"9 British Columbia home to 20 of Canadas top 10... \n",
"10 Canadian environmentalists fight pipeline expa... \n",
"11 Trump win could strain US trade relations with... \n",
"12 Retail sales strong across BC year-over-year i... \n",
"13 Satellite phones key in government operations ... \n",
"14 Bank of Canada maintains overnight rate but do... \n",
"15 Victoria the best place in Canada to be a woman \n",
"16 Vancouver once again has Canadas biggest jump... \n",
"17 Canadas foreign real estate buyers tax wont ... \n",
"18 BC growth decelerating as Canada cranks out ... \n",
"19 Legalised pot set to spark new joint ventures ... \n",
"20 Provincial Canadian premiers landlord linked ... \n",
"21 Vancouver to continue to lead Canadian economi... \n",
"22 BC expected to lead in Canadas economic growt... \n",
"23 Canada sets a low bar for carbon pricing \n",
"24 Food prices rise in Canada, sink in the United... \n",
"25 Vancouver outlines exemptions on new empty hom... \n",
"26 Canadas Competition Bureau takes action again... \n",
"27 Vancouvers burgeoning Latin American communit... \n",
"28 Ottawa to spend US$60 billion over next decade... \n",
"29 Canada approves Petronas LNG project, but wit... \n",
".. ... \n",
"90 BC Canadian employers forecast modest salary i... \n",
"91 Auditor General says better monitoring needed ... \n",
"92 Seven things you need to know about Canadian P... \n",
"93 Canadian Liberals nix fall Legislature sitting \n",
"94 Metro Vancouver home sales continue to plunge ... \n",
"95 Emissions cap for Petronas could be a boon for... \n",
"96 Microsofts Bill Gates, Satya Nadella champion... \n",
"97 BC Canadians have mixed feelings about economi... \n",
"98 August was busiest-ever month at Vancouver Int... \n",
"99 Google, Canadian company in global e-commerce ... \n",
"100 Canadian utilities commission nixes Creative E... \n",
"101 Vancouver resident accused of defrauding inves... \n",
"102 Vancouver real estate has highest bubble risk ... \n",
"103 Vancouver fintech named criminal organisation ... \n",
"104 Home data shows provinces 15 per cent tax on ... \n",
"105 Tech experts call for dedicated lanes for self... \n",
"106 What to expect from Justin Trudeaus trip to C... \n",
"107 Routific putting itself on tech map with logis... \n",
"108 Canadian Cancer agency spinoff faces fraud all... \n",
"109 Canadian city's business owners fear housing c... \n",
"110 Airbnb signing bonus could eat into Vancouver'... \n",
"111 Canadian company puts touchless tool in doctor... \n",
"112 Deep-fried dishes dominate at annual Vancouver... \n",
"113 Prices cut in Canadian property markets as sal... \n",
"114 Foreign home buyers in Vancouver hit with HK-s... \n",
"115 Into the wild: mama bear saves woman from stal... \n",
"116 More money in the pot: Canada poised to reap b... \n",
"117 Thousands to be airlifted to safety as Canadia... \n",
"118 Canadians are standing with you: PM Justin T... \n",
"119 WATCH: Crazy hat lady jumps into Toronto Zoo t... \n",
"\n",
" Content \n",
"0 Business in Vancouver A timeline for the c... \n",
"1 Business in Vancouver Owners councils ref... \n",
"2 Business in Vancouver Expansions to five t... \n",
"3 Business in Vancouver Provinces yearly to... \n",
"4 Business in Vancouver The regulator has se... \n",
"5 Business in Vancouver Canadian provinces ... \n",
"6 Business in Vancouver Monday saw the most ... \n",
"7 Business in Vancouver Vancouver company Lo... \n",
"8 Business in Vancouver The city is stillby... \n",
"9 Business in Vancouver Womens Executive Ne... \n",
"10 Business in Vancouver Increased tanker tra... \n",
"11 Business in Vancouver Deal-making likely t... \n",
"12 Business in Vancouver Top spending Canadia... \n",
"13 Business in Vancouver Busy signals, power ... \n",
"14 Business in Vancouver Forecasted growth no... \n",
"15 Business in Vancouver Report says city is ... \n",
"16 Business in Vancouver 24 per cent increase... \n",
"17 Business in Vancouver Royal Bank of Canada... \n",
"18 Business in Vancouver Unemployment rate st... \n",
"19 Business in Vancouver Prospect of legalisa... \n",
"20 Business in Vancouver Spokesman says Chris... \n",
"21 Business in Vancouver City is on track to ... \n",
"22 Business in Vancouver Bank of Montreal exp... \n",
"23 Business in Vancouver Under new federal be... \n",
"24 Business in Vancouver Walmart expansion sp... \n",
"25 Business in Vancouver Owners who are under... \n",
"26 Business in Vancouver Application to Compe... \n",
"27 Business in Vancouver Trading bloc clearin... \n",
"28 Business in Vancouver Liberal government t... \n",
"29 Business in Vancouver New LNG project will... \n",
".. ... \n",
"90 Business in Vancouver Increase is up 0.3 p... \n",
"91 Business in Vancouver Eight major projects... \n",
"92 Comments: Canadian Prime Minister Just... \n",
"93 Business in Vancouver BC Legislature will ... \n",
"94 Business in Vancouver This is the first ti... \n",
"95 Business in Vancouver Using hydro power mo... \n",
"96 Business in Vancouver Microsoft CEO says t... \n",
"97 Business in Vancouver Survey finds support... \n",
"98 Business in Vancouver Airport set to get b... \n",
"99 Business Insider Internet censorship, corp... \n",
"100 Business in Vancouver Provincial regulator... \n",
"101 Business in Vancouver Provincial securitie... \n",
"102 Business in Vancouver Report says the Cana... \n",
"103 Business in Vancouver Businesswoman implic... \n",
"104 Business in Vancouver Percentage of Metro ... \n",
"105 Business in Vancouver Report says the move... \n",
"106 Business in Vancouver Climate change decla... \n",
"107 Business in Vancouver Vancouver start-up o... \n",
"108 Business in Vancouver Perceptronix Medical... \n",
"109 Business in Vancouver 'This isnt just a h... \n",
"110 Business in Vancouver Short-term rental co... \n",
"111 Business in Vancouver Interactive device i... \n",
"112 Business in Vancouver Ten-pound burgers an... \n",
"113 Business in Vancouver Higher-end markets i... \n",
"114 Real estate board seeks exception for tran... \n",
"115 Comments: A Canadian woman has told of... \n",
"116 If marijuana is taxed at the same rate as ... \n",
"117 Comments: Canada prepared on Thursday ... \n",
"118 The blaze could cost insurers as much as C... \n",
"119 Comments: The Toronto Zoo said on Mond... \n",
"\n",
"[120 rows x 2 columns]"
]
},
"execution_count": 16,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"#Import CSV file to open\n",
"import pandas\n",
"news_articles = pandas.read_csv('scmp_news.csv', encoding = \"ISO-8859-1\")\n",
"news_articles"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": []
}
],
"metadata": {
"anaconda-cloud": {},
"kernelspec": {
"display_name": "Python [conda root]",
"language": "python",
"name": "conda-root-py"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.5.2"
}
},
"nbformat": 4,
"nbformat_minor": 0
}
| mit |
DoWhatILove/turtle | programming/python/notebooks/matplotlib/tex_do.ipynb | 2 | 1417 | {
"cells": [
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"import numpy as np\n",
"import matplotlib.pyplot as plt\n",
"\n",
"# Example data\n",
"t = np.arange(0.0, 1.0 + 0.01, 0.01)\n",
"s = np.cos(4 * np.pi * t) + 2\n",
"\n",
"plt.rc('text', usetex=True)\n",
"plt.rc('font', family='serif')\n",
"plt.plot(t, s)\n",
"\n",
"plt.xlabel(r'\\textbf{time} (s)')\n",
"plt.ylabel(r'\\textit{voltage} (mV)',fontsize=16)\n",
"plt.title(r\"\\TeX\\ is Number \"\n",
" r\"$\\displaystyle\\sum_{n=1}^\\infty\\frac{-e^{i\\pi}}{2^n}$!\",\n",
" fontsize=16, color='gray')\n",
"# Make room for the ridiculously large title.\n",
"plt.subplots_adjust(top=0.8)\n",
"plt.show()"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": []
}
],
"metadata": {
"kernelspec": {
"display_name": "python3",
"language": "python",
"name": "work"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.4.3"
}
},
"nbformat": 4,
"nbformat_minor": 1
}
| mit |
PythonFreeCourse/Notebooks | week13/resources/Create DB.ipynb | 1 | 9569 | {
"cells": [
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"# DB from https://www.kaggle.com/stefanoleone992/imdb-extensive-dataset\n",
"# By Stefano Leone - https://www.kaggle.com/stefanoleone992\n",
"# Published under CC0 license."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"import gzip\n",
"import os\n",
"import pathlib\n",
"import sqlite3\n",
"from typing import Optional, Tuple\n",
"\n",
"import numpy as np\n",
"import pandas as pd"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"from sqlalchemy import (\n",
" Column, DateTime, Float, ForeignKey,\n",
" Integer, String, Table, create_engine,\n",
")\n",
"from sqlalchemy.ext.declarative import declarative_base\n",
"\n",
"DB_PATH = 'imdb3.db'\n",
"\n",
"engine = create_engine(f'sqlite:///{DB_PATH}', echo=True)\n",
"Base = declarative_base()"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"class Country(Base):\n",
" __tablename__ = 'countries'\n",
"\n",
" id = Column(Integer, primary_key=True)\n",
" name = Column(String, nullable=False)\n",
"\n",
"\n",
"class Genre(Base):\n",
" __tablename__ = 'genres'\n",
"\n",
" id = Column(Integer, primary_key=True)\n",
" name = Column(String, nullable=False)\n",
"\n",
"\n",
"class Language(Base):\n",
" __tablename__ = 'languages'\n",
"\n",
" id = Column(Integer, primary_key=True)\n",
" name = Column(String, nullable=False)\n",
"\n",
"\n",
"movie_country = Table('movie_countries', Base.metadata,\n",
" Column('movie_id', String, ForeignKey('movies.id')),\n",
" Column('country_id', Integer, ForeignKey('countries.id'))\n",
")\n",
"\n",
"\n",
"movie_genre = Table('movie_genres', Base.metadata,\n",
" Column('movie_id', String, ForeignKey('movies.id')),\n",
" Column('genre_id', Integer, ForeignKey('genres.id'))\n",
")\n",
"\n",
"\n",
"movie_language = Table('movie_languages', Base.metadata,\n",
" Column('movie_id', String, ForeignKey('movies.id')),\n",
" Column('language_id', Integer, ForeignKey('languages.id'))\n",
")\n",
"\n",
" \n",
"class Movie(Base):\n",
" __tablename__ = 'movies'\n",
"\n",
" id = Column(String, primary_key=True)\n",
" title = Column(String, nullable=False)\n",
" original_title = Column(String, nullable=False)\n",
" year = Column(Integer, nullable=False)\n",
" avg_vote = Column(String, nullable=False)\n",
" votes = Column(Integer, nullable=False)\n",
" duration = Column(Integer, nullable=False)\n",
" budget = Column(String)\n",
" gross_income = Column(String)\n",
"\n",
" \n",
"class Name(Base):\n",
" __tablename__ = 'names'\n",
"\n",
" id = Column(String, primary_key=True)\n",
" name = Column(String, nullable=False)\n",
" height = Column(Float)\n",
" date_of_birth = Column(DateTime, nullable=True)\n",
" date_of_death = Column(DateTime, nullable=True)\n",
" children = Column(Integer, nullable=False)\n",
"\n",
"\n",
"class Principal(Base):\n",
" __tablename__ = 'principals'\n",
"\n",
" movie_id = Column(String, ForeignKey('movies.id'), primary_key=True)\n",
" ordering = Column(String, primary_key=True)\n",
" name_id = Column(String, ForeignKey('names.id'), nullable=False)\n",
" job_id = Column(Integer, ForeignKey('jobs.id'), nullable=False)\n",
" characters = Column(String)\n",
"\n",
"\n",
"class Job(Base):\n",
" __tablename__ = 'jobs'\n",
"\n",
" id = Column(Integer, primary_key=True)\n",
" name = Column(String, nullable=False)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"Base.metadata.create_all(engine)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"CREATE_TABLE_SETTINGS = {'con': engine, 'if_exists': 'append'}"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"names = pd.read_csv(\n",
" 'names.csv',\n",
" parse_dates=['date_of_birth', 'date_of_death'],\n",
" infer_datetime_format=True,\n",
" na_values=\"None\",\n",
")[\n",
" ['imdb_name_id', 'name', 'height',\n",
" 'date_of_birth', 'date_of_death', 'children']\n",
"]\n",
"\n",
"principals = pd.read_csv(\n",
" 'title_principals.csv', na_values=\"None\",\n",
")[['imdb_title_id', 'ordering', 'imdb_name_id', 'category', 'characters']]\n",
"\n",
"movies = pd.read_csv(\n",
" 'movies.csv', parse_dates=['date_published'], na_values=\"None\",\n",
")[\n",
" ['imdb_title_id', 'title', 'original_title', 'year', 'genre',\n",
" 'duration', 'avg_vote', 'votes', 'country', 'language', 'budget',\n",
" 'worlwide_gross_income']\n",
"]\n",
"\n",
"\n",
"movies.rename(\n",
" columns={'imdb_title_id': 'id', 'worlwide_gross_income': 'gross_income'},\n",
" inplace=True,\n",
")\n",
"principals.rename(\n",
" columns={'imdb_title_id': 'movie_id', 'imdb_name_id': 'name_id',\n",
" 'category': 'job_id'},\n",
" inplace=True,\n",
")\n",
"names.rename(columns={'imdb_name_id': 'id'}, inplace=True)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"names['date_of_birth'] = pd.to_datetime(names['date_of_birth'], errors='coerce')\n",
"names['date_of_death'] = pd.to_datetime(names['date_of_death'], errors='coerce')"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"movies.set_index('id', inplace=True)\n",
"names.set_index('id', inplace=True)\n",
"principals.set_index(['movie_id', 'ordering'], inplace=True)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"tables = {\n",
" 'names': names,\n",
" 'principals': principals,\n",
" 'movies': movies,\n",
"}"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"# Create M2Ms\n",
"\n",
"TO_CONVERT = [\n",
" ('movies', 'genre', 'genres'),\n",
" ('movies', 'language', 'languages'),\n",
" ('movies', 'country', 'countries'),\n",
"]\n",
"\n",
"for t, c, many in TO_CONVERT:\n",
" values = {\n",
" value.strip()\n",
" for vals in tables[t][c].str.split(',')\n",
" for value in (vals if isinstance(vals, list) else [])\n",
" }\n",
"\n",
" second_table = list(enumerate(values, 1))\n",
" tables[many] = pd.DataFrame(second_table, columns=['id', 'name'])\n",
" tables[many].set_index('id', inplace=True)\n",
"\n",
" # Create the M2M relationships\n",
" values_kv = {v: k for k, v in second_table}\n",
" titles_value = (\n",
" (i, values_kv.get(value))\n",
" for i, title in tables[t].iterrows() if isinstance(title[c], str)\n",
" for value in map(str.strip, title[c].split(','))\n",
" )\n",
"\n",
" tables[f'movie_{many}'] = pd.DataFrame(titles_value, columns=['movie_id', f'{c}_id'])\n",
" tables[t].drop([c], axis=1, inplace=True)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"jobs = list(enumerate(principals['job_id'].unique(), 1))\n",
"tables['jobs'] = pd.DataFrame(jobs, columns=['id', 'name'])\n",
"for job_id, job_name in jobs:\n",
" tables['principals'].replace({job_name: job_id}, inplace=True)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"delete_chars = str.maketrans(\"\", \"\", \"[]\\\"\")\n",
"\n",
"tables['principals']['characters'] = (\n",
" tables['principals']['characters'].astype(str)\n",
" .str.translate(delete_chars)\n",
" .replace(',', ', ').replace('nan', np.nan)\n",
")"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"WITHOUT_INDEX = {'movie_genres', 'movie_languages', 'movie_countries', 'jobs'}\n",
"\n",
"for name, df in tables.items():\n",
" CREATE_TABLE_SETTINGS['index'] = name not in WITHOUT_INDEX\n",
" print(name, CREATE_TABLE_SETTINGS)\n",
" df.to_sql(name, **CREATE_TABLE_SETTINGS)"
]
}
],
"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": "3.8.6"
}
},
"nbformat": 4,
"nbformat_minor": 4
}
| mit |
zseder/hunmisc | hunmisc/utils/plotting/notebooks/embedding scatter.ipynb | 1 | 4071 | {
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Initialization\n",
"===="
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"import sys\n",
"import cPickle\n",
"\n",
"import numpy\n",
"import scipy\n",
"\n",
"from sklearn.manifold import TSNE\n",
"\n",
"import matplotlib.pyplot as plt\n",
"\n",
"import seaborn as sns"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"%matplotlib inline"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"# some seaborn initialization for prettier plots\n",
"def init_seaborn():\n",
" sns.set_style('darkgrid')\n",
" sns.set_palette('muted')\n",
" sns.set_context(\"notebook\", font_scale=1.5,\n",
" rc={\"lines.linewidth\": 2.5})\n",
"\n",
"RS = 20151012\n",
"init_seaborn()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Reading data\n",
"===="
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"# read vectors, one word per row\n",
"\n",
"def read_data(fn):\n",
" d = cPickle.load(open(fn))\n",
" labels, vectors = {}, []\n",
" for k, v in d.iteritems():\n",
" labels[k] = len(labels)\n",
" vectors.append(v)\n",
" return labels, numpy.array(vectors)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"labels, vectors = read_data(\"all_feat_vectors\")\n",
"print 'nal', 'ies', scipy.spatial.distance.cosine(vectors[labels['nal']], vectors[labels['ies']])"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"training t-sne\n",
"===="
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"tsne = TSNE(random_state=RS, learning_rate=200, verbose=2, perplexity=20, metric=scipy.spatial.distance.cosine)\n",
"proj = tsne.fit_transform(vectors)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"proj.shape"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"proj[labels['lly']]"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Plotting\n",
"===="
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"f = plt.figure(figsize=(12, 12))\n",
"ax = plt.subplot(aspect='equal')\n",
"sc = ax.scatter(proj[:,0], proj[:,1], lw=0, s=40)\n",
"plt.xlim(-25, 25)\n",
"plt.ylim(-25, 25)\n",
"ax.axis('off')\n",
"ax.axis('tight')\n",
"to_annotate = ['ion', 'ity', 'ism', 'tor', 'age',\n",
" 'ncy', 'hip', 'ium', 'ney', 'cer',\n",
" 'ked', 'ged', 'red', 'ied', 'ced',\n",
" 'tic', 'ful', 'ary', 'cal', 'lar']\n",
"for l in to_annotate:\n",
" ax.annotate(l, proj[labels[l]], color='g', fontsize=20)\n",
"plt.show()"
]
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 2",
"language": "python",
"name": "python2"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 2
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython2",
"version": "2.7.3rc2"
}
},
"nbformat": 4,
"nbformat_minor": 0
}
| gpl-3.0 |
therealAJ/python-sandbox | data-science/learning/ud2/Part 1 Exercise Solutions/Matplotlib Exercises .ipynb | 1 | 172807 | {
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"___\n",
"\n",
"<a href='http://www.pieriandata.com'> <img src='../Pierian_Data_Logo.png' /></a>\n",
"___\n",
"# Matplotlib Exercises \n",
"\n",
"Welcome to the exercises for reviewing matplotlib! Take your time with these, Matplotlib can be tricky to understand at first. These are relatively simple plots, but they can be hard if this is your first time with matplotlib, feel free to reference the solutions as you go along.\n",
"\n",
"Also don't worry if you find the matplotlib syntax frustrating, we actually won't be using it that often throughout the course, we will switch to using seaborn and pandas built-in visualization capabilities. But, those are built-off of matplotlib, which is why it is still important to get exposure to it!\n",
"\n",
"** * NOTE: ALL THE COMMANDS FOR PLOTTING A FIGURE SHOULD ALL GO IN THE SAME CELL. SEPARATING THEM OUT INTO MULTIPLE CELLS MAY CAUSE NOTHING TO SHOW UP. * **\n",
"\n",
"# Exercises\n",
"\n",
"Follow the instructions to recreate the plots using this data:\n",
"\n",
"## Data"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"import numpy as np\n",
"x = np.arange(0,100)\n",
"y = x*2\n",
"z = x**2"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"** Import matplotlib.pyplot as plt and set %matplotlib inline if you are using the jupyter notebook. What command do you use if you aren't using the jupyter notebook?**"
]
},
{
"cell_type": "code",
"execution_count": 9,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"import matplotlib.pyplot as plt\n",
"%matplotlib inline"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Exercise 1\n",
"\n",
"** Follow along with these steps: **\n",
"* ** Create a figure object called fig using plt.figure() **\n",
"* ** Use add_axes to add an axis to the figure canvas at [0,0,1,1]. Call this new axis ax. **\n",
"* ** Plot (x,y) on that axes and set the labels and titles to match the plot below:**"
]
},
{
"cell_type": "code",
"execution_count": 41,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"<matplotlib.text.Text at 0x23d4bc6d68>"
]
},
"execution_count": 41,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"image/png": "iVBORw0KGgoAAAANSUhEUgAAAe4AAAFaCAYAAADYTL41AAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x23d3b959e8>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"fig = plt.figure()\n",
"ax = fig.add_axes([0,0,1,1])\n",
"ax.plot(x,y)\n",
"ax.set_xlabel('x')\n",
"ax.set_ylabel('y')\n",
"ax.set_title('title')"
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"<matplotlib.text.Text at 0x111534c50>"
]
},
"execution_count": 4,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"image/png": "iVBORw0KGgoAAAANSUhEUgAAAe4AAAFaCAYAAADYTL41AAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x10f0b1710>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": []
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Exercise 2\n",
"** Create a figure object and put two axes on it, ax1 and ax2. Located at [0,0,1,1] and [0.2,0.5,.2,.2] respectively.**"
]
},
{
"cell_type": "code",
"execution_count": 58,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"image/png": "iVBORw0KGgoAAAANSUhEUgAAAdoAAAFBCAYAAADQRW4vAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\noP8HJ6vBZ8lJosUAAAAASUVORK5CYII=\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x23d54cc2b0>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"fig = plt.figure()\n",
"ax1 = fig.add_axes([0,0,1,1])\n",
"ax2 = fig.add_axes([0.2,0.5,0.2,0.2])"
]
},
{
"cell_type": "code",
"execution_count": 39,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"image/png": "iVBORw0KGgoAAAANSUhEUgAAAdoAAAFBCAYAAADQRW4vAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\noP8HJ6vBZ8lJosUAAAAASUVORK5CYII=\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x1144d8160>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": []
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"** Now plot (x,y) on both axes. And call your figure object to show it.**"
]
},
{
"cell_type": "code",
"execution_count": 62,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"image/png": "iVBORw0KGgoAAAANSUhEUgAAAd8AAAFBCAYAAAA2bKVrAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nAABy7P8DFcKm1Cj6rZEAAAAASUVORK5CYII=\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x23d54cc2b0>"
]
},
"execution_count": 62,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"ax1.plot(x,y,color='black')\n",
"ax2.plot(x,y,color='red')\n",
"fig"
]
},
{
"cell_type": "code",
"execution_count": 42,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"image/png": "iVBORw0KGgoAAAANSUhEUgAAAe4AAAFQCAYAAAB5151TAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nraoRPgAAAABJRU5ErkJggg==\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x1144d8160>"
]
},
"execution_count": 42,
"metadata": {},
"output_type": "execute_result"
}
],
"source": []
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Exercise 3\n",
"\n",
"** Create the plot below by adding two axes to a figure object at [0,0,1,1] and [0.2,0.5,.4,.4]**"
]
},
{
"cell_type": "code",
"execution_count": 70,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"(30, 50)"
]
},
"execution_count": 70,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"image/png": "iVBORw0KGgoAAAANSUhEUgAAAfoAAAFQCAYAAABXkrzBAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nksAU9CIiIglMQS8iIpLAFPQiIiIJ7P8BvxqYRjA2YHsAAAAASUVORK5CYII=\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x23d69a3550>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"fig = plt.figure()\n",
"ax1 = fig.add_axes([0,0,1,1])\n",
"ax2 = fig.add_axes([0.2,0.5,0.4,0.4])\n",
"\n",
"#Large\n",
"ax1.set_xlabel('x')\n",
"ax1.set_ylabel('z')\n",
"\n",
"ax1.plot(x,z)\n",
"\n",
"#Inserted\n",
"ax2.set_xlabel('x')\n",
"ax2.set_ylabel('y')\n",
"ax2.set_title('zoom')\n",
"\n",
"ax2.plot(x,y)\n",
"ax2.set_xlim(left=20,right=22)\n",
"ax2.set_ylim(bottom=30,top=50)\n"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"** Now use x,y, and z arrays to recreate the plot below. Notice the xlimits and y limits on the inserted plot:**"
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"image/png": "iVBORw0KGgoAAAANSUhEUgAAAfoAAAFQCAYAAABXkrzBAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nH51ua+eImT++AAAAAElFTkSuQmCC\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x10c212cf8>"
]
},
"execution_count": 5,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"image/png": "iVBORw0KGgoAAAANSUhEUgAAAfoAAAFQCAYAAABXkrzBAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nH51ua+eImT++AAAAAElFTkSuQmCC\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x10c212cf8>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": []
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Exercise 4\n",
"\n",
"** Use plt.subplots(nrows=1, ncols=2) to create the plot below.**"
]
},
{
"cell_type": "code",
"execution_count": 80,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"[<matplotlib.lines.Line2D at 0x23d7e3ac50>]"
]
},
"execution_count": 80,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"image/png": "iVBORw0KGgoAAAANSUhEUgAAAX0AAAEACAYAAABfxaZOAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nIiJFRElfRKSI/H+D+R6mF3s3bwAAAABJRU5ErkJggg==\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x23d6aae518>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"fig,axes = plt.subplots(1,2)\n",
"axes[0].plot(x,y,lw=3,ls='--')\n",
"axes[1].plot(x,z,color='r',lw=4)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"** Now plot (x,y) and (x,z) on the axes. Play around with the linewidth and style**"
]
},
{
"cell_type": "code",
"execution_count": 51,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"image/png": "iVBORw0KGgoAAAANSUhEUgAAAX0AAAEACAYAAABfxaZOAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nyP8DG1xU47jRvuwAAAAASUVORK5CYII=\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x114be5e48>"
]
},
"execution_count": 51,
"metadata": {},
"output_type": "execute_result"
}
],
"source": []
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"** See if you can resize the plot by adding the figsize() argument in plt.subplots() are copying and pasting your previous code.**"
]
},
{
"cell_type": "code",
"execution_count": 82,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"[<matplotlib.lines.Line2D at 0x23d7ef5b70>]"
]
},
"execution_count": 82,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"image/png": "iVBORw0KGgoAAAANSUhEUgAAAswAAACRCAYAAADXXu4aAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\niIhU4P8D6PioFdc+baAAAAAASUVORK5CYII=\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x23d6a77da0>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"fig,axes = plt.subplots(1,2,figsize=(12,2))\n",
"axes[0].plot(x,y,lw=3,ls='--')\n",
"axes[1].plot(x,z,color='r',lw=4)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Great Job!"
]
}
],
"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": "3.5.2"
}
},
"nbformat": 4,
"nbformat_minor": 0
}
| gpl-3.0 |
intel-analytics/analytics-zoo | apps/ray/parameter_server/sharded_parameter_server.ipynb | 1 | 15408 | {
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# This notebook is adapted from: \n",
"https://github.com/ray-project/tutorial/tree/master/examples/sharded_parameter_server.ipynb\n",
"\n",
"# Sharded Parameter Servers\n",
"\n",
"**GOAL:** The goal of this exercise is to use actor handles to implement a sharded parameter server example for **distributed asynchronous stochastic gradient descent**.\n",
"\n",
"Before doing this exercise, make sure you understand the concepts from the exercise on **Actor Handles**.\n",
"\n",
"### Parameter Servers\n",
"\n",
"A parameter server is simply an object that stores the parameters (or \"weights\") of a machine learning model (this could be a neural network, a linear model, or something else). It exposes two methods: one for getting the parameters and one for updating the parameters.\n",
"\n",
"In a typical machine learning training application, worker processes will run in an infinite loop that does the following:\n",
"1. Get the latest parameters from the parameter server.\n",
"2. Compute an update to the parameters (using the current parameters and some data).\n",
"3. Send the update to the parameter server.\n",
"\n",
"The workers can operate synchronously (that is, in lock step), in which case distributed training with multiple workers is algorithmically equivalent to serial training with a larger batch of data. Alternatively, workers can operate independently and apply their updates asynchronously. The main benefit of asynchronous training is that a single slow worker will not slow down the other workers. The benefit of synchronous training is that the algorithm behavior is more predictable and reproducible."
]
},
{
"cell_type": "code",
"execution_count": 1,
"metadata": {},
"outputs": [],
"source": [
"from __future__ import absolute_import\n",
"from __future__ import division\n",
"from __future__ import print_function\n",
"\n",
"import numpy as np\n",
"import ray\n",
"import time"
]
},
{
"cell_type": "markdown",
"metadata": {
"collapsed": true
},
"source": [
"# Init SparkContext"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Current pyspark location is : /root/anaconda2/envs/ray_train/lib/python3.6/site-packages/pyspark/__init__.py\n",
"Start to pack current python env\n",
"Collecting packages...\n",
"Packing environment at '/root/anaconda2/envs/ray_train' to '/tmp/tmp7qvxc3o2/python_env.tar.gz'\n",
"[########################################] | 100% Completed | 34.4s\n",
"Packing has been completed: /tmp/tmp7qvxc3o2/python_env.tar.gz\n",
"pyspark_submit_args is: --master yarn --deploy-mode client --archives /tmp/tmp7qvxc3o2/python_env.tar.gz#python_env --num-executors 2 --executor-cores 4 --executor-memory 2g pyspark-shell \n"
]
}
],
"source": [
"from zoo.common.nncontext import init_spark_on_local, init_spark_on_yarn\n",
"import numpy as np\n",
"import os\n",
"hadoop_conf_dir = os.environ.get('HADOOP_CONF_DIR')\n",
"\n",
"if hadoop_conf_dir:\n",
" sc = init_spark_on_yarn(\n",
" hadoop_conf=hadoop_conf_dir,\n",
" conda_name=os.environ.get(\"ZOO_CONDA_NAME\", \"zoo\"), # The name of the created conda-env\n",
" num_executors=2,\n",
" executor_cores=4,\n",
" executor_memory=\"2g\",\n",
" driver_memory=\"2g\",\n",
" driver_cores=1,\n",
" extra_executor_memory_for_ray=\"3g\")\n",
"else:\n",
" sc = init_spark_on_local(cores = 8, conf = {\"spark.driver.memory\": \"2g\"})"
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Start to launch the JVM guarding process\n",
"JVM guarding process has been successfully launched\n",
"Start to launch ray on cluster\n",
"Start to launch ray on local\n",
"Executing command: ray start --redis-address 172.16.0.158:34046 --redis-password 123456 --num-cpus 0 --object-store-memory 400000000\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"2019-07-18 07:09:19,971\tWARNING worker.py:1341 -- WARNING: Not updating worker name since `setproctitle` is not installed. Install this with `pip install setproctitle` (or ray[debug]) to enable monitoring of worker processes.\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"\n",
"2019-07-18 07:09:19,855\tINFO services.py:409 -- Waiting for redis server at 172.16.0.158:34046 to respond...\n",
"2019-07-18 07:09:19,858\tINFO scripts.py:363 -- Using IP address 172.16.0.102 for this node.\n",
"2019-07-18 07:09:19,861\tINFO node.py:511 -- Process STDOUT and STDERR is being redirected to /tmp/ray/session_2019-07-18_15-09-10_137772_188428/logs.\n",
"2019-07-18 07:09:19,862\tINFO services.py:1441 -- Starting the Plasma object store with 0.4 GB memory using /dev/shm.\n",
"2019-07-18 07:09:19,887\tINFO scripts.py:371 -- \n",
"Started Ray on this node. If you wish to terminate the processes that have been started, run\n",
"\n",
" ray stop\n",
"\n",
"\n"
]
}
],
"source": [
"# It may take a while to ditribute the local environment including python and java to cluster\n",
"import ray\n",
"from zoo.ray import RayContext\n",
"ray_ctx = RayContext(sc=sc, object_store_memory=\"4g\")\n",
"ray_ctx.init()\n",
"#ray.init(num_cpus=30, include_webui=False, ignore_reinit_error=True)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"A simple parameter server can be implemented as a Python class in a few lines of code.\n",
"\n",
"**EXERCISE:** Make the `ParameterServer` class an actor."
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {},
"outputs": [],
"source": [
"dim = 10\n",
"@ray.remote\n",
"class ParameterServer(object):\n",
" def __init__(self, dim):\n",
" self.parameters = np.zeros(dim)\n",
" \n",
" def get_parameters(self):\n",
" return self.parameters\n",
" \n",
" def update_parameters(self, update):\n",
" self.parameters += update\n",
"\n",
"\n",
"ps = ParameterServer.remote(dim)\n"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"A worker can be implemented as a simple Python function that repeatedly gets the latest parameters, computes an update to the parameters, and sends the update to the parameter server."
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {},
"outputs": [],
"source": [
"@ray.remote\n",
"def worker(ps, dim, num_iters):\n",
" for _ in range(num_iters):\n",
" # Get the latest parameters.\n",
" parameters = ray.get(ps.get_parameters.remote())\n",
" # Compute an update.\n",
" update = 1e-3 * parameters + np.ones(dim)\n",
" # Update the parameters.\n",
" ps.update_parameters.remote(update)\n",
" # Sleep a little to simulate a real workload.\n",
" time.sleep(0.5)\n",
"\n",
"# Test that worker is implemented correctly. You do not need to change this line.\n",
"ray.get(worker.remote(ps, dim, 1))"
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {},
"outputs": [],
"source": [
"# Start two workers.\n",
"worker_results = [worker.remote(ps, dim, 100) for _ in range(2)]"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"As the worker tasks are executing, you can query the parameter server from the driver and see the parameters changing in the background."
]
},
{
"cell_type": "code",
"execution_count": 10,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"[19.16281869 19.16281869 19.16281869 19.16281869 19.16281869 19.16281869\n",
" 19.16281869 19.16281869 19.16281869 19.16281869]\n"
]
}
],
"source": [
"print(ray.get(ps.get_parameters.remote()))"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Sharding a Parameter Server\n",
"\n",
"As the number of workers increases, the volume of updates being sent to the parameter server will increase. At some point, the network bandwidth into the parameter server machine or the computation down by the parameter server may be a bottleneck.\n",
"\n",
"Suppose you have $N$ workers and $1$ parameter server, and suppose each of these is an actor that lives on its own machine. Furthermore, suppose the model size is $M$ bytes. Then sending all of the parameters from the workers to the parameter server will mean that $N * M$ bytes in total are sent to the parameter server. If $N = 100$ and $M = 10^8$, then the parameter server must receive ten gigabytes, which, assuming a network bandwidth of 10 giga*bits* per second, would take 8 seconds. This would be prohibitive.\n",
"\n",
"On the other hand, if the parameters are sharded (that is, split) across `K` parameter servers, `K` is `100`, and each parameter server lives on a separate machine, then each parameter server needs to receive only 100 megabytes, which can be done in 80 milliseconds. This is much better.\n",
"\n",
"**EXERCISE:** The code below defines a parameter server shard class. Modify this class to make `ParameterServerShard` an actor. We will need to revisit this code soon and increase `num_shards`."
]
},
{
"cell_type": "code",
"execution_count": 11,
"metadata": {},
"outputs": [],
"source": [
"@ray.remote\n",
"class ParameterServerShard(object):\n",
" def __init__(self, sharded_dim):\n",
" self.parameters = np.zeros(sharded_dim)\n",
" \n",
" def get_parameters(self):\n",
" return self.parameters\n",
" \n",
" def update_parameters(self, update):\n",
" self.parameters += update\n",
"\n",
"\n",
"total_dim = (10 ** 8) // 8 # This works out to 100MB (we have 25 million\n",
" # float64 values, which are each 8 bytes).\n",
"num_shards = 2 # The number of parameter server shards.\n",
"\n",
"assert total_dim % num_shards == 0, ('In this exercise, the number of shards must '\n",
" 'perfectly divide the total dimension.')\n",
"\n",
"# Start some parameter servers.\n",
"ps_shards = [ParameterServerShard.remote(total_dim // num_shards) for _ in range(num_shards)]\n",
"\n",
"assert hasattr(ParameterServerShard, 'remote'), ('You need to turn ParameterServerShard into an '\n",
" 'actor (by using the ray.remote keyword).')"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"The code below implements a worker that does the following.\n",
"1. Gets the latest parameters from all of the parameter server shards.\n",
"2. Concatenates the parameters together to form the full parameter vector.\n",
"3. Computes an update to the parameters.\n",
"4. Partitions the update into one piece for each parameter server.\n",
"5. Applies the right update to each parameter server shard."
]
},
{
"cell_type": "code",
"execution_count": 14,
"metadata": {},
"outputs": [],
"source": [
"@ray.remote\n",
"def worker_task(total_dim, num_iters, *ps_shards):\n",
" # Note that ps_shards are passed in using Python's variable number\n",
" # of arguments feature. We do this because currently actor handles\n",
" # cannot be passed to tasks inside of lists or other objects.\n",
" for _ in range(num_iters):\n",
" # Get the current parameters from each parameter server.\n",
" parameter_shards = [ray.get(ps.get_parameters.remote()) for ps in ps_shards]\n",
" assert all([isinstance(shard, np.ndarray) for shard in parameter_shards]), (\n",
" 'The parameter shards must be numpy arrays. Did you forget to call ray.get?')\n",
" # Concatenate them to form the full parameter vector.\n",
" parameters = np.concatenate(parameter_shards)\n",
" assert parameters.shape == (total_dim,)\n",
"\n",
" # Compute an update.\n",
" update = np.ones(total_dim)\n",
" # Shard the update.\n",
" update_shards = np.split(update, len(ps_shards))\n",
" \n",
" # Apply the updates to the relevant parameter server shards.\n",
" for ps, update_shard in zip(ps_shards, update_shards):\n",
" ps.update_parameters.remote(update_shard)\n",
"\n",
"\n",
"# Test that worker_task is implemented correctly. You do not need to change this line.\n",
"ray.get(worker_task.remote(total_dim, 1, *ps_shards))"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"**EXERCISE:** Experiment by changing the number of parameter server shards, the number of workers, and the size of the data.\n",
"\n",
"**NOTE:** Because these processes are all running on the same machine, network bandwidth will not be a limitation and sharding the parameter server will not help. To see the difference, you would need to run the application on multiple machines. There are still regimes where sharding a parameter server can help speed up computation on the same machine (by parallelizing the computation that the parameter server processes have to do). If you want to see this effect, you should implement a synchronous training application. In the asynchronous setting, the computation is staggered and so speeding up the parameter server usually does not matter."
]
},
{
"cell_type": "code",
"execution_count": 16,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"This took 4.21185827255249 seconds.\n"
]
}
],
"source": [
"num_workers = 4\n",
"\n",
"# Start some workers. Try changing various quantities and see how the\n",
"# duration changes.\n",
"start = time.time()\n",
"ray.get([worker_task.remote(total_dim, 5, *ps_shards) for _ in range(num_workers)])\n",
"print('This took {} seconds.'.format(time.time() - start))"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": []
}
],
"metadata": {
"kernelspec": {
"display_name": "Python (ray_train)",
"language": "python",
"name": "ray_train"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.6.8"
}
},
"nbformat": 4,
"nbformat_minor": 2
}
| apache-2.0 |
liganega/Gongsu-DataSci | ref_materials/exams/2017/A02/midterm-a02.ipynb | 1 | 57062 | {
"cells": [
{
"cell_type": "code",
"execution_count": 1,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"from __future__ import division, print_function\n",
"import matplotlib.pyplot as plt\n",
"import numpy as np"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"%matplotlib inline"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# 파이썬 기본 자료형"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## 문제 \n",
"\n",
"실수(부동소수점)를 하나 입력받아, 그 숫자를 반지름으로 하는 원의 면적과 둘레의 길이를 튜플로 리턴하는 함수 `circle_radius`를 구현하는 코드를 작성하라,"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"```\n",
"\n",
"\n",
"\n",
".\n",
"```"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# 문자열 자료형"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"아래 사이트는 커피 콩의 현재 시세를 보여준다.\n",
"\n",
" http://beans-r-us.appspot.com/prices.html\n",
"\n",
"위 사이트의 내용을 html 소스코드로 보면 다음과 같으며, 검색된 시간의 커피콩의 가격은 \n",
"`Current price of coffee beans` 문장이 담겨 있는 줄에 명시되어 있다.\n",
"\n",
"---\n",
"\n",
"```html\n",
"<html><head><title>Welcome to the Beans'R'Us Pricing Page</title>\n",
"<link rel=\"stylesheet\" type=\"text/css\" href=\"beansrus.css\" />\n",
"</head><body>\n",
"<h2>Welcome to the Beans'R'Us Pricing Page</h2>\n",
"<p>Current price of coffee beans = <strong>$5.94</strong></p>\n",
"<p>Price valid for 15 minutes from Sun Sep 10 12:21:58 2017.</p>\n",
"</body></html>\n",
"```\n",
"\n",
"---"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## 문제\n",
"\n",
"아래 코드가 하는 일을 설명하라.\n",
"\n",
"---\n",
"\n",
"```\n",
"from __future__ import print_function\n",
"\n",
"import urllib2\n",
"import time\n",
"\n",
"def price_setter(b_price, a_price):\n",
" bean_price = b_price\n",
" while 5.5 < bean_price < 6.0:\n",
" time.sleep(1)\n",
"\n",
" page = urllib2.urlopen(\"http://beans-r-us.appspot.com/prices.html\")\n",
" text = page.read().decode(\"utf8\")\n",
"\n",
" price_index = text.find(\">$\") + 2\n",
" bean_price_str = text[price_index : price_index + 4]\n",
" bean_price = float(bean_price_str)\n",
" \n",
" print(\"현재 커피콩 가격이\", bean_price, \"달러 입니다.\")\n",
"\n",
" if bean_price <= 5.5:\n",
" print(\"아메리카노 가격을\", a_price, \"달러만큼 인하하세요!\")\n",
" else:\n",
" print(\"아메리카노 가격을\", a_price, \"달러만큼 인상하세요!\")\n",
"```\n",
"\n",
"---"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"```\n",
"\n",
"\n",
"\n",
"\n",
"\n",
"\n",
"\n",
"\n",
"\n",
"\n",
".```"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# 오류 및 예외 처리"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## 문제\n",
"아래 코드가 하는 일을 설명하라.\n",
"\n",
"---\n",
"\n",
"```\n",
"number_to_square = raw_input(\"A number to divide 100: \")\n",
"\n",
"try: \n",
" number = float(number_to_square)\n",
" print(\"100을 입력한 값으로 나눈 결과는\", 100/number, \"입니다.\")\n",
"except ZeroDivisionError:\n",
" raise ZeroDivisionError('0이 아닌 숫자를 입력하세요.')\n",
"except ValueError:\n",
" raise ValueError('숫자를 입력하세요.') \n",
"```\n",
"\n",
"---"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"```\n",
"\n",
"\n",
"\n",
"\n",
"\n",
"\n",
"\n",
"\n",
"\n",
"\n",
".```"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# 리스트"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## 문제 \n",
"\n",
"아래 설명 중에서 리스트 자료형의 성질에 해당하는 항목을 모두 골라라.\n",
"\n",
"1. 가변 자료형이다.\n",
"* 불변 자료형이다.\n",
"* 인덱스와 슬라이싱을 활용하여 항목의 내용을 확인하고 활용할 수 있다.\n",
"* 항목들이 임의의 자료형을 가질 수 있다.\n",
"* 리스트 길이에 제한이 있다.\n",
"* 신성정보 등 중요한 데이터를 보관할 때 사용한다."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"```\n",
"\n",
"```"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"견본답안: 1, 3, 4"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# 사전"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"`record_list.txt` 파일은 여덟 명의 수영 선수의 50m 기록을 담고 있다.\n",
"\n",
"---\n",
"\n",
"```txt\n",
"player1 21.09 \n",
"player2 20.32 \n",
"player3 21.81 \n",
"player4 22.97 \n",
"player5 23.29 \n",
"player6 22.09 \n",
"player7 21.20 \n",
"player8 22.16\n",
"```\n",
"\n",
"---"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## 문제\n",
"\n",
"아래코드가 하는 일을 설명하라.\n",
"\n",
"---\n",
"\n",
"```python\n",
"from __future__ import print_function\n",
"\n",
"record_f = open(\"record_list.txt\", 'r')\n",
"record = record_f.read().decode('utf8').split('\\n')\n",
"\n",
"record_dict = {}\n",
"\n",
"for line in record:\n",
" (player, p_record) = line.split()\n",
" record_dict[p_record] = player\n",
"\n",
"record_f.close()\n",
"\n",
"record_list = record_dict.keys()\n",
"record_list.sort()\n",
"\n",
"for i in range(3):\n",
" item = record_list[i]\n",
" print(str(i+1) + \":\", record_dict[item], item)\n",
"``` \n",
"\n",
"---"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"```\n",
"\n",
"\n",
"\n",
"\n",
"\n",
"\n",
"\n",
"\n",
"\n",
".```"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# 튜플"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## 문제 \n",
"\n",
"아래 설명 중에서 튜플 자료형의 성질에 해당하는 항목을 모두 골라라.\n",
"\n",
"1. 가변 자료형이다.\n",
"* 불변 자료형이다.\n",
"* 인덱스와 슬라이싱을 활용하여 항목의 내용을 확인하고 활용할 수 있다.\n",
"* 항목들이 임의의 자료형을 가질 수 있다.\n",
"* 튜플 길이에 제한이 있다.\n",
"* 신성정보 등 중요한 데이터를 보관할 때 사용한다."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"```\n",
"\n",
"```"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"견본답안: 2, 3, 4, 6"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# 리스트 조건제시법"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"아래 코드는 0부터 1000 사이의 홀수들의 제곱의 리스트를 조건제시법으로 생성한다"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"[1, 9, 25, 49, 81]"
]
},
"execution_count": 3,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"odd_1000 = [x**2 for x in range(0, 1000) if x % 2 == 1]\n",
"\n",
"# 리스트의 처음 다섯 개 항목\n",
"odd_1000[:5]"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## 문제\n",
"\n",
"0부터 1000까지의 숫자들 중에서 홀수이면서 7의 배수인 숫자들의 리스트를 조건제시법으로 생성하는 코드를 작성하라."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"```\n",
"\n",
"\n",
"\n",
".\n",
"```"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"모범답안:"
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"[7, 21, 35, 49, 63]"
]
},
"execution_count": 4,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"odd_3x7 = [x for x in range(0, 1000) if x % 2 == 1 and x % 7 == 0]\n",
"\n",
"# 리스트의 처음 다섯 개 항목\n",
"odd_3x7[:5]"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## 문제\n",
"\n",
"0부터 1000까지의 숫자들 중에서 홀수이면서 7의 배수인 숫자들을 제곱하여 1을 더한 값들의 리스트를 조건제시법으로 생성하는 코드를 작성하라.\n",
"힌트: 아래와 같이 정의된 함수를 활용한다.\n",
"\n",
"$$f(x) = x^2 + 1$$"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"```\n",
"\n",
"\n",
"\n",
"\n",
"\n",
"\n",
".```"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"견본답안:"
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"[50, 442, 1226, 2402, 3970]"
]
},
"execution_count": 5,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"def square_plus1(x):\n",
" return x**2 + 1\n",
"\n",
"odd_3x7_spl = [square_plus1(x) for x in odd_3x7]\n",
"# 리스트의 처음 다섯 개 항목\n",
"odd_3x7_spl[:5]"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# csv 파일 읽어들이기"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"`'Seoul_pop2.csv'` 파일에는 아래 내용이 저장되어 있다\"\n",
"\n",
"---\n",
"```csv\n",
"### 1949년부터 2010년 사이의 서울과 수도권 인구 증가율(%)\n",
"# 구간,서울,수도권 \n",
"\n",
"1949-1955,9.12,-5.83\n",
"1955-1960,55.88,32.22\n",
"1960-1966,55.12,32.76\n",
"1966-1970,45.66,28.76\n",
"1970-1975,24.51,22.93\n",
"1975-1980,21.38,21.69\n",
"1980-1985,15.27,18.99\n",
"1985-1990,10.15,17.53\n",
"1990-1995,-3.64,8.54\n",
"1995-2000,-3.55,5.45\n",
"2000-2005,-0.93,6.41\n",
"2005-2010,-1.34,3.71\n",
"```\n",
"\n",
"---"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"확장자가 csv인 파일은 데이터를 저장하기 위해 주로 사용한다. \n",
"csv는 Comma-Separated Values의 줄임말로 데이터가 쉼표(콤마)로 구분되어 정리되어 있는 파일을 의미한다. \n",
"\n",
"csv 파일을 읽어드리는 방법은 `csv` 모듈의 `reader()` 함수를 활용하면 매우 쉽다.\n",
"`reader()` 함수의 리턴값은 csv 파일에 저장된 내용을 줄 단위로, 쉼표 단위로 끊어서 2차원 리스트이다.\n",
"\n",
"예를 들어, 아래 코드는 언급된 파일에 저장된 내용의 각 줄을 출력해준다."
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"['1949-1955', '9.12', '-5.83']\n",
"['1955-1960', '55.88', '32.22']\n",
"['1960-1966', '55.12', '32.76']\n",
"['1966-1970', '45.66', '28.76']\n",
"['1970-1975', '24.51', '22.93']\n",
"['1975-1980', '21.38', '21.69']\n",
"['1980-1985', '15.27', '18.99']\n",
"['1985-1990', '10.15', '17.53']\n",
"['1990-1995', '-3.64', '8.54']\n",
"['1995-2000', '-3.55', '5.45']\n",
"['2000-2005', '-0.93', '6.41']\n",
"['2005-2010', '-1.34', '3.71']\n"
]
}
],
"source": [
"import csv\n",
"\n",
"with open('Seoul_pop2.csv', 'rb') as f:\n",
" reader = csv.reader(f)\n",
" for row in reader:\n",
" if len(row) == 0 or row[0][0] == '#':\n",
" continue\n",
" else:\n",
" print(row)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## 문제\n",
"\n",
"위 코드에서 5번 째 줄을 아래와 같이 하면 오류 발생한다.\n",
"```\n",
"if row[0][0] == '#' or len(row) == 0:\n",
"```\n",
"이유를 간단하게 설명하라."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"```\n",
"\n",
"\n",
"\n",
".\n",
"```"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# 넘파이 활용 기초 1"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"넘파이 어레이를 생성하는 방법은 몇 개의 기본적인 함수를 이용하면 된다.\n",
"\n",
"* `np.arange()`\n",
"* `np.zeros()`\n",
"* `np.ones()` \n",
"* `np.diag()` \n",
"\n",
"예제:"
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"array([3, 6, 9])"
]
},
"execution_count": 7,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"np.arange(3, 10, 3)"
]
},
{
"cell_type": "code",
"execution_count": 8,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"array([[ 0., 0., 0.],\n",
" [ 0., 0., 0.]])"
]
},
"execution_count": 8,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"np.zeros((2,3))"
]
},
{
"cell_type": "code",
"execution_count": 9,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"array([ 1., 1.])"
]
},
"execution_count": 9,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"np.ones((2,))"
]
},
{
"cell_type": "code",
"execution_count": 10,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"array([[1, 0, 0, 0],\n",
" [0, 2, 0, 0],\n",
" [0, 0, 3, 0],\n",
" [0, 0, 0, 4]])"
]
},
"execution_count": 10,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"np.diag([1, 2, 3, 4])"
]
},
{
"cell_type": "code",
"execution_count": 11,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"array([[ 2., 2., 2.],\n",
" [ 2., 2., 2.],\n",
" [ 2., 2., 2.]])"
]
},
"execution_count": 11,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"np.ones((3,3)) * 2"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## 문제\n",
"\n",
"아래 모양의 어레이를 생성하는 코드를 작성하라.\n",
"단, 언급된 네 개의 함수들만 사용해야 하며, 수동으로 생성된 리스트나 어레이는 허용되지 않는다.\n",
"\n",
"$$\\left [ \\begin{matrix} 2 & 0 & 0 \\\\ 0 & 2 & 0 \\\\ 0 & 0 & 2 \\end{matrix} \\right ]$$"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"```\n",
"\n",
"\n",
"\n",
".\n",
"```"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"견본답안:"
]
},
{
"cell_type": "code",
"execution_count": 12,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"array([[ 2., 0., 0.],\n",
" [ 0., 2., 0.],\n",
" [ 0., 0., 2.]])"
]
},
"execution_count": 12,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"np.diag(np.ones((3,))*2)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## 문제\n",
"\n",
"아래 모양의 어레이를 생성하는 코드를 작성하라.\n",
"단, 언급된 네 개의 함수만 사용해야 하며, 수동으로 생성된 리스트나 어레이는 허용되지 않는다.\n",
"\n",
"$$\\left [ \\begin{matrix} 2 & 0 & 0 \\\\ 0 & 4 & 0 \\\\ 0 & 0 & 6 \\end{matrix} \\right ]$$"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"```\n",
"\n",
"\n",
"\n",
".\n",
"```"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"견본답안:"
]
},
{
"cell_type": "code",
"execution_count": 13,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"array([[2, 0, 0],\n",
" [0, 4, 0],\n",
" [0, 0, 6]])"
]
},
"execution_count": 13,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"np.diag(np.arange(2, 7, 2))"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# 넘파이의 `linspace()` 함수 활용"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"numpy 모듈의 `linspace()` 함수는 지정된 구간을 정해진 크기로 일정하게 쪼개는 어래이를 생성한다.\n",
"예를 들어, 0부터 3사이의 구간을 균등하게 30개로 쪼개고자 하면 아래와 같이 실행하면 된다."
]
},
{
"cell_type": "code",
"execution_count": 14,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"array([ 0. , 0.10344828, 0.20689655, 0.31034483, 0.4137931 ,\n",
" 0.51724138, 0.62068966, 0.72413793, 0.82758621, 0.93103448,\n",
" 1.03448276, 1.13793103, 1.24137931, 1.34482759, 1.44827586,\n",
" 1.55172414, 1.65517241, 1.75862069, 1.86206897, 1.96551724,\n",
" 2.06896552, 2.17241379, 2.27586207, 2.37931034, 2.48275862,\n",
" 2.5862069 , 2.68965517, 2.79310345, 2.89655172, 3. ])"
]
},
"execution_count": 14,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"xs = np.linspace(0, 3, 30)\n",
"xs"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## 문제\n",
"\n",
"0부터 1사이의 구간을 균등하게 10개로 쪼개어 각 항목을 제곱하는 코드를 작성하라."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"```\n",
"\n",
"\n",
"\n",
".\n",
"```"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"견본답안:"
]
},
{
"cell_type": "code",
"execution_count": 15,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"array([ 0. , 0.01234568, 0.04938272, 0.11111111, 0.19753086,\n",
" 0.30864198, 0.44444444, 0.60493827, 0.79012346, 1. ])"
]
},
"execution_count": 15,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"np.linspace(0,1, 10) ** 2"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# 넘파이 활용 기초 2"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"`population.txt` 파일은 1900년부터 1920년까지 캐나다 북부지역에서 서식한 산토끼(hare)와 스라소니(lynx)의 숫자, \n",
"그리고 채소인 당근(carrot)의 재배숫자를 아래 내용으로 순수 텍스트 데이터로 담고 있다.\n",
"\n",
"---\n",
"\n",
"```\n",
"# year\thare\tlynx\t carrot\n",
"1900\t30e3\t 4e3\t 48300\n",
"1901\t47.2e3\t6.1e3\t 48200\n",
"1902\t70.2e3\t9.8e3\t 41500\n",
"1903\t77.4e3\t35.2e3\t38200\n",
"1904\t36.3e3\t59.4e3\t40600\n",
"1905\t20.6e3\t41.7e3\t39800\n",
"1906\t18.1e3\t19e3\t 38600\n",
"1907\t21.4e3\t13e3\t 42300\n",
"1908\t22e3\t 8.3e3\t 44500\n",
"1909\t25.4e3\t9.1e3\t 42100\n",
"1910\t27.1e3\t7.4e3\t 46000\n",
"1911\t40.3e3\t8e3\t 46800\n",
"1912\t57e3\t 12.3e3\t43800\n",
"1913\t76.6e3\t19.5e3\t40900\n",
"1914\t52.3e3\t45.7e3\t39400\n",
"1915\t19.5e3\t51.1e3\t39000\n",
"1916\t11.2e3\t29.7e3\t36700\n",
"1917\t7.6e3\t 15.8e3\t41800\n",
"1918\t14.6e3\t9.7e3\t 43300\n",
"1919\t16.2e3\t10.1e3\t41300\n",
"1920\t24.7e3\t8.6e3\t 47300\n",
"```\n",
"\n",
"---"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"아래 코드는 연도, 토끼 개체수, 스라소리 개체수, 당근 개체수를 따로따로 떼어 내어 각각 어레이로 변환하여 \n",
"`year`, `hares`, `lynxes`, `carrots` 변수에 저장하는 코드이다."
]
},
{
"cell_type": "code",
"execution_count": 16,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"data = np.loadtxt('populations.txt')\n",
"year, hares, lynxes, carrots = data.T"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## 문제\n",
"위 코드에서 `np.loadtxt` 함수의 작동방식을 간단하게 설명하라."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"```\n",
"\n",
"\n",
"\n",
"\n",
".\n",
"```"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## 문제\n",
"위 코드에서 `data.T`에 대해 간단하게 설명하라."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"```\n",
"\n",
"\n",
"\n",
"\n",
".\n",
"```"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"아래 코드는 토끼, 스라소니, 당근 각각의 개체수의 연도별 변화를 선그래프로 보여주도록 하는 코드이다."
]
},
{
"cell_type": "code",
"execution_count": 17,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"<matplotlib.legend.Legend at 0x113899450>"
]
},
"execution_count": 17,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"image/png": "iVBORw0KGgoAAAANSUhEUgAAAVsAAAEJCAYAAAA6iYQRAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nhjMI71CKAAAAAElFTkSuQmCC\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x1139354d0>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"plt.axes([0.2, 0.1, 0.5, 0.8])\n",
"plt.plot(year, hares, year, lynxes, year, carrots)\n",
"plt.legend(('Hare', 'Lynx', 'Carrot'), loc=(1.05, 0.5))"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## 문제\n",
"\n",
"위 코드에서 사용된 `plt.plot(year, hares, year, lynxes, year, carrots)` 를 간단하게 설명하라."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"```\n",
"\n",
"\n",
"\n",
"\n",
".\n",
"```"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## 문제\n",
"\n",
"산토끼, 스라소니, 당근의 예제에서 1900년부터 1921년 사이에 개체별 개체수의 변화에 대해 어떤 분석을 할 수 있는지 그래프를 이용하여 간단하게 설명하라."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"```\n",
"\n",
"\n",
"\n",
"\n",
"\n",
".\n",
"```"
]
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 2",
"language": "python",
"name": "python2"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 2
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython2",
"version": "2.7.13"
}
},
"nbformat": 4,
"nbformat_minor": 2
}
| gpl-3.0 |
nholtz/structural-analysis | slope-deflection/sdutil2-doc.ipynb | 1 | 7118 | {
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# sdutil2\n",
"Eventually will document `sdutil2` - for now just some examples."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"**NOTE:** *The slope-deflection sign convention may seem strange to those used to matirx stiffness analysis, but it makes sense. None of the slope deflection equations explicitly state a \n",
"member 'direction' and it doesn't matter. For example, whether you consider the column AB as\n",
"going from A to B or as going from B to A, a +ive shear at end A is still directed toward the\n",
"left. In matrix analysis, that direction matters.*\n",
""
]
},
{
"cell_type": "code",
"execution_count": 1,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"import sys\n",
"m = 'sdutil2'\n",
"if m in sys.modules:\n",
" del sys.modules[m] # so we can easily re-import the library if it changes during debugging\n",
"import sdutil2 as sd"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"EF(4,-96.0,96.0,144.0,-144.0)"
]
},
"execution_count": 2,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"sd.FEF.udl(4,72)"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"EF(4,-57.6,38.4,100.8,-43.2)"
]
},
"execution_count": 3,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"x = sd.FEF.lvl(4,72,0)\n",
"x"
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"EF(4,-38.4,57.6,43.19999999999999,-100.80000000000001)"
]
},
"execution_count": 4,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"y = sd.FEF.lvl(4,0,72)\n",
"y"
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"EF(4,-96.0,96.0,144.0,-144.0)"
]
},
"execution_count": 5,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"x+y"
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"EF(4,-960.0,960.0,1440.0,-1440.0)"
]
},
"execution_count": 6,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"(x+y)*10"
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"EF(4,-614.4,441.6,1051.2,-532.8)"
]
},
"execution_count": 7,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"10*x + y"
]
},
{
"cell_type": "code",
"execution_count": 8,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"EF(4,-96.0,96.0,144.0,-144.0)"
]
},
"execution_count": 8,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"s1 = sd.FEF.lvl(4,72,0,a=0,b=2.5)\n",
"s2 = sd.FEF.lvl(4,0,72,a=0,b=2.5)\n",
"s3 = sd.FEF.lvl(4,72,0,a=2.5,b=1.5)\n",
"s4 = sd.FEF.lvl(4,0,72,a=2.5,b=1.5)\n",
"s1+s2+s3+s4"
]
},
{
"cell_type": "code",
"execution_count": 9,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"EF(4,-96.0,96.0,144.0,-144.0)"
]
},
"execution_count": 9,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"s1 = sd.FEF.lvl(4,72,0,a=0,b=1.5)\n",
"s2 = sd.FEF.lvl(4,0,72,a=0,b=1.5)\n",
"s3 = sd.FEF.lvl(4,62,10,a=1.5,b=1)\n",
"s4 = sd.FEF.lvl(4,10,62,a=1.5,b=1)\n",
"s5 = sd.FEF.lvl(4,72,0,a=2.5,b=1.5)\n",
"s6 = sd.FEF.lvl(4,0,72,a=2.5,b=1.5)\n",
"s1+s2+s3+s4+s5+s6"
]
},
{
"cell_type": "code",
"execution_count": 10,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"EF(10,-121.6,102.4,73.92,-46.08)"
]
},
"execution_count": 10,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"sd.FEF.lvl(10,15,a=0,b=8)"
]
},
{
"cell_type": "code",
"execution_count": 11,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"EF(10,-121.6,102.4,73.92,-46.08)"
]
},
"execution_count": 11,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"sd.FEF.lvl(10,15,a=0,c=2)"
]
},
{
"cell_type": "code",
"execution_count": 12,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"EF(10,-121.6,102.4,73.92,-46.08)"
]
},
"execution_count": 12,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"sd.FEF.lvl(10,15,b=8,c=2)"
]
},
{
"cell_type": "code",
"execution_count": 13,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"EF(10,-121.6,102.4,73.92,-46.08)"
]
},
"execution_count": 13,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"sd.FEF.lvl(10,15,a=0,b=8,c=2)"
]
},
{
"cell_type": "code",
"execution_count": 14,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"***** Error: Cannot specify all of a, b & c\n"
]
}
],
"source": [
"try:\n",
" sd.FEF.lvl(10,15,a=0,b=8,c=3)\n",
"except Exception as e:\n",
" print('***** Error:',str(e))"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": []
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": []
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 2",
"language": "python",
"name": "python2"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.6.3"
},
"widgets": {
"state": {},
"version": "1.1.1"
}
},
"nbformat": 4,
"nbformat_minor": 2
}
| cc0-1.0 |
planetlabs/notebooks | jupyter-notebooks/analytics-snippets/roads_as_vector.ipynb | 1 | 92924 | {
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Roads as Vectors\n",
"\n",
"This notebook demonstrates converting the roads raster that is the output of the Analaytics feed into a vector dataset.\n",
"\n",
"It demonstrates the following techniques for converting to vector:\n",
"1. GDAL CLI\n",
"2. Rasterio (no processing)\n",
"3. Rasterio (with filtering and simplification)"
]
},
{
"cell_type": "code",
"execution_count": 1,
"metadata": {},
"outputs": [],
"source": [
"import os\n",
"\n",
"import fiona\n",
"import matplotlib.pyplot as plt\n",
"from planet import api\n",
"import rasterio\n",
"from rasterio import features as rfeatures\n",
"from rasterio.enums import Resampling\n",
"from rasterio.plot import show\n",
"import shapely\n",
"from shapely.geometry import shape as sshape"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {},
"outputs": [],
"source": [
"# if your Planet API Key is not set as an environment variable, you can paste it below\n",
"API_KEY = os.environ.get('PL_API_KEY', 'PASTE_YOUR_KEY_HERE')\n",
"\n",
"analytics_client = api.ClientV1(api_key=API_KEY)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Obtain Analytics Raster\n",
"\n",
"### Identify road feed feature for download\n",
"\n",
"We want to download the most recent feature from the feed for road detection in Kirazli, Turkey."
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {},
"outputs": [],
"source": [
"# This ID is for a subscription for monthly road detection in Kirazli, Turkey\n",
"SUBSCRIPTION_ID = 'f184516c-b948-406f-b257-deaa66c3f38a'"
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"96 features in collection\n"
]
}
],
"source": [
"results = analytics_client.list_collection_features(SUBSCRIPTION_ID).get()\n",
"features = results['features']\n",
"print('{} features in collection'.format(len(features)))"
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"2019-07-01T00:00:00Z\n"
]
}
],
"source": [
"# sort features by acquisition date\n",
"features.sort(key=lambda k: k['properties']['first_acquired'])\n",
"feature = features[-1]\n",
"print(feature['properties']['first_acquired'])"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Download Quad Raster"
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {},
"outputs": [],
"source": [
"RESOURCE_TYPE = 'target-quad'"
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"'data'"
]
},
"execution_count": 7,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"def create_save_dir(root_dir='data'):\n",
" save_dir = root_dir\n",
"\n",
" if not os.path.isdir(save_dir):\n",
" os.makedirs(save_dir)\n",
" return save_dir\n",
"\n",
"dest = 'data'\n",
"create_save_dir(dest)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"We want to save each all of the images in one directory. But all of the images for a single target quad have the same name, L15_{target_quad_id}. We use the function write_to_file to save the image, and that function pulls the name from the resource name attribute, which we can't set. So, we are going to make a new object that functions just like resource, but has the name attribute set to the acquisition date. It would be nice if the write_to_file function just allowed us to set the name, like it allows us to set the directory."
]
},
{
"cell_type": "code",
"execution_count": 8,
"metadata": {},
"outputs": [],
"source": [
"from planet.api.models import Body\n",
"from planet.api.utils import write_to_file\n",
"\n",
"def download_feature(feature, subscription_id, resource_type, dest=dest):\n",
" print('{}: acquired {}'.format(feature['id'], get_date(feature)))\n",
" resource = analytics_client.get_associated_resource_for_analytic_feature(subscription_id,\n",
" feature['id'],\n",
" resource_type)\n",
" \n",
" named_resource = NamedBody(resource, get_name(feature))\n",
" filename = download_resource(named_resource, dest)\n",
" return filename\n",
"\n",
"def get_date(feature):\n",
" feature_acquired = feature['properties']['first_acquired']\n",
" return feature_acquired.split('T',1)[0]\n",
"\n",
"def get_name(feature):\n",
" return feature['properties']['target_quad_id'] + '_' + get_date(feature) + '.tif'\n",
"\n",
"def download_resource(resource, dest, overwrite=False):\n",
" writer = write_to_file(dest, overwrite=overwrite)\n",
" writer(resource)\n",
" filename = os.path.join(dest, resource.name)\n",
" print('file saved to: {}'.format(filename))\n",
" return filename\n",
"\n",
"class NamedBody(Body):\n",
" def __init__(self, body, name):\n",
" super(NamedBody, self).__init__(body._request, body.response, body._dispatcher)\n",
" self._name = name\n",
" \n",
" @property\n",
" def name(self):\n",
" return self._name"
]
},
{
"cell_type": "code",
"execution_count": 9,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"afb9e074-b180-4e11-9524-eb9bd4def5c5: acquired 2019-07-01\n",
"file saved to: data/1176-1272_2019-07-01.tif\n"
]
}
],
"source": [
"filename = download_feature(feature, SUBSCRIPTION_ID, RESOURCE_TYPE)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Visualize Roads Image\n",
"\n",
"The output of the analytics road detection is a boolean image where road pixels are given a value of True and non-road pixels are given a value of False."
]
},
{
"cell_type": "code",
"execution_count": 10,
"metadata": {},
"outputs": [
{
"data": {
"image/png": "iVBORw0KGgoAAAANSUhEUgAAA14AAANeCAYAAAAC2N/yAAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADh0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uMy4xLjMsIGh0dHA6Ly9tYXRwbG90bGliLm9yZy+AADFEAAAgAElEQVR4nOy9b8z3T1bfdWa3hSCKBVmQ7ILd1K0IRNH7lhIwEUOVjU+gWsxiUvYBuoaA2qTGQJ/UJ6t9Yk1IBLOmhCWxpVtbAlGw3WyI+IcKv0upy5+u/IQt/Loru4haUnXrrh8fXNeHe+655/+cmTnnzPuVXLmu6/v9fj6fmTNnzpwzc2a+7rouAgAAAAAAAAAwjzftLgAAAAAAAAAAWAeBFwAAAAAAAABMBoEXAAAAAAAAAEwGgRcAAAAAAAAATAaBFwAAAAAAAABMBoEXAAAAAAAAAEwGgRcAAAAQ4Jz7Bufc5Zz7P3aXBQAAgA0QeAEAAAAAAADAZBB4AQAAUItz7vfuLgMAAABQAwIvAAAAanhK/7ucc3/cOfdrRPSRp9d/v3PuLzrnPu6c+9+dcz/lnPtD3nX/tnPuV5xzf8c59ynn3F93zv1R7/2/3zn3F5xzf9s599eJ6J8Mnuucc/+ec+43nq7/X51zf8U59w+sqjsAAADduOu6dpcBAAAAqMI5dw9a/zcR/cWn33+CiD5MRG8nop8mot8ion+RiP4vIvrHruv6X5xz/xERfQkRfZyI3vL0/qeJ6Muv6/qoc+79RPTtRPQGEX2QiP4lIvo8Ivo/r+v6fc65P/z0+t8kop8goi8kon+aiL7uuq6Pzq43AAAA/fye3QUAAAAAOvju67p+kIjIOfcv02PQ9atE9A3XdV3OuR8lom8hou8goj9JRP8OPQZT7yCiv0tEnySif5CIvs459xtE9K6n+/4r13X91865/5GIvs973p3S+DoRfYCIfunpHm5eFQEAAFgCgRcAAACN/Lfe37//6fdHrhdpHH/j6fc/5Jz7LCL6a0T0VZH7vIUeV68+677H0+//OfjcXyWi7yeiP0ZEP/X02s8R0TfT4yoaAAAAkAV7vAAAAGjkU97fH336/Qedc/cK1D/y9PtvEtFX0GPQ9Rl6XPF6Ez2uWBE9rlj9Fj2ugvnX/cHgeW8mou8mot9HRP8wEf0wEf1TRPSvDtYDAADAIWDFCwAAgHb+C3oMvv4AEf2Uc+63iOiP0OP+rx8kov+HiP4/egye/gwR/T30GIAREdF1XZ9xzv0FelzN+nPOuQ8S0R+ll/k6IvohIvoZIvptIvr6p9fxPV8AAACqwIoXAAAA1VzX9XeI6BuJ6C8R0ZcT0R8mov+KiL7xuq7Xr+t6g4j+DSL6TSL6Z4jogYj+u+A2/yYR/Wf0uKL1NUT07wfv/y0i+pWn5/xr9Bi8/cdE9L4JVQIAAGAQnGoIAAAAAAAAAJPBihcAAAAAAAAATGZ54OWce6dz7iPOudedc9+z+vkAAAAAAAAAsJqlqYbOuTfT4xG9/xw9fknlzxHRt13X9UvZCwEAAAAAAABAMatXvL6GiF6/rutXr+v6u0T0I/T4HSgAAAAAAAAAYJbVx8m/lYh+w/v/DSL6Q+GHnHPvIaL3EBF97ud+7rMv//IvX1O6w3l4eKBnz55Nv8YqDw8PRETD8uC6z0qgB31obGuwH9jqNUBmAIAePvrRj9Jv/dZvudh7qwOvWCFeyXW8rut99HRE7/Pnz6/XXnttdrmOxzlHvWmnI9da4P6+Vm4ZzLrvDJxzhH5aj6a2BTJptbvOOXp4eIDONQC7BjRyuk8mgefPnyffW51q+AYRfan3/9uI6GOLywAijHTS67p+15E8Cefc7xq4GUbuvu/9HMmcqgM9zNQZcA61tsG3U/f/oI7Vdm30WWjb9UDmoFUHVgdeP0dE73DOvd0591lE9C4i+vHFZQABMBztrHSeNQVgAIB1+DYoZh9CO+XbEmCHMLgG6wj7n/8DbMHVpktTDa/r+rRz7ruJ6K8Q0ZuJ6Aev6/rFlWUAL+BMd7oHc+uGf2eKWDhjbV3WVkG7gRm0rGidYq9H0SAjDWU8gVgb+H1xZRtBH2Szeo8XXdf1E0T0E6ufezoxA8DdOa0O5n6gI6FuCMD0YrF/AFn4+pVbCYEeyqLXNqAd5bIjvRdjjHyWB15gHbsCBisdX3pg4xt1qWUEL0A7gdUgrRAAAObSOq6v3uMFFrFrA78Vx1LTAQjY/wUASIHga4wVssOkDOAC/X0epXTSWhB4GWS3Edfc8bVuUpayaR4BYBptOgXA6ezss7CldoDttwNHn0TgZRAJnVzbKszso+FXsSv4CtNatbT7KiAPsBP0SR34Y6b2sQgAEAeBF5iGhu+NsRJw+exyskL5SW73VUjfJwjskjpeHtSzMvvB2jgE9oGJFtngcA0wFckn8GlMKazFN7yz6xgz8Dj44+y6g/2Euoc+KROJYyPgBX3ODhynd2PFCyxB2pcAn2AIV644pmR58sybdf0COjm5T7awcoyArQDcoJ/PY1S2CLzAUiSkH54QdN3MlneNLE+RNQBagFOWZ9VJhieNRWA96OfzGJEtAi+wnJ0HMJw40M1abTxRlgBYAU5ZHm7bdttfzsMzZrQf9gbaA+23noeHh+R7CLzANlamHmJ2kXf1C4YcAGCRGePErJMKZ4xnsTKePG5aQEKmkUVy3+v17Nmz5HU4XANsJWYQZgx6GDge4TzsBDIFQDccG8UtweWYhjKdId9d7db7XBwisofZeqgRbl1snVhB4AVE4CvryICSO2EPvGDkhDM4agDYAcHXy4zIYeVJsrvaq0dfTtavk+suFY4TXsO+3pJVhcALiCPce5Bazo29zhXAnQKcLgDAyq+fkAqnE2ad1nqeIBfJ46jksq0g1T97Jp1T1/pbZ0r3ReAF2ODs3Lkv4y0t5542CI7SEnydbsABsEpsz+3Mvi7JlowGXSvrIUluJTSVdZTY5MVJ9ZdMyV8MJ+xj16U+F6Zy1qQr43ANoII72Ep1IO7Tok4DJ5wBAHw76wdiMw5B0mRzQjmE4w14lRODjp0nNgMefP/R7+uxFa5U1lWp/bHiBdjYtdF317NP48SBFICTiWUecJ/KJ92uSCqfpLKU0FLOlExD57mlPqWVkdVo6Ge7iMkmtwrWcs8UCLwAKys7NwwJLzDOAIAcM2yEBLujwebtlpFVUtsaal+vuTdWSNfR009K5wr02KjccfJINQTsrPpeLhiwdUDeAACiOd+/OCM9q/Z+pc/B7p1DLjgK029b7hmm74I5jPgppe0snHYAgRdgZcUghSBgHhgYAAAlep3Q0j2578cB7OFZlPTGX8Wacf+ZWNdlLX4hUg0BOzNTRxB0rQcyBwDE4N7Lsuo0OP/+pcmm1lNfw2u5gA2eQ88pni0TlJLaTUJa7yw01QuBF2AHQZduLBvnHiALAMrUBE1+cMa9pyL1vJoDQkrHTZeeEbtP7Xf6tABbxE+vrvk6LLlNrK9yaawfAi8wRGyGD0GXHU6Wu5+bDwCoI3VK2P2e/7nw/ZDRFaTUAQmt90gFUaU9QdzBF2yRLGonGyQcHBNOeuRO8dOExnIj8ALNrP6+LM1GAegC3wUHwDitKYgtfa01NawnlSwkta+nZj/QjJUvwMto22hZ/fK5dRK6uR4EXqCKMCce2OZEg3xafQGYzYw+FR7RHbt/zQRK796e3L1WrXwBedSsLEkhDBSlltMqCLxAkZ2dEgYBrAB6BgA/M/tU7nS52j1bHOlhNZ9F8HUOo6ce7kDjip1mcJw8EAsGqb20nNykFThDAOjmXtWqOaWwNpW4xnlutR0aHXIwhqRxpeb0TknltQwCL5Bll1MKZ1gGJwRf0DMA9BOuZPnfCeYHSeF7OcK0xvBZo4d9ADCLW2dTen7C2C4VpBqCJOiU4MZiIGyxTlaY+X1IwA6xMSp8LXd8fO0+r5bj5wEg2q8jNaeGYn/XPB4eHpLvIfACIoEhkAP2J4DZlAIt6B644TzoaceR2nB2wWpKkwrQR36ePXuWfA+BF3iF3ZssYQRkwZFiIxEMODJoOZwg9jo4ixkHASC7A1gDY5tcsMdLCFIM/+4vjYWxkEWYipPa86AV5LnrwT9EATbibGYdUb8K2B37aGpf6ONaEHgJoSYfdzYIeoBPGITHTgSzEoCBPYzYnJZDEoAt0OYAAK0g1VAgO1KgJARdEsoA0t+Lk9qH4zu/aD9QA5e+lCasoI/2mBV0rR53kepsm53tCr2SDQIvoaw0yuikgOhVZ7jWwUk5v9ApvcwMpGfZm9g9cTqiLXzd4QzAwqPmoSdgBI06pK28mkHgJZgV3yYuxUBIKcdMYk6glHqnjlxuPUEsXAWTUDfQRujccrbhap2InY6Yeg/IZvWpg/ffK54FXQQcIPNEBwi8hDPLAQrvD+YTk7WEgbfl+bWfje0LA/IJ25dLP6XsyYmtzkI35TPbRua+52v2qq+EMQDwsjpTgMuWQQ/XgMBLCTXGWetsrhSnbAd3m+4yeKscGji5OuCaHEidhikROBsykTCerVz9gh7aYuVWEaK1fQTj+RgIvBQRHnCQmqXz0WLMNZRxBtJnPbnKk9qXIa2+IE6LfmoZlMPVL+nltUpM9r4OpSbmuG1m6l4zVr9iadxIzwY5Qp1ZOSEQKwvoB4GXMlpTZaSvOFgfaFrqJzX44iTnYAHZlGb/NbVlKqXS/x+sITYpUxvscNrM0j247XNqD62mfgTWMjsF1gd6OA8EXopp6RArOyx4gXRZ7y4f9FIXsYmf2HuSKa1s+J/LvZ+7//15rTLaQa9srE1Y1QZgNSn6VmQC1gPdmQcCL+W0DjhwdMFNS+rYbF3Z6TxZc9xWsXL/Cxct+0ljgVjL6nXsHqAeidkC3CujuXKn0rNbno00sX1okzPGwHUg8FLO6CzhyD1GQUd/lVUOBGQPONCoQ1wrK5K/HkI7PXJcGXxx3y9X7pHnaU8JBsAiCLwM0DvYYPXLLhhcwSnUrkBw2LnShBX62160Br+ryy1h4hWMo1HXdyFJ39+0uwBgnNhegpXXg72k0kn8k7LCn97nSDBaAPjcus6RmtXyPCCT3vFs9xi4chwOV253190qs+Q6I1CXYNNG5RVeL/VrTbDiZYTRE5G0zhRapKUNak79Sl0HHoHe9yNRdqn0KmnlBPPQ2tYryu1PyFk4LEcys/0qrntLsY8z0nglghUvY4QzvyObycE+atpt5SrnbMO8y/BLGXDAPKy1MSZN6oCc0sRWh/2VY+5sCfAqoTxr5Jv6ygUr7WKlHiUQeBmmJv0GyKPGUeTIV5aSYoqgC8zCUhvHVikA6KUmNTf8yX0exImNs3c/9vtyTL7h32HfDwPlHtCe60HgdQgYrG3gO18cbRrObOY+B0CIdL2QXr5aOPv8KUBWL1PaC1wDZNpHrYzD9mg5BKUn+JJ04AT34UeSwR4vsA0JnV0aMePD+b0xMXJ5/rPBDD4AedBHABe57wxrccKhk+3MTu9v+XwssNsJpz5pOK8AgRfYgvSOIYEds1GpgwlmsNPgQ//GgQznMXuyRTpcdYaOvsBPZUsFX/f7/v83Ehx07YT7slK6OTMgkrTKNQvpwRcCL7AcyR1iN77B2CmjVW20a28XAFI5wTHKgfFhLqXVrdxertYDHdCOcWpknPq/F6l2ZaS/1wav0uqMwAssQ2onkIYE+ZRmR7VjsU6r2TWwnXDstcU6SUf6LDknNfY9J4eW8QHjfhrOVcWUfK3K/65XjQ5L69cIvMBSJCk/2AdOMtTPjpXD2HcPjToWNbPOqzhdP7nrf7IsaxgNNmuul+r8SqQ34yUlW6syj40DROn+Lm1SBYEXWIIkpQcA9LM6FTa138m3KaNBYGnmeUVdYSP3I81B00DtyjdkW8dqOWlrj9Sx+qUxQJL+4Th5AMBysNqlk9Uy9IO8Gqeuh9i1Nc/kBroph5P2gXIc5FC790vKKXqnoMGmtK7s5e5TG3ztBiteYAkaDABYy0onHkHXOLuCrt1lSc2Ucn7vHfTzEQkykOKcrYRjNSCW+hV7735fQltLhaM9NMi4tozW9qkh8AIAbKHWiMZSzTQMKtbYeYDGbkJnvGY1rBQ4hvcHcpCUlqSR1CTF3W9Kx6mDNkJ9rbWftYejzGon7qCrds/h7kAOqYYAgG3UHkkcOrocG4+BbLhXu0ZXqFrTD0srJ9DJl5EebFtnZn3DgOsm9ffpcIxZo9fPDk5q2ntWGXx93AECLwDAclYaPGsO7u28rJQh97Ny9+t1OnLX7Aq+Y4O7X5bdDgDIg7bhJTeBFu4Tg9zbaNXV3EmIEiYrW4KuMBuhVg6xyYAVINUQNNPTKSV0ZCAH7O3qY3eKBAd3m/Sk2/Xs+5KkA2E6kJRygTQnpB2utithHwhTEUEfvq7WHvN/s0sHYuzYw7sSBF6gmZYcWQuO4snMbDfMaLZjqR+1Dvw9q2QSHGYEWmWky8Zy8JWaCFmBv/LrlyX8G7RRCr5ibb56D2/NBFqtDnCkZa7UN6Qagi5SOdupzwK9zBiQEZDrxk8Hak1vSVEz2611bx8mGfSzKy1pJpL6iI/UcmkgTF+O7aPzA43Vq4wtbVv7uVj2RMsespZrOMCKFxjCH4xiM9gwnsAHAZc+Uv3Yn5WsvY9/HUcZ/LJIni3nDCjBPqTqlyUsBbY78fUzFXxJZOXqU/iMVc/GihcYJrZBHAMT8PGDc+hFGSnOR82ESs1M4cyga/TzUpDS5jvQ1l4WVr+k9RPs83oB9+pLuLK1U77cz56hx7P7NQIvwEZu9Qucy66AC3o4Rmmlq/U+pc3UHKDN9aE1eIlNOGpmZ12syFA6Guxjaxm567NCPgi8ACvWBiMAUoT7nLTPgM+gZXN0THYaHIUeYvWC3eRhtQzRbmP4fcFiX+9h1iqONfmW9gxL7ZcIvAAAWUaM9S5jP/O5YdpkmMbBYex3D5C507Bi+wVa7pEiltJYi+Q01lBeFh0gKUC2Y+xyVtFufUgNLFLUpKRzrXZJ1ikcrgGmIFXhQR+SjdhK/GN4YycpWd58H9unNdtR4z79ahWpY5qlldMK0g8M0MTKww3Qbq/Seupfzec1jEdSyuifBjmrPAi8wDSkdCQAOAiP582t0HA4L1L6T8k5mrG3U0rde+h1JjXXuReOPkK013E/sd04gezGKI01UvRzdjk4JgBDezLLviDwAgBMY+Xs6UxqDHDs+F6Nde85eji28tVbb40yC0HQNYdwcgMya6P25MBVcpU8PkgtV4xc0CWBmScPpnSote6pjI7UeyMg8ALTkGxUAaih1+COpuCtmB0sOV6luqcGu9Z+zxWwSaC13SSs2EinNlgAeSSPxRL7gaSy1OLbXyI5deBejQtT/mevmoeTqqNyReAFpoLgCxDJHvRTjA5eM1LwOMgdnEHU32d7DhfxnyNNTq3Ulj+1/+s0rOxNkYwGXdt1oEcNGvVPYplTafg9mQH3Pfz7xeA6pCP22VEZI/ACAExF8sCagmvwkhZ85YKunQGQNv3wqQ3Qpc1CS0dKn9GMFvlpHCMkEU6aSek7MZs3khXAncLdK6eafXU5VAReGLB0g1UvsJoenZs1O9zqVMzqK7n7jsxCxu618rpdtIxLsH82QbvyAT+hjVRa9+yDIcIypGgdS1r3HHKlGPYSjuthiufz58+T16oIvKykopwMjKpeONpNWvvn9hXNKOPu+tc8e0fZdsulFwRd85EuM6zQ9JPrPyf3lxadKtnOUb853C8WuzdXO61o81w9egmvr72fisDr5tTOCIAVVg6qsYEpNeCvWlWflfow8kzQBoKu+UiXW84pBWlKq+rSUuVWEqZ7+4yMV+FKTGtZdgREpc/m9hPXyGp3v1UVeAHdaJ3dBjzscFR8nYvp3so05pJToS3oOrEfI+jiQ+N4gG0P/dS2tW8n7/+tUwq0ws+0yqQ1oJ0p89iYXPIL/IC9VI+aYCx85mo7hMALAJCE2yD1zL5xsDvoulkdfGpwWjSUkaitL2gLKECZkr0o7Xc5nd79PlYDsNZAKjZ+tcpUympi7aRL7DOlMTQmp9x9Yqtns+WDwAsAEGWWgV45AOxOLYyRGnQ0rXadRE+aDqhHup7WtD9nCrF0eawmdIx3TJTNsM33/Xuv77m2Rpar9G/1loOQXKA1OwgTH3g9PDzsLgJgRGN6yYnMbqMVg6nEoCssx+4y7Ea6DEYdHJBH8j6pGjtRSpfy98lwBHAnI2nFhoORoIvr2RL63ur2rO3X4edL17QgPvAikuMoAWCd1X1tVgBW2lC725bMdiKsOCe76NVHyL0diZNxLeXxg6vctSVn997DAtLsCL649ZPjXlxlKe0l2wln6u7IFoeUDzFSLhWB144cTABOYvfkBmcAlgu6JNkNazO4VkB77GGm3Gv3k9ysWO0HfUheJV2BJf1JtaM/XnMcRsU1/pdWq2vL9KahUizCF9r9c1fy5A4IwAjhyVESDPpoCoSWoOtmRsrHrtQNK0jUE+uM7Hes0b+Ss+6fmiZlNQKk8X1ATYzYZo31zVGSRSxNt/a+4c8K/8Yfy0vlddKNxPPnz6/XXnst20hYCdMDZpP7qdkMWiNbyYGIT6uuaAu6fLSUM8Zon95pE7hSR1L31Nieuwj1oGbMl6Z3GN/WoqmvjaQv39fN1q9V+us/p7bfh22d+l+KHjzVIzrAqEg1JMorndR9HOBV0CZ9lIxTKR1XY79oMabh57TVV3Paobby3pR0q7c9aoMH0Ab36jyCLv34Y4QG+Y9OJK4IviQQmxBLrYrX2OkdMnv27FnyPfGB18PDQ1PEH1vus66kmjjBaMwgZnRiskzJVqvMWwISzn1iO9Be/hS5wwTu37vSI0tjyWgw3LKvqPYaq4R64P+vYdyQXj6r5FK8pLTJjv1IWuidqGqZlF09tpSeJ36P17Nnz353hsuf2Sjhz4qN7AfDXjJgAc36W9oHFaYt+NdoJOdIaGnH0G76+2dacuFnle0uU4mS7o2UIdxX5I9zWtoZACmkbMxuWvcnlVZ0Z41tKwOU0ef02HBJiF/x8okFX6054C0pSC055wCspmUmWMuscYrU6oM/I+5/Vjup4CRmzyS0bWs7tKSJcNITmIf1ml0GXzYWdHkEbtmDc9g5GdczHu2chCJaJ5/cHq5WuyyRmnKpCryIXjXEpTSW1OulVEQY+zlI7Sxa6Qm+Yq9rIAy+QmdfSz1aCIPMsK7h752MBDWzg4xd9jxVrxMmSziBDEAP/hgx2pdqr++ZsI9dQzRf7zX2Kws2UV3gRcQzIOWUPKb0Fhob2KQ1+IqhJXgJU7G0lJuDmM0KX18NRzlmr2pIkRVR36zu6WPPyXUHfaQmqCRO8MTKtGqlV8r+pxXtIsmOqAy8iOYMSCc5ccAWo/1B6ySDlnJyszsI434mpz3PbbDvfUbPNS17kkEeTTYJ7KWkKzN0qdce5soSy+bgLvvKiZ1c0DV6jxLSbMfQ4RrOuY865z7snPt559xrT699gXPug865X3n6/fne57/XOfe6c+4jzrlvGi38KKkNzvfr/sApreEAmIVkXZe0eiGFlO3idvjD+97P5Lq3/3u0bNzl6yUcW3rLc3rwtrsdgQ5KvtqO1L3U6lWNX7nChq1YXfPte2yr0Gn9m+NUw3/2uq6vvq7r+dP/30NEH7qu6x1E9KGn/8k59xVE9C4i+koieicRfb9z7s0jD+5VmDDgit03TDsEQDrh/sWeH8nMcPitETr4o+27MqBptbl+2WIBTky/V+oN17P8+gAA4rSk288Y73J9NHxPWl9eYV9i49KpY/mM4+S/mYje//T3+4noW7zXf+S6rk9d1/VrRPQ6EX1N6WYPDw/Z91sVpjbCDpVCg2MKAFF8lr32h0ieroerLJLK1suKOvhtmgtGSoE4x4pNTVl9m1tTtlKZuFabbnbr3YkOCtF+uQN79Eysp/pfT8AnMeBYNbaO2GNpMutlNPC6iOivOucenHPveXrti6/r+jgR0dPvL3p6/a1E9BvetW88vfYKzrn3OOdecy/SF4sFqf1MT4NLdkoB4EbKim/M+beQ9ruzDr3BuMRygrPBOGyXllX63nS11s/H0uR6dFCy7ZI8sSm1XD2MHq7x9dd1fcw590VE9EHn3N/IfDYmtagGXtf1PiJ6HxHR8+fPr9deey3buUInMfxMa8dMOUb+a6fmpoZwzBgBuewIdsK0NktYCBx3AvmdRa69oQc2Cds8pwP3e712oeba0Nfr9f202K4ZY74f0GmQwWyGAq/ruj729PsTzrkfpcfUwd90zn3JdV0fd859CRF94unjbxDRl3qXv42IPtb4vCpDHAsGuBs79qyTFKqn3qfKSjurjGZJP7QMXCm0l382JT2D/ACwT9jHV0z+pe5fMwlvkZnBF+z4QKqhc+5znXN/3/03Ef3zRPQLRPTjRPTup4+9m4h+7OnvHyeidznnPts593YiegcR/Wzrc3NLoak9CTMb+aQ0xJb9FTGkpLCBdvxUP05S+4nC58BY96FNbn77x/Z07cKfWd9ZhpPY3eZANlz6kfJJpOmftPL0oL38XIyseH0xEf3ok7L+HiL6c9d1/ZfOuZ8jog84576DiH6diL6ViOi6rl90zn2AiH6JiD5NRN91Xddneh5cuzzc08gjS9b39daWVLlXqkaX7ME+uGbCUjoldePxKBYGzZVIlhXaEoC1hOPOjNUY/76nTXLEmJXpsmP7ApEsu90deF3X9atE9I9HXv/fiOgbE9e8l4je2/vMkFApZnXKFqwEYCvSAv2Z7ZnPAbz0Gs5wMKvdMynJYPawq/za5SYJX+dhs9YA2QKilyfjZgdEvi/Z+6wZdneXLZ8Z6K6ukyR7Mnq4xjZSQpTibNQ6ldJYvQ8rDFQ1yAjUkwu2/Pel9+detJcfvEw4617Sb05O0iP0GxBj5cRcLDgoPas1UNGi57tWqbiRUgc1gdesZc8Yp6XWSTj0Ahsv9ZDbiBwjNYDd7+WeA/pAP+Knxo7n+gAoAzmBVlbpTItNDVfIY/fQZqM5g6/W+/iyHHm+lABSTeBFVHbOOQQacw65iG3i3KUAEsoQgjSeOXC29X2vVgcznDgplcOCDqLzMRwAACAASURBVEgw8ICfnB1PjUujffAkXcKEC/DRqvclJ7+lXlplAOKoCryI4rOOMwz1zIGuZx8YlyMqMeDyQeohP72OHvc9W6/R3PY7dXf02eh3daQm6ULZhWNWz2zvaUD/AJEMWzSSDh9Lrd9dnxF2rBjNOkhlJ+oCr5vZAdjK/U1E5c7dkmccEjoFGjo/Vr/mU9Nfdshdg36m8DeCa0Vz2XdRM2HUM+EWXgvAKUgYB0b9Sgl14EbrhOKse/WgNvC6saLULfvAauucmoXVFHwR8aTqnEzr6hWC3XYgM0BUZ8exqg+AHmZlVWlmld3yn2PJTqoPvG6sDGCj+8Bqr9MSfBHFU3XC91YgdYWIaDw1sGX/FXgZLf0IrKO0H7n2M/fnADgJaTaVy5eSVCeNcOnFbv9XfeDVc1qaBlKrPbH3Y69bJNbOs4OwnoBklg7mgqvRZ0nRG2kDbg3Sy2vFJmqjdvWrZkJHY78AoAfrum6hfrv8jd0BExcqAq+atI3wfwuNQ5RWtNNT72LyyAWoPfTq0IgOzjjUApxNSxoz4Ke0smXFmQBgFMn9gHO1BZSxbC9VBF4tefOp/y1yQh1riXXGVDDmX5OCq2OXgubWcgF9SBkksFdhHzWTMVxHTwOgkRE7OTvzJ2c3Z2aiAJuoCLxuWvc/3R0ZM71nUtKNkhPK5aRyr8QBXaycocN+IdmkdKFmv5eUAB6cxQq9484O6fX5UteltnaE7wMerNs6VYHXTWzmNhaMhb+tNKaVenAza5Mr5A04gB4BorHgC4DVrNDF1vG49jrur22w5ktK5ISsjDftLkAP/nflhIcfIJ3mbGa0PwwsGMW3TbBPIGWnSquVEvVHWnkAL5Lat/Wwq9JKVek5uftKkoslatqWa6/dLnuqLvCqmWnwBer/aCS2bwmBQB4YRSCVmZNDNbYB/UI+Of2QaPsllgnMZYdP1Zs+2BN81Tjl8DP4iZ1WXfrsCLXjMXc7q0w1rMHiYICgqx4LJ99YJbUR+qRBLDaZ0nNSaWvaDfqDHHI2qvc9ALjJHWa2Wg97n9VT3toVNfRFHiSO/7POh1AVeKXy4q2CDj0GjKIsSkasdnD072NhRTi2F7VGBrXpNuF1qedrWmU5BQ36rKGMYA6rxljOo9xHnOnc3kz0gXF2yXC1L6Eq8NrBrhMRJTqUsdn12ce4jgKjKAOufpRqy95ARCqhgxDWOyeD++/S/WteC+8L1iH1sI2Ybu0aJ8F+Zo+xM/Zs9/ar3KShtH6qid624JZ56x7CXtQEXiuVOib8nUvqs57dcs9c2kv4uZ77A7v06G/r6osFXQud15ogKtffuGQSmyXG6tgaUk7iakevZoIt3BMDXTiHWfo4K6CXOqlxIpIy2VY9V03gtRIpHXFFzmvthvzevGgMxGczMrCdpi+tKZipFa4ZzkTtwIiVD358J/H+fwWttjvUzdHDEAAgmqcPCL70wxHw79hbpuJUQwkdY2cEvrPuo7K/yx86DyvxZ+zBOnp1p+Wa3XZhNXcfCoOuXSvzIbWpjqB/QitMQZ1RrtGUmzBbpLac0Bu9cOvjClsGe7WXWBu3tgWHjqz2s1WseElxrlandqx41o70ydaZUI6ZXuRgrwey5oN7ZZqDXPoxZpL56QlkapmVmRArM3QClFgZCMFe7YFT3r332tXmKgKvE1kZKOSeM3NvWaszybH6BuO6BrQVL7nUvl2yyj0vNpOMtnxkNAAJZdtzv9WHItUEYOjvgGhPurKUjIFT4JZzbB9y6fm1n50BAq8GWhuX63lWDYH1+p0M2nYdktNkYily/uunBWYzHI77vj16sCtYxwoDyKF5ewXYg4aA6waBVyM79gvNNgQ5J3n3s1c+D/AyKmu0VR1a5JNKSySSHTxyMZIO0/NeiCQ9OTXwBnl223ukHc6nJNtZ476kNkXgJZxwxliK4nASW0lMHTFqVQZWQZvNw5JcoSfpL7e2LJMdE5lAJlL0ADo5j1q5co4HUla5fBB4NeLn1e9I07DqnNQevGFZBgCczGl9O7R1uXpblkut7QfyGW07SW1vuc/tpFamXOOBxDZE4NWI1Q3BUpyekedLKD94FRyyAcAjLal1p6XhIQCzQY+9lmbjserFT08bWz30RMX3eElmZQf1TzBbhURlD9Mv/ZzskmysdWANcOgrBsEzsOzw3GmDNemDt52ynmoY46R9f8DOmGyhDrPgOOX4vo8FEHgpYUfQJV3JW50TKwZeOn4wzIE1o8uBZV22HHyVwMb+R26bzmlHwBpadBdtax/ONrbiCyDwYmCFo7Ai5Uqbw9O7bA3mEQuGOeSOtjsPTbaIA7/vgEd8R+s0fbAOUkrPgbuNtesMAi9F+I4sBiEgDUwMAC60D6wtYJUrjx+Mov/bwGLQBd18Fdi1OAi8GJitXCtn+2JHuwNQAgbWPqtXHawH2n7Ahb5TBumHOqjZZ00k27+Afo2zQoZc33G4GgReQgkHl9R3Ws18vkYkG3MwjnVnXCIIEPiBPPtB+qF8Uu2C9jqLWfatFLxLnwhG4DVI2MBcg0G4Ryb15ZoIvspYqYdkJBs5i6xYYZcQcCHQBjGQfigbf2UyddgS2s0WfnvODHxqVrmk+yP4Hi8G/MaOfe9AqzJIiNbh8AAgk9mDGse9JdgwyeA7q3iAHHnglF/sILDY/aX7FzidsY9V41Moc00ZBAi8Bkk1tG98ar8ELmX8ctetOMxAizITxVcgNZVfM6tkrVEvOZlRb07H6+S2aQWBAw/h3i/IsY5SQNQrR99Gh86y1baxWq9adkzWa5U5Aq+J1AYAucFih6Hyn3e6kwtkgkNg+OGSI44ObscPHE6o7wzCvV+QY56UjDiCsPu62DOsrQ5B115mVTZG63MktRMCr82UArKSonArkyTlBLrYNUEAnR1Dg/w0lHEUrNTzgFXEMrXBTy41sGaPXSndUCKtgaH0+qwitsLJfe8RJC0iIPBaQE9j16yQEa3p9JIUFoAQrH71g34tg1B/0SbjIP0wTq88arc75O5vrQ1gP19FYtB1I6WtcKrhAlIphCmFqlG0MK2CY/keRgRoBXrbDvq7DPy9wDvSsKylfvmEe6zBI7P2iaZOQYWt0U/pNMFZOmVRb7DiNZmaPOpWcrOitcv+sety72PVC0gHOloHVgDkEgZfK1ZzT+g3SD98ZFYa2I0VuVqpByerU/st2yQEXpNpXdEaVbZScBX7jCUFD50IK/UCgAOtfd1icJCyx6m9XjPrb1G+MXCICe9XRtycKkugd0zZCVINN7BLSbk2JwIgFehonNNn+qVSm1J+E+q2/7+fdt7TB07pO35a5wn1veFKaQ33zZ1sU06uO+gHK16CWDFzEJvZbHnmKTOjQDfQ0Ue0BFyp9Dr/tdNtT2qvUmpfzcgzTuCk9EOOo7h7rwW2WGGHrU+IIPA6nN4OdLIDBGRjdea+tc9p6qOldDsfTfVKMaKfI3WvCTIsyLcFy6cfjtQpdXy8FrSXXwOctuKkLSIIvA5kdMZCep68xDKBtVhcHbEadJXw29JCUL3Twa8JMqzoTQv+6peVAKynHtqDldLKnCW7uBNuO3xauyDwOhSO4ItIboe5yyW1fGANVpyoVqzVNxZ8aaujFF0MnaaYLDXKl4NYABa+p4FS+6Wc5hl1XKFLtX1rhu04ua/ga4z6QOAliNOUbyYWZsZBP6ExX+loABAizbkI9zeFjqvW4JaLcGKx5iCOcMwpHYwym5qvltHcxr37zk7XbWmc2A4IvA6Fy/BIN2JSywXmktsz5CNZd8HLhIcDaGo7qeWMBWBSy7qD1J6TWFCTO3EyvAfk3I+UlWMAekHgJQTNhliqEyS1XEAO0BGwCsl6FjsxUWpZJTAqm5Nky2VjoZvywPjZB77H60DQUcAKtKR6Ii1VJ7Bh/PhpdSPfCQbyQKZ1+Dp47+/kuCen7RgdP6AL54EVL2YkBzUzO7jUmQ9p5TkJqTqRQlNZgT606FbqNLjUewBwEa5q7dK3nrGgd/xAnzoPrHgJYWXnm/UsqSsHEssEZIHBTyfo22vwT5OEzMeROlbuILaqtXNPLoKnejBZ+TK19hGBFzPSlXCF0ZI2oEgsE5AH9EQnWtpMSzlz+AEYGGO3r7DKac49pybYqrkPWA9swKvUrtIi8ALsSDWOMBQA2EKqrQmxZnuw+qUbCUGMhDJwYakuNSD1eAwEXuAIYCAAsMnKFZie51h1UrD6NcYpcosdxd8aqKwObFr12lrfzmHVnq0Eh2uAozhtZgrUA93Qzez26w26rOuU76Rarys3J+hH7vvMLHBCG4acVl9usOIFjgHG4mxyqVGpL0Q9ZVZaO7HvoeJk5SlnGpktf4ucJLPafVwxdvajE9qmhZNs2kwQeDGBDqoDpMasRZKRjqVG5b4jBie56UKSrp0I+ks71nV21FGf4ejX3q/mcycFIifVdTZINQQATKHW+VppzO8ytaRG+Z9BStV5+A5H7XfTneykhCs5p8pBKiv3Q0oLulrvmevvJ00unGzPZoDACxyHti/21UiLfHcea9wzEIfXp94D61nVt0tHZIOzArBcm9eunNR+thf/Gdw6yl1+LeOzhjKOIsGeWpPz8YEXV6NqcOaxCfoFGtpLIz36NVsvc/ce1YPRQA7oI+e4wr6+ih+AWZSLX6/R0/pm6U/4nNnpezWBXerQDWkTF7ExgkuXpfeJVeVbEditeE4tx+/x4uzsGvYPSTVuQD+pvVI1zNLLmjJxGmMphv0UYvqywg7HniFtcJeGhvGxhZ5j0UNS+0rv+4+SK+PMg2hiZfB/iF4+cCMWFM4aC3rwdVd6sGQNroURSW12/IrXTW42w1pHszYAgv1w9JHYgNt7z1Yn2FofB/PbNLSj0J8y1mQUC5w47z0SzK9M4b4JA8fUamCpbNyrpFz+DsaJecyUrbR2Q+AVIez0LQYQKWx6QFvxwB3Ej6behHsZStdCB/SSardV+4ugN2fCsV+05vNcKYw91O5bK/XB1DWr7S7HxCDHKqdUSunTPdTIKZeVYlXWCLwypJa/LSgCgg7AyWzHtlZXEXQBH2t22xoS26VUJs594S2frZ1EqC1fKfNldrvU2GgOP4V7PxZn8LVD91sCqZaMkdrXQ12eLQOJNgaBVyOjy/+SQPBlqz13sHLzba6delO+0OZn0GvrTrePs5Eo21wK6e5A5S5D6lk9+iqxDXyk+SlcwdeOVNAb7omFlgDNL4+kdl0JAq8ORlMMJCHNqO0As+J9cKYYtqbdhK/Hri3pNtr7PFraHPtgzyVna3KssCmxSSitk4e1/seIn8IxuRo+W3LaYavOhteumkDwn5l6X+uqYgkEXgxIbFjQDoLQdnbIqsX5KaWxgHOodVK1OrGAH6k6kNs/1Xqf3WPequCLiLdvSwp6R4KtlaQmSG/CFbHUNbXs0O2aZyLwAiKML9DFzAM1JN0L2CO1mnG/B/0BN63j4m7nW6vurvJBZmS3+Ktf/jNm05pCWHO/3futYmm9EgLbFLH61JQTgRczWtNTEHyBWqToOPQVjBBLeeE+wADopqeNtemGlLG/phxcZeWusx/QzQoUSitBI+OylDHdJ1e/WtmuTpusBYEXI5Ij81okGOCdSBmEpOPnzXMBuYOdxGZbaz4LeIEdWI+UcW9lOWY8izsAW5FCuNJv5UgXJeL5rs+dIPBiZIYzupIZy/AakTIISWSmXCBvIIXS/i8wD9iBPUgZ91b6IbP2l9VO4uyc4AmfrTXg1ThJhsCLESuDskRFXY2UQUgSsdm3nTJC28wHfeAFkAWoYYddtKibKTlqq2esvDPaayQ4nFkuLmpWuWI+uMSDRxB4AQCqkWC0wDpiM7en6cDtjJxWb9CPVl2RVu4wmNgZsHAF1BKCGwllqKE1pTC30lizJ26VTBB4AZBg94qOJFJy4JQRZC2bcFCz3lYIuIAGuPuitL7tjzGSyiWN0XZb1e6x56SyxUbKUzqcI7Xix/HsEgi8AMgQ7ts70fDX5FD3Gu3a79wCsrA8KXHqyh7Qx6ztDdL6thR7w1GOWQdk1N5313dbxf5fOfbXHM4RHo4yq1wIvJiQYBTAHE5u11on1DdYNZ8PrwP6kOIM9ZKbedVaJ3AWM/RU6iFb/iSopHK1MEOmktMf/faqecbKAz78Z8Zen1keBF6MSDNUgJ8T27ilvmEAxn1/IA8r3311l88/CpoI+glkMrs/rdhf1VOmuyz+/6vLIEEWkgnbp1Zeu9rzJrT7s1a+EHgBAKKMGJyW67TPYp5Ma1Au1WG5dTC1CiaxzOBc/EmC2bopcX/VTKd4FhJWu2bLa3Qs392eYarhDXe53sR2JwAOINzzZZWVBvAe1E+Q6+lIcpLC3P6UcwndBJJYbZul0pNdwflsCTaBq304UhVzAbq2rQf+eJAKxkZA4MVITapNK35jS+jo4OXZNovsmnWSMpgBMBvL9gPMZXXqn+TxjmvSbvbYM3sP1c57WMtYSR0z7+ua/9MDAi8GWvJXWxpK4hI/eMTqKs3upX4ALBPODMdO+5Lq5IJzkT4mcAWHrXuTaz6/Kr2vBs5ylFa5/M9ZgmMlDIEXAz37HHqfY02JtWOlTaTky1uRJ5BPOHM5I+gJ7xs6KaG+hwM6ADfQhzyxFYnW64l0yTms881Mu1YbcJ1A70oYDtdgotZp9TfFEqWDNglO8Cp8WWhdttbcXtJkLvkQBqCTMLjxf6c+H9qjWRvZY6ljpcMDpPVZMBeL9nBWnWKn1LU+J1Y2yW0QBo21K1AccrFGr/8R6t3Dw0Pyswi8BumN/MPBtnY2wpJTGjMSLQZECqtz7jnRJGcAehh1onpWYTmCtNQYEa6Kte4tRl/Xh6Vx/6a3Lq19d+Qa/38tsm+Z7LkpBWPwE9q4roueP3+efB+B12LCqLinU2teGSKq68SagxkNaNAftD0YpTboKr1WE3yNzCSXbGGuv7asimlKowIvA1v4SI8cenymWOphanJEAiPjZclGSKrnCmbXF4HXICNLtSNphrnAxH9Nahpfz2yzhHKn0BQkaChrzyoDsAOHjnI5Iv5ruXvO7FOp1MOYc7iiPGA/q9tX+6qb34fu/1uuJUpPZuR8udmM+kelYDKX8myZmXVG4LUY7kAi5qDGBmb/b22dSGP6oUS0yU/7QA/64bKN3KR0coWe5iYj0EceGVl11MgJ9jHVpr11b53Ui/lNPamLM+Bq/10TSqeCwMsAszseN6MzM9oCCAloHaARfIEeZjoSmldjrfcnv20wTuinph/3rmDV7o+MfTa0AeH7te/16mbO/szq35btxmqKx8k7537QOfcJ59wveK99gXPug865X3n6/fnee9/rnHvdOfcR59w3ea8/c859+Om973NaRy4mavY3rXy2pg6VS6/ZhWT5aTeYmh1d8ILRNgydmdy9OfdY5a7xf7RgvT+F7WG5ruDV9m657k6jK+lIzme6n+/fK7QN/mRA+HpYhlRZUvdfhSYbJ52a7/H6ISJ6Z/Da9xDRh67regcRfejpf3LOfQURvYuIvvLpmu93zr356ZofIKL3ENE7nn7CewJQTWi4JCClHD7ag64b687iycScjxipFOrY/63P1wJXP5BmO2fgO6bW6wrKxAKbWHDUQrh1JBUMpVbYYpM3uRU+rmArFfCVZNArI43MLHcx8Lqu66eJ6LeDl7+ZiN7/9Pf7iehbvNd/5LquT13X9WtE9DoRfY1z7kuI6POu6/qZ67E2P+xdA0A3I0ZzRlkklOPGStB1I02+oJ7UvqjY7HD4vsQ2txC4SJy8moHl1S/LNpGjXr5ul4Ks1FhZsxpWE7BIGotTAZ+kMlqmZsUrxhdf1/VxIqKn31/09Ppbieg3vM+98fTaW5/+Dl8Hk8gtV+9k1vNPcCBakGboOUEb66J3v5X0ND4rgYuVeuTA6pcuRvfnhcFWSO19a+2P5QAY8NMbeKWIad6VeT1+E+fe45x7zTn32ic/+cm6Bw8aU0vGWKLTvUK+kla/dpJaXbCANL0GccL9CDGstOVquzNLbhL3znJjMQCzoHvcz6q5Nmz/WZPVq/0x+EDy6Q28fvMpfZCefn/i6fU3iOhLvc+9jYg+9vT62yKvR7mu633XdT2/ruv5W97ylqoC9c6Mhuku2o2xtKBr52bQHUitn9Ry9YCBRS6x1J7cZyXA1TdW6uXMzIET+pfl9EOphPuKcoFOS58cab/cvtGee7XumbLO6fVP0Rt4/TgRvfvp73cT0Y95r7/LOffZzrm30+MhGj/7lI74O865r3WPLfHt3jVbCZ2DloFHmlLVGqzazewj5QgDrpXsDqB36oW0wHsWFp1DLfXJOVDhBIv0Os2yf0A+liYCV9nDnmek9nJyrYT3tmFLumHLPcM9U7t0bIYPZKW/7Kb4PV7OuT9PRN9ARF/onHuDiP4UEf1pIvqAc+47iOjXiehbiYiu6/pF59wHiOiXiOjTRPRd13V95ulW30mPJyR+DhH95NOPalqVcDRvuXTv2lzkVLlS79c+/75eQuf0gy8J5QFzsNS+t85KqU9Ylh77VdrDZQ0rdkeaLs7ED1o013dFm/WsRGmW6Y22vjBzRVebLGbSq+PFwOu6rm9LvPWNic+/l4jeG3n9NSL6qqbSbYTbiEk2QmEnrS0jR8A2k3DWfVUZJTot0srDgRUn10ea7nBNyqScW0l1HSWsX0sGQnhdyOrUOL/NrLRPDiu2ZEUQWSMj7XIM0VYXX/5h2bEiz0evj1kMvE6Ga+CZbYQ475+rc9hhtRgjK4NqL5brbTHlcDczHLeUDlrQy5S8WsaP0l6TXPDKTei0rRi/fHbphAVdJGoP/HtI6aHkCeYQTWXtYVb7nzQhU0urH4LAq8CokmlU0JgSSUkh7GX16pck4yShDKCeXRM+s1awY86ZpP4x076X6tkSmK0OumLP9l/juHdMF6TohXZm6kxsdVabj6CprK3Mnmyutd+WZTwCAq8KJDkJq7Ba113phwC0wDXhk9qnFXse+kI9LfaDywGe3T45fQt1qEc3a1c8TxxvZzEjmPXbx1obWdK92IQJZ+aBJVmtBoFXJVAyW8Rm7PzXgQ7QJ/NInGhI7T1YTe8qy0jQMbKyI0XXT0vvlSL3EeC/nMnsdl+1Eq+Blvoj8GqgR4lh8GQzs33Q9nOxLltO/ZEmJ2ltNzPo4rqPdHtSOhQkRyq9UUJdJZUFgFZmB0epSexSWVL3yWFlohyB1wJW7CcC/cweWFcN2piFBkQ6ZCPRmS2Vh6O8Eut901qm0oEfd0pi6d659CcpspJUll64J3K0yyOFxbqtWKWO9X+utEaOayWBwKsRi50SzGvXGTn2J3NaWkOLXmqTjZZyEq07OXYXI6mWset8XUztCavR19bZ9JlIai8AgBxa7TkCrw4kDpxgnJlL8qtW1ayveqHPvcBK2sUOSqf3+e/NmpCRkm7Ivb8tlKEffPm0puzXlK31vqAfLX6QhjJKAHLqp1VuCLw6gYLaZGbwMmugsm4wTw8wYnpjvc13sXKFevbeixpmHSoSC2pnE66OoX+8ysoJhPCo+V30PNui7pT8Dy2BtAXetLsAmmlx0K2vRFhiRfDFxQmG8rqul35is+cncNf7hDaXxExZt6TS+des1v+WdNfduunbCTAf3yaH+/o0t4XGMgMdYMVrgN0DDDiXk2d1tQ7kHJzY3tzUBAcrV6NW6nPPKoSEYKoHzOCvQ6qM0f5gBqN6hRUvACJIXfUqnRR2clBiGTgPtmnptz2DfmwV4v6f+1mSgD08F+26O4NSf0B/KcOhV1jxAmADNTPqqe/F8Tt+7B69s/Ulgyt1EDt59Q+0MbJPaTer9RyOK9BGuKILHW4HcpsPAi8ANpGbXardBJv7zH2f2rLUnlbm338n4ZHVlsFAOE6PjqyUe+45vUHX6HUWgCN5DmjjMjUy4ki1RlukQeAFQIIVA3bqxDr/7/D5rRvxuZB0ql7s1LTdZZqF1XqtRMuqaO/3XdXeq/a6lc8DQCKWdTnXx8NJ3d7UZsvyGwV7vADIsHo1xTd6pVWvnUhcZTr5xENQZnef6WFXEKRRVjkk2quT2KVP1vSYk1hWTMxuYFx9GQ5bgsALgAKzB+3ejfUwhK9iSTY4Op4HrTJckSYYOlQjq2QaZQzWoc0maytvC6HPUbI14VaE8OsDQjti3RaM+oRINVyA5Q4M+Aidl5pVLzg8r+LP0GmVjeayS0KbHGOzzS20fN+Wf/8TxihOe6lNrwCI0avHpQPBTugbI/YEK16LOEERLTNj1cufKap1liTpkaSyhEguWwlp7awVrXLsnTRotSPhJE/NATsjz5UAlx1H6qJ9rLfxjD6rxQ5w0asjWPECoIEaJ6OmI8acnHAGJfV37prVaHK6NGB5oAf1zOpTnP1V8+w27NZ6NMp79/iqkdNk1lNfrHgxIPX7j+DE8VIbcPmzx6mfFk4yYpzs3BTce3Q5kU4HRSInybFkI7hTbzXrKleZNdZ9F/BFZDJrVc/6auEoCLwmA+WzRcqg7AyOdho56QY2PBZXIr5TDGfuTPwJgtiEQWwDu/9ebs/FLN3SrKtcdkuqTZEGdOU8TpJbq34j1ZABzUYFtBMzKJyzqLVHys94fg+hPE7vD70HHAB5rEibSR3h7L/vv1Z7sumscltZgT8tJWonkLNs0D5jINVwE6dE9uCRkdTBmnv3sFMHfVlI7QsSy4bB7mx694PmVrJmnuhpzUGTaBNWsrLuJ8tZMrP3k6LdXwUrXkxg9gxw0qNLUnRQSjlixFYKdux7kSofTayS4Qx9HlntrF0N48aqzo60r3Z5rCp/TmfBfvwAqSVbo3RP/96W2xuphsJoVWgAiF42Wj3XgTy+oxobRGY6YZgBHEf7QM59wMPMMeaE/YcnOIcpVtc7DMKsyNxCXWIBcux9rZPCM+ipF1INGUktq1pUNjCXXafxnUaYMjr7MA5M8KMuwAAAIABJREFUwvSTOmBiRT+RnjKDVa59SNaLGnbvDyaSP97V7rOWXIcSsb3a3GOiZvm0UKonAi8AADsWnLWagdQPBkoHHZywcjCDUHahDO92OmVQX8HMfWJS0e44j7C73rCL8hndw229fVu2LyDVcAGnDWDgbCzpe02KRO1sKGijZXVwRcqd5HQZjnLhZFIA0kjt+5zUjne9dlayDe2lp05Y8QKq2T1TB87F2gAiid7VQX9WdoZtsLa65tcltpp4Gqeuep1ab25OkeOInT1FRjkQeDESc8TgnI0R66B+apdV2Vqtl1RyAwgGinWU+nVtO4SOAWf7jabccNNjB5H6mkZS265EW713lFWTfHpp0QNptlALSDVkwh/8LAcEq4mdsgPZzqfluNja16UT21zsg1SsedT27VYbmzqliyMtj0sHwrGDqO2EzJ6gq+UZ4Bw0pYJJLau08qygtS2ktl0vrfVA4DWAZEfMklLDUZhPGGjVyHqF/veUa+RZqUA/zGu31L9209O/e2TPNYnDNbsbW3HyV+k49UvyWCWRk/t47AQ7qXIY2W808qwS2nWnR/9PD75a6oLAq4KwYyMQWMfJsp5d59GBddZqgn+/Vam7sZWUsH5Y0eYnFuyukG1vgDNSvtpJhNqypVLbU/cEoIWZ9p2TWZMVPv4kSc1zpMlIMieOpwi8Kgg3EZ6mJLuBvMeIOWPcezv8oKTXkNYM7rNnOFMDa7gqAfrJtfNK2bZOpI3odWt/qy1bKEvoJh/WZuRH2DE50sKK4Os0Vq56WaBFTgi8KuntzDAC/ZxqRLnrvdqZbTGkPTOploz1SUidNa9x2nr3Uo0GQ6lVh9j7gB8EX68iVSarxgWp9ZfCSPClXaa19caphgAIo+RsSSdcIfYJT5nrdUxnDrII7Hhpbe8dsvfTiMLntzgRMzMjfNlhdUsOaAc5wHbzsjKYJdLp7/SAFa+JYFYEjBIaPi36lDKkuRRCYAsLeltT7pX1PMUxkUTNDP5pY73kVR9LKygSWNXWJ6WLIvACQDipDfQajNOqgwu4kVIObXCcQilB9mEAlgt4VpX37h8IvtYjOdAAr+L331l7gQE/s9tNCgi8JoDBEczEqkGS5txIKot0NE0GtCClPtDF/eTsk8a2qfFRcvXSUGesfvGwWo7SfAFuxAdeDw8PahtAY5klAfmdR4vBnW2coX9l4NTMR+v4ZxELDiFnGq0GWYR7prWuwu/mpFTA2Yg/XOPZs2e7i9AFFFMnq1YquZ9jaYW1ZcXYHwxmyMCSXDnxB2DYunnAyZFHzj5Jtxetffb+rJ8CNtPezqbnEAetbT2D3CFEM56lScYttlp84EWkrwF8tJb7VFY5Odw6bc05a6mP7xxwDwiabc9MEHDNZyStDTo7l5RdkGovuFYqfFvr21xthIFkC1jlXxuAWURF4EUk16ABXk5qY+g0P5qdAQBCevXYSh+QXodc8CWl7AgU0pTGizBYxQr0y4wEsLX3l9KPSrTohZrAi0hXI+w2dlKMQ2t77Sz3ru8PAvxw2gpNdgfYoSctKrxeu95Kto+lgyd2j2Wr0oGlBZpcpIJq8DKxVFQOfB9aun61lk1V4EWkazDRUs5ZtM4OnSqvU+s9Gw0GG4AcHMHXyPUgT8kf8b+OYMXeqDDYWpk6fz9fIyk5IdCqh1PffN/R12et+hUi/lTDGBpOFgqVZvcK2A44OyH3PWPsXJ2Urs9aCR2CXhmjjcAuwrGk9x7Q3znkHMJb3qHcZ4xpu30MDj2VgoU67IJjzPXtlX+9FV9aZeBFpKeTxwY8C4ozk1A+MTlJb/dWVtUlN2NkSZ4hcF6Bdnz969HDGqdFo37ngphVY21PW/iMBGLS/IkwEJVSrlqsrKrsxs84qdUBX5dLq5AabdWN2sDrRosz5JfRguLMZJdMdrfHiufn7q95sKxl1F5osTfAJrEJvNh7ueu5TriTQmmvFdF+214i1a65ALn0mZ2M6ukupAWx2snZm5hetMhd81isPvAikr/8mEpF0Kw4MzhdDrvr3zr4awbBF9BOjVMeuyYVjFjWaU19Nhe0xD4jnV2rkL1ILZdW/ODLf41DztJ9/xQmAi8iPTNbIZrKap3dg7Mk3fX7k6RycRAOBL056ABIpGZV+0T93W3fe9BU1lqk+mrSymOJmX2PY0/ZatSdalgitbokmRnl1SYDbrQdwSzV6N8zU9ZOB+SacQNAE35/9m2dVPsD7CJpXIH+z2e2bxWu5q86SfR+dgvmAi8iWR2aCJ2amxp5juiAxuB9Nr7DJpme8kmvEwAho+OJ9gMQgA0kTOzB/q9jlQ9x65X/I6mdTQZeRHJmtEtBgqWgTIpia1pyJtKlA9IMWEirHCUaZQBq4NDZ03T/tPpqYZcdXu0rQPf2MUu/eu5pNvACNmkNUno726pBQFPQ5WNtAIFDBrQxYjdip+zer1sHfV0uK9tmR9ClcaznZmf/m/HsnjZF4DURdLT1xGTupzK0dLqZKRCaD63QWOYaUkYZThqQSqtuluyO1b4N9DDbMd8x9mod67UjdexG4DWJ2o42qzOeOKtXcih6T6/jluVdTs2GWLJ+jZQrVq/dexCkcHr9JdLSD7WlYM8EfVo2s9pnx9iLoOtVVu71koiZ4+Q1M7NjntLpazrxiCw4vi8Cjo9eatKx0K5gBzV6d8o40ILUY83BI37wNdo+u8Ze6NY+UrKX0B4qAy8ocx2h4ZptfHZuUF3x7JFZGos66x9LfRKp+p4oCyCDnO5BL4FWOIKvXfqPfrcP6YfaIdVwElJSsXzlCr/ngJOVB1HEfmqvHUVKu4I0uwxqSTe06w2cCH3sdjAAGIXrEJnUvbm3EeQCRe1jACczTxmUbvNUBl6tQj1d2XP7VjhYoeiS2rB1X4UGQ9CL1EBUUpms6wDYT6wfQueAFXrGmZX679v43GSw1PHSErVnK+xsB5WBF5EsxypFbafflaLH8dyVQZckJ6J2la32s8AO/qynhYNUgA6gY8A6kv2+Vf6eZBnspEcuu2SpNvDSQqlhVzV8KsVQYie24LhqLXcPu2ePYozIfbQ+YRqsNNkAm/h6htUuYI1Qn0M/IUaN7eUIhFZmYfm+nPaxhWt87GmDWdtualB5uAboI5VyKIlZDsOqgyCwyjUHDY6k5FOUONDQBgAAu6QOCQuDkNZJr17bttMmwhaP+1ucJ2e2cMSK104FLT17ddl8YyRxtmSmPFasPpyyyhUyW7bWZWph9hLsZZcToRnISh+xMfb+3w+4ZttT2Ov9cPhbO1a+jgi8pBOmiKwIDnYt7+buBcawLsOezdW9z2jtI6N9gTMt0bch1nUCAABCZqV6c22BQLD/gt424mzX1RPmalMNLSlueOQ70Tkpa6vraVmeYerF/dqps7o9de6RF6d8c/fJDTSpFJxwUDtRD04CewrBqcwa63y7CvspA+0+jdrAS7vgS8xOG5EyQK9owxMdz5jzfUrdY7TUPwxgd8utdnIi9X5sU3rPZvDdcgCAG+i0LWJjXe/4d8rk927utqltIwtjkdrASwsjShLmKmtXth1Y6KQakC7nkdUvnzCIX1Fv7vtjFQyAl5Fuv0A9qUnlVBunbPxMfYC+vUrNeGpFbmoDLwvCryGVPgbqgKweSaWiaaI3DfC+dvSZ4bMlrBj3kpJH6sQwACyi3SaCV8mNETFbFh44Bl3YSy5AttI2KgMvSw3QgrQ6SytPDIu60rta4e8f1CqTlUFXeK/USVozWd1WsTRLrboCQA3Qb3mM2u6Wr/bYFXSVvtpH6qRA7QQkZ5aJtXFIZeB1OlIcZynliCHVaHEwaqC1rn616Jtk3dQC5KcT7OkE2umdJPQ/X3vt6v7SUi7/mvC1XcyegAzbQ0KduVEXeGkaUDSVtRXJdZNcthn4A43/f+mamYe3zCKVKhJ+ZsZgAIAmtPXtHUA+8ymlKufaYNWhC9L1QGoQxkFL2rsVVAVemgYSTWUFNuiZ6Wv5fM29ZpEzwtx9DXua4sCm1VMjq1jKMNehJ6kDBsCrQK/n0OI459LuOPbn+s/R3tbay1+D9VV7VYGXlkawrDBAPq1GS7KRq0k5yB2A0fqs2HPgwIJWwnTemGNZ2oPCeTAMAKvo0dvcZ2vtb27lBH1AH5btl5rAS0sDrDpeWos8VmJ5abqV3uDr/lsKftA1qvOl1YQVq2ktoI/rZnTW3tpMvUQgV16kpXnHsjosroJZxmL7qAm8tHBKUCSpfhIDBgn0BF9E8gzdSI5/TDck1U0TkNs+ThlXVhBOwJROlwNlNIzBseALfUo2VlOmEXhNYMXqAYfRsGB4NBj83cRSnrg2NK+gxviWPtOjJ5JkAIC0fqmJ3Gp3bLLmVBn3HA2uZQwOJ99ObmdNWGwrBF6TuJXFmsLcSKiXFoO/k5STUWo/icautSz+rGbP9QBIo6VfSuu/u2iVw0k2o2a1L+fHQMfagLwAEQKvqWjY6yXRwc4RSxPRVH4p1DgXknSjN+jqubb3mSOEaTCrnw/0IKlfSqdXTpLTrkNq9aFnNct/v/aAGC2s7kfSdGk1I1kn1mweAi8DWFDI1iPQ/f8t5gCvoCZw1TL7GwtctBCTv2RZg/2MOiIa+vQotcFIzcr//fdqwonGmpS/FKPlt6wrs+E4GEq7/Hsmyq2mASPwmsxsReEIPHoGce46ca7aWemcK6iRmaaVRY0OJYIu0MNI8AVb+UjLZF/pVFROWu3YyW1YomZs45BfSj8QdL1gxO5YmmRH4DWZFR2GK+WwFUkGYffMpHZK8pvhfHAOeDPaX5J+A5AidZpnbVARO16bA0t9J5zYmxGErQzswAs4g68ZWRcWdWFE5hbGZRWBl5UodybWcmB74JxlOpGaPUacMuXQ2fB6bW0OPQUj+KvRo/e4/7YQSHD2q5KN6d07FV4LO7CP0Ym7Gh05NchI0ZtpZSEeEB94PTw8mBE20dyOtDr4khrszS6TxDpzEpv9lqizs9NLtaxWg7MZXbWapX+xFSJtel4jm537rkCekf1E/j1yn+nVkdx1WvtLKyPBl2bZiA+8nj17pkrIuQ6zqh6a5MXJKmNloePXIn0VcVabYwIDaCLUoVqdWqF3Kydy/GdIYCQoA2PMSJnl1N/YpOHs/hHKRMKY0zr+Weg34gMvIj37d2Iz8KtTCVavDkprDzjL85C06hUbrLiwYNgB8BlJh+NklYPJdd+T7LsFZgcWsWCpNqW0JgjfkakjwbeO+a2pgHdmlssqVAReN5qE3NIpZzx7pVLu6gASZ2+IdBuEXbRMrtyfmxkg7XBIoTdghFTKoe+sSOEuK/derPveXMzok36boM/zENPz1N5HrtUqDn2TEvTc7NbH8NkS5DMDNYGXdCMlrWzS5TWClM6YkvHucs1itj6lZrJqAmyuslntM+AMah1CCXrOGXxJGRNKxA5hkNAWWilNLJT2UOU+U6L1utg4Jm1yXFJ2WanMmvvOm3YXoBWkAbXBIa+aDcKr2kXqDG4I9LQf3yG7//d/ZlFqs9ltujpN+DSsyrZ1Vl+Ss+Kv/LRy2whNY0KsjOj3fYzo8Yrx5CbUz9W62qpbI33yfp5/be19/GdKslEzULPiRTQnPWEEfx+XhPKEcBn0lpmSHTnKO8mtekksrwZ65MYl79xqWk2OfuoeLc+H3szBqmwt1KdnrNJUb8uz9zs4UVY9dR4NTu97+P+nnhNe5//fWo7az2rtN8UVL+fcDzrnPuGc+wXvtX/XOfe3nHM///TzL3jvfa9z7nXn3Eecc9/kvf7MOffhp/e+z3VGBH40vnOWyA+6JKOhjFaxJnvfEI/MUIevcTJD3iWj7gdmo/n+GgcRTfhjh/9jgVJdpOtXuLJd+gHnUjsZvJtYamnPPWL9tiZDg0MGNStgpX5Z6wu1+kxabXdNquEPEdE7I6//h9d1ffXTz08QETnnvoKI3kVEX/l0zfc759789PkfIKL3ENE7nn5i96wmDMB2DKBScmFH4JTZ7EBDo5ytBV9EfakIqVUjTvmEM3Qt9M4m+s/tuTZ8vkYd10boJGjvo2G6XWxiEvq1j1rbol0PJSFxQqUmUIqVO9Vvc+PdjAmW1KRVTyohZ5k09pti4HVd108T0W9X3u+biehHruv61HVdv0ZErxPR1zjnvoSIPu+6rp+5HrXhh4noW3oLHZQvOuDMbAjf4dIwkOUUc0b5tXWCUWpmgyzKpHbluSbNhrNMq1bEU+VOPdcvkwa7cQKag68w4PLxX5O8ymUdyH4fEvp0rO1TgUvvam4uKOMkltkhyQfe3dYtjByu8d3Ouf/pKRXx859eeysR/Yb3mTeeXnvr09/h61Gcc+9xzr3mnHvtk5/8ZFOhwgFnRiqTJGWTxiy5aOpUKSzUIaS08tzieHCmQrUEYDOdI18mpVQMAGppCeAlOKAngqBrD5Lknsts0JQ2G5Pp6ARnbjGg9Z5+2TTYut7A6weI6A8Q0VcT0ceJ6D94ej1W4yvzepTrut53Xdfz67qev+Utb+kqYGoVLLUilvpMrxMpDa3l5nTGZ1Kz6mWZ1Mpzqd61gclIue7npJ4/wzEdncUEoESLTiH4Wg/6/Hqk+Qp+v5NULi5KE68+Ydtw26T7fhrk3HWq4XVdv3n/7Zz7T4joP3/69w0i+lLvo28joo89vf62yOtLKKUE+cumNZ8Debg6gJZOdFMqqybDwEGunr7Bne0Upu7vtwVHm8BO6EZL/+wto5b6SWLEcYas15JbldnVDhZ0oKYOqZTKEtpl00vXitfTnq2bP0JE94mHP05E73LOfbZz7u30eIjGz17X9XEi+h3n3Ne6x9b4diL6sYFys+BH60T51ETtCrJ6tpPDmcYsrWxyM1ux98PVLaI1e2xiA0duMOkpx2jQBT2Xg+S2GHHkJNdLMqGfkGLG1gZQh4YAR0MZOYlle8zuH1p8xuKKl3PuzxPRNxDRFzrn3iCiP0VE3+Cc+2p6TBf8KBH960RE13X9onPuA0T0S0T0aSL6ruu6PvN0q++kxxMSP4eIfvLpRwQxp8x/76TO0krOmHDJzdJKwu4ZOC5q291P5atdEQzvwU0p6Gp5tiXdBPHUVCtt69fNSp1WknLqOFfNQTslfd415lroZ1x1SAVf2uXTSzHwuq7r2yIv/9nM599LRO+NvP4aEX1VU+k2EXMcQZzZsrHYFpqDr9ZAo7Wu3M6hf5+astc+kzPg0qoLlvF1Rkr7cDpBUuqkjV0TQuBVWmzwbrmibV8Q2lb/tVFaJnp3MnKq4RGEs1waljFTzEzlWvk8sJaRAzBG96NwgUAJ9BBbAdOM3w+s1EkqK+zkqfSMRSux0J6z6+CnInKl6dbuK98NAq8EobMZDliSGrEGbgUP7z1bHhYMmY8Wx8cf4LRuLucapGP702YPFmA/tXt8NJBLqwftcMhPip3UgKQV6Fq0lXcHLVkpKcIUxnDBRFIbIPDK4DtrfqNpngWdpXySlBqMwZGDzRWQSAlsZuzl8id3gHysBF83sNnjQIbrkS5zaU5+L6vrMDrBFSuv1D3YCLwi1B4EoHn1C2UGKThWhjjuI4VcfXr10r+nVjtyItbskLX6aMOKk74CjbLSWGaivYsKPePhrvL2Prfre7zACziWSMGZaNgE2oqVftC66tfTjqVVdO0ytIrlfutjqX5SgYzr0DI5YM0u7MIPvnoP3OI+qCtXztb7I/CK0HsYAJEdx3M3cAT0IVX3c4YxNaDPNPC5z8088QnwsTr4iu015qKmb0AHgVQkBztSy5VDSpAbjqspmyu5/VMg8AoYbcRYpH4qI/uDTpGdlNlzrjLsrkeMXDoVtwNbkmPtoObfAwFYmp3BwaxAKCQ80CV8bRapgzigh2kk2HKNpORW078hb34kybRkZ2u2Bc1e9WoFgdcEsPrVDwau9dx6mtPXmoBiJNVuBjGHtfW6VlKDxIgtwGROGgnymDmwp+67Y8JmZdCnFSkTaVoI7WL4fywFG5yH9DGwtVwIvCaSMxq7lQcDhBx2t0HN82OrRqtyqFcz+syeWbnae0qfzJFuU7hWyFLB9WjdU30sxi4bjrEjD+QSp8YPsrCqpb1vSC37iL8hzWYh8FpALGVDkhKA/WjQh9WpRxxpv9aQPvMnbYALqS1XLkCL1a/XKQifo8WhkNq+QB6j44RkexLi22f/NcDHqhTvFlrtMAIvj1UdvHX5fNZstxSl9eGeQQbzkBwESCwTF7VOPnfKY2v5NBOTWey92HUlGfemwAKgCWnO8UpWT1KeRo/vIWlSEIHXRnr3xHB24pmKyJl6I6GzzERj/UaOeq1Bo0xWkzrliejVAC08GW91ubTCaadn9RNL8gb64dRHbXod64/a6qAFSbavpSz4AuUnpDReDXdKyh3xhz9W6Em9AWvIOfycz+jlBL3x7cDN3S6xQX/lKssJ8i8BGYDTkJoBsYIwvdCSLyaZWHpn6bOzy1ICgRfpCrp8bgcr/JEQgHHJVGvbWCa3x4UTrtVS60hN+TxF/jlWOWCS2h3YJDXB67928mRp2NcRfM0lnFyU4vvWtDtSDcneoBUq4Or67VZ8MIeSPs1Y9pcWTEilVUYr5CopDUQKM2UBWYOZ5Oz+TDTptZZyaielE6nzE8IgbbdOIfAySmpz+OyVilP2ZJ1GraHiMGrh4QM9OrXbsGpiZp+VMMjtJpTB6fIA4BTQz/cwextEzfNzz0SqoWF8hyq2FDtLGWFseJCyb2+3oxjby5Rjt7w0EAa0M9NikHIDAABgF34KbGpVjJtnz54l30PgZZiYwzNT6ZBmxs8qIxFD2r6hFgdeSpklE1v9nhXonx58Sd2HBwAY52TbpgVJX+OBwMs4uTzYVVg9dXEVq4OvkY3SHGXMPbPGgd9tVDXjtzl3fz05+FqRgg29B2APJ9s2rexsMwReh8LtzJe+T6jVicfM8MusCL7C9uqF6zTL3P1T72Pw42OGzp3soMCeAdCOln4j5VS9E9CiEykQeB1AyhDM3NuRCsRKs+lIx3lB6iCUGd+XxSFzznYr3Semt9AbfmYc03tS8AV7BsA57NwaANrZNRbhVMMDKJ0sturkMf85Ncd+gleDiVa51RiVGSdbcq16taYd4lTNOYR6NyrfE047tF4/AMCr4PRSfaxuKwReC5DWAWPl4XSESkFe7n9QR0xusSBLs3xrj6+/wZHd8+GSL2aEAQCWOWFyyQI7Vr2QargACR2vxmEaVUA/rQaO1Xr8vXSj+7R6n+//3onVfHsp9Rnt47OPsAcA2ECzjbA6Du2GW56rxyIEXguR0Plq9870lDV09iXUF5zDqj1xO5EQ1PqMytaqY7J6ptua/ACwgsVxyCqr2giB10KkOU0pUrPR4bHwpZPnYHDG0JqmIKm9La2sSKsDl2yttZHWfgsAmIMlG2eVlf4qAq+F7Op0IytY4f/hj7/ZHkaFBzhv/JRO0wT9cMlW+0RN73ffAQDOAH7SHmr9qVVjEA7XWMiOAXmWA+87GTjUgI9QfqnTC/3XpclbWnl8NOup9PJynXwYTuhIR0JZNcgJAIBDNziYKbsVwTECL8Os6Nz+YRr+fg3NDu4u7s5eE1TFgrPdctbU1lKP0N91f05GArDU101IrbukdpFUFgC4sKjTCL7GaJGdRDkj1dAIsf1XK5TNP8kudUy9FqSkALSeSqhJxlaZPUumMUXFXxGvLbsm+yFxQAcA6ECjTZeCdruLwMsA/qzwrqPE7+drPk5+Z2ce3R8iQeYSyrAT7YPBLMIALBWIpXTndL2qBXICQBfoszKZ3S4IvIwgxenzFRYGpQ5LcsJAMq89tcs2nBTyA7Ddq0ctJ7beSGwPiWUCAJRBv+VnZFyJTRZytRH2eClnl8NSeiYcgDqk72Hp4fT89Zn1tyLb3AEypc9zUdpLKfkAmxxWdASAk0B/lUeqTUbHBgRegB0YkDq4nSNJDpeksuwgnHiw2s4c1B6kwVHnljZJBYexk0WlYU1HAACghZn2L8zaaH0OAi/FYGDVibVVrpQenu78hY47gq88M08y5Dji/r6PlpUwizoCgDXQP/lZebhczwQcAi8AFmLREfKNT+xUOot1bmVm2uGs++8iFYD16hKn/vll0ypzLYEjANbRbEd2Ugp2Vsq0Z1xC4AXAAjgc5F17YWKEzlvJIcUAMwd/A7A1+Y4GObODUg0yjzkF0ssMAAA5avZeSQaBl1I0DPpaWP2dZz1IWNkIv7YgRmzp3XJwIAELqzAlWus2u79ApwEAQB4a7DGOk1eIlsFeQxlXwNVeq+UZHqGaC7h8/GPD/TQxnHQ5F6vyDVdrSnUc/U68nnJJRks5ATgNqzZ7F7tsXWs7IvACYCIcRnXnVwZwO7AYaEAPtQGXlkmp1aDPAQAso8nGIfCayAxFgGPBywp5zt7XpQ2LwZe1+kgkpzerVrlSzwYAgB7gz/GwW44tfg0Cr4nsVgQJSHZKZgddo/ff6UyuQLJutGK1jTSwczIK7W4HS/YIyCOnX9C9cTTJEIGXIrDapYeYEQi/A6h0veW2tli3mlS42VhcTfQJ9ebuJzvrvPv5AAD55MY82BAb1Po1ONUQHMmuFMPSdy9JOL0Q9IE2W0OsD0n4vrjVz4+dHgr6gfxALbG+ju/H24sUmdeMAwi8FCFFsTSzIrBJGWXfSQzLc4M21k3ue8wAH7nvcdl1EM3qwz1yNgboBZNv8gnHcI4tARImj7SjRX4IvJSgRaFCJJVZogyllWcWMdmfMtCsToc7Ra4hu+u7+7u9Tm13TnbIL7Z6C+Qyc7IUfXgMKfIrjfUIvMARjK5E1HZkCZ1eIqfKZKc+QBfXszv4Ino10Pe/Qw/6kGdXwAz0cLcX+hNIcV0XPX/+PPk+Ai8FaO/gksof5mFzBVRIJQMhu0/bg07uYWfwlXIKb12QZIsB0MjsVFBMkowhRX4PDw/J9xB4KWC3AmlndNOr77TkyDm7aMMzqDlNXSeVAAAgAElEQVS5avUBDNC99ewMvmI2yA/IQB70GVBixcFc0EO9OOfo2bNnyfdxnLxwLAyUO1NuRje++tfHfojqUnkstOMMTluZWV3fU+QrsY6+XdjxbNAHZAdyQD/ks3vcK+kIAi9gDq5jXUuBVMuMlD8DLtFJBOOkvrttF7sHH/ACKe0AnQBgjNWHJIE+RuU301czE3hZVFBLS807ZvlnBF2pVbSa53EcOWsRKwNMWI9UW9fUl1MeVuSbQ3IdV6581do9qbICQDr+JCqQzUhbzbTbZgIvS+B7PPLUdIRZQdfove/rYbTHkCq/lrZNrZCtPn7eChpkJqF82O8FwBiYRNWD31a1QVj4GW5baSbw0jDopgiVwWKHbq3TrrYszRinArLW+mnW1xlYkkdL+inRi/4frqRyyuQUZztM6R2ZmY5dOyK/2Xa9NfX5vgYAAE7AX8VK2b4VmWZmAi8inc7b6OEPmmhpm5rT4TjZdQIZeIHG/pui5QRMP9CKfY6TE/Q7dQhOaxAWXnu/Nlo2KTqO4CuNLxOklQFgi5TtC31A3z/PBWqtk3zmjpOPOTRSOcnR99OnpNVZi75IhrNdJepIKzW67n9mVX0tyLYXf7BtkQG3zGr3ArbQW0Z/9vdUvcgBuQBgk5jty+3bD8f0nA0vBWCmVrxudh7jW8tpBl1qXU9acdTAae1QW18OW3aazUnROjbMkFlsNW0X0Ik46C9gN9C/uaRihVTfD4O13AFax36Pl9Tg6zSDHs4McLTJ6rbd2WYaUl1mrgjMft4MZqySSLiHFbgD3t7+WVOO2J618MdSCuRukAFhHw26rqGMlrjlnTtcjWui3lyqYYi09DZJZdmFlDbZHUzVsltOO5CaMlyrM7mUhR1IKIM0amTSelgFB6FtKN0bDhofEm0O4MFvW+ntiz69llzKta83XO1iesXrRsps3onOT0ruHG2yql1ntZsFXZgpfympWNo50e7sgDMdNDwYZAVSxsldIOiyi8YtBZrKaoGYvxE7AIvDRh4ReBHtd+JOdn5SysqVPtXTprXtMaPdLOnC7HpIk1OPvu2uw+7nS4Y7jXfkXj12wZIt2QmCLptobVdt5bWCHyekTjTkOBzpmMAL7GFFmkyrM7zbWfGXtXM/4JHTZ+JHwAD+Kq0pR7W6VzrUKXef3TbpRkIZVqPVOQd5pPSpVjDW7SNlC1LBV29bmd/jFaKxI1ogta+Lqz1m7Bvr2c9Ty67VNq1I2Rd4l0ULUmS2m9gMZSp3f5aNytHzDKzGj4Ogyyaa9VhruTUTswOhz9Hqgzw8PCTfw4oXWMbslYua+88yyDPqNTqrYhEpspBSjhIYxB+J7Zny//ffn7X3NDxwBewFQZddtLYp7MJaSicVhj5Y7aFfzrlzj5OXhFZDMINdwVdL0CVlxkzbhuCZ7N6n6YP0R7twBl+5w4Vi1KYaS7FPmghTuWFb5XKqbYU+rqPWBqQyJO7rQ12tsc3HpRruAIPkC3Y5rK1BlzQk6dDOsvhGcLc8dj8fyCY1URAbyMP3Uu/fr0P3yqTkDuRTu7IAzoDT5vXoVRhsxd5rSUVE4AWWM3u/TsxRqQXGvowE2Uja81VCSzmls1KOnMFO6rrSJFRskgG6lGf0tDEgh1xqbmxSQ3t7o2/nGZXPqG8XSzsM36vFbOAlwYGWUAaprHacLR5mcbp+aQq+wBg72jncWM19X0wI8RA7pVJi1gJop6T3lvqDpbpwMzLxxGk7c3a75f4mA6/dzhgGyb2MdE4NxPTL0sxfCwi+7LO7fbn7VphGFatfzT7VlnLV2LfYPUbs4uw2y42zsAf74VihQDvqZbcfPOv5pUmzI/d4SemsEsognZlOc4+zNFKOVQFA6hlhELYzHxq8QIo9An2kAp1RwnuG9ioVBPXsB4u9HiMsy+jhE9x7rFrvh743l5J8R8ZEtJ1+ZqRHt0wwzdSfUX/PVOAlobNKKIM2ZsjMUspJ68ZNrmcS6djYL3XVS2KZtDArza+VWKDD7Uhw3C9l71ruy12/kSDQ/3zqfjXPRx+cR41cEXSBWRPrNRNPMxmxL2aOk0dn1cnsNmvdSzH6LG78WeiWlbtcqlLsJ3zePdsdvn8KJ9YZ1MExqRP2vVI/q+3//iqVtPEw3INV+rk/P7ryBvbR2kdgd0EtMVuxmpivVeNzmljxkhR0SSmHJnpmDlKf360L3M/nzpEv3S/1eekDIvfs9uj9dushGKekA70rRLHZ2rCfWd8bs6p8WPXaR63cuVJbNQMdzRPzP6TIq8fGqF/xkuIQSimHVmauTK1efh6FQw4zUjelGLocM/bh9JRBg6xAmRq71JMCl3pGKcgrAd17lZVZD6CN2tUKtJ89Siv8udVvaTYutDGl8qkLvMKUDCmNIKEM2mkZIFvlze08jTyrdB33qs0p7Fidi6WIwRaAEcJ+W6vPnHuzTrIbYA45XWwd62am8UvgxDEjZuc0pxmH9Xl4eEh+Vl3gpakhwBxy+5d6Zox3G+CZG0NP6yur6wt7ZJ/WQIRrxdq3C7V7wQCQjpRVWQllINrvf+witGnaAq0YtfviVQRe0hRTWnksMXO2NXbfGc8aTT3iRkL63UpOPhQE7IfLhvl9zT8wI3b/GXtLLfUfa/XRQExHa/R0RTtBF9ZzyuE5NXVREXhZahRQZsUgOfM0nFLZtRt9DeUPDy/QUGbwAkmOck85WsvfM1njzxjPQFIbcGCtPppoGWtn+nu1wd9KJJWFEz/IIqKqQMtK/7yui549e5Z838Sphqux2lEkkTspJvZajTHlPDlsBOjPGnCimW4ktV9PGWrtTW8dffmsCL4ktAPQxazJzZ5yEMnTYSn2jQOOvc5WZFECgVclljqINVraRqoBBgC8wJK99QMw7jr5KYizZCZlwoqDWjmlAlnNdd+BBHlZsiWSmHXI3QnthcALiKVmkOzppNY7dchp9fXBjD0YRdLK202sPLPLNzOAlELN6mQMq/LoRYqOxMohpWzaWHWa+Altg8CrAnTUfeScntb0mlXtKElXoLuPQAY6kdJuHMEXZwC3Uy7WJjNChzJHKSCzIA8raBj7JJcPX5MyDwReBTR0XuuEDkvsmOUSVjZtAmCd0OZascH+fizuVfzV3PXQUFafcBzhmrnnWg20EMBJ14nYxIH0Mq8AgRYfJX8TgZcHOp9sYoNSyyzyaW17uj6HAfrJsgDj9OpReE0pqNSip1qd1pnBzYit0SbHGJLqkCuH1j7HCQKtOdx94Pnz58nPIPDyQC6wDGKpH9IHNOiKTNAm+rDWZr4N87FiMzRNbKxKNW+Vhxb55ZBUB0llkUJsFQYy4qNlQufIwCungOF3DoA95HLpY7NVuw2tNF2ZKQ9pdY2xWx8AH2hH+UiwwdKolYdmubXsj1uJpLLsAoHWWlpke0zgVQqoLORWW0fqapc0Tt7Phn5skxP7MdBLbRqmdL1O2VPJdla6TGciNRAGL2M+8Ko1EFrz1a1R+x0ru3O0peqJ5AFxBal6Y0YeSMOaLqKPvUpMJpqc45w9lYrksnGCFS29mA+8oIi6kLqqFTsNSxKnB1xgDIk67SO5bADk8E/Ru/8nOjszAfShKWgHad60uwDSCI0kkEWLgzjLmZRm8BB01YO+HUey3ZNarhEs1gmkubc4wEaDkJwtuL+yIfzqBuiRboqBl3PuS51zP+Wc+2Xn3C865/6tp9e/wDn3Qefcrzz9/nzvmu91zr3unPuIc+6bvNefOec+/PTe9zmho8/thAgtHgCvAENcBjLSh/SVOPAykgN4ACTi95lYoOX/wB7aoGbF69NE9Ceu6/pHiehriei7nHNfQUTfQ0Qfuq7rHUT0oaf/6em9dxHRVxLRO4no+51zb3661w8Q0XuI6B1PP+9krAsrSAfQRRgon9Bu4SwYACNIc5ql6bYk2QAA9OP7LbFAK/ycJHsI+ikGXtd1ffy6rv/h6e/fIaJfJqK3EtE3E9H7nz72fiL6lqe/v5mIfuS6rk9d1/VrRPQ6EX2Nc+5LiOjzruv6metRe37Yu0Yk/iwDBl3Z+IZqRuqdRIMnsUzSkRZcSEOSTu0uS2j3ucYD6w4U+lgdkNN5lFa0UtcQ7beHgI+mwzWcc7+fiP4JIvrvieiLr+v6ONFjcOac+6Knj72ViP6ad9kbT6/9v09/h6+LR/KhCqcRG6xibTKjnSS1P4wxmIUkPY+x6jSv0gmZLZzYX6XrEQCzqfFVaq5HP7JFdeDlnPt7iegvEdEfv67rb2cGntgbV+b12LPeQ48pifRlX/ZltUWcDnJsZbBL/pLaH7Ol/UhqR4mskEvr6Vwtn7/bdpbT0qo74edP0DvYJ3AqXCcPYoyyS9Wphs6530uPQdd/el3XX356+Tef0gfp6fcnnl5/g4i+1Lv8bUT0safX3xZ5/RWu63rfdV3Pr+t6/pa3vKW2LkvAgCKb2cZKSupprJ5+CgMAvazQnzC9JqW3sZScUlnDwJqzPqNBl/+6dTBWloGM9JNLHxy5H4Iuu9ScauiI6M8S0S9f1/VnvLd+nIje/fT3u4nox7zX3+Wc+2zn3Nvp8RCNn31KS/wd59zXPt3z271rVAFjeTb+Xg9JhKcfSSufJCS238mEehs6MbnriF51fsL3ewjvxxV0nQT6GbBGzz6tlntz3AfIpibV8OuJ6I8R0Yedcz//9NqfJKI/TUQfcM59BxH9OhF9KxHRdV2/6Jz7ABH9Ej2eiPhd13V95um67ySiHyKizyGin3z6UQnSlYDktse+RKCRXl3NpRb22Orw8717ulKcNH6cVNceIB/ZjO7TankGdOAMioHXdV3/DcX3ZxERfWPimvcS0Xsjr79GRF/VUkDJwGAC6UBH4/j7gCAbG5RWxmrauhS81QAnCgC9rAi0Ys+DvTiHplMNAQD6QIDxAgxy51KzgjUSvLXqFvoluIEu7GN1oBU+F20unzB9fbSvIvAaBAYTrAb61gfkVscJMppx0mHvfXuCQABAP1z7QEeej/6th1gGhE9reyLwYgDBlxzCWW20CbiJ6QL6LmghtpdsJOiqSX20oJvoZ2Ugo3nsWtWKgTa2R2vfReAFTGF9705PvXCqGAB8cARcLc+yasvAq6C9x0l9xYQUJJUF8NFyoFnV93iBMjg2dz/+sc/gEf94WhwxDwAP2J/RDsZIwEV4pHvueHcpfRS6fwY1dg6BFyNSOvjJSDK03IzOhPqywSDwAjiErwJ5pFn9XTuW9NO3P5gIimOpvXvJBVZEFA2upI/9kssGeCm1NVINmbCe4iYdyD1OSidPkxX0A3CwS4+sjS1W6jELa+19UxNQSg+gABgFgRcjfqobDMderA1cvXXxBzqcngZqgU68igR7IqEMYA3axzDpe60A2AUCLyZGj5cEsmj5stUUXO3PoUupe5yQ0lIjP/RVkEOCPUcK2nloCr4knRwIgGQQeE1Ck8G0APdR4eGx9LH3au47ugmfY1/X6dTKAP0VxIBegN1I1cHd34cFgEYQeAEzzBqcckFYy7WjzwYArEWawyupLGAN0lY6EWwBMAYCL2Aa7pVHDDQA6KUlHUpa0AXATvAVCgDwgOPkJyJtpso6kDcYAfpjl9R3/KTaW7IeSC4bmMMu2xT7fizQDvos8MGK10Ywo7oG7Ld7ldjsJWY0gRXCdKgWnUY/ABJZOY6hDwAwxsPDQ/I9BF4LCY0mTkLkB0FWPbmgC3n8QAucx1bDdgDJzBzfYPMB4ME5R8+ePUu+j8BrETjSeh3cJxxaJza7eWra3aiexK6F3s2BY1Y+1HMt7QSdOhdundWo/wBIpcZvwh6vybQcPQ7ALmLBwv36iQFYCyXHBX2fH65UKP8+ktvp3mdDJL+sYD7+PkWivj1EsX2PAIBxSn0JK14TwawkkE6ooxZz+1u/6Lpl1UtjH5da5pqZd079lCqHG798kss5C+ntI4lam4XVLQDmUWuzEHiBY0C64auEM6Y9skntsZEk61l7IqTUrwVJZc6t4nDu3QrvK32GX6tuAZlYnFDTBORun5ZVZ5OBl6RBCwZPFtICgl2Eq1w5eYQyq11BSn1+pex727qkJ9ChcWr0ruZz1jitvilgq8fA6hYAa6ntZyYDL0lGpmYWt3QNALNocWxqJhH8fQM3YZC38khkroMXWgJV8Cojq1fcspfedtLLB2TScugGbBgAddSm8Lb0J5OBlwRSDQFjtx/MpD7SIoOWz+UmF+73W/ZQ9ZSFq31jqZin602J2Aonp8xGgmH0e12grepoXd3CGLgOyFk3M7JfEHgBcCA7B4Oa4Cu3urYjfffUtLdaVqU1pe7dcqgA0AX6XJqRfofgC4AxevsPAi9wJBh05lAr01wgM7r3Z2a7Qm9eoGUPCdpLN+hzr8I1+QTZAlAm1k9G+o25wEuCEZFQBlDHiW01q849qwqx1MTRtMbT2nM1uw4MatXbsJwn9nUrpPZbprDY1rMmOhB8AVDG7yej/cVc4AUjAmop7UeyyOz69p4guPJ5HJxoY3Yfwz56CMxp7WWNcL9l7D2LrJroONGmrQAytQOXz/gmhrKI40SHGvRxkq7MHMBPGrRPqWeIlnrjABTb3O3r/8RWzbXbdX8f7Gx9Rn+Zg3YdBC/D5UOZDLxu4UDpQQ0WBukSCLr4sa4zmkBbnI0VG34HWztWl3MrigCARzj6pLlUQ84NcOAcLKSolo5xn/VMzTLrxYqjp53WCYVT9fUEQhuuxaZLO6RGi9wAWMGM/mkq8IoZCxgPUMuqAYfzGTMHbQy+eeCg7KVH9mgr22gJvqQFWyHS5Faa5IqdjJt7fwWS5AfG4ewTZlINJSk5ZsP1Ir3t/FQUopf3O3BTuqekPgfOoeULuGPXxv4GdghtuCSbHrPdUtkpt9bAKfb5mWNjCSn6BsaYtUXDxIqXVQcw1Xkt1lUS0mb7brStxgHADedACD0/h51tLX11K8eusTD2FRBaZLfr6zYAL7F2RKrhE1IdxVGDJbVep7Ay7fB+Xulz0Ad5SA3SLcLh0Piz+Ggzu0jol5oDLp+dfUab3GBbbJBrRw67ojrw2m1YZ4COK4eZA07s1Kodh2PEygSANLj74W49R19bg683q2RuJeDyia1AgZeB72aDFe2oNvCyaAAs1kk74RG7M1Ocds/OQveARKwFXVLKYBV/UmtVqprFYCsGVozjYPy0Ra4tOSZxVAZe1pQchkw+XAGYFd3F5mEwG8t20Yod2ElMP0py5Zb7KQGXzyn1rMGyjTqRVXZZZeBlSckxAOtixgrYbnp10ELdOZCwn0QTYfpXilnyRFvZIJamHbZrSsdGjhs/MdgCr2LJBwBt48LomK8m8NI2WNbMvBGh02olDMD812qu0w6CDdBDqDM79EeCzkoogyVK8kwFZDWHGrU+C9gH/lsbkn2FHW0p/nu8Hh4eRDfaCBbrdBL+XoJ7IPe/pyX2eWCT0uoNeAHkBLQQThJgzAYIutqROj6OtOVIncQHXs+ePVOn4KvzzIEMWoIw7UCHQQ9wXsEOYra4NEb7X9StRWetjzu70KoPkpAafO1oSzWphlpBqoJNSoGHb2R8HbDwpdgIuuIg/RIAWYT9sTTDrXE1wy+zpnJrQKM+SEbSGMlRjt76IPBiImX8JCgY4KF1gPP3gdV8fmTD9yqkGE0AACgR29tFlD7gRaNt01hm6SDgmoeE4Gv388WnGu6gdzkUndQWYdpGLuAqnc5Wk/7hpyrWXrOS3cYKAPAykuyDJnoPRwL2QTrhfHamHXI/t6cuWPGK0BqRwyG1SUublnSmdfUrvKanTDW0rMZBxwEA2vBtV86WwsYB6MAZ7G5jBF4AMFGbftiaxtCyRyHHPaj07gmwsD8N6AKOUBrIpp4auykhBQrsA22/lh2yntXGrbYDgVeClu/5QGcFLfSsfvnX+qcr9Vzbe3RqCQ171IAuoENghJ5JLuzvOQ/4cfaR1MYIvAaQ1JCAlxVt2zvI9wRu4Wby1pWulsNE/OcBAPjB2JNmNDU7Zccgb5v4mSBoY5tIa1scrgHARmJ7D1qurbku3CwcXlf60meOdEQAQqQdHqMFaU6EFGoPQmph1mFHK/UefSwN+tJeVuimxDZG4JUh59hKbEygE39g77k2p6P3Z1Lk9Li1POgTcr8kUgKh44qTwwAXK77LasRO3+xIY4RNioPxyj5S9R6phh2gw4IZ9KY7hCmLPRvJw/97ywFACujHOBh7XmZnINP6zJ1tB515mZhDDhnZRGK7YsWrkZnGE6k3YGTli6juO0j8wznCFYjegzsAAPNAv3wVTYdgoO3kEWsP+F+gh5g/lQOBV4HVM2lIC5DBzkGSI/hKkdvv1fKdXrVlOQ04WGAW0KsXaAm6EDDLA+1xDlLbGYFXBa2O6SgIvmww0obcwVfJAQjTDlPUfsUCAIAHOIpxpMtES3B4EqW+BN/LDpLbEYFXJb2DX2/jwwAAov7TDmMrWanUith7qVMP4UQAAHayOxCtPUmWCPZSCi0rj/C91mBFxj32CIdrVLLDgMJon83oAJDbBF46jKP2kA6gg93OKhgD7feIdGcN3/u1l1Q/QVvIYoU9m3n/UTuEFa9K7hmT8KfEaONLH2hAGgnGPvWdXeE+L5/RgOvkGUMJbR5DarlAGRy7/0iPXeK2QzXp2hra6lT7XMuKNjy1DVZNIs2Qb+g/9fZ3rHhlaDH0sZkuDgXTYMTBPHqPLo7d4/479ZnS3yAPViXATE7Wr97JIG55WZE/x7hindnygex14rdbr45gxStCbFWghB/9+qthp85qnI60dq858XDG8wAAPJy6kqwh5Tl0wDS0lWR59qJB7qdjpY1iQVdtn0Lg5dETcMWIHddtQdFOYbStuGfKuAzVquDrVF23MqAAuZyqYxKDBH/LgcTygTHgu4EQXydye+dLIPB6YmZ+dnjKHJCL1DbiDr5mAScEgLmcNJZItie+v3BKe0hl1sm7q3Uvp0ctZwtIZ7YNm3VvrjgBe7xojnFPLUNKHkjAIyPtM7N9a/Zq5QxOuAo7kqsMPQZgHyeMJZIdTF/+seDLcrvE2KmLK/Zi1Ryqwvms2Osce4tOQENqMgIv4m8gyQMGyCO5sxKVZ4pWlT+1zC5dfrM5wSEGMjhBxyTXMezroWN8EtbtXq5+sTF5dKvKSHm0MKMOu4Ou2jodn2o4y0Bq7hCnctJgeVK60g4gWwD60eJU5uzoaWmIu8aUlZONqXQ//3C1VXLAGP4yu4OuFo4OvGZE21oGDDCHFcZwln6NlBt6/wLs6QQA3JwUgIVjwOw9Satl6gdXpc+tDL606haXzyAt6Cq1x3GphrPzsKU0vHVOdfRn5JOHe7/uv+/n1d4LvIyFdBAAVqPNiQz7eU1K2ik2oSQTy6yy/9on+UZkJLE/3e3x8PCQ/MxRgdfMTnCiYQFxZm/0nQk28AIAdjPD7qywZyUHOJzQOsW+nhyAreCUSb7UwSPSuK6Lnj9/nnz/qMALQRcA9UCnAQCnU9rDdf/dEkzFDuI4wd6GqzO1dY7JSIPftTIg0hp81Zwa6X/WAkcFXjPQqOja0SBzDWW80VRWAIBdJKVM/f/tvV/Ir8t137dGqiKL2sJyc2xOzhG1apQLWbSy90GouITimEpRQiRfBE4hkS4MCkYGh7YEqYFiXwjaEidBlAjkxFhK0gpBUiyEBVWUiBJQrJ5t669l1ceViI510DlNMZFv1Fp+evH+Hu/Zs2fmmT9rzaw18/3Ay3737/f8mVmzZs1aM2vmLQmISk+zy9nY3WxvbRp7mMYJ0lgNvmKsPCGBwKuDFiOwohKBR7HUzpbKCoBVVnGIpJGSUc1zS9uq5Ah5iUO8VtCjkjqEdbVY79FlXiH4sl7+K7Y+1ZCDFuXArE07Z4eMneTTK1eJvycBAABgLjX2mNvp631WeIT5Sg5prl1Wcr6l/IEr+VnEarlrWHbFSzovdCWjYIXUH6s86dm4rG2wBQCsxQoz0avT2j697ZpLWYS+gBRX6ava7U3J/skVMRV4lTrU0sq2Q0SujZo2LUn/AAAAoAepcbv0uVxjRU09VlnFam076/XWjtbJntY/W7MKpgKvFsdbCo70AW2dgYuWADm1Qnle0yqrmateFtitvqOBfAHQD4fjVxNErRJwnXDXYzdHXBpNvk1O9zWVUxJTgZcGOBTDyjJwKWFHugqmYp/t3AlnAfnKAvkCYIfwqPNSavs57MIDcsfEI/jiYaYsS/7u1o7tvEzgpeGPI9aidRn4pPQ43dJjcsMVrqt6z5KL5jYBAIDVmGlzR25N6MneWIlcqpk/gYuxmIfRsiz9u2uWgmxO+S1zquEohZI4+S52Qt9s/I4TK9tZ5hp5+KcRlqQhjiR2cIe2NuEEAxoA40Gf001r+8RO2PXHUARcD4iNPblDs8ADenyS0QFOqo2vPtuBZVa8pOFyVHPpd1qc4VjqINesnaZ9ehbKAGyyctAOgCVKMzZ6xt/YGAnSpIKv2N/t0uIXtcI5FnBscxmxwlv7fCurXlwg8BpESdqeBiOTKqdlw3fFbJmDdVht0zwA0oxwuHJpTlzP3SFToocdbSNXYL8DfkbU+X8tcLedyVTDGWloPffWnMw3eyMkUiLWBwOADOg/IAYc8UfJ/d0qCcJxNZfuBuq52i7R40xrWw0pLUtq9W42WsoRI8yyCvts6scaJle8ZhhIX1lL9ye1OmEzVr7gjO+BRSNlAfQfEAN6EUfbGI52aidc1SF6dPXPQopcDVdlSX2vrR5ayenU1fUWUL/idf/+/anOYphK4CtDakMtx6x37f2tkX/LIRkrsWMgsmtbAzAa9LU8I+0vgi4ZwlWdHfa7haf1Xa2mxu6d6XtYWPWqCdo11yeG+sDr3r17Kjuub2A4Ay6fGkWKGbzS52uU7wh2qz8cDACANkbaJJysNoYWmZYELBqc69RhY7WT2LP1Tos8Y7T4s5rrE6I+8NKO9J4OKaX7AZgAACAASURBVEXafT/K7vUHvFgy+kCO3XSgd//zKDDpNIdVZZ4K4Fet7yxWDb4QeDER2wzI8Uwi/gFq50Fo99TKFTn7hwWDC9ZlN7vCUd8R8hrdLjvpABelbWTFsbaChK5yt1FtGbl9cQn7YfJwjVHUClzi2NCrfNdULnX4eezY293YLbVwB0K9Dvve6nsNgB520i8rQaa0k56SgRX5zKTWNkOmMkjIdXY7zX7/FVjxEkBiVia1jB0e+OE7n/53u88UIbVwPXx9PwnbN+wH530AgHmMcKKlJ9pgR/JcySdmm0vAGG4D7lWvGf1Nyk5hxStBr8B9ReE+cCMVgCGNLg5ksiatK7jQBQDa6XWARjlQs/t5rJ6zy7QCyFzhJZYpAmRB4BWBa2BoORaT650AM5IAAMAJ599lkkKLYz77/S1wOeDcjnwspVz6nQBIgVTDBJwdODxutOdvbrWWa2ejtGu9fXZu/5Eg2AerEv5R09YxTBKklPehPejyg+qav50FxmO9PSR1CoFXgKSw/Zzm2pNXrq6D4YkDmYBR9Dp8CNrqgczGEDucqXXfBbdN9sfRUfY+N95iLOaj5rRD68y0ZdgHORakGnr4x1KPTAvMHQ7AMaDsOBDsWOcUkMU4UrK+aoPUaYzIv88DuczB18tS/eTYGxbbOz1SB+BIpuEOfiFrMAvpMfdyxcs592rn3L90zn3FOfdl59zP3z7/Befc7zvnPnf7eat3z3udc886577qnHuz9/k959wXb9+93ynqWeFJgCOLljvdp3QWHQ7Iw0AeQAslQdf5r++8hI6lInMJNiM1NnE8p4ZZqYRhv8T4EodbLuFkFORuC7RXnJIVrz8iov/6OI7fdM59HxHdd8598vbd3z2O42/7FzvnXkdETxPRjxLRnyGif+6c+7PHcXyXiD5ARO8ion9NRL9ORG8hok/wVKWdWIeWOpVQklg9YKwA0E1p/8TqF9BGzWoXRxruLN1PZaLErtPaT6XLNOL05tLvwXzQRmkuV7yO43j+OI7fvP3+bSL6ChE9kbnlbUT0keM4vnMcx9eI6FkieqNz7nEieuVxHJ857lrjw0T09u4aCDJj9YsbKD6wrsOWwKZvsBMjUgxL3zOKMDsmtgqmrbxEsvt4Wg9bqXmH/68m+a4E/IQ7pH2mqsM1nHM/TEQ/RkS/cfvo55xzX3DO/Ypz7lW3z54gom94tz13++yJ2+/h57H3vMs594xz7pkXX3yxpojVlDhGtQdhzAIONsgB3bhmhBMBwCqUHPpE1L9HWQuWJlIkgxTfTtYcGObfF/7416T2u1qRfQsr1c1SP0kh6U8XB17Oue8lon9KRH/jOI5/R3dpgz9CRG8goueJ6JfOSyO3H5nPH/3wOD54HMdTx3E89dhjj5UWsZoaoZYaltkg+AIxsE+oDM7BXULWpf37yvkBgIMSfazpTzGHXIsDp6ksObiD3ViAlNrrFmYJ5e5LrRb6799thQu2OY6VvldK0amGzrmX0V3Q9U+O4/hnRETHcXzL+/6Xiejjt/8+R0Sv9m5/koi+efv8ycjnU6ltTAuNb3F/GpBHS1BuQTc5DgHgeE6M1B6S2Mywf50FuQN7pMabGmdJu25acvy4bFfP8857WstiRdYrceo4x37MVdpPar9myamGjoj+IRF95TiOv+N9/rh32U8T0Zduv3+MiJ52zr3cOfcaInotEX32OI7niejbzrk33Z75DiL6NaZ6VNPrgGpwYHOssD8N8KNBJ6ykjLTKqdfpKH1HbGUgNgOtbeUArEcY7IcrFjlSuquF2fZyBhraQnswvhL++KDBR9CEhDxKVrx+goj+GhF90Tn3udtn/y0R/ZfOuTfQXbrg14nor98K+WXn3EeJ6Lfp7kTEdx93JxoSEf0sEf0qEb2C7k4znHqiYU+HtrKqpLlsYA5Sszgl7BIAcMw6l+w9HVEWsCZS41doX3LvsWIPLJSRAy3tEeqPhjKtSkq+tSufUsHaiu1/GXgdx/GvKL4/69cz97yPiN4X+fwZInp9TQF9uBqA6zmWDIOFMoKxcPank9zzoIPzmRl0A11I6UCoX7n9pdr10Epf4SinprpqKcdoNNU7F4yFKcWxzzTVpRfucbNoj5cWtOafWlj9gsMFfDiXz2N7ja4M88rsVl9gG85xIWdTesdt8Cja0zRrSQXn8F1kSMk1Jeudx3VOPVQfeN2/f59tNodITkmwgR1YQ2pAC4M6q31hZrml94cBcMJtByT0a8a+EwvOPmfGgpa6xvRRS9lGMELvStr8qhyt39Ww6oRL1d/xmsG9e/eyqQoljJwV8g+10KY02DQJQkp0olZn/P6204B5YqHOsAOAGymHccbhMBaCLg40rJiFBwQRwVeRZESgzdl2K/ZD9YGXT0tQM8uA9gaLUsCggRgpnVjpSOhacv1E48RKLSvUAfDQOy5I65HW8dQqs086TQVbPrv6KpL11hBoAwOphjH84EuzAl2lH9aeGsNZLu2yAzqo0ZFd9OlqD5t/lHaLTEakaa68ERrMYRUdWr0/zJ4gq5EvfJV59MicKwVWU7tve7iGT2oGTFNDnaSctNjnI8ukTbHBOGJ6CH14mNSpTrGZWf+71qBmlkMEWwCI2vVglO6Mes8O/UFT0LW6rLWwWj8dDaddMJVqGCPcS+IvYZ/fa1quTi3zziinNtmAMaTSDaAPcWJpMTFik0E1Mg3t2Ei07ksFY6m1AdL6cva5Gc7cin1hdnphCys68avC1Wd8PV2xH5pd8YpxlcoH7LLqLMpoSuS4u6zDSZsaWaSutyBTrHwConq9l9KVmUHX1TYBS4xIXS4tg2U5Wka6D8XS7Vdsay57ZH7FK4e/GmYhCJu16gX2oMRgrKoPtYfxpDZ8l9wb0iLTVdsB2KBkLBrhXK3qwHHhr1L7K/Nh1o//74wyznw/qKdl1dtvXys+9yyWWvFagdEzfBjYQMiKexxK6tJT55I0xNLnz5b9Su0O7DLTebMQLIR2IlVWDemFmuUIHqV0nMpd6/ffldqfwz9aesXLx1IEPmq/hRV5EK3VcWdRI0NL/YWDnrpyyElDOpDPTm0P6pk9OTACzfWzIP9Sp3u3saYEDTLJvb+mbbF/+FG2CbysIZ0imTpgQSPosECSnlm50n5UejBHWCYAUkjrSG4FRQpNeq+pLCdWHNha/0JDoAHKaAn6awIwC5MKvWwVeFns3L6yWiu7FJDDGCz2l1p6JyCkZLSD7EE7ozbLp1jdMdI4U29hsrTk9FdgF79tW8YoS2cu5OjV7+32eFncvxKbDbdU/h5idd2l7tbRnN89smw79Vcgy2zne/Qq2+x+o+V0Qws2pFdGV76ZtnTsHeGSe2m/sqD3Ke7fv5/8brvAi8hm8OVjvfzADrW6Fs6IaYTTibqSTY+jqlV+QJaYTs12/H00lGE0vqM4uv6pyUcNPsCIYEiT7o9EQ31rDtAIPy8h16806Hcrzjm6d+9e8vutUg19rC93hjMGrfdrxnLHO4kd8Vv6o4nSsmpuLz8NZkTQdaJZJkAfvr5w62wPM2ySNjs4kt60vdw4wnUg0KmX3CshsXdYgFtfZ/W5qwlUf+z3dSBM0e3xZbS3ea5vXZV9yxWvlehRTu2BjeayET0qP392iGOWTtNMn4YytCAVFI4cELX3U8CPpr7vo608o5FeaeKwVzHdOcvMPUZJYsnuhQGipbKHcKR6poK1q+ssy60G9YFXLk+yFy3L9eBRLLRLbj8CR9ljq5opg6ZdVqORXIULnRgAuJndn1OpjuAOyaCl5pmxyb/Y/bHVC9gwPrSmgnLApesryOKK0jZXH3jdu3cv2fCrKLYlRsySoV0fpmR2CTIbt9+gZg8bR9us1q7QVZto2NukhXBSbEY5OQKn8BkaVsJWCQa5bH/vMzj6kbQu7DYJoD7wIkrvZ+rd2Oc/Q6uB14b0Urr2ttBcPomyaa7vyShHYbUNwLOAvHSTSqEGjzIrAPNT23onnFIrZle2Tcp3Svl7FpGS0YhxZ9TYVjuZWfosaXreYyLwOsktoQP7aHdiNZcvNzmxMrNnZ0uwOLlzNQNpqS6gn1krOlZI7fWVQNLGx1IRLbWDRrhXrWqe1frekUEvt/6O1NnWd5kKvAAvSH8qx8oAdFXGGqdAc51nzTCHaJVPL6XyHZHeKY1mPZ8NZPMwJfJozQopuX50e1ylHkI3yugJCM57a8e8nqDLclrxTJ0sffeWx8n7SrzCcvYsdpLdKgNMLD0lhmaHa2TQ1XMcro+lHPbwhK4c53Whc5B7NtCNJV0dSa1N5JbjLJt8NWZAV8ro0YdSewzmUdO22wReqRkDKHQ7GKDtknOUNQddJ5zlC+Vw/r8mAFmJ1n5dGtRrwoKuz2KmbCzpUI6aCYmcrDXoqbW+rZFeGZbqkyU4dduKXJZONfQDrdYNpCCPVH45kCe2MXy3/hCrr1T9NcuXO9UjNQBqq7u28mgBTjYfV76GVpsQ4yr1EMSRSKWzemjHzPdJ0NIflgy8UoIIZ7BXaPRVQFvMY+f+sFt9U3A7VKln7KhjFpkddGnUkd6Jk9Y+pq3PjFh10VZnjUjuv1vp1ERpWupgItXQT/0puS6WHjRyZtsKswdXC4QpaKuy457HmrpqthVcbbZjWiVIM1MXVrVDYTpuzWEd2tBaLo2E21s4nxuuTveeYYCgq5zWOphY8QrTBEuu81mlkTnRJBNtg+wpm1BGWlLFuMsQPk9DHVNw6orlWWcfiXJp0XUwHm32eEVK+9fIPhi2eyzddIRdgN2phzNjYUTQtTsmAi8fdMi18IMcLZw6llq+nz0wcJYhtRI8u45EcUdAm67UokW2JUikoFqq/87Mbh+NesJdnll1C8fcq/Eudq902VYlNanLhdaxPESiPBrrmcJEqmEPlhpjFFpk4pfDkkOtQXYc8rpaPZ55elLOKeituwY901CGUiz1TdAH2lkXEn3Pd8Zrx7LR2zVW00cNvsNMJH1PS7JdPvACsqxmGEvro6HeHINyzlj5Kx61pII2f79m7JrcPs2wbK11n5W6E74/JR+NcM/0a60n0OPAaCnHimiVrT/h5zvpsBcghzX9MJdqWIOWlR1NcMpkFfn6ndZafVpTCUrvqU03uwqacvswc9/XPGs2pYGjf234OwCj0ah/GssEHjBqBSP3jquxIxzfNejUyHLUvAtppGNYOvAC68Mx09FysIQWA36Whag+OKp9x9V9PfIoWd3SIOsSamTEuSl6BJbaAdhHU9/XUIZdaJmAu8ow8P+vRadGsVNdLbBs4LVbxyphJZlocFa1GXDpcqTqq6EtJBlx+pgV2fWW09cVTX1nd9AWeWbJR3O7aCtbbbBWO25J1FfykKzYNURzxhptujKbJQMvNPKjSMik93mt92vaoGlNzzgChNhpg7tSs0k9JXtrQQjXyqa11T4ARoN+IUeY6XJ1bWxlraV9YvuaR6Ycjh5rzvpaGuOkWS7wQuM+yioygZOmix3aIdV3ak8FW6UP9hCTAfa3zUf7xnQtExMzHFYNKyypLActk59S7yypN9eq2SjfJgwepfd0xd4JFjvVUINxHknqZLjwGusyKT3pbjVaTxMcgXUHpBfuldHYSuJqaGtD8AC0TRp/7LHcR1vtaE1QwcGVTzOKXHaC///wM98vCz9PyXK0fpW8r6csscBOysZI+wcSbbLcitfqpJa5V1gNSi39j66TFke/ZaY3dy1HvVaY9W19d2tZau7Tons5SvWypi7a6wzmomXVK5Yey1ku/9mcdR0hu9ltM4qYDvj6GY4ZNb7Z6PRrrveVbj/Q0IdrkCrvMoGXtQatocQYj17O5ZZ3+LxV27KWXF55SV46JxpmIUfh62PvwFQTfMzIwW+BM+hquR7wALnnyaXH+v/vmZTxnyPRFiPamPMdFlYWuSZDU9eHq2WlGRM9K5otPljt2KghdVbDs5cIvFYdPFocvlhOsnZmbTZNoWV21SeV9pAjDB446jNaJjPaQEJupVgKvq72vwHAyQy7XDtpUpuWZykg8vHrK+FjWLQfvfp5lc0k7culMqhSe7VaJwpSk5qp+uVWz6SQtjPmA6/VBvmZKXY1cJcttVQ/Cw1l6IVzUJzRzzSkGc54f26lTZO9sxJ0aSyTBiCX8YxMIxvVvhZWqCxQsqqVo3fCzr83tvLFqbs1e8BqA7JeRvQb04HXKgOHlWDrhDunPdbZAQ8cKaiz+tmMGW5NeyVjq18aV2MliOmrdKonAFKMtisjbYT0tgPLcGWZlMikJwC+yvKQSCWtqRd3GWZjNvCy2AijI3cprJUXtLdZjYG0jqag68TabDLn4BxjBz0cAeQoy8x0ZWt7ulZFKlgpee95TwlXz5Vs69ptNNJj9Ci9NnucvLVO76cuhT+WsOQEgjs4ZsFmMTrwmF3fGKEMrAVjnNTWfVc5rYpm3Q8dwxl7NbXKphTJfWMj6F11ar3G17XzJzygI3Z9TD/9CQN/fxlnm7TKSeP43ILJwMtap7RW3hQtg8gqdbdK68Af3jdzMBzxbg1B5hXoS3eU6oOFNp3BCnLR2hdikyQzyjAKyW0HWtu4BKn0y1hAlVoFygVgqQAtDMhiiwTcQVgtkroxqu+YDLysUTIDIfluDmoNSW62z7JB1Y5vOIl498VYd9ZyaK9bbFDduR+VzOgCMJqZQZflgNp3/E+s1qU1kyll02OToH4QdH6We65/be6a2LNj184efyTeP7I+5vZ4WTYuVnO+a+7nMgSgDo37kziR3mc2266U9pvZ5Ywxq0w5O6JNRlrQqD8taO0LRLZ0T8O4EbbjlV+gVb4S+ljyzJK+0OJ7Xj23pw9y+X4a9LcFc4EXGEdLznHrZknQjlXj04JWZ6uX1oFxNhr6rwY5gDFo7v+ay5aiZuUkd38ruT1GqeuvnncF5wS4Lzfu1MLzublr/JWq8LMYvs+Vui6V8pnKgmnJhmq9N/Vuaz6QqcDLomELmVWHls5RktPbWhdrHUUrO8lxhdzuK2pnGWfak7M8wAbWx0/N5ddcthJa7IlGG1A78XtSWxfJupe2QWsgFKZpl6w2hmNvb73DwI7reT3tMnIV3UzgpWFmtZdZdYjthyidSSm5vhbrg5Q2IEsetOhljRM0orxhoCfxXg1yBzrJHR6gpc/OhksOI2WqaRI6lrYcXmNl/1ltKmFpvVIBWs14ldv33xosxf6v3S6YCbyIdCt7yfJt7ntpUkYk9rl00KUB7R2zBC2yHIn1NpvJyL2eQBfW2y63Arw73G1bGnyN3iowIrjM1Ula16T66FUGxRUxeXH5uVKTerE0xNy1YVmkMRF4aR40QmWZ1WlrSZV3RDk1yEJDGThYpR7gUVIOwuiZfqwsgFn0zKLvwKy699qEnq0PkqQO+rCsX6MCi1BuuUn9mFwlAvnSOo+exFYfeN2/f188zaUV6dUhSWIBo9UZnR3RsM9nFjvVV0PQM/v9oA+r7We13KOQdBZzdmeFdilZAYoFClf3tzIiEL1aHCi5t+b6VPDV8rwUnO8YqdPqA6979+79ye+aHE0NZeghNqujYcPoKu+VJDYIrFjP0WiVXyrVA5Ml4Iod05FHM7OPjHxvzj9oCdJqViNGr3aVBFozFwJaUvn8e1vsQm07hO+RktOMrK1ezP4B5XBT4Oh3a2/cWtkcx/UfzhtVFs53am+nVlIrrXC01iQ2WJ6foc1BDos20MIYS2SnnL2c9YxN/vgTtykn2P9ci8yuynRVxlMemu1vrHyle59iz6rBl6nkZH7408JofTQZePUKeQd6FFDCmIyemdtVN7QPBNqxJjvJgHuELKzJ2xKW7aDVcq9Krj1iju8ZhKUCs1Lnf4YO967sSL+vlrBNalbL/PtqCIMuC3Z+ZBlNBl45pIVnQYFqiXXE2Gz6iOXpHlZf5SIqk6cVQwfqSLVr6WRJ7R6C2nuAHiz3f+mZeC5mB7aSdr63bqmViNj/c469dEASBiM99W71j2ZRoz8tY0fLu3pSKHsZ2ZeXC7xGpFyt5Izk5BRLLah99siga6V26QXBVxva5RbOXobf5cpuxaEFPFhrv5YZec191SK1bVDz3NRn/sRR6qf1HSVwBF0tfudI3Q1TRaXfnZrML+FqDFvB11N/uEaMkg17HJ0p9l7rDT6CkbPlsdzzVWlNgdhBNrsRDqB+G/e2OdcMMJiD5TYbmeIFHjBizC7J1EgR2qTYPb0rVb11D1fvQpsce0f4/xLftoaS96bsRcv7OWxPym+xbNdCTAZetcaZaK1Gm0WJDEfLeZc27ZnNW033petiSWahfYulz4TX1j5fUhaWZG0Bq4FIqw5Ab/qYOXFZ0/f9a7lSAf10Ry5SdSp9RyoQkwqCYqme2vqUxrT33jKZSjXsGVR6l1jhHFwDGcnSmwaxCiOcS2sy82f/U7bu6v/nZ6Prbk3W2tlNnlaDTQ56ghBr43XtCmeYqngGbX7wJlF/zpXYWBpmjNS+qtJ3xN7lP7ekPpz6FE4gatFTrjKZWfHialQtDWiB1AxR6tpRsrU2YPSyW31BG+Gs8JVzdZW6E3su0AvaCOQIAxANSNmW2X2Bu149aZo97/LHiNwqj9Tkx6w2lE51NBN4STbAlUBnd2JJUvVK1Tl00nL5zFfvAADwkgq+TlpnRhF86cZy21gu+2xK0/+J+Mfhmnbjsh9Xz9GYlqaZnu0jpemJPWnEs2xDmMLPXQ4zgZckuwZdRI/W78pwpQKsXCfklOHq7RGyW313YfZeyJb3Qw91snOqHbhGqt/OsgdhKl/oJGtB+2RV7559rfXiRColFYFXBs2dhouUESu9dyQ7tIcEkJs+UqvFIX7bpa4P2zc2Wxd7fwvQJZ1YbRPL+qTBsbYqu15qJotnokFHSoiVk6Pcvc+wIr9aTB2uAWQ405IkZ8Z6Z2VHdD5tM8ecG3SBDlIztSUHXYTpg1c59xLtLvFMbf3OEis6JbVwjC+WsV53Dv3V3Ac06ueo7TVcz8gdLGIRBF4JMKDpYee22LXeK+KvQMVO2/I/zw0y/iRJScCmGUtl1YZ1R2SFttfoVFukRYb+4UHaHXNtehLrd9rK6JMb7yyCVMMIKwwIJVipp4UycmOlbUA9XDnzsb0NlvTGUlm1AvndMVsO0OVxxFb6r9KxtVGqL6Prozm1z+IYlwIrXkA1I2c4rHdmoBfJwULzTGUMf6YatAH56WG12XgJuPTVP7E1Rpg9YJFYNgT382fcy4W18S4GAq8ADGi6GNUWmjoydBDUkpup1KRLVmakNaPJVoE7EHylKRnPSpzpGtn6AZg2cvK4Ciw5GLG3SxrrwTUCLw8rSseF9rpa7VQA+INC7nRBTiz0Z2mnQgKNdsiaDGOsUAefWfXRGmBI0JKOrUk2KR/TyoSUpvJpKkstCLxu7BZ0gQdoantNZdHIqCCmBz/ACIOvXdHcXgBwMcs2aV0BqC1P6voe+zlCNqlDjkpOoM19PhKLY5R2XyAFAi+yqXCAB01tr6ksFigxuLONsvSfaihhtgxOrOq2pnKvYiO06ORKaNILfx9nTRpm6Ehz7QeVSgXNlc+3/b1jAPpLGovBl4nAS3qmQpPB0oIGRZbuULll/5jxLznqm7ss4GFOGWmVFdrxYXCQBihBw3jDxUxd1+CEplKKS1ee/Os4J62kDqmoeW6r/wD7uRYmjpOXOOLSSk6tNBY25EsQq3dKJ3I52VfXlZSj9d7d0Xb0raayzAZ6DWrQ1pd7mV2XWf3vqt6pI8G5xtMrrvSstt1ayugHx6vo+2ysydRE4EXEa5hHGsXZBlgL2mQQph7UzqzlDHfNM7TJBYBeJGwe7ChkAMoIA5oROlPr9IYBWLgapl3Pa+Uart5prJ/GMtUQ8+m0YibwIuIJvqQNUdjomhufSK58vpw1OgxSnVNbPXcHEyx3aC9fCguDKKjDqi7WoKV+kisB/iRjz8EX57NmBIyp8nDiHwilRS9WJrafT5vcTQVeRHpTEqw7CNwy1ZqXTbTHwL8isXZDO14zet9HSZuUDIrop8AyWvQ33DPVSmhDOLOPUvvBNMgvRkt2jNa6rEoqlVVDO5g4XCNEwpFoOfY0XBrX0KAgj2ZjDuoo7bMjgg7tOjUq+Cp9x2kvU/snZx8QAMBK9AZcfoA06sCL0RNGRLwn5VqzYdbKe0UYbPl6O3uMMbfidcI9I1KyJK8tauZih2DE+ookKDv0pOQ+bnboPyOBLNcl11diKUKx70EbLT4Tp21rSSeUWPnKPRM6tgezt8GYDbyI2jrl1QwLUT7yX61j7uA0hkfDn1zpD4I1PeygpwDk2EX/V0g91VrWUp9JYuzrmTiTSHMM0brdArRRIn/JPZA5TAdeRHXBV+l1JSteK6B1cODkqlOVBuKry8kSmtpjNZsAdKJJ52ehfd/PySpl1JQpEF7HLePTjoeHgpWUBdhmhp9nco9XiL95FJRhYXDoJbVxt5YZueYn0OmHB0UifQOftvKkkNZjKzYFY8UcOPRjpi0uwUIfKFntGlEHbXIKfYVwT1uJL6FZN3ejti1G2pYlAi+i6zRBCwZxFCXpddaRygsfze46G27q1gRsii3CQwKATTQHX1b0KiXDUTZt9kSmP1nPedqjlfYHcUbZlmUCL6J08MUlyB06leZBrZRVHGLr7dDLavXXUJ8V+nctCLjms4pNzmG9X41so95Ap+X+8z7uk6hX12urtLbLiAy6pQIvogedKZzN6O0c1o0qERXP7lh1zjhmr3KMTGndwVEpQasMrtonpiMz6yI9K6tVXxFwrYnVMUoTvgy19t9WYpPvZ/046ol05bXx4wgJTByuUevE7N4hYif3nb+XYmUj88nIvHTpAM+S3LmxkLahYWN6LdKb07WhdT8g4CN0jKyNWVpYUWZh9pNE/VaS2Yo6wLmnlFs2JgKv2g2N/vVcObwWlDJlZHqXXHueIR2gEI3/49UzTsHZBcgT9LJav1ytUq8GegAAIABJREFUPpxwjXW7YnHlsNQZxuTL3nC1u0TwZTrVMNaxUoa41bhYMEqSexikl1xr4U4h1cbOTpYWHcuxc/tYAO2jD7QJGMnK/gGog/N8B07/xMSKV46SWY2W9DArsyU7DWra2oR7JmSntrSEhfTHWlaphw/6jz4sTKbsziopmtr8g15GnjC5isxCOOvF2U9Mr3idlAiiZeVLszJK7zMKmS2LHWawVq5bCdrqPyuVVZoVneGVnQer7GCzT6B/YwlXIFp1TXO7aco0Ag/gaJPLwMs59z3Ouc865z7vnPuyc+4Xb5//gHPuk8653739+yrvnvc65551zn3VOfdm7/N7zrkv3r57v+uoQUtHqwm+tHZGonkzOzACj8JlHHeXrab6j57UsE6LrDS1t2as6WGYCg6ANK261qOjo4Ii6fegn9bhb73paZeSFa/vENFPHsfxnxDRG4joLc65NxHRe4joU8dxvJaIPnX7PznnXkdETxPRjxLRW4jo7zvnXnp71geI6F1E9Nrbz1taCt3bYUqVWaNzUBpwSpSdQ+HAo+xu/DTpU65/7dxGnHDr++79RwPhvppd2mPnIHO23e5J/dq1zQAPvQHYZeB13PGHt/++7PZzENHbiOhDt88/RERvv/3+NiL6yHEc3zmO42tE9CwRvdE59zgRvfI4js8cdxr/Ye+eYrg6jMVlXA3GomWVsTdY01DvFJrLphF/RlxTTn6pA2XBZlyVkXOVtkX/EXSVYaVuO6UU+miyXyPRFmy22DIuH3IEKXttYSxandbD+4r2eDnnXuqc+xwRvUBEnzyO4zeI6IeO43j+9vLniegHb5c/QUTf8G5/7vbZE7ffw89j73uXc+4Z59wzL774ov+5yGa53PdaqK27prIT6SuPFjQNYJLEZsT9es+aCAnLtQKtg0ENrc/mLNMufUczaAP7lLahxj2vratdXCD4AS06WBR4Hcfx3eM43kBET9Ld6tXrM5fHNPHIfB573weP43jqOI6nHnvsMdEZliuHT0PHqq27hjLvQK9O7uK0aAxsWgMuKyvloW5xldl/To3sfHlbkN9sLNgGC2VM0ZuBodGmSbHryl4J3PYszAjxM0PCz3rbA3aYj1o9qDrV8DiOPyCiT9Pd3qxv3dIH6fbvC7fLniOiV3u3PUlE37x9/mTk8yz3798/311T1CpyQpttbGan8gAZLDstNdTUM+yHEgMDh9OkNXjIpW9e/T/1vJCauodOQuqdLbLcpf9oxrL8W8u+UhASToak0tlWqGsLNXaJI5AnejCZFfvxv+9l53aVokaeJacaPuac+/7b768gop8iot8hoo8R0Ttvl72TiH7t9vvHiOhp59zLnXOvobtDND57S0f8tnPuTe5Oy97h3ZPk3r17QxQk51DMcrLQOfTS2zY7tGvrpEFslo+jLNJBHTe1A/85KJfKrEcGuXTGmCNx9SwL7TEKC3Z/hfZqrYPGtqm1FeFkiG87wms009KGpfamZsKwtiylk1Lhe1oC/3DlLPY9GEvJH1B+nIg+5O5OJnwJEX30OI6PO+c+Q0Qfdc79DBH9GyL6K0REx3F82Tn3USL6bSL6IyJ693Ec370962eJ6FeJ6BVE9InbjxpOxW6ZHdaEBYMJ1qZHB8P7ep2A1L29ZdTaz3xHoLZ8YSphLbEUxxqnonQlTqPcOVi5btrQ3IdrqUn5zV1rSSY9q4+lQUyJLPxrSoMY6S0KqYmvsM61k14rrfjO5DLwOo7jC0T0Y5HP/y0R/fnEPe8jovdFPn+GiHL7w6ajRaGsGD9pNMpBY5lWJjWo9c5c9joZWpyUVBmuArCY/GqDptCBwSpwOxp0qQQr5SxhlXqUUDMJoh2OAMDPDkhNzPn/+velyiAtu5KMhpwdzo0DJangubqDckpWvLZihUFFi0MI9kRK93LBRWs5rAdfpXXMfVcTmMWed+XAlLD7ahfSfYAkravfmvsaR9nCyamSSaTZQceo917V82risySQy123Mgi8gCjajbcGVjJEWtq7phxag6+aFBcpamRI9GiZYrLRoB+a0NJnwHpwpONp002pMllYERzVHqV6czXxWXpPbYq7Rr2sAYHXomgwmrPfLwH3zPRKMtJSl5ZyaOgvMbSUiSswjQ3oWuo4A2t1t1benUFblaFRTiWZBpLv5lxNrL22dPvA7JXHHhB4BWhpRC3l0MKVMZhpqFqwbDRCNNVlVsrWyMDtfI+2gbgm7bFWVpp0jAuNDh/Io3WChkgmc0JjfbnKotGm5Mo0oh0kDkOqpSXLouY+DSDwUow2g1cDd9lzy9M1+di1S9rhs3odx6vyWkOTjvYaYAtOxsjy1cizNuCNyTr1Pm1twsGKdQLjSY2Bq+4Z5Oo3mvpfrg1HTuSdZbDGOZbMaNPWCX8EXorpVSKNjmQLYZoSUXnwEl4TC8BKy3D12VVwuCJa6sUl55ie1d7LPSMr8eyS97aka9Zef74rVbcV7FeI1TpZLTc3s8fVK/skMemppe01lIGL0tUtrT6ERr0YLavW/rdd4KVFUUqxVl5upGYzUkYj9dl5j39/jNj9O7ffSLjk7BvxWr3jGIxyq7TccOlr6Ypz7D7uoCs1UaOB3e056KMk5X5m+pcFNPTBmjJIllWDLDjRHqyevGR2AUajtSGkmFFfqTRDbgfUD+rCz05qO3DJLD7gQzLFoFXvQh0qJdTF8HfpAVjimSUr07F+eH7e+s7zfk1pvZbtgeWyr0CJnZvVRpbSGjXosYYycKG1HiMmLa/enWO7wIuIHnKMtRuNXsXWXr9SJIMv3/ELdcN33Erf3ep4g3Ji7SPBqODLr0sYMEjWkeO5YT1bVwpTAVhNObQ6AprLBuoZaeNH2LketJYrhfax2Zqt0CpPf0ypJfQHa++5um+7VEMfbcuS1jrcaCQHu6tUqZbnaTVIlpnRV6XSXYn02B4uemXk2+Ta52iVoXW7br38VqmxDWijMjTISEMZTjjKommvV4xc2nnKRwu3G5S+x3/u/fv3k9duFXilDFlqVnu0ImlV3FqkVyBGdvIeR7CX3DuvDMaKzAxSWvTgSlcRdKWJTVxoTLO6Qmu5SsHk0Xha7IImHbOu87MZKTuutvLttca2D8tUWtbWuhzHQU899VTy+20CrxIFm6kwq22KlTS+M2ZYat/lpyzG7k85lbG0rdh1V+XRbARb0DKYp1YyU2XLrXxqqA8Xpe3Ts6ncmk5r0dleVqiDBJxy8XW7ZazR1EYtZTnrMKIumu0Id/1zYw/3u2ZOUtcyu3xLB16p5UVtSCkqx3M1G6lRtMoxDKZy+liSx1+T7maxvXLBqKb6xMqSKqMVG9TLiLqlglht+rGKzdQm1xVZRVdCanTHv5bb5sfsr9ZxhZMrvVq57hZYJvCKOW07KhaXoxc7WEKTPEeVpaXe4T0lq1QlM1BaUmKluApcNHO1Eia1R4wLreUK0V7OVRxpK/1uNj19WrM9GEVKBlxBV8l4qoVefajx/aS3g4A8SwRelg1Yq2N/EgZGnHsscv/fAY6gK/V57LqS4MuyrreQS9XTRklgrbH9RjmPXCvwWm3TKkHXySr1kMS3TzXy4t4PaRFJW6jRzl7BMVE+Gy3l0I754+R3bOgzwIqtqgA+JPKf/f+nVkhSgYaVAIQbK8HXVRlTK5ZWqdnXJW2nZ8rUr98KdnjHMbWHs91PPbiCU75abclV/ST2MpXKXyOt5dY6mQfymA68rCucpfKHRs2qgZOktj1rAorVZtNXoqZtNLVfTx+uvZdzdr/mcBNp0C/BSZiab2E/4ihC30FCBn4AbEnGreXVONljZZJ0NuZSDXtO/9GElHJKGB2thkxTubhTR/znaarnLLTWv6VttDgH/t7DmtUr/96S67nrGQ7uM+S4asClQS9XIGW7JfqCBVpTMkuQlrEkvUGXVrSXbzZmAq8VBzptdUkFg9rKqY0eI5O6DzLXT2+7WxucNNngq/2SEswO9oA9LPZzTqQnyneUrfYVJax6XaM+8Dr/+vNKnUujseAuk/SAo6Vjj6ijNl0BPO1uxSnz9XBWiuHVO6TkuFOwZUEXLSKtn9rbTLLe2uueQ2LCFthA/R6ve/fuzS4CK9KGUnLZOnRCRgQ/WlfhRmxqXe0whpWYrX/cpA4HCIN/rfXmskf+Jn3fudNaby4sOPDgUbS3GbefsNJhEiumGJ5g1SuP+hWvldCoiDUdecZeFm2rAqNXoUbv89Ia6K7KaP3OvcsPvrT1OwlCXd8hwIqxejvPZoR8NWdHcO7v0lg/AGpB4DUYScMx0jkf6aRIbswtYZd0o5z+aB7YrTNSplfvSq18taLNqd+lL5eAPr0G2vpYjJrDeHrut4imU1q5sKCTM0HgNQitithbpqt6cc2cj1r5iT1fQ7uN2DNX0o5w1vgZJdMS/QlXu7TarRoQbD0K+vEacPbPq8mWnvdclXMnfSwJtGLyWMEWW0Nq7EDgtTGtHdm/p9c5yzl4qYEgN0DUOJcxtBo2DWmqqzji2ghT/GaXJfy/hnK1gIDrUXZycDWgcU93jFK94F6d2Ukfa+rKnXkA0uR0WirwReA1gFGOiyXjFaYrxtIJWweB0tWbkndoYtZqF5BnxIpubbqP378spQAh4Ipj0eaBNL2TjCGlAUH4/FZ2Gnda+t4usplNTf/g0lkEXsLsMGPRopAcK05+AOXfG34ee77v6Na+dzQzUwy1P381pCYFSlN9/HLUPDtkVptb6M8zQD8cD7fMfd0ufXbJNSN0Ixaw7aaPu9V3VTh0V/1x8iswYu9GCxwpArPxV83CPUixn9T9WuoTQ0J/RqW2hQEuuIZbJ2v2V7SmHsf6mb9yJpk2Ez4bDs7D7OjkzkYi6JIYq3rLqX3s1MBu/W/l+pb2wavvEXgZp1XJRxrLUZ2w1XE875V2EDnhSPFodbJb3r2qIZaEy6lJyd4Pvv1ruNoqF5CFP7X49+YmVnZnZSdoF8I2tN6eO+nkTnVdnXByLzV+lbQ5Ug2N0ptS02sMYukOOQdqdCrDrGeMorWsXI48UrpsM7P9Yu+s1ctcqjKcHaRczmRG6l4LXOVEnwM7ktrvWNIPEHgJI5UmJvXsGsL3l8ysS+K/o3dVZsWBJNSbnjoixWQMEk7Nlf2Yofvc71u1/6YG+5NwDysYz6i9mbU63rOP84rcZNxqK3alaLJBqVRsqZTY1SipV029EXgNgFMZYyeNaVf0WDAkUWbulRgr8r0iVQ+OVU9gi1V0OseqkwKj7CiwTyzIGrkKVxqErYgmG3tVFmSvzAGBlyGuZr+I5p1iVPK8EsPsf19bRs56rTRg5MrNVS/L8lkJjpm5FVKHVqhDihXrBB6lRn9jEw0z9WTHFS4iPUFXzT7u0u0isetj71wNiXoh8BpAaj9UrVHNXZ/qPFepKbPIzd76p6GF3/mEqYWSnT4s2yoGhstBXdnRtQRXkL1Ce65QB2CLWfqmdZwfOUbPRItfMGLvvxbdsgwCrwG0nHoSUnP91QzGbONw4huJ0mDRv2fGrNqIAMxqKpGlsmojZROwItkHgi9giVNXS1euNev2Lg66hjYYGfjNrutIpNoWgdcgenJpexpe40xYTA65QeQqMJtB7X6LUrmn0gMkDasGee5OTxtcDQ4aHIPZQAZAml4d8++3rqu79LdZ9dz1wJKRSLYtAq8BaOogs2fJcu8uCb5myy8kFyT513Ck8p3P1iYDIENNsJ57hmTaiQXCiRsANLJKf6tZtbPMzPqtLFctSMoYf0BZGH+/UmtDanIanOv7o6c7cAZaHAFX7Nm7yHF3Tv3x+1tN26/u+NSAfgOk0TS+z9L3VNC1mi1arT7gYaT7Dla8hNHWOWsNRphKx7Vyk/s+llYHQ/eAHWYTOdGy8bmF2Opp6X2S6YtW6bF/RPmTWVeVGShj9Em8MecwHK9HpeiH+7BX7geYwFmbEfqLwEsQy0vuM53VWMBlVY5SQB5lhKdqEekMwHxbcdI60cHhGGiUUS/+6mHpfszcdeF3mCgCNbTqiD8mnv8/maGTObuq2eb2sFp9wB2j7DYCL0G4GjCVNpA6OKPGWQjvvbpuJLENpHBoHgB55EnptDaZcW6q53B0tMmHk5izmqNVFqvKD/QTBk69lD4nlU3SytWK74p2JJbyLbXHFoxnVFsh8BKGq+PFHIbQ8JUGVbHnajMOMFhlQEZxWg9xGV0eqf0dIE9NyiZsESihREc4dKnn/pJVstay+HZ15XS8KzseysT/HnYEECHwEkVqwK7d52Gts185zQDkKOl3o4OvcLZZYiDmqA+CjIcpTU8Ee1OqH1wrTVzBW0+AdDXRu1p/6fFLYEeAD041NERNp125g4eDxcqza6COGr33B8MROhTuVdQWdIE4kCvIYXn86VmdSqXdob/E8e0/2BsEXoJwGaCdOmrLqWMwZqCVczAcpUMSARJXuRG8pYGNATFGZ5Vo7KMtWxx2BrYEIPBSTrjxvqTDajTORLyHjYS/a6wvGEuv3ksOiFJpJqum9QCgHa3jbC01WTQSz90RBF97gz1eiokZdsvGTPI42/A0pSssyxE8CtcgJrH3a8YxzgAAUErpvtjSa2ueuyszDnkCOsCKl0Jys+McQcWMmRbJlMBwr46fPuangUinleWeaX12K5bPrwF/fxbniqqV9D3OZ8MBAKCcXftLTb13lVEN2sZUIA9WvJhodbBK/j4XN6ON4YhZndJThUqulSiD5QEolu6qoT4aypE6Rlha16TSFmfLUzOadB/siaQOcq5koY+UUTJJDlmuBwIvJmo7R84xy6XOzUqBanUkJR3E2mdzHKF7VaaV8OUUBl8zad3QLhEMjeinISvqGgCWWK0P1qzyr1Z3zUDOdqjpFwi8BlPi/F3t68o1cO1x2qWEQUvJvVx/VygsQ28AsNrGVu49bbE21iCv3hPEanS4V15SwOkBYG8k94vGPg/HgVFjPwBWqNV3BF6D4HTOUqs9I1P6Wp393EpVuI+ox8keycxBpmXVModmmXPu4SJKy67GueAsV+n7gH7geK6LhrblLEPKlyBqm3BdFaQaAy4QeA1glNFq3WPWch/XSpb/nJEO7I7Gc8c652hd+Rz9t3vOdxKhDWdT4nztal+APa6CLtgdAPK02HsEXsJIGS7rhtB6+U/gZNkmt/Ia/n5eP+NwmvPdYD5Xq/Zop7XR0L4cZbj6MyzQ5UexuuplscwWaJUrAq8BaD7ZbPSs1mwDMPv93KxUFw2E+qFFvlrKAR7Quv8F2GW1Ng73UGOSZz1GHda2Gz22AIGXIFaMtIUyasRK+3KwSz21MVLH0Mb1tJ6wCsAsUsEVV9C1el+w3t8xWdRP735rBF5CQKGBNKN1bHWd1lY/beUBd2ClC1jk6rRaifTFVdEcfMXS4wE/PXJ9CWM5tgOnjNWzSpphyQZ7SbQafatok+eM8sCepXHOJVcEWg9pAdecctcgX8t9MnYgEPchQdpsqDRnv9egmyd+m5a0rcY6aIdDz7HiJcBuBqiUHeSSS9ewnlttscwWmTUIon0fpSb9agf7NoqrdLiT1f+Ug9TKCvde1l11X8tx+z3v94OvHdtwBgi8Orhyrq2ADsfDlRy50pPQXrxokufsARw8oKYtsOr1KD3ySMm8Z19SrDxX96X+nlUKiX7LGXz5z+G0e7vbq5kBGNcha1zPAtcg8GrgqnON3AyPjjKHmANQ0w49bYb25kNj/9FWnh1p0Ytd7XGqziPkUBoQ+demSD2jZmWIe0WOOx0wnCDgCBR21PkUqQCsRLd638mBrx8IxuRA4FWIr4wpJYSC6kVj22gs0xUWy5xDU11Wk61Vetoh5risjBadne3ApgKu3vRyLn2K7Uv06dV58DC5FbBw1TF230zCfV8ayqQFLnuHwKsALYNLjFE54Nqep4nWulmcIbdU1is0yV5TWXaGO20n/GwloLPl2S89jmzvykNJemHrWAQdyFO7R1FbIDb7/auyfeAVOxrYp3SZf4X8fl8WCLrKQdog6GH1/mEFiXaYufdDipXq0kPMd8g50r0rCS3BEfchGrEygTin7EuypU5yvugopPYBWodTFlsHXmHHILLpREulI3CwaseF8wE4WLV/WEO6HXyne2Z7txwwEbt/d52NtWPtPrdUGlru/py/0tu2FjMwNBK2K0facs9zSonpI3TiDm4ZbBt4+UHX7PxwLsKOen42g5UH6JXrtgsaBpMVVslXQEoXUo65tO7lNvLH3ltiz2Dz7uiVQ07+Jd/nrtm9bTTAbdNjgbX0BFHss531S6Lu2wZeJ6spU+teI060DdKcxlBb3YBNoEc6GBEE5VYtuN5dk84UEtuTFn6/q56GQZF0gB4Lwkr2AXGVa3cnmwMp2Y22U7sjOUZvGXhxr3blnjNKoTV0Gs3OJFc7cz0LzEVDG2oow+7MTg/vsSncaUjQxwfMsvUlqYNoJ52sHLzsFpBL13XLwOtEY0BUO5hqCQa0lEOK1eu3GzMHkZ0GMJDGX2mqWZXv3TuyI6X7nzTZ+XBldNQR3xIrsjtgVVal49EuwdeIOm4deGmktMFDgzyLHQz0DsYGjAG6tBelDg2QoWb/2tV1M0jtC5Qed0v3I8Ke3WFVDrXlXj34GlU3BF5M1BjC2BGz/v/962L591rSDVbugCc71BGMAbq0J2j3OdTM5Ftj1EEtPe+B3q/J6sHXCLYMvDiW0ms3M+euT93vf65NyVfueD0b1QGIsXJ/AUADGicnuUg5u7HPJU42bgnAVpJ/Dqu2vafcKwZfI+uzZeBF1J6i5zdOTVrgiJzsUUqzWofz2SF1cndG6y+Ojd+bFZ0UTexis6+CL///Ppy6t7qMd4FDJ1aya6PrsW3gVUto3GvzYldhlY4WssvgDcYCvQJEazkpWtitb+UmcHIy4Na9EXpspW2tTapZkevqvGR2AWZRYzzOa3dX1lUdB7QvkAR6BU6sOWra2alv9dR19iFctZzjseYyWwpizhMxuf0c7W1Uwgy/FiteBUgu3Vth5TpjNnovkJILZrCCk0Jky+Fcjd6xiuMEx5HtzvmuVN9reYeVPjDqzw+MetcqbBt4QTnK2cGBXDW4XqUe1oDcQYyr/Tia0eRYoX/VE5OXVV1sIVW/Ul2SOLREktF9ZNRJmyuwZeAFxShnBVmlBpyRefGzWKUeloC8QY7wBDoLuqKpnJrKMgNOmx47DbF2//oK7RHWPbY6Fsrq6pqdyekSJnA2Dbx60aAwI1jBoJ60HDXsz06vIgcgywqpZGAc2m2MtrKtNCb1IBHwtKaMWW2PVLC1wt9/m9FPUnvIUr6XNtsyEgReIMpqDqTf2Ws6/ArL56vMSmpn54EEtKNt75e2v2OorTyrE1v1WUXuuTpp64etjBrrSyezaz8fweyxGoHXInA41ysa2pDW1ArtM9NXWCyzJSzrBpjP7MmRMLiZ4YD69UewdY20zsTGyvBzC9SUvWSitUTms/ryrH1dPcwKFGeCwGshSldnUrnJs5VxNOfAtUJqAZgP9ANYI5UeNFKXY5MW6Eu66N0LNoOSfVstz6yZqNUuox446yYVFGkKtnwQeC1IrNNrVcDZrJJeUMrqg8EMIFPAwQxnLTern/u+F6xo8TBTZzT6FJJlqpXz6CwZq+NQbE9Yaz1a9vLPAIHXooRBl1YFBMAy6FtgJUY5iugzfMxaXdHiY5QGW71yarlvhT3iowknw0vkJmm3JNruJaxPA6qoSaMDe7DbCp8k6FuAm1n7QkauQMH+8DPbrku9/9RN/8f/7nw3t95y94HZ7WONU/4rBl1Em6547ZB/ayUPWwM76APgBfoCrIMxYi00jGNSR9yH70h9V/K8WTIaeRjKLlgMuog2Dbx2YceO2ApkBUqZ7dyAtZHWLwRcgJvUiZTc77D03BjSMlqJ0rMKrAVdRBsHXisrPBzDenaSmYaZ0VFw13MXuYF5jDjQYhboP+sRtinn4RsSh2WEh17MWv0iQn+4omW/lwW2DbwAAOvDZag1OK0AtKBFd3tPK5tdfgtoOhkztVLB9fweNOiSNZ2emaIZw3La5raBlzWlB7LstAq0KlLtp8VxBaCGFfQ29XfGgC2stN/INF8rMiHS5y+POKhEss6Xpxo6577HOfdZ59znnXNfds794u3zX3DO/b5z7nO3n7d697zXOfesc+6rzrk3e5/fc8598fbd+93gY178k3E0KRE3K9cN8AAdKWMF5xXshT/GadHbsx/VDvlayg9AKbmTGDX1SYuMTDuUPImyZMXrO0T0k8dx/KFz7mVE9K+cc5+4ffd3j+P42/7FzrnXEdHTRPSjRPRniOifO+f+7HEc3yWiDxDRu4joXxPRrxPRW4joEyTIivmhOVYPKiXZbdVrp7q2gKALzKCkX8au0aqvYbmu6ge71Mdu45g2whTLEe0g+R4tuuRP3owqj1RfulzxOu74w9t/X3b7yZXibUT0keM4vnMcx9eI6FkieqNz7nEieuVxHJ857mrxYSJ6e1/x04SzfhoUBwBgA20rBgD4+EGMxhWuk5py7ZCNMgrJ2XpQjvV20NgfR5dHog2L/oCyc+6lzrnPEdELRPTJ4zh+4/bVzznnvuCc+xXn3Ktunz1BRN/wbn/u9tkTt9/Dz2Pve5dz7hnn3DMvvvhiRXUeRpvCAP1YN5Q1oH88Cpw/YAE/hUljwEVU57RpDRwts9NYphnL7aChP4bpmjPgbsOiwOs4ju8ex/EGInqS7lavXk93aYM/QkRvIKLnieiXbpfHSndkPo+974PHcTx1HMdTjz32WEkRHwKOEwBlzDZomtCaqgXAiebVrZDcaXe+3cF4LYdlp38lLLaDhvKm/lTBDDjbsCjw8l78B0T0aSJ6y3Ec37oFZH9MRL9MRG+8XfYcEb3au+1JIvrm7fMnI58DoAqLRhK0Y8mZBXuS0lGt+lpjP7XWAQBOTr/Cim8xs1/6ctI0NnP5hiWnGj67dxI7AAAgAElEQVTmnPv+2++vIKKfIqLfue3ZOvlpIvrS7fePEdHTzrmXO+deQ0SvJaLPHsfxPBF92zn3pttphu8gol/rrgEAIIkVI89Bi2HGKhfQTMmkgLY+XlIeTG6NI7bCaCkAGMWIlVepAIK7P83UjR1OgSw51fBxIvqQc+6ldBeoffQ4jo875/6Rc+4NdJcu+HUi+utERMdxfNk591Ei+m0i+iMievftREMiop8lol8lolfQ3WmGIicarthQpexcd05ihqz0VC6gi1hwhYDLPiu2Ycssr6b6wzbqwm+PFfsLkGGWjljQUY6TDi8Dr+M4vkBEPxb5/K9l7nkfEb0v8vkzRPT6yjKCQjDo8bLCsbxXZZ9dt1GzjP77Yp8DW1gYoHuwVq+W9rBWR2v4thWyBtqx7mvVULXHywJYPgdcWDcEpX0hdp10P5rhOGs/BQ6UoSnnn4Mw7ctKvTiOssd4zQ9OZgXWsKivPbarJNUQgK24ciQsGIheQyZVx9kBF7CNxQE6hVW9tFruHUDbrEvO9q1kF7VzyhqBFwCdYDXkAVJGfOTgYHEFwRK5QUdKd6SePZpcXbTWT6I/YbVLBq06tCrS41qJ7eN4P4K3PJxjEAKvRUCnaWMlh+5E22rXiNSw1CzUSu2qgZL+wt2nVrBtJYGL1npKlktjfa2iVX+s0Co7iUO3ZkwEz9QdzXqbGs9ED9cAYCU4jirVPMD1lk1q8JAiPCY5dZAG6Ke0PUvboEQvNPe1FDhJ7hqL7aoZ2DoeevSS6zAu9A09SLUFAq8FQEfNg9OdyrGwShFzZlNpW+gbPLQGED0rYxbbLjcRYBGJOqwgFy0gpXoddp2k0WoPaspVW4flTjUEIERjp5ag1YBxBluSg0d4WldJCuMubc+Ff1Idx6l1Oc5npk7VtNZ2vu7XrkBoqy9OxtNHGNQTPehDNe2E1bE0vYcmtN6/a9CllVrbF2Z5XOkAAi/jYHAcj1Z596YY9iC5j6sl4ArvB3nCACv8AXlCO+wHlf5AXKKLJQO3FJKBNjcz5TSDqxV+wANX8FX6DCv9bSd698hf3Y9UQ7AkI04a0mgoR5dLeoUrnElqXdFrube1blp1I4aGmdZY+1iT3/l7agN2qZxnBlxE4+TO8T4rOgLs0ZumXrPXGHq8H0sFXpYcHg52q28Nu8pl1L6mUY7aTB2PDZ6+fFMnKIblrU1Byw3anLLQZD8s7ccL99X07lWbvU9ndNDFMZkCgDRcNgn6DUKWCbx2SjkA89HsKNbOstcw8qRCzndwzmCmAqyre1tW8FJBXM09qfu06a6k3nIQlqtmH1RO/mFq0ior1iVotqMAQD+BBGYCLyzXPgyMgQ40t0PoyPqfxa4tmZm3FnD5zB5EU2loPc+IcRVkaZ+kqtHbWlJ1L9H70sCwZZUytboqQXgww2xm90sAcmgYG8FamAi8oNRAI72bcEdR4nSXpEKNcARBPzWraLPT3HKUrPaVlvmqnrHVrNL7w+84UpMkThXcoZ/BV7jGkny0tGc41nNMooF9MRF45dDSMaVocZTBOKRna3PPbnlvzfXcjlpqr9NIXUa/eZirtERN8ipdYaq5N/Z9Tu/9gKikf7TOlEsFX5q5Gut2ksXOxOzPqBT33PNHrkrvwq592mTgpS1VQoqYUu6qqNqRapcVNtuHAdesvSxw4MpIreholVmrc9YyERDb6ycF54p6bH/h+Q4N1KR6AhuUTBrH0nJTExHhPTP1ITWWYGwBJZgLvHZQbA2GBZQzI+VQsh9IBlyzAklNfcla/x4569zDVTlTKYHa9IObWP1m90lpVq0XJ1IyqlkJbskYaQ3CribfWmRhZbuBJrRmVIzEROBVmtaxAtypZWAM0imH0nAOHloCrpOU0yldplwaWe57rVgpd2oGOlVmzXWRxIrTWNNf/bbWrqcljJ5g63lfjY/GtXJcY5OkVqut69godvHhS3jJ7AKMwE9v8n/XRMlJWVBY3YxyZDh1wQ+MOMofm1WcmS45470agk1JrDjsRPMH+Z73c+hQyb4VC22542qEtA2JyaY3CJrV32b3c5Bml61BNZhY8TppbbTYTIeW5U6ODdrgUWbKzEJ7SQZFIx2G1H6BktlP7nLWPM+CjuSwpDezbH3tLHzqGSXX9bDK6hCoo3fFCzoDUpQEWzvrj6nAi7OhWvOEuSh9587KaRELs6wSAYePtL5qTBmDw6ITy7KOpUyW2BaplKqZWG7HFmbUt2evlSZ20xVNIJ2wDBOphtIdKbYKJknJasPq6UorIxl8jcrBL3mOn7ZrMY2AM13zqi/HfgcPw50+y/nMljKUrmaV4K9KXf20oHnCqMfmaa5XCmvlBcCntK9a7JtcqA+87t+/P2Tw5NrjckXNgGzNkdXEbNlpMioS+hQ6gSea6l1CT1lbJ0dm66ZGOPYWnlicBCihZkW19fn+HsXZ7Dj5OGtf7Cpy1qC3IRrLJMUKOjQC9amG9+7dG/o+yXz30qALygs4aAm4cvq/4oRAz+ph7b3o22l65QK58jA7BX/me2cz0z6sJGttdfEzqrSVbTa77i9VH3jNYIYy7DrYrAy3HpU+izOl0F/Bgm7eseNAIYlVefr9onZWW7LO3PugR7WP5Mo8SAMZjQEyTrObDiLwSjDDad5J8UawW2eWSFNZWYZ+YCm9qXxlOe5GLEBYuW1H1o37XeH+ba3tNHOiV6tMarFkY7Xr40h2XBFE4JWBK/gq2Xy/i8LtxojVU8wUA6tY0zNr5e1llfr6+9dWqA8HK8rBSvtaCzZyK/oWyq8NBF4X9DrOCLp0sorcueoRHk6wE6MO7wEPY6kPYobaPlZWv0awYt0tBtfayzmyr+y030v9qYZakDiZZhclG4l/KldOtqPTZ7j1h3Mf14ppJwBwAUd9LcIAbDbwA/g4xzAtbWuZESds7tpOWPEqoLUj5wwqjC0vvc6RlfbgDLg4nhN7rgU5jgTyeBQrAy63PiOI00Es+NqtTVa21TutnkgwSnbhO3ZpNwRehdQqxE5Hcs+ES57aO7z2gOt8tlb5xbBU1pUIT8vcBdh+ffhtYc1+9aJ9zAPj0WKjVtdLpBpWUOosIOiSx08n5Dw+WcoZ7Fk15dAbCXlZZzfHXxs7BV/oe/rx9wgB+2i3L9rKpslGaSiDJFjxquRqlijVmTiDrtVnA2KEcpWqvya5+u3cGrSdSNdrR51ciZF/q8mKnnAeXGOlzrPQIqPRzvrseq++6rV6/biAjMaCwKuBq84cfj7674GtQBio7lDnk96Zp9H7FnbRyVUZGXT5/2rWGQRdY2fkNclolLOuRTdWD05WrRewCwKvRmLGKma8rBg0LeXUtNx9Mtoxjb2r5P07bxRvATIaR842arE9JxJ/osEqo9pFmw4Q1WUc1Oz9Pq/XVt+V0TjZg7LE0VQWKRB4deA7DZaDLqJ5yh4GCxrlxeUc5vTk/D7G1btnBlyWdBw8ysyZfY16wx10aayjVjTakpoU95JAW1v9TvwUS61l7GHFOnGhqd9pKoskCLw6uXKmJd53Ra3yjl7RIbKXQsg5Mx/OpPamFFqRoUZWN/Qt+1E5301El7quxenj0AUN9bCIloMQriaxarYYWENLG+yC9NhTMiG7+vinFQReneRWvLgVWqqTjOp4ljs4Z1pU7YpXrCw110uxg9FesY4j0nlr5Xb2Levy5phQ2ZUZaaepvcS7BiDQ37FIZKvk/IOd985rAoFXB2Gnkd6zUPrMGgd+1EqX9Q4u1b7hHsHUZ6nvZ7FCmxJdy1LjHqQaYno7K7Xwipkz7txtrGUVzxqjgy60TRzrds8CXFtTarZroD11gMCrE1+RLSj16M29NcZEu6MiHVynAq7z/1rlAmwwIuDKvaek38xc9eAmXD1B/9UD7Ok1CL5068mKdmWluuTAH1BuJNUhR+bt1nzvp/GMUu5SWcwoWyu9s/Kl94cBvXa5rAz2Plxj3QmQLPfZf63pkNW2vKJnjPZTYqUoffZZDv+n5Tk5LOotN7H6S+vAFVb8pRp20jMzK16aZh40laWEGeUteadVZ23EfhSrsgH7UaOr2vR5pG3ECsJcuGxqz0pmSfuHwU4qHR16JE9qz18qO6V1/7aUXYC90YmZwEsLV6eEjUrfI0rnCIflmJE3n5PRiWWDMCL4AsAKnPtKc99z9jf03Wusy0gyUEk55VfX117bmt7OuQ/Zuh70UrpvKhWgXQVnpZPUJeXJvX/nNtSEicBLS6fXsLRMpNvpT6Vf5r63im/UOOul3Uhq1DtpVqkvt25pnKm9uld7/wI8jLJTtXuYpd8TvhN6Po7c/taT0iCK02+S8lW4kZjE0Ij6wOv+/fuqhDxrtSt8dtiRYoHZjJmqWMfR1H4S+KkhvXW14BRqLhtIIzFBMFoXOGfxwRqknDVtbVy6QhZb7ZoNVr3itIzXYfumAjEJWWtvR87MCc2oD7y0MLOhU0FV+H3q8xHlO7HcGXpI5eHXYCHosm7wdmaVdpNcDQP2kHJQpZiRLgj44bAlaN82tAeQV6gPvO7duze7CGqCrhhXS9u9ZS+ZnbOq/FK0pIIi6AKWGL3CP+peAE5WsHfWy6+VFXRDEy3y9DONzv9bQX3gNZvSjY8jGz0V7cfKUDMzEFvytqTMpYxor9AohN+F5Yl9romdjnoFeWY7HS2r+1r6mPWZWmALTl2Dzt6B/qsHjWm5JSDwUk5uRSumaKmAzHc8UvuwrChtL6PqWRrsapa7FocVtKFdv1KUrPSHKdi5Z11dA0DICkGy5bIDUIMlXUfglUG70a0ZGPxrNNdpJ7Tr14mFMoKHkVqh7N1fVQqXzmkMujgP4wFgJFbGLEl2r78EK9v7GC+ZXQCt1KTnaUi78f8PrpmdOjdbb67Q+ucKQB7Jv1ukaTN5TVk06vDZPrPtUA70/3zKuBWsl18TkCU/nDK1Yq8QeEWwOODEjiUFaWa2r2b98gMurWUEaUr2fc6gRedz9szCpBhYA+gQAHawMFmCwCvA4mBtrbzaGNlJtRsEIugTeJRZdvGcCFh1YsmCkwDACnDYkBXGxhXqYB0EXh1oC9I0lcUSUs6PNYdKmz7Pxlr7STFDL/xVV/8HAFAH+s0d3PuILGK57DVon9BC4OUBx3NfRnRU35nUZhSg+0ALHH3Dkj5rtAdgLbj0C7pqG2020ZKd5sRU4IUOHwdy4YF7UAn/xoTkuwCI0atjIwfGcH8h+gcAQBvWAoVd7ajmMQTHyd+odTC0ROpaygGuSf19tdntp6EMQBecx/Km9Cs8jVXrwSAj0GILfDSVBYBeOJ1wbX01h5VySuG3lZYTwE0FXrsrUAzIhBcJByj3vNl/08fSADKaXeUiqRO1A1/raYhW205T2TWVBbSDdnwAlxw0TpTE0FzGEWULA65YFtIM+ZgKvKTQrJxgPKON6vm+0XoIvU+zq2w46x2bYa599k5toCk1Zlf9B9fk9HQnnZk9aVqC1nKNJiaHXFAmDQIvACJwBV+lz/CNwAgDAMdqD2akT3MNZKusdtWs8s2eSdfuSII6pHQp9UzN+iMlBy2TJaCN0b4XEQKvZQZ3sAYjHC/orw5mtLPvGMUOfJF4ZythGUsnMDTpd0tZZtVBk9yATTQHIqODUJBGo60ZuYK5deCl1UAAHfQ6QK33ShkAzbOROzIy0Im1PbceSAymqcDRCtb62tmGsBWgFW2THyPYrb6rUnIIB8eE5ZaBF44sBtL0GmLufV8YGNbkyskZ5UCP0K9wMLSi01ZWvcKgHGOjfWb1D0v9E4zFgl7kgq3Yd7UThFsGXj05nZqURlNZVmWmfLlyj6En9VhK9cw9a5X9grF3zNwcXYPWcoG1gd0HgIeSMTbMFsixZeC1CqOcqtHv1Mbsv/HWmvqj3SHVivRx6miPOq5kZiUAswRWu8BqwC7MZ+XxL1zlfeqpp5LXvmRUobSxsgL0cqa4ET34w6ZIzSxDSj6x3OOSckDHx1DaLqPaYwX7VptqCxvVjy/zUJaQK6hBk/2R1F3tthb9dgw1eoAVrwq0d7BeSpx15G5fw5Ui2PpctM94dpS3ZHDf82zYqDZS6Zz+55jQscPMNtql/1moJw7LkaVFttsGXlDAOCVy2dGxKalv6KBIySclfxhXcCK9R03yHRx9J1ytsdgnRpc59b7QccMMOsihzTeQ1FdN9cwxwi+pKccK9IyD26YagkepUaCdBuASucw4gcxPCUXQBTjI9ekwDU3q/VzPliznjpyy3Mn2W2Vm+2jUD9iBOyAHHnrHwa0DL23GYSYcsthVnqGzOCoI8/dhwMkEJ639sERvLZ32eKLREbxCW3lhW0ANmvRlxOp/7zNSP2BNtk01rGX2Eq1Gdkw5DMkdcz0K5HDzgcEujnQ/392OALASO/RnrrFip/3aluvDWfatV7xAPxZnk7nQZERqTz0EabS0qSSxmdWUDq0QdO1spyTpnZnHzD6wyKiJznBLARhHanzkYOvAS7ODZamj7bjypbG+CL76kTyhbxRXQUbsT0Wk7teo560g+OIldex86tpUsA/nkpeV+iwH3Ho1OrvEt9HW+4glvfT3tPpw9C+kGioEhlMfVo5T1nJ6EdBJ7emc0jpkVU8ly21BJqXlK7WXOKF1PSzocS2z9dLyJLfVckuw9YqXNmZ1anSIMmYb3Rowu68DTX1LU1lmob3+VvtsaG96T8DU3k7gGi1tyFUOLYdYWV0hni03TSDwUoJlp8hy2UuxFHSdIPiag8a/H1XTR0eVeQe70YJ1mXA5qLBfbWiQm4YyEPFNZGizVaUpvlqwUk6fVJtzyB2BVwErbC7PocmgaMRi0HVidXZsBj3HsGsLtlr/vMEoPZlp87T2hdnjAADgYfyVW434WwskxvkRJzdqY0SbI/DKID1Aa3DotTohWtDQRr2ssjFXKxrST2Jodxpmgr7Aj+X9J4AXDXrAVYbZ9SjBPyiJKwDT0IajKV2t7131wuEaHuGs9YijQmchsQS/WkddIeg6WbWNuFhNLpp1d7asOfoCd/lny4SLFeoAAJHdPukHX63lt1r3HrjqXOJbI/DyWE3RYgoQm5VsVbjwvpVmPFepR8hKbcSB5gClFc3tq61s2soD6kD7AfAoPSsyu/Wp2j2pJfI5joOeeuqp5PcIvJQRdpbUMbux70Ji38eUpsUZX7lzrlw3IgRfRGsGXBzspBu9K1+cctpB3pz4/Tc1wbgzs+s/+/272DBudpNbS33P63sWMRB4KSLWaFeBWC2p+8MBrCcFx2r64U7O+E4OdsiIdp4lW473SumGVn3rqS9nnbTKRxOxMap0gnEnZh9es7PsrbLjvldOPa3RewReSsgdXTkKrkMYfEfGigFeJeiqPTZ810FyxzrPRPugzjXRBGSotc+7t8uu9SZC4NcCJpDaaK0nAq8CVlei1F6tHrgHPsnOvErQRVRfh90dFEuU9EnNqW/aday1H6APyRAbl2rwxzG0zTgg633ZzQ6e9a2tNwKvQqRSbzQwe6WtBMmgS1tdRwMHRTelbROzJxr0W0MZSugJoBB8ycFx+uRubWM51VkL1vt0OK6n/E2uzCTLsmoFK14GKDk4o7Uhc51qFtoNl+ayjeZsq9VlomWyo5ZexxOUwRF8nb8DsCMrjSHafZgUsa0eqTpw1M+ijLhoqTsCL2FqTyFMNWJsQC95duwajkM0rLJz3a9YdXY41Qcs1LF2zx7op0fe1vRLK2efhRxtAf0fy+ixLXUAnKW25i5vy7MQeAnSelRlbGUsFnCVPPvqPksnvPU+B0FXGavM3FuvQ42+Wxv8RlMrn155tsyUW51dlwJysIVvb1fSZc116d0m0mKj/Pu0ykU7CLwE6D3Rrzag6nlubPai9fml74y9r/T6Fka8YxSjDJ3Vmctw8J8FUjf04Muy1L5xyD6cRCu162h7uXFgF7mOrmcoW3/FckZ5wDU9E3u5PWPa0aCLCLyYsW7cRzvcNcvWLeVZbWZmdB2srH5pKuPVgFQyGTFydWYEWjb7j17tb5nYWsleaSHmKK4u41H1i70npfex78Lvc9fNYrU+WRs0jbab0sxuSwReDIxOYRthBGLvqOlwPRvUw/flylRSBi2pjpJIlu2Uocb6ax0EcuXJlVmjjHvRqj/hKpj/WQmtqeSl92qT1yqEJ72lZNyiF1rabNRqRGl9rwIx7bbQ6upOjJ4xU1ObWAaBVwG5TjdLEUcFX7H/lxghrlQdn5ZZGk4ZaTY40vqgLfVQa8BVIp+ULDkmKzSjddZ4xmzulSwsp/JoxJ+EI3q0zXM6UNIOpcHcCGa//wore1fD4HCF/tgjT+16ZYmXlF7onHupc+63nHMfv/3/B5xzn3TO/e7t31d5177XOfesc+6rzrk3e5/fc8598fbd+50hTdY4IzPLGJwzVLkfyfeWDoQS7aNZZUeuts7kbNeSVSXN+CtBs23JCHznV2v7+DZGesLtqi9pl5UlzrYK7UY4psRsS66d/L7b49D6P72MSi+sLWvtpEbJinDsp5XYc/x21TD2tbLLGGOF4sCLiH6eiL7i/f89RPSp4zheS0Sfuv2fnHOvI6KniehHiegtRPT3nXMvvd3zASJ6FxG99vbzlq7SDyClrFqUeMfB+arOkrPWlo0vFzNlsFqKlvRkhRZizi5RmwPX+t7a+2J2hru8JaujsDljqLUbqXGmZXLQ/2nVsVF+QFjmUmr7YjhRE/7kJn6v5BAL1EZNII+Gyx9aRR4aKAq8nHNPEtFfJKJ/4H38NiL60O33DxHR273PP3Icx3eO4/gaET1LRG90zj1ORK88juMzx10Lfti7xxyalHAlI1FKKg1kRKoQHCG9MrAUdAFddjRGzBmeYW+19rdVuGrTmPxz94SraKmgKHZ/72SBJDPsK1dWTSpoW5kR/pBFZsujdMXr7xHR3ySiP/Y++6HjOJ4nIrr9+4O3z58gom941z13++yJ2+/h54/gnHuXc+4Z59wzL774YmER+bHqxO0yQMfSRM7PLWKt3WY4g1fphTVpLBJlh4N8R8rJrPlcExocNQtyAg/w2yuV0lhybynQjYfJrZCtDkcKbPg8wMdl4OWc+0tE9MJxHPcLnxlroSPz+aMfHscHj+N46jiOpx577LHC1/JiNegi2m+APus70qhKyLin7DPbW+LdsWdK77kBPLS2xU42qwfIaQ5h2lvpPWGardX2G21jW9+VSh+cWaZRcAdcK6Kh/5WcavgTRPSXnXNvJaLvIaJXOuf+MRF9yzn3+HEcz9/SCF+4Xf8cEb3au/9JIvrm7fMnI5+rYwUnznr5S7G+ysXFrPr7zghnGVLPSr1nZvtrMOSa8CdCfHI64qcO796Xc1h23FeiR0ev7q0d00bt67KAbz+kyqzRRsEPssXlitdxHO89juPJ4zh+mO4OzfgXx3H8VSL6GBG983bZO4no126/f4yInnbOvdw59xq6O0Tjs7d0xG87597k7rTkHd49arBiYACMjRZGDHThLJ4/6zx7tY9j/wG4IxdwgzsQfM1lRP+ufYek3bEwzkpM/pW8TwOj9rWvgoa69Pwdr/+eiD7qnPsZIvo3RPRXiIiO4/iyc+6jRPTbRPRHRPTu4zi+e7vnZ4noV4noFUT0iduPOjQ0DMijYTCAnjzAX/lKfV/LVRunVlZGoXHmUws9bSMl15Xaa7bu74pGeUvqgoZxNseM8oWTf7OzLaTfP7uOLaTKrKUuVYHXcRyfJqJP337/t0T05xPXvY+I3hf5/Bkien1tIUehpVFAHrTTAzTJwi9HWK7WAfIqNW0WmuS+GhKOJNoLWCFc0SzRW6k+U/r+0Wgo2+yVZ9i0NH7bhHssNVDzd7yWBkqsHw0zTKCMcAVM2wrGrHt3ouUwgvBekAYyWhc/bbnmIA9NBz5xENan9vAIzf2jp2wYg64J+48mXUDgRboaZEdK5K9hhivGbN3RJg+fcHCscQxKBpYeR0Oz3EJm61gLYdDdIm8EFmVARmtTM4Fhya7V0uoDaOwfmPgbxykrTTJD4HVDU6PsxpWDpTXoIoJzWIsGeWGmUQaOYCuES1+u9iBaBbq4B+HBPX4gFlsV6kWbnatZ5fKRrsPo8WxGu2jSg1XYPvDSZmB2JWXANAdds7HuRF61t1T9oEs8hE6f1Mlq3M9cLQDTMJkBxuIHYqtOKpy0+Gj+PRr7B8agvdk68ELQpRsrQZdGw64Zro3gqQM8gCy1+yxOelKEevuYX94VnVXYoL3ReIAAF5pWzjloLQf81XXYMvDCIQ06iRlHK200o5xWZMNBLJ0mrP8IZ3onmceoDbhaHZ5c+3LBEYBpceaIdDmXAPTS46PF7tPQP3rev/vYsxLqA6/79++zdhYrqyi7g8B4baQGwdqTwGqYMWjPdhROeierauqRe09LoHRVbk0rYL1l0OBcgjlwtb2GsVdDGbhp3ad23gvWQX3gde/ePZYO2JoeA8bClYa2AysY49YVzpITDy0THlQxswy9drM2GC5pW4kAo+W53G3DEQQi+AKWkRz/Z9pSnGIITtQHXjlKB5fZDgwoB0ZmH3KObklAtppzGTsVcHRf0FCGUqRWqTQELr3BpZYVPDAOiX2zoxnx/tF9YlSmALDDvze7AD2UrI7MNiSgHLRVHavIyncwazaJj6y/hG6GdZasT0y+sUFdsgwSq9nns3KTay3v07LynuobpfcSwa7uRHggjRVWnRzn6HuryQQYD7yI4gOkVeOzM3AO9qbEgV6JXn0vvb8kIBnZ96QCmpj9732XtuCrtSw9wRuwQ9i2LfoyQj9i2QzQS7ATJgOvVBoSBhebaHBugH5m6Unte3PXc+0zrbm/ZOVwtO20so9DU/BFlE89yqXjYowEV0jq+Y6T4RrsBtCJycArNrOT+g7oBsYJ+LSuwqQczPOzcBXEv5YTv9zSM7ucadYjV79i+4802wAtwVdpWXJy7V09A3bQ0r476prWOvHO53MAAAXWSURBVGst12447Y3gnPs2EX11djkAERH9aSL6v2cXAvwJaA89oC10gfbQA9pCF2gPXaA99MDZFv/hcRyPxb6wsOL11eM4nppdCEDknHsGbaEHtIce0Ba6QHvoAW2hC7SHLtAeehjVFqaPkwcAAAAAAAAACyDwAgAAAAAAAABhLAReH5xdAPAnoC10gfbQA9pCF2gPPaAtdIH20AXaQw9D2kL94RoAAAAAAAAAYB0LK14AAAAAAAAAYBoEXgAAAAAAAAAgjNrAyzn3FufcV51zzzrn3jO7PLvgnPu6c+6LzrnPOeeeuX32A865Tzrnfvf276u86997a6OvOufePK/k9nHO/Ypz7gXn3Je8z6pl75y7d2vDZ51z73fhX/IFRSTa4xecc79/6x+fc8691fsO7SGEc+7Vzrl/6Zz7inPuy865n799jv4xmExboG9MwDn3Pc65zzrnPn9rj1+8fY6+MYFMe6B/TMI591Ln3G855z5++//cvnEch7ofInopEf0eEf1HRPSniOjzRPS62eXa4YeIvk5Efzr47H8kovfcfn8PEf0Pt99fd2ublxPRa25t9tLZdbD6Q0R/joh+nIi+1CN7IvosEf2nROSI6BNE9Bdm183iT6I9foGI/pvItWgP2bZ4nIh+/Pb79xHR/3mTOfqHnrZA35jTHo6Ivvf2+8uI6DeI6E3oG+raA/1jXpv8V0T0PxPRx2//n9o3tK54vZGInj2O4/86juP/JaKPENHbJpdpZ95GRB+6/f4hInq79/lHjuP4znEcXyOiZ+mu7UADx3H870T0/wQfV8neOfc4Eb3yOI7PHHfW4sPePaCCRHukQHsIchzH88dx/Obt928T0VeI6AlC/xhOpi1SoC0EOe74w9t/X3b7OQh9YwqZ9kiB9hDEOfckEf1FIvoH3sdT+4bWwOsJIvqG9//nKG/YAR8HEf1vzrn7zrl33T77oeM4nie6G3SJ6Advn6Od5KmV/RO338PPAR8/55z7wi0V8UxRQHsMwjn3w0T0Y3Q3k4z+MZGgLYjQN6ZwS6X6HBG9QESfPI4DfWMiifYgQv+Ywd8jor9JRH/sfTa1b2gNvGK5kzj3fgw/cRzHjxPRXyCidzvn/lzmWrTTPFKyR5vI8gEi+hEiegMRPU9Ev3T7HO0xAOfc9xLRPyWiv3Ecx7/LXRr5DO3BSKQt0DcmcRzHd4/jeAMRPUl3M/Svz1yO9hAm0R7oH4Nxzv0lInrhOI77pbdEPmNvC62B13NE9Grv/08S0TcnlWUrjuP45u3fF4jof6W71MFv3ZZa6fbvC7fL0U7y1Mr+udvv4eeAgeM4vnUbVP+YiH6ZHqTWoj2Ecc69jO4c/X9yHMc/u32M/jGBWFugb8znOI4/IKJPE9FbCH1jOn57oH9M4SeI6C87575Od1uWftI5949pct/QGnj9H0T0Wufca5xzf4qIniaij00u0/I45/5959z3nb8T0X9BRF+iO9m/83bZO4no126/f4yInnbOvdw59xoiei3dbUAEfFTJ/rZs/m3n3Jtup+68w7sHdHIa6xs/TXf9gwjtIcpNdv+QiL5yHMff8b5C/xhMqi3QN+bgnHvMOff9t99fQUQ/RUS/Q+gbU0i1B/rHeI7jeO9xHE8ex/HDdBdH/IvjOP4qze4bradySP8Q0Vvp7rSk3yOivzW7PDv80N0pkp+//Xz5lDsR/QdE9Cki+t3bvz/g3fO3bm30VcKJO73y/1/oLgXh/6O7GZafaZE9ET1Fd0b994jofyIiN7tuFn8S7fGPiOiLRPSFm5F+HO0xpC3+M7pL7fgCEX3u9vNW9A9VbYG+Mac9/mMi+q2b3L9ERP/d7XP0DV3tgf4xt13+c3pwquHUvuFuDwQAAAAAAAAAIITWVEMAAAAAAAAAWAYEXgAAAAAAAAAgDAIvAAAAAAAAABAGgRcAAAAAAAAACIPACwAAAAAAAACEQeAFAAAAAAAAAMIg8AIAAAAAAAAAYf5/hMVCipP6yOkAAAAASUVORK5CYII=\n",
"text/plain": [
"<Figure size 1080x1080 with 1 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
},
{
"data": {
"text/plain": [
"<matplotlib.axes._subplots.AxesSubplot at 0x7f64d938b550>"
]
},
"execution_count": 10,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"def _open(filename, factor=1):\n",
" with rasterio.open(filename) as dataset:\n",
" height = int(dataset.height / factor)\n",
" width = int(dataset.width / factor)\n",
" data = dataset.read(\n",
" out_shape=(dataset.count, height, width)\n",
" )\n",
" return data\n",
"\n",
"def open_bool(filename, factor=1):\n",
" data = _open(filename, factor=factor)\n",
" return data[0,:,:]\n",
"\n",
"def get_figsize(factor):\n",
" return tuple(2 * [int(25/factor)])\n",
"\n",
"\n",
"\n",
"factor = 1\n",
"figsize = (15, 15)\n",
"\n",
"roads = open_bool(filename, factor=factor)\n",
"fig = plt.figure(figsize=figsize)\n",
"show(roads, title=\"roads\", cmap=\"binary\")"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Convert Roads to Vector Features\n",
"\n",
"### GDAL Command-Line Interface (CLI)\n",
"\n",
"GDAL provides a python script that can be run via the CLI. It is quite easy to run and fast, though it doesn't allow for some of the convenient pixel-space filtering and processing that rasterio provides and we will use later on."
]
},
{
"cell_type": "code",
"execution_count": 11,
"metadata": {},
"outputs": [],
"source": [
"gdal_output_filename = os.path.join('data', 'test_gdal.shp')"
]
},
{
"cell_type": "code",
"execution_count": 12,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Creating output data/test_gdal.shp of format ESRI Shapefile.\n",
"0...10...20...30...40...50...60...70...80...90...100 - done.\n"
]
}
],
"source": [
"!gdal_polygonize.py $filename $gdal_output_filename"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Rasterio - no filtering\n",
"\n",
"In this section we use rasterio to convert the binary roads raster into a vector dataset. The vectors are written to disk as a shapefile. The shapefile can be imported into geospatial programs such as QGIS or ArcGIS for visualization and further processing.\n",
"\n",
"This is basic conversion to vector shapes. No filtering based on size (useful for removing small 1 or 2 pixel road segments), smoothing to remove pixel edges, or conversion to the road centerlines is performed here. These additional 'features' will be provided in sections below this one in the future."
]
},
{
"cell_type": "code",
"execution_count": 13,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"wrote 1507 geometries to data/test.shp\n"
]
}
],
"source": [
"def roads_as_vectors(filename): \n",
" with rasterio.open(filename) as dataset:\n",
" roads = dataset.read(1)\n",
" road_mask = roads == 255 # mask non-road pixels\n",
"\n",
" # transforms roads features to image crs\n",
" road_shapes = rfeatures.shapes(roads, mask=road_mask, connectivity=8, transform=dataset.transform)\n",
" road_geometries = (r for r, _ in road_shapes)\n",
" \n",
" crs = dataset.crs\n",
" return (road_geometries, crs)\n",
"\n",
"def save_as_shapefile(output_filename, geometries, crs):\n",
" driver='ESRI Shapefile'\n",
" schema = {'geometry': 'Polygon', 'properties': []}\n",
" with fiona.open(output_filename, mode='w', driver=driver, schema=schema, crs=crs) as c:\n",
" count = 0\n",
" for g in geometries:\n",
" count += 1;\n",
" c.write({'geometry': g, 'properties': {}})\n",
" print('wrote {} geometries to {}'.format(count, output_filename))\n",
"\n",
" \n",
"road_geometries, crs = roads_as_vectors(filename)\n",
"output_filename = os.path.join('data', 'test.shp')\n",
"save_as_shapefile(output_filename, road_geometries, crs)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Rasterio - Filtering and Simplifying\n",
"\n",
"In this section, we use `shapely` to filter the road vectors by size and simplify them so we don't have a million pixel edges."
]
},
{
"cell_type": "code",
"execution_count": 14,
"metadata": {},
"outputs": [],
"source": [
"def roads_as_vectors_with_filtering(filename, min_pixel_size=5): \n",
" with rasterio.open(filename) as dataset:\n",
" roads = dataset.read(1)\n",
" road_mask = roads == 255 # mask non-road pixels\n",
"\n",
" # we skip transform on vectorization so we can perform filtering in pixel space\n",
" road_shapes = rfeatures.shapes(roads, mask=road_mask, connectivity=8)\n",
" road_geometries = (r for r, _ in road_shapes)\n",
" geo_shapes = (sshape(g) for g in road_geometries)\n",
"\n",
" # filter to shapes bigger than min_pixel_size\n",
" geo_shapes = (s for s in geo_shapes if s.area > min_pixel_size)\n",
" \n",
" # simplify so we don't have a million pixel edge points\n",
" tolerance = 1 #1.5\n",
" geo_shapes = (g.simplify(tolerance, preserve_topology=False)\n",
" for g in geo_shapes)\n",
"\n",
" # apply image transform \n",
" # rasterio transform: (a, b, c, d, e, f, 0, 0, 1), c and f are offsets\n",
" # shapely: a b d e c/xoff f/yoff\n",
" d = dataset.transform\n",
" shapely_transform = [d[0], d[1], d[3], d[4], d[2], d[5]]\n",
" proj_shapes = (shapely.affinity.affine_transform(g, shapely_transform)\n",
" for g in geo_shapes)\n",
" \n",
" road_geometries = (shapely.geometry.mapping(s) for s in proj_shapes)\n",
" \n",
" crs = dataset.crs\n",
" return (road_geometries, crs)"
]
},
{
"cell_type": "code",
"execution_count": 15,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"wrote 838 geometries to data/test_filt.shp\n"
]
}
],
"source": [
"road_geometries_filt, crs = roads_as_vectors_with_filtering(filename)\n",
"output_filename = os.path.join('data', 'test_filt.shp')\n",
"save_as_shapefile(output_filename, road_geometries_filt, crs)"
]
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.7.6"
}
},
"nbformat": 4,
"nbformat_minor": 4
}
| apache-2.0 |
atulsingh0/MachineLearning | ML_UoW/Course00_MLFoundation/PhillyCrime2.ipynb | 1 | 6274 | {
"cells": [
{
"cell_type": "code",
"execution_count": 1,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"\n",
"ACTION REQUIRED: Dependencies libstdc++-6.dll and libgcc_s_seh-1.dll not found.\n",
"\n",
"1. Ensure user account has write permission to C:\\tools\\Anaconda3\\envs\\gl-env\\lib\\site-packages\\graphlab\n",
"2. Run graphlab.get_dependencies() to download and install them.\n",
"3. Restart Python and import graphlab again.\n",
"\n",
"By running the above function, you agree to the following licenses.\n",
"\n",
"* libstdc++: https://gcc.gnu.org/onlinedocs/libstdc++/manual/license.html\n",
"* xz: http://git.tukaani.org/?p=xz.git;a=blob;f=COPYING\n",
" \n"
]
}
],
"source": [
"#importing the graphlab\n",
"import graphlab as gl"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {
"collapsed": false
},
"outputs": [
{
"ename": "AttributeError",
"evalue": "'module' object has no attribute 'SFrame'",
"output_type": "error",
"traceback": [
"\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
"\u001b[1;31mAttributeError\u001b[0m Traceback (most recent call last)",
"\u001b[1;32m<ipython-input-2-dc9ac8af02ff>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m()\u001b[0m\n\u001b[0;32m 1\u001b[0m \u001b[1;31m#importing the input data into graphlab SFrame dataset\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m----> 2\u001b[1;33m \u001b[0mcrimeData\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mgl\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mSFrame\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mread_csv\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m\"Philadelphia_Crime_Rate_noNA.csv\"\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 3\u001b[0m \u001b[0mcrimeData\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
"\u001b[1;31mAttributeError\u001b[0m: 'module' object has no attribute 'SFrame'"
]
}
],
"source": [
"#importing the input data into graphlab SFrame dataset\n",
"crimeData = gl.SFrame.read_csv(\"Philadelphia_Crime_Rate_noNA.csv\")\n",
"crimeData"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"# setting plot canvas to this IPython notebook\n",
"gl.canvas.set_target('ipynb')\n",
"\n",
"# plotting scatter plot \n",
"crimeData.show(view=\"Scatter Plot\", x=\"CrimeRate\", y=\"HousePrice\")"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"# Calculating Linear Regression model\n",
"\n",
"crimeData_model = gl.linear_regression.create(crimeData, target='HousePrice', features=['CrimeRate'],validation_set=None,verbose=False)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"crimeData_model.coefficients"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"#importing matplotlib library for plotting\n",
"\n",
"import matplotlib.pyplot as plt\n",
"%matplotlib inline"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"plt.plot(crimeData['CrimeRate'],crimeData['HousePrice'],'.',\n",
" crimeData['CrimeRate'],crimeData_model.predict(crimeData),'-')"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"# Tryting the same linear model withput high influencial point\n",
"\n",
"crimeData_woHI = crimeData[crimeData['HousePrice'] != 96200]"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"crimeData_woHI_model = gl.linear_regression.create(crimeData_woHI, target='HousePrice', features=['CrimeRate'],validation_set=None,verbose=False)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"# getting linear model coefficients\n",
"crimeData_woHI_model.get('coefficients')"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"plt.plot(crimeData_woHI['CrimeRate'],crimeData_woHI['HousePrice'],'.',\n",
" crimeData_woHI['CrimeRate'],crimeData_woHI_model.predict(crimeData_woHI),'-')"
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"\n",
"By running this function, you agree to the following licenses.\n",
"\n",
"* libstdc++: https://gcc.gnu.org/onlinedocs/libstdc++/manual/license.html\n",
"* xz: http://git.tukaani.org/?p=xz.git;a=blob;f=COPYING\n",
" \n",
"Downloading xz.\n",
"Extracting xz.\n",
"Downloading gcc-libs.\n",
"Extracting gcc-libs.\n",
"Copying gcc-libs into the installation directory.\n"
]
}
],
"source": [
"gl.get_dependencies()"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": []
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 2",
"language": "python",
"name": "python2"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 2
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython2",
"version": "2.7.11"
}
},
"nbformat": 4,
"nbformat_minor": 0
}
| gpl-3.0 |
radu941208/DeepLearning | Sequence_Models/Emojify+-+v2.ipynb | 1 | 69323 | {
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Emojify! \n",
"\n",
"Welcome to the second assignment of Week 2. You are going to use word vector representations to build an Emojifier. \n",
"\n",
"Have you ever wanted to make your text messages more expressive? Your emojifier app will help you do that. So rather than writing \"Congratulations on the promotion! Lets get coffee and talk. Love you!\" the emojifier can automatically turn this into \"Congratulations on the promotion! 👍 Lets get coffee and talk. ☕️ Love you! ❤️\"\n",
"\n",
"You will implement a model which inputs a sentence (such as \"Let's go see the baseball game tonight!\") and finds the most appropriate emoji to be used with this sentence (⚾️). In many emoji interfaces, you need to remember that ❤️ is the \"heart\" symbol rather than the \"love\" symbol. But using word vectors, you'll see that even if your training set explicitly relates only a few words to a particular emoji, your algorithm will be able to generalize and associate words in the test set to the same emoji even if those words don't even appear in the training set. This allows you to build an accurate classifier mapping from sentences to emojis, even using a small training set. \n",
"\n",
"In this exercise, you'll start with a baseline model (Emojifier-V1) using word embeddings, then build a more sophisticated model (Emojifier-V2) that further incorporates an LSTM. \n",
"\n",
"Lets get started! Run the following cell to load the package you are going to use. "
]
},
{
"cell_type": "code",
"execution_count": 1,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"import numpy as np\n",
"from emo_utils import *\n",
"import emoji\n",
"import matplotlib.pyplot as plt\n",
"\n",
"%matplotlib inline"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## 1 - Baseline model: Emojifier-V1\n",
"\n",
"### 1.1 - Dataset EMOJISET\n",
"\n",
"Let's start by building a simple baseline classifier. \n",
"\n",
"You have a tiny dataset (X, Y) where:\n",
"- X contains 127 sentences (strings)\n",
"- Y contains a integer label between 0 and 4 corresponding to an emoji for each sentence\n",
"\n",
"<img src=\"images/data_set.png\" style=\"width:700px;height:300px;\">\n",
"<caption><center> **Figure 1**: EMOJISET - a classification problem with 5 classes. A few examples of sentences are given here. </center></caption>\n",
"\n",
"Let's load the dataset using the code below. We split the dataset between training (127 examples) and testing (56 examples)."
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"X_train, Y_train = read_csv('data/train_emoji.csv')\n",
"X_test, Y_test = read_csv('data/tesss.csv')"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"maxLen = len(max(X_train, key=len).split())"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Run the following cell to print sentences from X_train and corresponding labels from Y_train. Change `index` to see different examples. Because of the font the iPython notebook uses, the heart emoji may be colored black rather than red."
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"You are incredibly intelligent and talented 😄\n"
]
}
],
"source": [
"index = 59\n",
"print(X_train[index], label_to_emoji(Y_train[index]))"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### 1.2 - Overview of the Emojifier-V1\n",
"\n",
"In this part, you are going to implement a baseline model called \"Emojifier-v1\". \n",
"\n",
"<center>\n",
"<img src=\"images/image_1.png\" style=\"width:900px;height:300px;\">\n",
"<caption><center> **Figure 2**: Baseline model (Emojifier-V1).</center></caption>\n",
"</center>\n",
"\n",
"The input of the model is a string corresponding to a sentence (e.g. \"I love you). In the code, the output will be a probability vector of shape (1,5), that you then pass in an argmax layer to extract the index of the most likely emoji output."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"To get our labels into a format suitable for training a softmax classifier, lets convert $Y$ from its current shape current shape $(m, 1)$ into a \"one-hot representation\" $(m, 5)$, where each row is a one-hot vector giving the label of one example, You can do so using this next code snipper. Here, `Y_oh` stands for \"Y-one-hot\" in the variable names `Y_oh_train` and `Y_oh_test`: \n"
]
},
{
"cell_type": "code",
"execution_count": 9,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"Y_oh_train = convert_to_one_hot(Y_train, C = 5)\n",
"Y_oh_test = convert_to_one_hot(Y_test, C = 5)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Let's see what `convert_to_one_hot()` did. Feel free to change `index` to print out different values. "
]
},
{
"cell_type": "code",
"execution_count": 11,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"2 is converted into one hot [ 0. 0. 1. 0. 0.]\n"
]
}
],
"source": [
"index = 59\n",
"print(Y_train[index], \"is converted into one hot\", Y_oh_train[index])"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"All the data is now ready to be fed into the Emojify-V1 model. Let's implement the model!"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### 1.3 - Implementing Emojifier-V1\n",
"\n",
"As shown in Figure (2), the first step is to convert an input sentence into the word vector representation, which then get averaged together. Similar to the previous exercise, we will use pretrained 50-dimensional GloVe embeddings. Run the following cell to load the `word_to_vec_map`, which contains all the vector representations."
]
},
{
"cell_type": "code",
"execution_count": 12,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"word_to_index, index_to_word, word_to_vec_map = read_glove_vecs('data/glove.6B.50d.txt')"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"You've loaded:\n",
"- `word_to_index`: dictionary mapping from words to their indices in the vocabulary (400,001 words, with the valid indices ranging from 0 to 400,000)\n",
"- `index_to_word`: dictionary mapping from indices to their corresponding words in the vocabulary\n",
"- `word_to_vec_map`: dictionary mapping words to their GloVe vector representation.\n",
"\n",
"Run the following cell to check if it works."
]
},
{
"cell_type": "code",
"execution_count": 13,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"the index of cucumber in the vocabulary is 113317\n",
"the 289846th word in the vocabulary is potatos\n"
]
}
],
"source": [
"word = \"cucumber\"\n",
"index = 289846\n",
"print(\"the index of\", word, \"in the vocabulary is\", word_to_index[word])\n",
"print(\"the\", str(index) + \"th word in the vocabulary is\", index_to_word[index])"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"**Exercise**: Implement `sentence_to_avg()`. You will need to carry out two steps:\n",
"1. Convert every sentence to lower-case, then split the sentence into a list of words. `X.lower()` and `X.split()` might be useful. \n",
"2. For each word in the sentence, access its GloVe representation. Then, average all these values."
]
},
{
"cell_type": "code",
"execution_count": 16,
"metadata": {},
"outputs": [],
"source": [
"# GRADED FUNCTION: sentence_to_avg\n",
"\n",
"def sentence_to_avg(sentence, word_to_vec_map):\n",
" \"\"\"\n",
" Converts a sentence (string) into a list of words (strings). Extracts the GloVe representation of each word\n",
" and averages its value into a single vector encoding the meaning of the sentence.\n",
" \n",
" Arguments:\n",
" sentence -- string, one training example from X\n",
" word_to_vec_map -- dictionary mapping every word in a vocabulary into its 50-dimensional vector representation\n",
" \n",
" Returns:\n",
" avg -- average vector encoding information about the sentence, numpy-array of shape (50,)\n",
" \"\"\"\n",
" \n",
" ### START CODE HERE ###\n",
" # Step 1: Split sentence into list of lower case words (≈ 1 line)\n",
" words = sentence.lower().split()\n",
"\n",
" # Initialize the average word vector, should have the same shape as your word vectors.\n",
" avg = np.zeros((50,))\n",
" \n",
" # Step 2: average the word vectors. You can loop over the words in the list \"words\".\n",
" for w in words:\n",
" avg += word_to_vec_map[w]\n",
"\n",
" avg = avg/len(words)\n",
" \n",
" ### END CODE HERE ###\n",
" \n",
" return avg"
]
},
{
"cell_type": "code",
"execution_count": 17,
"metadata": {
"scrolled": true
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"avg = [-0.008005 0.56370833 -0.50427333 0.258865 0.55131103 0.03104983\n",
" -0.21013718 0.16893933 -0.09590267 0.141784 -0.15708967 0.18525867\n",
" 0.6495785 0.38371117 0.21102167 0.11301667 0.02613967 0.26037767\n",
" 0.05820667 -0.01578167 -0.12078833 -0.02471267 0.4128455 0.5152061\n",
" 0.38756167 -0.898661 -0.535145 0.33501167 0.68806933 -0.2156265\n",
" 1.797155 0.10476933 -0.36775333 0.750785 0.10282583 0.348925\n",
" -0.27262833 0.66768 -0.10706167 -0.283635 0.59580117 0.28747333\n",
" -0.3366635 0.23393817 0.34349183 0.178405 0.1166155 -0.076433\n",
" 0.1445417 0.09808667]\n"
]
}
],
"source": [
"avg = sentence_to_avg(\"Morrocan couscous is my favorite dish\", word_to_vec_map)\n",
"print(\"avg = \", avg)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"**Expected Output**:\n",
"\n",
"<table>\n",
" <tr>\n",
" <td>\n",
" **avg= **\n",
" </td>\n",
" <td>\n",
" [-0.008005 0.56370833 -0.50427333 0.258865 0.55131103 0.03104983\n",
" -0.21013718 0.16893933 -0.09590267 0.141784 -0.15708967 0.18525867\n",
" 0.6495785 0.38371117 0.21102167 0.11301667 0.02613967 0.26037767\n",
" 0.05820667 -0.01578167 -0.12078833 -0.02471267 0.4128455 0.5152061\n",
" 0.38756167 -0.898661 -0.535145 0.33501167 0.68806933 -0.2156265\n",
" 1.797155 0.10476933 -0.36775333 0.750785 0.10282583 0.348925\n",
" -0.27262833 0.66768 -0.10706167 -0.283635 0.59580117 0.28747333\n",
" -0.3366635 0.23393817 0.34349183 0.178405 0.1166155 -0.076433\n",
" 0.1445417 0.09808667]\n",
" </td>\n",
" </tr>\n",
"</table>"
]
},
{
"cell_type": "markdown",
"metadata": {
"collapsed": true
},
"source": [
"#### Model\n",
"\n",
"You now have all the pieces to finish implementing the `model()` function. After using `sentence_to_avg()` you need to pass the average through forward propagation, compute the cost, and then backpropagate to update the softmax's parameters. \n",
"\n",
"**Exercise**: Implement the `model()` function described in Figure (2). Assuming here that $Yoh$ (\"Y one hot\") is the one-hot encoding of the output labels, the equations you need to implement in the forward pass and to compute the cross-entropy cost are:\n",
"$$ z^{(i)} = W . avg^{(i)} + b$$\n",
"$$ a^{(i)} = softmax(z^{(i)})$$\n",
"$$ \\mathcal{L}^{(i)} = - \\sum_{k = 0}^{n_y - 1} Yoh^{(i)}_k * log(a^{(i)}_k)$$\n",
"\n",
"It is possible to come up with a more efficient vectorized implementation. But since we are using a for-loop to convert the sentences one at a time into the avg^{(i)} representation anyway, let's not bother this time. \n",
"\n",
"We provided you a function `softmax()`."
]
},
{
"cell_type": "code",
"execution_count": 24,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"# GRADED FUNCTION: model\n",
"\n",
"def model(X, Y, word_to_vec_map, learning_rate = 0.01, num_iterations = 400):\n",
" \"\"\"\n",
" Model to train word vector representations in numpy.\n",
" \n",
" Arguments:\n",
" X -- input data, numpy array of sentences as strings, of shape (m, 1)\n",
" Y -- labels, numpy array of integers between 0 and 7, numpy-array of shape (m, 1)\n",
" word_to_vec_map -- dictionary mapping every word in a vocabulary into its 50-dimensional vector representation\n",
" learning_rate -- learning_rate for the stochastic gradient descent algorithm\n",
" num_iterations -- number of iterations\n",
" \n",
" Returns:\n",
" pred -- vector of predictions, numpy-array of shape (m, 1)\n",
" W -- weight matrix of the softmax layer, of shape (n_y, n_h)\n",
" b -- bias of the softmax layer, of shape (n_y,)\n",
" \"\"\"\n",
" \n",
" np.random.seed(1)\n",
"\n",
" # Define number of training examples\n",
" m = Y.shape[0] # number of training examples\n",
" n_y = 5 # number of classes \n",
" n_h = 50 # dimensions of the GloVe vectors \n",
" \n",
" # Initialize parameters using Xavier initialization\n",
" W = np.random.randn(n_y, n_h) / np.sqrt(n_h)\n",
" b = np.zeros((n_y,))\n",
" \n",
" # Convert Y to Y_onehot with n_y classes\n",
" Y_oh = convert_to_one_hot(Y, C = n_y) \n",
" \n",
" # Optimization loop\n",
" for t in range(num_iterations): # Loop over the number of iterations\n",
" for i in range(m): # Loop over the training examples\n",
" \n",
" ### START CODE HERE ### (≈ 4 lines of code)\n",
" # Average the word vectors of the words from the i'th training example\n",
" avg = sentence_to_avg(X[i], word_to_vec_map)\n",
"\n",
" # Forward propagate the avg through the softmax layer\n",
" z = np.dot(W, avg)+b\n",
" a = softmax(z)\n",
"\n",
" # Compute cost using the i'th training label's one hot representation and \"A\" (the output of the softmax)\n",
" cost = -np.sum(np.multiply(Y_oh, np.log(a)))\n",
" ### END CODE HERE ###\n",
" \n",
" # Compute gradients \n",
" dz = a - Y_oh[i]\n",
" dW = np.dot(dz.reshape(n_y,1), avg.reshape(1, n_h))\n",
" db = dz\n",
"\n",
" # Update parameters with Stochastic Gradient Descent\n",
" W = W - learning_rate * dW\n",
" b = b - learning_rate * db\n",
" \n",
" if t % 100 == 0:\n",
" print(\"Epoch: \" + str(t) + \" --- cost = \" + str(cost))\n",
" pred = predict(X, Y, W, b, word_to_vec_map)\n",
"\n",
" return pred, W, b"
]
},
{
"cell_type": "code",
"execution_count": 25,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"(132,)\n",
"(132,)\n",
"(132, 5)\n",
"never talk to me again\n",
"<class 'numpy.ndarray'>\n",
"(20,)\n",
"(20,)\n",
"(132, 5)\n",
"<class 'numpy.ndarray'>\n"
]
}
],
"source": [
"print(X_train.shape)\n",
"print(Y_train.shape)\n",
"print(np.eye(5)[Y_train.reshape(-1)].shape)\n",
"print(X_train[0])\n",
"print(type(X_train))\n",
"Y = np.asarray([5,0,0,5, 4, 4, 4, 6, 6, 4, 1, 1, 5, 6, 6, 3, 6, 3, 4, 4])\n",
"print(Y.shape)\n",
"\n",
"X = np.asarray(['I am going to the bar tonight', 'I love you', 'miss you my dear',\n",
" 'Lets go party and drinks','Congrats on the new job','Congratulations',\n",
" 'I am so happy for you', 'Why are you feeling bad', 'What is wrong with you',\n",
" 'You totally deserve this prize', 'Let us go play football',\n",
" 'Are you down for football this afternoon', 'Work hard play harder',\n",
" 'It is suprising how people can be dumb sometimes',\n",
" 'I am very disappointed','It is the best day in my life',\n",
" 'I think I will end up alone','My life is so boring','Good job',\n",
" 'Great so awesome'])\n",
"\n",
"print(X.shape)\n",
"print(np.eye(5)[Y_train.reshape(-1)].shape)\n",
"print(type(X_train))\n"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Run the next cell to train your model and learn the softmax parameters (W,b). "
]
},
{
"cell_type": "code",
"execution_count": 26,
"metadata": {
"scrolled": true
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Epoch: 0 --- cost = 227.527181633\n",
"Accuracy: 0.348484848485\n",
"Epoch: 100 --- cost = 418.198641202\n",
"Accuracy: 0.931818181818\n",
"Epoch: 200 --- cost = 482.727277095\n",
"Accuracy: 0.954545454545\n",
"Epoch: 300 --- cost = 516.659063961\n",
"Accuracy: 0.969696969697\n",
"[[ 3.]\n",
" [ 2.]\n",
" [ 3.]\n",
" [ 0.]\n",
" [ 4.]\n",
" [ 0.]\n",
" [ 3.]\n",
" [ 2.]\n",
" [ 3.]\n",
" [ 1.]\n",
" [ 3.]\n",
" [ 3.]\n",
" [ 1.]\n",
" [ 3.]\n",
" [ 2.]\n",
" [ 3.]\n",
" [ 2.]\n",
" [ 3.]\n",
" [ 1.]\n",
" [ 2.]\n",
" [ 3.]\n",
" [ 0.]\n",
" [ 2.]\n",
" [ 2.]\n",
" [ 2.]\n",
" [ 1.]\n",
" [ 4.]\n",
" [ 3.]\n",
" [ 3.]\n",
" [ 4.]\n",
" [ 0.]\n",
" [ 3.]\n",
" [ 4.]\n",
" [ 2.]\n",
" [ 0.]\n",
" [ 3.]\n",
" [ 2.]\n",
" [ 2.]\n",
" [ 3.]\n",
" [ 4.]\n",
" [ 2.]\n",
" [ 2.]\n",
" [ 0.]\n",
" [ 2.]\n",
" [ 3.]\n",
" [ 0.]\n",
" [ 3.]\n",
" [ 2.]\n",
" [ 4.]\n",
" [ 3.]\n",
" [ 0.]\n",
" [ 3.]\n",
" [ 3.]\n",
" [ 3.]\n",
" [ 4.]\n",
" [ 2.]\n",
" [ 1.]\n",
" [ 1.]\n",
" [ 1.]\n",
" [ 2.]\n",
" [ 3.]\n",
" [ 1.]\n",
" [ 0.]\n",
" [ 0.]\n",
" [ 0.]\n",
" [ 3.]\n",
" [ 4.]\n",
" [ 4.]\n",
" [ 2.]\n",
" [ 2.]\n",
" [ 1.]\n",
" [ 2.]\n",
" [ 0.]\n",
" [ 3.]\n",
" [ 2.]\n",
" [ 2.]\n",
" [ 0.]\n",
" [ 3.]\n",
" [ 3.]\n",
" [ 1.]\n",
" [ 2.]\n",
" [ 1.]\n",
" [ 2.]\n",
" [ 2.]\n",
" [ 4.]\n",
" [ 3.]\n",
" [ 3.]\n",
" [ 2.]\n",
" [ 4.]\n",
" [ 0.]\n",
" [ 0.]\n",
" [ 3.]\n",
" [ 3.]\n",
" [ 3.]\n",
" [ 3.]\n",
" [ 2.]\n",
" [ 0.]\n",
" [ 1.]\n",
" [ 2.]\n",
" [ 3.]\n",
" [ 0.]\n",
" [ 2.]\n",
" [ 2.]\n",
" [ 2.]\n",
" [ 3.]\n",
" [ 2.]\n",
" [ 2.]\n",
" [ 2.]\n",
" [ 4.]\n",
" [ 1.]\n",
" [ 1.]\n",
" [ 3.]\n",
" [ 3.]\n",
" [ 4.]\n",
" [ 1.]\n",
" [ 2.]\n",
" [ 1.]\n",
" [ 1.]\n",
" [ 3.]\n",
" [ 1.]\n",
" [ 0.]\n",
" [ 4.]\n",
" [ 0.]\n",
" [ 3.]\n",
" [ 3.]\n",
" [ 4.]\n",
" [ 4.]\n",
" [ 1.]\n",
" [ 4.]\n",
" [ 3.]\n",
" [ 0.]\n",
" [ 2.]]\n"
]
}
],
"source": [
"pred, W, b = model(X_train, Y_train, word_to_vec_map)\n",
"print(pred)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"**Expected Output** (on a subset of iterations):\n",
"\n",
"<table>\n",
" <tr>\n",
" <td>\n",
" **Epoch: 0**\n",
" </td>\n",
" <td>\n",
" cost = 1.95204988128\n",
" </td>\n",
" <td>\n",
" Accuracy: 0.348484848485\n",
" </td>\n",
" </tr>\n",
"\n",
"\n",
"<tr>\n",
" <td>\n",
" **Epoch: 100**\n",
" </td>\n",
" <td>\n",
" cost = 0.0797181872601\n",
" </td>\n",
" <td>\n",
" Accuracy: 0.931818181818\n",
" </td>\n",
" </tr>\n",
" \n",
"<tr>\n",
" <td>\n",
" **Epoch: 200**\n",
" </td>\n",
" <td>\n",
" cost = 0.0445636924368\n",
" </td>\n",
" <td>\n",
" Accuracy: 0.954545454545\n",
" </td>\n",
" </tr>\n",
" \n",
" <tr>\n",
" <td>\n",
" **Epoch: 300**\n",
" </td>\n",
" <td>\n",
" cost = 0.0343226737879\n",
" </td>\n",
" <td>\n",
" Accuracy: 0.969696969697\n",
" </td>\n",
" </tr>\n",
"</table>"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Great! Your model has pretty high accuracy on the training set. Lets now see how it does on the test set. "
]
},
{
"cell_type": "markdown",
"metadata": {
"collapsed": true
},
"source": [
"### 1.4 - Examining test set performance \n"
]
},
{
"cell_type": "code",
"execution_count": 27,
"metadata": {
"scrolled": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Training set:\n",
"Accuracy: 0.977272727273\n",
"Test set:\n",
"Accuracy: 0.857142857143\n"
]
}
],
"source": [
"print(\"Training set:\")\n",
"pred_train = predict(X_train, Y_train, W, b, word_to_vec_map)\n",
"print('Test set:')\n",
"pred_test = predict(X_test, Y_test, W, b, word_to_vec_map)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"**Expected Output**:\n",
"\n",
"<table>\n",
" <tr>\n",
" <td>\n",
" **Train set accuracy**\n",
" </td>\n",
" <td>\n",
" 97.7\n",
" </td>\n",
" </tr>\n",
" <tr>\n",
" <td>\n",
" **Test set accuracy**\n",
" </td>\n",
" <td>\n",
" 85.7\n",
" </td>\n",
" </tr>\n",
"</table>"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Random guessing would have had 20% accuracy given that there are 5 classes. This is pretty good performance after training on only 127 examples. \n",
"\n",
"In the training set, the algorithm saw the sentence \"*I love you*\" with the label ❤️. You can check however that the word \"adore\" does not appear in the training set. Nonetheless, lets see what happens if you write \"*I adore you*.\"\n",
"\n"
]
},
{
"cell_type": "code",
"execution_count": 28,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Accuracy: 0.833333333333\n",
"\n",
"i adore you ❤️\n",
"i love you ❤️\n",
"funny lol 😄\n",
"lets play with a ball ⚾\n",
"food is ready 🍴\n",
"not feeling happy 😄\n"
]
}
],
"source": [
"X_my_sentences = np.array([\"i adore you\", \"i love you\", \"funny lol\", \"lets play with a ball\", \"food is ready\", \"not feeling happy\"])\n",
"Y_my_labels = np.array([[0], [0], [2], [1], [4],[3]])\n",
"\n",
"pred = predict(X_my_sentences, Y_my_labels , W, b, word_to_vec_map)\n",
"print_predictions(X_my_sentences, pred)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Amazing! Because *adore* has a similar embedding as *love*, the algorithm has generalized correctly even to a word it has never seen before. Words such as *heart*, *dear*, *beloved* or *adore* have embedding vectors similar to *love*, and so might work too---feel free to modify the inputs above and try out a variety of input sentences. How well does it work?\n",
"\n",
"Note though that it doesn't get \"not feeling happy\" correct. This algorithm ignores word ordering, so is not good at understanding phrases like \"not happy.\" \n",
"\n",
"Printing the confusion matrix can also help understand which classes are more difficult for your model. A confusion matrix shows how often an example whose label is one class (\"actual\" class) is mislabeled by the algorithm with a different class (\"predicted\" class). \n",
"\n",
"\n"
]
},
{
"cell_type": "code",
"execution_count": 29,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"(56,)\n",
" ❤️ ⚾ 😄 😞 🍴\n",
"Predicted 0.0 1.0 2.0 3.0 4.0 All\n",
"Actual \n",
"0 6 0 0 1 0 7\n",
"1 0 8 0 0 0 8\n",
"2 2 0 16 0 0 18\n",
"3 1 1 2 12 0 16\n",
"4 0 0 1 0 6 7\n",
"All 9 9 19 13 6 56\n"
]
},
{
"data": {
"image/png": "iVBORw0KGgoAAAANSUhEUgAAAQwAAAD3CAYAAADormr9AAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x7f76e6e718d0>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"print(Y_test.shape)\n",
"print(' '+ label_to_emoji(0)+ ' ' + label_to_emoji(1) + ' ' + label_to_emoji(2)+ ' ' + label_to_emoji(3)+' ' + label_to_emoji(4))\n",
"print(pd.crosstab(Y_test, pred_test.reshape(56,), rownames=['Actual'], colnames=['Predicted'], margins=True))\n",
"plot_confusion_matrix(Y_test, pred_test)"
]
},
{
"cell_type": "markdown",
"metadata": {
"collapsed": true
},
"source": [
"<font color='blue'>\n",
"**What you should remember from this part**:\n",
"- Even with a 127 training examples, you can get a reasonably good model for Emojifying. This is due to the generalization power word vectors gives you. \n",
"- Emojify-V1 will perform poorly on sentences such as *\"This movie is not good and not enjoyable\"* because it doesn't understand combinations of words--it just averages all the words' embedding vectors together, without paying attention to the ordering of words. You will build a better algorithm in the next part. \n"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## 2 - Emojifier-V2: Using LSTMs in Keras: \n",
"\n",
"Let's build an LSTM model that takes as input word sequences. This model will be able to take word ordering into account. Emojifier-V2 will continue to use pre-trained word embeddings to represent words, but will feed them into an LSTM, whose job it is to predict the most appropriate emoji. \n",
"\n",
"Run the following cell to load the Keras packages."
]
},
{
"cell_type": "code",
"execution_count": 30,
"metadata": {},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"Using TensorFlow backend.\n"
]
}
],
"source": [
"import numpy as np\n",
"np.random.seed(0)\n",
"from keras.models import Model\n",
"from keras.layers import Dense, Input, Dropout, LSTM, Activation\n",
"from keras.layers.embeddings import Embedding\n",
"from keras.preprocessing import sequence\n",
"from keras.initializers import glorot_uniform\n",
"np.random.seed(1)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### 2.1 - Overview of the model\n",
"\n",
"Here is the Emojifier-v2 you will implement:\n",
"\n",
"<img src=\"images/emojifier-v2.png\" style=\"width:700px;height:400px;\"> <br>\n",
"<caption><center> **Figure 3**: Emojifier-V2. A 2-layer LSTM sequence classifier. </center></caption>\n",
"\n"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### 2.2 Keras and mini-batching \n",
"\n",
"In this exercise, we want to train Keras using mini-batches. However, most deep learning frameworks require that all sequences in the same mini-batch have the same length. This is what allows vectorization to work: If you had a 3-word sentence and a 4-word sentence, then the computations needed for them are different (one takes 3 steps of an LSTM, one takes 4 steps) so it's just not possible to do them both at the same time.\n",
"\n",
"The common solution to this is to use padding. Specifically, set a maximum sequence length, and pad all sequences to the same length. For example, of the maximum sequence length is 20, we could pad every sentence with \"0\"s so that each input sentence is of length 20. Thus, a sentence \"i love you\" would be represented as $(e_{i}, e_{love}, e_{you}, \\vec{0}, \\vec{0}, \\ldots, \\vec{0})$. In this example, any sentences longer than 20 words would have to be truncated. One simple way to choose the maximum sequence length is to just pick the length of the longest sentence in the training set. \n"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### 2.3 - The Embedding layer\n",
"\n",
"In Keras, the embedding matrix is represented as a \"layer\", and maps positive integers (indices corresponding to words) into dense vectors of fixed size (the embedding vectors). It can be trained or initialized with a pretrained embedding. In this part, you will learn how to create an [Embedding()](https://keras.io/layers/embeddings/) layer in Keras, initialize it with the GloVe 50-dimensional vectors loaded earlier in the notebook. Because our training set is quite small, we will not update the word embeddings but will instead leave their values fixed. But in the code below, we'll show you how Keras allows you to either train or leave fixed this layer. \n",
"\n",
"The `Embedding()` layer takes an integer matrix of size (batch size, max input length) as input. This corresponds to sentences converted into lists of indices (integers), as shown in the figure below.\n",
"\n",
"<img src=\"images/embedding1.png\" style=\"width:700px;height:250px;\">\n",
"<caption><center> **Figure 4**: Embedding layer. This example shows the propagation of two examples through the embedding layer. Both have been zero-padded to a length of `max_len=5`. The final dimension of the representation is `(2,max_len,50)` because the word embeddings we are using are 50 dimensional. </center></caption>\n",
"\n",
"The largest integer (i.e. word index) in the input should be no larger than the vocabulary size. The layer outputs an array of shape (batch size, max input length, dimension of word vectors).\n",
"\n",
"The first step is to convert all your training sentences into lists of indices, and then zero-pad all these lists so that their length is the length of the longest sentence. \n",
"\n",
"**Exercise**: Implement the function below to convert X (array of sentences as strings) into an array of indices corresponding to words in the sentences. The output shape should be such that it can be given to `Embedding()` (described in Figure 4). "
]
},
{
"cell_type": "code",
"execution_count": 33,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"# GRADED FUNCTION: sentences_to_indices\n",
"\n",
"def sentences_to_indices(X, word_to_index, max_len):\n",
" \"\"\"\n",
" Converts an array of sentences (strings) into an array of indices corresponding to words in the sentences.\n",
" The output shape should be such that it can be given to `Embedding()` (described in Figure 4). \n",
" \n",
" Arguments:\n",
" X -- array of sentences (strings), of shape (m, 1)\n",
" word_to_index -- a dictionary containing the each word mapped to its index\n",
" max_len -- maximum number of words in a sentence. You can assume every sentence in X is no longer than this. \n",
" \n",
" Returns:\n",
" X_indices -- array of indices corresponding to words in the sentences from X, of shape (m, max_len)\n",
" \"\"\"\n",
" \n",
" m = X.shape[0] # number of training examples\n",
" \n",
" ### START CODE HERE ###\n",
" # Initialize X_indices as a numpy matrix of zeros and the correct shape (≈ 1 line)\n",
" X_indices = np.zeros((m,max_len))\n",
" \n",
" for i in range(m): # loop over training examples\n",
" \n",
" # Convert the ith training sentence in lower case and split is into words. You should get a list of words.\n",
" sentence_words = [w.lower() for w in X[i].split()]\n",
" \n",
" # Initialize j to 0\n",
" j = 0\n",
" \n",
" # Loop over the words of sentence_words\n",
" for w in sentence_words:\n",
" # Set the (i,j)th entry of X_indices to the index of the correct word.\n",
" X_indices[i, j] = word_to_index[w]\n",
" # Increment j to j + 1\n",
" j = j+1\n",
" \n",
" ### END CODE HERE ###\n",
" \n",
" return X_indices"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Run the following cell to check what `sentences_to_indices()` does, and check your results."
]
},
{
"cell_type": "code",
"execution_count": 34,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"X1 = ['funny lol' 'lets play baseball' 'food is ready for you']\n",
"X1_indices = [[ 155345. 225122. 0. 0. 0.]\n",
" [ 220930. 286375. 69714. 0. 0.]\n",
" [ 151204. 192973. 302254. 151349. 394475.]]\n"
]
}
],
"source": [
"X1 = np.array([\"funny lol\", \"lets play baseball\", \"food is ready for you\"])\n",
"X1_indices = sentences_to_indices(X1,word_to_index, max_len = 5)\n",
"print(\"X1 =\", X1)\n",
"print(\"X1_indices =\", X1_indices)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"**Expected Output**:\n",
"\n",
"<table>\n",
" <tr>\n",
" <td>\n",
" **X1 =**\n",
" </td>\n",
" <td>\n",
" ['funny lol' 'lets play football' 'food is ready for you']\n",
" </td>\n",
" </tr>\n",
" <tr>\n",
" <td>\n",
" **X1_indices =**\n",
" </td>\n",
" <td>\n",
" [[ 155345. 225122. 0. 0. 0.] <br>\n",
" [ 220930. 286375. 151266. 0. 0.] <br>\n",
" [ 151204. 192973. 302254. 151349. 394475.]]\n",
" </td>\n",
" </tr>\n",
"</table>"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Let's build the `Embedding()` layer in Keras, using pre-trained word vectors. After this layer is built, you will pass the output of `sentences_to_indices()` to it as an input, and the `Embedding()` layer will return the word embeddings for a sentence. \n",
"\n",
"**Exercise**: Implement `pretrained_embedding_layer()`. You will need to carry out the following steps:\n",
"1. Initialize the embedding matrix as a numpy array of zeroes with the correct shape.\n",
"2. Fill in the embedding matrix with all the word embeddings extracted from `word_to_vec_map`.\n",
"3. Define Keras embedding layer. Use [Embedding()](https://keras.io/layers/embeddings/). Be sure to make this layer non-trainable, by setting `trainable = False` when calling `Embedding()`. If you were to set `trainable = True`, then it will allow the optimization algorithm to modify the values of the word embeddings. \n",
"4. Set the embedding weights to be equal to the embedding matrix "
]
},
{
"cell_type": "code",
"execution_count": 35,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"# GRADED FUNCTION: pretrained_embedding_layer\n",
"\n",
"def pretrained_embedding_layer(word_to_vec_map, word_to_index):\n",
" \"\"\"\n",
" Creates a Keras Embedding() layer and loads in pre-trained GloVe 50-dimensional vectors.\n",
" \n",
" Arguments:\n",
" word_to_vec_map -- dictionary mapping words to their GloVe vector representation.\n",
" word_to_index -- dictionary mapping from words to their indices in the vocabulary (400,001 words)\n",
"\n",
" Returns:\n",
" embedding_layer -- pretrained layer Keras instance\n",
" \"\"\"\n",
" \n",
" vocab_len = len(word_to_index) + 1 # adding 1 to fit Keras embedding (requirement)\n",
" emb_dim = word_to_vec_map[\"cucumber\"].shape[0] # define dimensionality of your GloVe word vectors (= 50)\n",
" \n",
" ### START CODE HERE ###\n",
" # Initialize the embedding matrix as a numpy array of zeros of shape (vocab_len, dimensions of word vectors = emb_dim)\n",
" emb_matrix = np.zeros((vocab_len, emb_dim))\n",
" \n",
" # Set each row \"index\" of the embedding matrix to be the word vector representation of the \"index\"th word of the vocabulary\n",
" for word, index in word_to_index.items():\n",
" emb_matrix[index, :] = word_to_vec_map[word]\n",
"\n",
" # Define Keras embedding layer with the correct output/input sizes, make it trainable. Use Embedding(...). Make sure to set trainable=False. \n",
" embedding_layer = Embedding(vocab_len, emb_dim, trainable=False)\n",
" ### END CODE HERE ###\n",
"\n",
" # Build the embedding layer, it is required before setting the weights of the embedding layer. Do not modify the \"None\".\n",
" embedding_layer.build((None,))\n",
" \n",
" # Set the weights of the embedding layer to the embedding matrix. Your layer is now pretrained.\n",
" embedding_layer.set_weights([emb_matrix])\n",
" \n",
" return embedding_layer"
]
},
{
"cell_type": "code",
"execution_count": 36,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"weights[0][1][3] = -0.3403\n"
]
}
],
"source": [
"embedding_layer = pretrained_embedding_layer(word_to_vec_map, word_to_index)\n",
"print(\"weights[0][1][3] =\", embedding_layer.get_weights()[0][1][3])"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"**Expected Output**:\n",
"\n",
"<table>\n",
" <tr>\n",
" <td>\n",
" **weights[0][1][3] =**\n",
" </td>\n",
" <td>\n",
" -0.3403\n",
" </td>\n",
" </tr>\n",
"</table>"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## 2.3 Building the Emojifier-V2\n",
"\n",
"Lets now build the Emojifier-V2 model. You will do so using the embedding layer you have built, and feed its output to an LSTM network. \n",
"\n",
"<img src=\"images/emojifier-v2.png\" style=\"width:700px;height:400px;\"> <br>\n",
"<caption><center> **Figure 3**: Emojifier-v2. A 2-layer LSTM sequence classifier. </center></caption>\n",
"\n",
"\n",
"**Exercise:** Implement `Emojify_V2()`, which builds a Keras graph of the architecture shown in Figure 3. The model takes as input an array of sentences of shape (`m`, `max_len`, ) defined by `input_shape`. It should output a softmax probability vector of shape (`m`, `C = 5`). You may need `Input(shape = ..., dtype = '...')`, [LSTM()](https://keras.io/layers/recurrent/#lstm), [Dropout()](https://keras.io/layers/core/#dropout), [Dense()](https://keras.io/layers/core/#dense), and [Activation()](https://keras.io/activations/)."
]
},
{
"cell_type": "code",
"execution_count": 41,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"# GRADED FUNCTION: Emojify_V2\n",
"\n",
"def Emojify_V2(input_shape, word_to_vec_map, word_to_index):\n",
" \"\"\"\n",
" Function creating the Emojify-v2 model's graph.\n",
" \n",
" Arguments:\n",
" input_shape -- shape of the input, usually (max_len,)\n",
" word_to_vec_map -- dictionary mapping every word in a vocabulary into its 50-dimensional vector representation\n",
" word_to_index -- dictionary mapping from words to their indices in the vocabulary (400,001 words)\n",
"\n",
" Returns:\n",
" model -- a model instance in Keras\n",
" \"\"\"\n",
" \n",
" ### START CODE HERE ###\n",
" # Define sentence_indices as the input of the graph, it should be of shape input_shape and dtype 'int32' (as it contains indices).\n",
" sentence_indices = Input(input_shape, dtype='int32')\n",
" \n",
" # Create the embedding layer pretrained with GloVe Vectors (≈1 line)\n",
" embedding_layer = pretrained_embedding_layer(word_to_vec_map, word_to_index)\n",
" \n",
" # Propagate sentence_indices through your embedding layer, you get back the embeddings\n",
" embeddings = embedding_layer(sentence_indices) \n",
" \n",
" # Propagate the embeddings through an LSTM layer with 128-dimensional hidden state\n",
" # Be careful, the returned output should be a batch of sequences.\n",
" X = LSTM(128, return_sequences=True)(embeddings)\n",
" # Add dropout with a probability of 0.5\n",
" X = Dropout(0.5)(X)\n",
" # Propagate X trough another LSTM layer with 128-dimensional hidden state\n",
" # Be careful, the returned output should be a single hidden state, not a batch of sequences.\n",
" X = LSTM(128, return_sequences=False)(X)\n",
" # Add dropout with a probability of 0.5\n",
" X = Dropout(0.5)(X)\n",
" # Propagate X through a Dense layer with softmax activation to get back a batch of 5-dimensional vectors.\n",
" X = Dense(5)(X)\n",
" # Add a softmax activation\n",
" X = Activation('softmax')(X)\n",
" \n",
" \n",
" # Create Model instance which converts sentence_indices into X.\n",
" model = Model(inputs=sentence_indices ,outputs=X)\n",
" \n",
" ### END CODE HERE ###\n",
" \n",
" return model"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Run the following cell to create your model and check its summary. Because all sentences in the dataset are less than 10 words, we chose `max_len = 10`. You should see your architecture, it uses \"20,223,927\" parameters, of which 20,000,050 (the word embeddings) are non-trainable, and the remaining 223,877 are. Because our vocabulary size has 400,001 words (with valid indices from 0 to 400,000) there are 400,001\\*50 = 20,000,050 non-trainable parameters. "
]
},
{
"cell_type": "code",
"execution_count": 42,
"metadata": {
"scrolled": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"_________________________________________________________________\n",
"Layer (type) Output Shape Param # \n",
"=================================================================\n",
"input_3 (InputLayer) (None, 10) 0 \n",
"_________________________________________________________________\n",
"embedding_4 (Embedding) (None, 10, 50) 20000050 \n",
"_________________________________________________________________\n",
"lstm_3 (LSTM) (None, 10, 128) 91648 \n",
"_________________________________________________________________\n",
"dropout_1 (Dropout) (None, 10, 128) 0 \n",
"_________________________________________________________________\n",
"lstm_4 (LSTM) (None, 128) 131584 \n",
"_________________________________________________________________\n",
"dropout_2 (Dropout) (None, 128) 0 \n",
"_________________________________________________________________\n",
"dense_1 (Dense) (None, 5) 645 \n",
"_________________________________________________________________\n",
"activation_1 (Activation) (None, 5) 0 \n",
"=================================================================\n",
"Total params: 20,223,927\n",
"Trainable params: 223,877\n",
"Non-trainable params: 20,000,050\n",
"_________________________________________________________________\n"
]
}
],
"source": [
"model = Emojify_V2((maxLen,), word_to_vec_map, word_to_index)\n",
"model.summary()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"As usual, after creating your model in Keras, you need to compile it and define what loss, optimizer and metrics your are want to use. Compile your model using `categorical_crossentropy` loss, `adam` optimizer and `['accuracy']` metrics:"
]
},
{
"cell_type": "code",
"execution_count": 43,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"model.compile(loss='categorical_crossentropy', optimizer='adam', metrics=['accuracy'])"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"It's time to train your model. Your Emojifier-V2 `model` takes as input an array of shape (`m`, `max_len`) and outputs probability vectors of shape (`m`, `number of classes`). We thus have to convert X_train (array of sentences as strings) to X_train_indices (array of sentences as list of word indices), and Y_train (labels as indices) to Y_train_oh (labels as one-hot vectors)."
]
},
{
"cell_type": "code",
"execution_count": 44,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"X_train_indices = sentences_to_indices(X_train, word_to_index, maxLen)\n",
"Y_train_oh = convert_to_one_hot(Y_train, C = 5)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Fit the Keras model on `X_train_indices` and `Y_train_oh`. We will use `epochs = 50` and `batch_size = 32`."
]
},
{
"cell_type": "code",
"execution_count": 45,
"metadata": {
"scrolled": true
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Epoch 1/50\n",
"132/132 [==============================] - 0s - loss: 1.6064 - acc: 0.2273 \n",
"Epoch 2/50\n",
"132/132 [==============================] - 0s - loss: 1.5287 - acc: 0.3561 \n",
"Epoch 3/50\n",
"132/132 [==============================] - 0s - loss: 1.4868 - acc: 0.3864 \n",
"Epoch 4/50\n",
"132/132 [==============================] - 0s - loss: 1.4493 - acc: 0.4015 \n",
"Epoch 5/50\n",
"132/132 [==============================] - 0s - loss: 1.3394 - acc: 0.4697 \n",
"Epoch 6/50\n",
"132/132 [==============================] - 0s - loss: 1.2390 - acc: 0.5076 \n",
"Epoch 7/50\n",
"132/132 [==============================] - 0s - loss: 1.1274 - acc: 0.5909 \n",
"Epoch 8/50\n",
"132/132 [==============================] - 0s - loss: 1.0545 - acc: 0.5455 \n",
"Epoch 9/50\n",
"132/132 [==============================] - 0s - loss: 1.0787 - acc: 0.5833 \n",
"Epoch 10/50\n",
"132/132 [==============================] - 0s - loss: 0.8138 - acc: 0.6970 \n",
"Epoch 11/50\n",
"132/132 [==============================] - 0s - loss: 0.8153 - acc: 0.6439 \n",
"Epoch 12/50\n",
"132/132 [==============================] - 0s - loss: 0.6856 - acc: 0.7727 \n",
"Epoch 13/50\n",
"132/132 [==============================] - 0s - loss: 0.6075 - acc: 0.7879 \n",
"Epoch 14/50\n",
"132/132 [==============================] - 0s - loss: 0.5096 - acc: 0.8106 \n",
"Epoch 15/50\n",
"132/132 [==============================] - 0s - loss: 0.3918 - acc: 0.8636 \n",
"Epoch 16/50\n",
"132/132 [==============================] - 0s - loss: 0.3511 - acc: 0.8561 \n",
"Epoch 17/50\n",
"132/132 [==============================] - 0s - loss: 0.3512 - acc: 0.8636 \n",
"Epoch 18/50\n",
"132/132 [==============================] - 0s - loss: 0.3295 - acc: 0.8788 \n",
"Epoch 19/50\n",
"132/132 [==============================] - 0s - loss: 0.2745 - acc: 0.9091 \n",
"Epoch 20/50\n",
"132/132 [==============================] - 0s - loss: 0.3838 - acc: 0.8561 \n",
"Epoch 21/50\n",
"132/132 [==============================] - 0s - loss: 0.3591 - acc: 0.8712 \n",
"Epoch 22/50\n",
"132/132 [==============================] - 0s - loss: 0.4562 - acc: 0.8485 \n",
"Epoch 23/50\n",
"132/132 [==============================] - 0s - loss: 0.2616 - acc: 0.8939 \n",
"Epoch 24/50\n",
"132/132 [==============================] - 0s - loss: 0.4970 - acc: 0.8030 \n",
"Epoch 25/50\n",
"132/132 [==============================] - 0s - loss: 0.2813 - acc: 0.9091 \n",
"Epoch 26/50\n",
"132/132 [==============================] - 0s - loss: 0.2332 - acc: 0.9167 \n",
"Epoch 27/50\n",
"132/132 [==============================] - 0s - loss: 0.1991 - acc: 0.9394 \n",
"Epoch 28/50\n",
"132/132 [==============================] - 0s - loss: 0.1979 - acc: 0.9167 \n",
"Epoch 29/50\n",
"132/132 [==============================] - 0s - loss: 0.2164 - acc: 0.9242 \n",
"Epoch 30/50\n",
"132/132 [==============================] - 0s - loss: 0.2225 - acc: 0.9318 \n",
"Epoch 31/50\n",
"132/132 [==============================] - 0s - loss: 0.1607 - acc: 0.9394 \n",
"Epoch 32/50\n",
"132/132 [==============================] - 0s - loss: 0.1071 - acc: 0.9697 \n",
"Epoch 33/50\n",
"132/132 [==============================] - 0s - loss: 0.1062 - acc: 0.9621 \n",
"Epoch 34/50\n",
"132/132 [==============================] - 0s - loss: 0.0687 - acc: 0.9848 \n",
"Epoch 35/50\n",
"132/132 [==============================] - 0s - loss: 0.0611 - acc: 0.9773 \n",
"Epoch 36/50\n",
"132/132 [==============================] - 0s - loss: 0.0499 - acc: 0.9848 \n",
"Epoch 37/50\n",
"132/132 [==============================] - 0s - loss: 0.0366 - acc: 0.9924 \n",
"Epoch 38/50\n",
"132/132 [==============================] - 0s - loss: 0.0354 - acc: 0.9924 \n",
"Epoch 39/50\n",
"132/132 [==============================] - 0s - loss: 0.0229 - acc: 1.0000 \n",
"Epoch 40/50\n",
"132/132 [==============================] - 0s - loss: 0.0159 - acc: 1.0000 \n",
"Epoch 41/50\n",
"132/132 [==============================] - 0s - loss: 0.0178 - acc: 1.0000 \n",
"Epoch 42/50\n",
"132/132 [==============================] - 0s - loss: 0.0202 - acc: 0.9924 \n",
"Epoch 43/50\n",
"132/132 [==============================] - 0s - loss: 0.0309 - acc: 0.9924 \n",
"Epoch 44/50\n",
"132/132 [==============================] - 0s - loss: 0.0116 - acc: 1.0000 \n",
"Epoch 45/50\n",
"132/132 [==============================] - 0s - loss: 0.0433 - acc: 0.9848 \n",
"Epoch 46/50\n",
"132/132 [==============================] - 0s - loss: 0.0461 - acc: 0.9697 \n",
"Epoch 47/50\n",
"132/132 [==============================] - 0s - loss: 0.0924 - acc: 0.9470 \n",
"Epoch 48/50\n",
"132/132 [==============================] - 0s - loss: 0.1134 - acc: 0.9697 \n",
"Epoch 49/50\n",
"132/132 [==============================] - 0s - loss: 0.0896 - acc: 0.9848 \n",
"Epoch 50/50\n",
"132/132 [==============================] - 0s - loss: 0.1454 - acc: 0.9318 \n"
]
},
{
"data": {
"text/plain": [
"<keras.callbacks.History at 0x7f76cec0df28>"
]
},
"execution_count": 45,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"model.fit(X_train_indices, Y_train_oh, epochs = 50, batch_size = 32, shuffle=True)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Your model should perform close to **100% accuracy** on the training set. The exact accuracy you get may be a little different. Run the following cell to evaluate your model on the test set. "
]
},
{
"cell_type": "code",
"execution_count": 46,
"metadata": {
"scrolled": true
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"32/56 [================>.............] - ETA: 0s\n",
"Test accuracy = 0.839285705771\n"
]
}
],
"source": [
"X_test_indices = sentences_to_indices(X_test, word_to_index, max_len = maxLen)\n",
"Y_test_oh = convert_to_one_hot(Y_test, C = 5)\n",
"loss, acc = model.evaluate(X_test_indices, Y_test_oh)\n",
"print()\n",
"print(\"Test accuracy = \", acc)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"You should get a test accuracy between 80% and 95%. Run the cell below to see the mislabelled examples. "
]
},
{
"cell_type": "code",
"execution_count": 47,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Expected emoji:😄 prediction: he got a very nice raise\t❤️\n",
"Expected emoji:😞 prediction: work is hard\t😄\n",
"Expected emoji:😞 prediction: This girl is messing with me\t❤️\n",
"Expected emoji:🍴 prediction: any suggestions for dinner\t😄\n",
"Expected emoji:❤️ prediction: I love taking breaks\t😞\n",
"Expected emoji:❤️ prediction: I love you to the stars and back\t😄\n",
"Expected emoji:😞 prediction: go away\t⚾\n",
"Expected emoji:😞 prediction: yesterday we lost again\t⚾\n",
"Expected emoji:❤️ prediction: family is all I have\t🍴\n"
]
}
],
"source": [
"# This code allows you to see the mislabelled examples\n",
"C = 5\n",
"y_test_oh = np.eye(C)[Y_test.reshape(-1)]\n",
"X_test_indices = sentences_to_indices(X_test, word_to_index, maxLen)\n",
"pred = model.predict(X_test_indices)\n",
"for i in range(len(X_test)):\n",
" x = X_test_indices\n",
" num = np.argmax(pred[i])\n",
" if(num != Y_test[i]):\n",
" print('Expected emoji:'+ label_to_emoji(Y_test[i]) + ' prediction: '+ X_test[i] + label_to_emoji(num).strip())"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Now you can try it on your own example. Write your own sentence below. "
]
},
{
"cell_type": "code",
"execution_count": 48,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"not feeling happy 😞\n"
]
}
],
"source": [
"# Change the sentence below to see your prediction. Make sure all the words are in the Glove embeddings. \n",
"x_test = np.array(['not feeling happy'])\n",
"X_test_indices = sentences_to_indices(x_test, word_to_index, maxLen)\n",
"print(x_test[0] +' '+ label_to_emoji(np.argmax(model.predict(X_test_indices))))"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Previously, Emojify-V1 model did not correctly label \"not feeling happy,\" but our implementation of Emojiy-V2 got it right. (Keras' outputs are slightly random each time, so you may not have obtained the same result.) The current model still isn't very robust at understanding negation (like \"not happy\") because the training set is small and so doesn't have a lot of examples of negation. But if the training set were larger, the LSTM model would be much better than the Emojify-V1 model at understanding such complex sentences. \n"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Congratulations!\n",
"\n",
"You have completed this notebook! ❤️❤️❤️\n",
"\n",
"<font color='blue'>\n",
"**What you should remember**:\n",
"- If you have an NLP task where the training set is small, using word embeddings can help your algorithm significantly. Word embeddings allow your model to work on words in the test set that may not even have appeared in your training set. \n",
"- Training sequence models in Keras (and in most other deep learning frameworks) requires a few important details:\n",
" - To use mini-batches, the sequences need to be padded so that all the examples in a mini-batch have the same length. \n",
" - An `Embedding()` layer can be initialized with pretrained values. These values can be either fixed or trained further on your dataset. If however your labeled dataset is small, it's usually not worth trying to train a large pre-trained set of embeddings. \n",
" - `LSTM()` has a flag called `return_sequences` to decide if you would like to return every hidden states or only the last one. \n",
" - You can use `Dropout()` right after `LSTM()` to regularize your network. \n"
]
},
{
"cell_type": "markdown",
"metadata": {
"collapsed": true
},
"source": [
"Congratulations on finishing this assignment and building an Emojifier. We hope you're happy with what you've accomplished in this notebook! \n",
"\n",
"# 😀😀😀😀😀😀\n",
"\n",
"\n"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Acknowledgments\n",
"\n",
"Thanks to Alison Darcy and the Woebot team for their advice on the creation of this assignment. Woebot is a chatbot friend that is ready to speak with you 24/7. As part of Woebot's technology, it uses word embeddings to understand the emotions of what you say. You can play with it by going to http://woebot.io\n",
"\n",
"<img src=\"images/woebot.png\" style=\"width:600px;height:300px;\">\n",
"\n",
"\n"
]
}
],
"metadata": {
"coursera": {
"course_slug": "nlp-sequence-models",
"graded_item_id": "RNnEs",
"launcher_item_id": "acNYU"
},
"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": "3.6.0"
}
},
"nbformat": 4,
"nbformat_minor": 2
}
| mit |
syeddanish41/kaggle-question-pairs-quora | averagging.ipynb | 1 | 13651 | {
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Simple average is taken as final submission."
]
},
{
"cell_type": "code",
"execution_count": 1,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"import pandas as pd"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"sub1 = pd.read_csv('final.csv')\n",
"sub2 = pd.read_csv('xgb_final.csv')"
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"((2345796, 7), (2345796, 6))"
]
},
"execution_count": 7,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"sub1.shape, sub2.shape"
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"sub1 = sub1.drop('test_id',axis=1)\n",
"sub2 = sub2.drop('test_id',axis=1)"
]
},
{
"cell_type": "code",
"execution_count": 18,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"dup = (sub1.fold1 + sub1.fold2 + sub1.fold3 + sub1.fold4 + sub1.fold5 + sub1.fold6 + sub1.fold7 + sub2.fold0 + sub2.fold1 + sub2.fold2 + sub2.fold3 +\n",
" sub2.fold4 + sub2.fold5)/13"
]
},
{
"cell_type": "code",
"execution_count": 19,
"metadata": {
"collapsed": false,
"scrolled": true
},
"outputs": [
{
"data": {
"image/png": "iVBORw0KGgoAAAANSUhEUgAAAZQAAAD8CAYAAABQFVIjAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nYII=\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x7efefee20b50>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"import matplotlib.pyplot as plt\n",
"plt.hist(dup,bins=50)\n",
"plt.show()"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {
"collapsed": false,
"scrolled": false
},
"outputs": [],
"source": [
"sub = pd.read_csv('sub_av.csv')"
]
},
{
"cell_type": "code",
"execution_count": 23,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"sub.is_duplicate = dup"
]
},
{
"cell_type": "code",
"execution_count": 24,
"metadata": {
"collapsed": false,
"scrolled": true
},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<style>\n",
" .dataframe thead tr:only-child th {\n",
" text-align: right;\n",
" }\n",
"\n",
" .dataframe thead th {\n",
" text-align: left;\n",
" }\n",
"\n",
" .dataframe tbody tr th {\n",
" vertical-align: top;\n",
" }\n",
"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>is_duplicate</th>\n",
" <th>test_id</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>0.000206</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>0.130279</td>\n",
" <td>1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>0.188597</td>\n",
" <td>2</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>0.000090</td>\n",
" <td>3</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>0.075666</td>\n",
" <td>4</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" is_duplicate test_id\n",
"0 0.000206 0\n",
"1 0.130279 1\n",
"2 0.188597 2\n",
"3 0.000090 3\n",
"4 0.075666 4"
]
},
"execution_count": 24,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"sub.head()"
]
},
{
"cell_type": "code",
"execution_count": 25,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
" adding: sub_final.csv (deflated 59%)\n"
]
}
],
"source": [
"sub.to_csv('sub_final.csv',index = False)\n",
"! rm -rf test.zip\n",
"! zip -r test.zip sub_final.csv"
]
}
],
"metadata": {
"kernelspec": {
"display_name": "Python [default]",
"language": "python",
"name": "python2"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 2
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython2",
"version": "2.7.13"
}
},
"nbformat": 4,
"nbformat_minor": 2
}
| mit |
freininghaus/adventofcode | 2020/day03-haskell.ipynb | 1 | 6639 | {
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Day 3: Toboggan Trajectory\n",
"https://adventofcode.com/2020/day/3"
]
},
{
"cell_type": "code",
"execution_count": 1,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"inputLines = lines <$> readFile \"input/day03.txt\""
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"testInput = [ \"..##.......\"\n",
" , \"#...#...#..\"\n",
" , \".#....#..#.\"\n",
" , \"..#.#...#.#\"\n",
" , \".#...##..#.\"\n",
" , \"..#.##.....\"\n",
" , \".#.#.#....#\"\n",
" , \".#........#\"\n",
" , \"#.##...#...\"\n",
" , \"#...##....#\"\n",
" , \".#..#...#.#\" ]"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"import qualified Data.Set as Set"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"For a given line, determine the indices at which the character '`#`' occurs."
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"parseLine :: String -> Set.Set Int\n",
"parseLine line = Set.fromList [ index | (index, cell) <- zip [0..] line, cell == '#']"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Test the function:"
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"fromList [2,3,5,7,11,13,17,19]"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"parseLine \"..##.#.#...#.#...#.#.\""
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Part 1\n",
"Zip the positions which should be checked in each row with the rows themselves, and check if the position modulo the row length corresponds to an index with a '`#`' in the row:"
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"solution1 rows = length\n",
" $ filter id\n",
" $ zipWith Set.member (map (`mod` n) [0, 3 ..])\n",
" $ map parseLine rows\n",
" where n = length $ head rows"
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"7"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"solution1 testInput"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Solution, part 1"
]
},
{
"cell_type": "code",
"execution_count": 8,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"242"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"solution1 <$> inputLines"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Part 2\n",
"Make the function more generic:\n",
"* the argument `dx` determines the positions which are to be checked in each row: in row `i`, we check position `i*dx`.\n",
"* `dy` determines which rows are to be checked. If `dy` is 2, every other row is skipped."
]
},
{
"cell_type": "code",
"execution_count": 9,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"genericSolution :: Int -> Int -> [String] -> Int\n",
"genericSolution dx dy rows = \n",
" let\n",
" touchedRows = map snd\n",
" $ filter (\\ (x, _) -> x `mod` dy == 0)\n",
" $ zip [0..]\n",
" $ map parseLine rows\n",
" in\n",
" length\n",
" $ filter id\n",
" $ zipWith Set.member (map (`mod` n) [0, dx ..]) touchedRows\n",
" where n = length $ head rows"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Verify that the generic function yields the same results as before if `dx=3, dy=1`"
]
},
{
"cell_type": "code",
"execution_count": 10,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"7"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"genericSolution 3 1 testInput"
]
},
{
"cell_type": "code",
"execution_count": 11,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"242"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"genericSolution 3 1 <$> inputLines"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Multiply the results for the given `(dx, dy)` pairs:"
]
},
{
"cell_type": "code",
"execution_count": 12,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"solution2 rows = product \n",
" . map ((\\f -> f rows) . uncurry genericSolution)\n",
" $ [(1, 1), (3, 1), (5, 1), (7, 1), (1, 2)]"
]
},
{
"cell_type": "code",
"execution_count": 13,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"336"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"solution2 testInput"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Solution, part 2"
]
},
{
"cell_type": "code",
"execution_count": 14,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"2265549792"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"solution2 <$> inputLines"
]
}
],
"metadata": {
"kernelspec": {
"display_name": "Haskell",
"language": "haskell",
"name": "haskell"
},
"language_info": {
"codemirror_mode": "ihaskell",
"file_extension": ".hs",
"name": "haskell",
"version": "7.10.2"
}
},
"nbformat": 4,
"nbformat_minor": 0
}
| mit |
sandrofsousa/Resolution | Pysegreg/Pysegreg_notebook_distance.ipynb | 1 | 12856 | {
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Pysegreg run - Distance based"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"** Instructions**\n",
"\n",
"For fast processing, you can just change the following variables **before running**:\n",
"* path/name at **Input file** cell (select the file you want to use)\n",
"* bandwidth and weigth method at **compute population intensity** cell\n",
"* file name in the variable **fname** at section **Save results to a local file** (the file you want to save results)\n",
"\n",
"*make sure you don't use a name already used or the file will be replaced*\n",
"\n",
"With the previous steps in mind, just click on **Cell** menu and select **Run All**"
]
},
{
"cell_type": "code",
"execution_count": 1,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"# Imports\n",
"import numpy as np\n",
"np.seterr(all='ignore')\n",
"import pandas as pd\n",
"from decimal import Decimal\n",
"import time\n",
"\n",
"# Import python script with Pysegreg functions\n",
"from segregationMetrics import Segreg\n",
"\n",
"# Instantiate segreg as cc\n",
"cc = Segreg()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Input file\n",
"\n",
"**Attention to the new data structure for input !!!**\n",
"\n",
"Change your input file with path/name in the cell below to be processed.\n",
"\n",
"**Data Format** \n",
"**ID | X | Y | group 1 | group 2 | group n**"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"matrix([[ 3.33245530e+05, 7.39477232e+06, 7.70000000e-01, ...,\n",
" 3.65000000e+00, 1.49000000e+00, 3.45000000e+00],\n",
" [ 3.33657950e+05, 7.39531053e+06, 5.10000000e-01, ...,\n",
" 8.41000000e+00, 2.12000000e+00, 1.14000000e+00],\n",
" [ 3.33381780e+05, 7.39420259e+06, 1.42000000e+00, ...,\n",
" 2.20800000e+01, 2.88000000e+01, 9.11000000e+00],\n",
" ..., \n",
" [ 3.00003210e+05, 7.39509971e+06, 2.00000000e-01, ...,\n",
" 7.60000000e-01, 2.49000000e+00, 1.69000000e+00],\n",
" [ 3.04217510e+05, 7.40542844e+06, 5.00000000e-02, ...,\n",
" 1.40000000e-01, 6.20000000e-01, 2.20000000e-01],\n",
" [ 2.97174290e+05, 7.41325052e+06, 0.00000000e+00, ...,\n",
" 1.00000000e-02, 1.30000000e-01, 7.00000000e-02]])"
]
},
"execution_count": 2,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"cc.readAttributesFile('/Users/sandrofsousa/Downloads/valid/Segreg sample.csv')"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Measures\n",
"\n",
"**Compute Population Intensity**\n",
"\n",
"**For non spatial result, please comment the function call at: \"cc.locality= ...\" **\n",
"\n",
"* to comment a code use # in the begining of the line\n",
"\n",
"Distance matrix is calculated at this step. Change the parameters for the population \n",
"intensity according to your needs. Parameters are: \n",
"* **bandwidth** - is set to be 5000m by default, you can change it here \n",
"* **weightmethod** - 1 for gaussian, 2 for bi-square and empty for moving window"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"--- 0.30095577239990234 seconds for processing ---\n"
]
}
],
"source": [
"start_time = time.time()\n",
"\n",
"cc.locality = cc.cal_localityMatrix(bandwidth=700, weightmethod=1)\n",
"\n",
"print(\"--- %s seconds for processing ---\" % (time.time() - start_time))"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"For validation only \n",
"Remove the comment (#) if you want to see the values and validate"
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"'To select locality for a specific line (validation), use the index in[x,:]'"
]
},
"execution_count": 4,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# np.set_printoptions(threshold=np.inf)\n",
"# print('Location (coordinates from data):\\n', cc.location)\n",
"# print()\n",
"# print('Population intensity for all groups:\\n', cc.locality)\n",
"\n",
"'''To select locality for a specific line (validation), use the index in[x,:]'''\n",
"# where x is the number of the desired line\n",
"\n",
"# cc.locality[5,:]"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"**Compute local Dissimilarity**"
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"diss_local = cc.cal_localDissimilarity()\n",
"diss_local = np.asmatrix(diss_local).transpose()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"**Compute global Dissimilarity**"
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"diss_global = cc.cal_globalDissimilarity()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"** Compute local Exposure/Isolation** \n",
"expo is a matrix of n_group * n_group therefore, exposure (m,n) = rs[m,n] \n",
"the columns are exporsure m1 to n1, to n2... n5, m2 to n1....n5 \n",
"- m,m = isolation index of group m\n",
"- m,n = expouse index of group m to n\n",
"\n",
"Result of all combinations of local groups expousure/isolation \n",
"To select a specific line of m to n, use the index [x] \n",
"Each value is a result of the combinations m,n \n",
"e.g.: g1xg1, g1xg2, g2,g1, g2xg2 = isolation, expousure, // , isolation"
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"expo_local = cc.cal_localExposure()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"** Compute global Exposure/Isolation**"
]
},
{
"cell_type": "code",
"execution_count": 18,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"expo_global = cc.cal_globalExposure()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"**Compute local Entropy**"
]
},
{
"cell_type": "code",
"execution_count": 19,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"entro_local = cc.cal_localEntropy()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"**Compute global Entropy**"
]
},
{
"cell_type": "code",
"execution_count": 20,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"entro_global = cc.cal_globalEntropy()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"**Compute local Index H**"
]
},
{
"cell_type": "code",
"execution_count": 21,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"idxh_local = cc.cal_localIndexH()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"**Compute global Index H**"
]
},
{
"cell_type": "code",
"execution_count": 22,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"idxh_global = cc.cal_globalIndexH()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Results\n",
"**Prepare data for saving on a local file**"
]
},
{
"cell_type": "code",
"execution_count": 23,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"# Concatenate local values from measures\n",
"if len(cc.locality) == 0:\n",
" results = np.concatenate((expo_local, diss_local, entro_local, idxh_local), axis=1)\n",
"else:\n",
" results = np.concatenate((cc.locality, expo_local, diss_local, entro_local, idxh_local), axis=1)\n",
"\n",
"# Concatenate the results with original data\n",
"output = np.concatenate((cc.tract_id, cc.attributeMatrix, results),axis = 1)"
]
},
{
"cell_type": "code",
"execution_count": 24,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"names = ['id','x','y']\n",
"\n",
"for i in range(cc.n_group):\n",
" names.append('group_'+str(i))\n",
"\n",
"if len(cc.locality) == 0: \n",
" for i in range(cc.n_group):\n",
" for j in range(cc.n_group):\n",
" if i == j:\n",
" names.append('iso_' + str(i) + str(j))\n",
" else:\n",
" names.append('exp_' + str(i) + str(j))\n",
" \n",
" names.append('dissimil')\n",
" names.append('entropy')\n",
" names.append('indexh')\n",
" \n",
"else:\n",
" for i in range(cc.n_group):\n",
" names.append('intens_'+str(i))\n",
" \n",
" for i in range(cc.n_group):\n",
" for j in range(cc.n_group):\n",
" if i == j:\n",
" names.append('iso_' + str(i) + str(j))\n",
" else:\n",
" names.append('exp_' + str(i) + str(j))\n",
" \n",
" names.append('dissimil')\n",
" names.append('entropy')\n",
" names.append('indexh')"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"** Save Local and global results to a file**\n",
"\n",
"The paramenter **fname** corresponds to the folder/filename, change it as you want. \n",
"To save on a diferent folder, use the \"/\" to pass the directory. \n",
"The local results will be saved using the name defined and adding the **\"_local\"** postfix to file's name. \n",
"The global results are automatically saved using the same name with the addiction of the postfix **\"_globals\".** \n",
"\n",
"It's recommended to save on a different folder from the code, e.g.: a folder named result.\n",
"\n",
"**The fname value should be changed for any new executions or the local file will be overwrited!**"
]
},
{
"cell_type": "code",
"execution_count": 34,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"fname = \"/Users/sandrofsousa/Downloads/valid/result\"\n",
"\n",
"output = pd.DataFrame(output, columns=names)\n",
"output.to_csv(\"%s_local.csv\" % fname, sep=\",\", index=False)\n",
"with open(\"%s_global.txt\" % fname, \"w\") as f:\n",
" f.write('Global dissimilarity: ' + str(diss_global))\n",
" f.write('\\nGlobal entropy: ' + str(entro_global))\n",
" f.write('\\nGlobal Index H: ' + str(idxh_global))\n",
" f.write('\\nGlobal isolation/exposure: \\n')\n",
" f.write(str(expo_global))"
]
},
{
"cell_type": "code",
"execution_count": 85,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"# code to save data as a continuous string - Marcus request for R use\n",
"\n",
"# names2 = ['dissimil', 'entropy', 'indexh']\n",
"\n",
"# for i in range(cc.n_group):\n",
"# for j in range(cc.n_group):\n",
"# if i == j:\n",
"# names2.append('iso_' + str(i) + str(j))\n",
"# else:\n",
"# names2.append('exp_' + str(i) + str(j))\n",
"\n",
"# values = [diss_global, entro_global, idxh_global]\n",
"# for i in expo_global: values.append(i)\n",
"\n",
"# file2 = \"/Users/sandrofsousa/Downloads/\"\n",
"# with open(\"%s_global.csv\" % file2, \"w\") as f:\n",
"# f.write(', '.join(names2) + '\\n')\n",
"# f.write(', '.join(str(i) for i in values))"
]
}
],
"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": 1
}
| mit |
danietzio/Artificial-intelligence | Coursera ML Course - AndrewNG/week 2/ex1/LinearRegression One Varible Project ( Python ).ipynb | 1 | 419934 | {
"cells": [
{
"cell_type": "code",
"execution_count": 99,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"import numpy as np\n",
"import pandas as pd\n",
"import matplotlib.pyplot as plt\n",
"import seaborn as sns\n",
"from mpl_toolkits.mplot3d import axes3d, Axes3D #<-- Note the capitalization! \n",
"%matplotlib inline\n",
"sns.set_style(\"white\")"
]
},
{
"cell_type": "code",
"execution_count": 100,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"df = pd.read_csv('ex1data1.txt',sep=',',header= None)\n",
"df.columns = ['X','y']\n",
"m = df.shape[0]"
]
},
{
"cell_type": "code",
"execution_count": 101,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"[<matplotlib.lines.Line2D at 0x7ea41ed2e8>]"
]
},
"execution_count": 101,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"image/png": "iVBORw0KGgoAAAANSUhEUgAAAocAAAKACAYAAADuEdJaAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nQhxsAAAAAElFTkSuQmCC\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x7ea41ed0f0>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"sns.lmplot('X','y',data=df,size=9)\n",
"plt.plot(df['X'],df['y'],'rX',markersize=10)\n",
"plt.xlabel('Population of City in 10,000s')\n",
"plt.ylabel('Profit in $10,000s')\n",
"plt.plot(X['X1'],h_theta,c=\"green\")"
]
},
{
"cell_type": "code",
"execution_count": 102,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"from sklearn.cross_validation import train_test_split\n",
"X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.33, random_state=42)"
]
},
{
"cell_type": "code",
"execution_count": 103,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"X = pd.DataFrame({'X0': np.ones(m), 'X1': df['X']})\n",
"y = df['y']\n",
"theta = pd.Series(np.zeros(2))\n",
"iterations = 1500\n",
"alpha = 0.01\n",
"h_theta = 0"
]
},
{
"cell_type": "code",
"execution_count": 104,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"def cost_function(X,y,theta):\n",
" h_theta = theta[0] * X['X0'] + theta[1] * X['X1']\n",
" J = sum((h_theta - y) ** 2) / (m * 2)\n",
" return J"
]
},
{
"cell_type": "code",
"execution_count": 105,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"def cost_function_train():\n",
" h_theta = theta[0] * X_train['X0'] + theta[1] * X_train['X1']\n",
" J = sum((h_theta - y_train) ** 2) / (m * 2)\n",
" return J"
]
},
{
"cell_type": "code",
"execution_count": 106,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"def gradient_function():\n",
" for n in range(iterations):\n",
" global theta\n",
" cost_function(X,y,theta)\n",
" theta = theta - (alpha / m) * np.dot((h_theta - y).T,X).T\n",
" return theta"
]
},
{
"cell_type": "code",
"execution_count": 107,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"theta0_vals = np.linspace(-10,10,100)\n",
"theta1_vals = np.linspace(-1,4,100)\n",
"theta0_vals_len = theta0_vals.shape[0]\n",
"theta1_vals_len = theta1_vals.shape[0]\n",
"j_vals = np.zeros((theta0_vals_len,theta1_vals_len))\n",
"\n",
"for i in range(0,theta0_vals_len):\n",
" for j in range(0,theta1_vals_len):\n",
" t = pd.Series([theta0_vals[i],theta1_vals[j]])\n",
" j_vals[(i,j)] = cost_function(X,y,t)"
]
},
{
"cell_type": "code",
"execution_count": 108,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"image/png": "iVBORw0KGgoAAAANSUhEUgAAAqsAAAHBCAYAAABOnPJQAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nEEusAgAQS6wCABDrA0x01sDJg0s5AAAAAElFTkSuQmCC\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x7ea4451358>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"fig = plt.figure(figsize=(12,8))\n",
"ax = fig.gca(projection='3d')\n",
"ax.set_xlabel(\"Theta 0\")\n",
"ax.set_ylabel(\"Theta 1\")\n",
"ax.plot_surface(theta1_vals,theta0_vals,j_vals,linewidth=0)\n",
"ax.invert_xaxis()"
]
},
{
"cell_type": "code",
"execution_count": 109,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"# from matplotlib import animation\n",
"# def init():\n",
"# global fig;\n",
"# return fig,\n",
"\n",
"# def animate(i):\n",
"# ax.view_init(elev=10., azim=i)\n",
"# return fig,\n",
"# plt.rcParams['animation.ffmpeg_path'] = 'C:/ffmpeg/bin/ffmpeg'\n",
"\n",
"# # Animate\n",
"# anim = animation.FuncAnimation(fig, animate, init_func=init,frames=360, interval=20, blit=True)\n",
"# # Save\n",
"# FFwriter = animation.FFMpegWriter(fps=30, extra_args=['-vcodec', 'libx264'])\n",
"# anim.save('basic_animation.mp4',writer=FFwriter)\n"
]
},
{
"cell_type": "code",
"execution_count": 111,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"<matplotlib.text.Text at 0x7e9f85a400>"
]
},
"execution_count": 111,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"image/png": "iVBORw0KGgoAAAANSUhEUgAAAsgAAAHfCAYAAABNkC7IAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\njIJRgAQAcmCfda+3uN0AAAAASUVORK5CYII=\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x7ea663a630>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"fig = plt.figure(figsize=(12,8))\n",
"CS = plt.contour(theta0_vals,theta1_vals,j_vals,30,cmap='viridis')\n",
"plt.clabel(CS, inline=20, fontsize=10)\n",
"plt.title('Simplest default with labels')"
]
},
{
"cell_type": "code",
"execution_count": 67,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"0.0"
]
},
"execution_count": 67,
"metadata": {},
"output_type": "execute_result"
}
],
"source": []
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"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": "3.6.0"
}
},
"nbformat": 4,
"nbformat_minor": 2
}
| mit |
romeokienzler/uhack | projects/lstm/sine.ipynb | 1 | 64064 | {
"cells": [
{
"cell_type": "code",
"execution_count": 11,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"u'1.0.0-SNAPSHOT'"
]
},
"execution_count": 11,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"from systemml import MLContext, dml, jvm_stdout\n",
"ml = MLContext(sc)\n",
"ml.version()"
]
},
{
"cell_type": "code",
"execution_count": 18,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"script=\"\"\"\n",
"source(\"nn/layers/cross_entropy_loss.dml\") as cross_entropy_loss\n",
"source(\"nn/layers/l2_loss.dml\") as l2_loss\n",
"\n",
"source(\"nn/layers/lstm.dml\") as lstm\n",
"source(\"nn/layers/sigmoid.dml\") as sigmoid\n",
"\n",
"source(\"nn/optim/sgd_nesterov.dml\") as sgd_nesterov\n",
"source(\"nn/optim/rmsprop.dml\") as rmsprop\n",
"\n",
"X = seq(0, 999, 1) * 3.14 / 100 #0 to 10 pi radians\n",
"\n",
"X = sin(X)\n",
"#print(toString(X))\n",
"\n",
"in_TS = 75 #in_TS\n",
"out_TS = 25 #out_TS\n",
"N = nrow(X) / (in_TS + out_TS)\n",
"M = out_TS\n",
"\n",
"idx_mat = outer(seq(0,N-1,1), t(seq(0,in_TS+out_TS-1,1)), \"+\") + 1\n",
"#print(toString(idx_mat))\n",
"\n",
"idx_col = matrix(idx_mat, rows=nrow(idx_mat)*ncol(idx_mat), cols=1)\n",
"rordrd_X = table(seq(1, nrow(idx_col), 1), idx_col, nrow(idx_col), nrow(idx_col)) %*% X\n",
"\n",
"X = matrix(rordrd_X, rows=nrow(idx_mat), cols=ncol(idx_mat))\n",
"#print(toString(X))\n",
"\n",
"Y = X[,in_TS+1:in_TS+out_TS]\n",
"X = X[,1:in_TS]\n",
"\n",
"max_iterations = 2000\n",
"iter = 0\n",
"learningRate = 0.01\n",
"decayRate = 0.95\n",
"\n",
"[W, b, out0, c0] = lstm::init(N,1,M)\n",
"rmspropCache = rmsprop::init(W)\n",
"\n",
"while( iter < max_iterations ){\n",
" [a1, c, c_out, c_c, c_ifog] = lstm::forward(X, W, b, in_TS, 1, FALSE, out0, c0)\n",
" loss = l2_loss::forward(a1, Y)\n",
"\n",
" if(iter %% 100 == 0) print(\"iter=\" + iter + \" loss=\" + loss)\n",
"\n",
" loss_grad = l2_loss::backward(a1, Y)\n",
" [dX, dW, db, dout0, dc0] = lstm::backward(loss_grad, c0, X, W, b, in_TS, 1, FALSE, out0, c0, c_out, c_c, c_ifog)\n",
"\n",
" [W, rmspropCache] = rmsprop::update(W, dW, learningRate, decayRate, 1e-6, rmspropCache)\n",
"\n",
" iter = iter + 1\n",
"}\n",
"\n",
"[a1, c, c_out, c_c, c_ifog] = lstm::forward(X, W, b, in_TS, 1, FALSE, out0, c0)\n",
"print(toString(cbind(a1, Y)))\n",
"\n",
"\"\"\""
]
},
{
"cell_type": "code",
"execution_count": 19,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"iter=0 loss=1.482576498588268\n",
"iter=100 loss=0.1612714328013795\n",
"iter=200 loss=0.058938287626221386\n",
"iter=300 loss=0.04897157601036786\n",
"iter=400 loss=0.031392670989147206\n",
"iter=500 loss=0.03271373060275306\n",
"iter=600 loss=0.029034160789182445\n",
"iter=700 loss=0.0377526211023614\n",
"iter=800 loss=0.009473446532038406\n",
"iter=900 loss=0.021804000351799663\n",
"iter=1000 loss=0.032295088972713305\n",
"iter=1100 loss=0.016885715019442922\n",
"iter=1200 loss=0.032142600672863356\n",
"iter=1300 loss=0.011399926950937665\n",
"iter=1400 loss=0.009869723200786508\n",
"iter=1500 loss=0.014297125319484428\n",
"iter=1600 loss=0.015026303897219703\n",
"iter=1700 loss=0.010265573574590854\n",
"iter=1800 loss=0.16290252619536108\n",
"iter=1900 loss=0.0019342772124357182\n",
"0.701 0.670 0.657 0.628 0.607 0.592 0.557 0.541 0.504 0.478 0.449 0.431 0.392 0.361 0.335 0.302 0.289 0.247 0.235 0.204 0.143 0.164 0.134 0.084 -0.010 0.708 0.685 0.662 0.638 0.614 0.589 0.563 0.537 0.510 0.483 0.455 0.427 0.398 0.369 0.340 0.310 0.280 0.250 0.220 0.189 0.158 0.127 0.096 0.064 0.033\n",
"0.691 0.658 0.647 0.619 0.595 0.577 0.546 0.524 0.489 0.465 0.435 0.410 0.377 0.347 0.321 0.291 0.273 0.232 0.213 0.185 0.130 0.139 0.105 0.070 -0.014 0.685 0.662 0.638 0.614 0.589 0.563 0.537 0.510 0.483 0.455 0.427 0.398 0.369 0.340 0.310 0.280 0.250 0.220 0.189 0.158 0.127 0.096 0.064 0.033 0.002\n",
"0.679 0.643 0.634 0.608 0.582 0.559 0.534 0.505 0.472 0.448 0.420 0.388 0.361 0.331 0.304 0.278 0.254 0.215 0.189 0.163 0.114 0.113 0.075 0.051 -0.019 0.662 0.638 0.614 0.589 0.563 0.537 0.510 0.483 0.455 0.427 0.398 0.369 0.340 0.310 0.280 0.250 0.220 0.189 0.158 0.127 0.096 0.064 0.033 0.002 -0.030\n",
"0.664 0.626 0.619 0.595 0.566 0.539 0.519 0.484 0.453 0.429 0.402 0.365 0.341 0.312 0.285 0.261 0.233 0.197 0.162 0.138 0.094 0.085 0.043 0.027 -0.028 0.638 0.614 0.589 0.563 0.537 0.510 0.483 0.455 0.427 0.398 0.369 0.340 0.310 0.280 0.250 0.220 0.189 0.158 0.127 0.096 0.064 0.033 0.002 -0.030 -0.061\n",
"0.646 0.606 0.601 0.578 0.547 0.516 0.500 0.460 0.430 0.406 0.381 0.341 0.319 0.291 0.262 0.240 0.208 0.175 0.133 0.111 0.071 0.054 0.009 -0.002 -0.039 0.614 0.589 0.563 0.537 0.510 0.483 0.455 0.427 0.398 0.369 0.340 0.310 0.280 0.250 0.220 0.189 0.158 0.127 0.096 0.064 0.033 0.002 -0.030 -0.061 -0.092\n",
"0.625 0.583 0.579 0.557 0.525 0.490 0.476 0.433 0.404 0.380 0.357 0.315 0.292 0.266 0.236 0.214 0.180 0.150 0.102 0.080 0.043 0.021 -0.026 -0.036 -0.057 0.589 0.563 0.537 0.510 0.483 0.455 0.427 0.398 0.369 0.340 0.310 0.280 0.250 0.220 0.189 0.158 0.127 0.096 0.064 0.033 0.002 -0.030 -0.061 -0.092 -0.124\n",
"0.600 0.557 0.553 0.531 0.499 0.461 0.445 0.404 0.375 0.349 0.326 0.287 0.262 0.237 0.204 0.181 0.147 0.120 0.069 0.046 0.011 -0.013 -0.061 -0.074 -0.082 0.563 0.537 0.510 0.483 0.455 0.427 0.398 0.369 0.340 0.310 0.280 0.250 0.220 0.189 0.158 0.127 0.096 0.064 0.033 0.002 -0.030 -0.061 -0.092 -0.124 -0.155\n",
"0.570 0.529 0.522 0.499 0.466 0.430 0.408 0.373 0.343 0.314 0.289 0.258 0.228 0.203 0.169 0.142 0.113 0.085 0.037 0.012 -0.024 -0.048 -0.092 -0.111 -0.116 0.537 0.510 0.483 0.455 0.427 0.398 0.369 0.340 0.310 0.280 0.250 0.220 0.189 0.158 0.127 0.096 0.064 0.033 0.002 -0.030 -0.061 -0.092 -0.124 -0.155 -0.186\n",
"0.537 0.499 0.489 0.462 0.428 0.399 0.370 0.341 0.309 0.279 0.251 0.229 0.192 0.166 0.132 0.103 0.078 0.047 0.008 -0.020 -0.058 -0.079 -0.115 -0.143 -0.157 0.510 0.483 0.455 0.427 0.398 0.369 0.340 0.310 0.280 0.250 0.220 0.189 0.158 0.127 0.096 0.064 0.033 0.002 -0.030 -0.061 -0.092 -0.124 -0.155 -0.186 -0.216\n",
"0.505 0.473 0.459 0.425 0.393 0.372 0.339 0.310 0.278 0.246 0.220 0.203 0.157 0.131 0.097 0.069 0.048 0.012 -0.017 -0.046 -0.087 -0.103 -0.127 -0.165 -0.202 0.483 0.455 0.427 0.398 0.369 0.340 0.310 0.280 0.250 0.220 0.189 0.158 0.127 0.096 0.064 0.033 0.002 -0.030 -0.061 -0.092 -0.124 -0.155 -0.186 -0.216 -0.247\n",
"\n",
"SystemML Statistics:\n",
"Total execution time:\t\t66.906 sec.\n",
"Number of executed Spark inst:\t0.\n",
"\n",
"\n"
]
}
],
"source": [
"prog = dml(script).output(\"a1\").output(\"X\").output(\"Y\")\n",
"with jvm_stdout(True):\n",
" result = ml.execute(prog)"
]
},
{
"cell_type": "code",
"execution_count": 20,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"X = result.get(\"X\").toNumPy()\n",
"a1 = result.get(\"a1\").toNumPy()"
]
},
{
"cell_type": "code",
"execution_count": 21,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"import matplotlib.pyplot as plt\n",
"import numpy as np"
]
},
{
"cell_type": "code",
"execution_count": 22,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"complete = np.vstack((np.transpose(X),np.transpose(a1)))"
]
},
{
"cell_type": "code",
"execution_count": 23,
"metadata": {},
"outputs": [
{
"data": {
"image/png": "iVBORw0KGgoAAAANSUhEUgAAAY4AAAD8CAYAAABgmUMCAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nRK5CYII=\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x114a466d0>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"plt.plot(complete)\n",
"plt.ylabel('some numbers')\n",
"plt.show()"
]
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 2",
"language": "python",
"name": "python2"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 2
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython2",
"version": "2.7.13"
}
},
"nbformat": 4,
"nbformat_minor": 1
}
| apache-2.0 |
markovmodel/adaptivemd | examples/tutorial/4_example_advanced_tasks.ipynb | 1 | 36322 | {
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# AdaptiveMD\n",
"\n",
"## Example 4 - Custom `Task` objects"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### 0. Imports"
]
},
{
"cell_type": "code",
"execution_count": 1,
"metadata": {},
"outputs": [],
"source": [
"from adaptivemd import Project, File, PythonTask, Task"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Let's open our `test` project by its name. If you completed the first examples this should all work out of the box."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Open all connections to the MongoDB and Session so we can get started."
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {},
"outputs": [],
"source": [
"project = Project('tutorial')"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Let's see again where we are. These numbers will depend on whether you run this notebook for the first time or just continue again. Unless you delete your project it will accumulate models and files over time, as is our ultimate goal."
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"<StoredBundle for with 215 file(s) @ 0x11055dc50>\n",
"<StoredBundle for with 3 file(s) @ 0x11055dc10>\n",
"<StoredBundle for with 45 file(s) @ 0x11055dbd0>\n"
]
}
],
"source": [
"print project.files\n",
"print project.generators\n",
"print project.models"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Now restore our old ways to generate tasks by loading the previously used generators."
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {},
"outputs": [],
"source": [
"engine = project.generators['openmm']\n",
"modeller = project.generators['pyemma']\n",
"pdb_file = project.files['initial_pdb']"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## A simple task"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"A task is in essence a bash script-like description of what should be executed by the worker. It has details about files to be linked to the working directory, bash commands to be executed and some meta information about what should happen in case we succeed or fail."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"#### The execution structure"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Let's first explain briefly how a task is executed and what its components are. This was originally build so that it is compatible with radical.pilot and still is. So, if you are familiar with it, all of the following information should sould very familiar.\n",
"\n",
"A task is executed from within a unique directory that only exists for this particular task. These are located in `adaptivemd/workers/` and look like \n",
"\n",
"```\n",
"worker.0x5dcccd05097611e7829b000000000072L/\n",
"```\n",
"\n",
"the long number is a hex representation of the UUID of the task. Just if you are curious type\n",
"```\n",
"print hex(my_task.__uuid__)\n",
"```\n",
"\n",
"Then we change directory to this folder write a `running.sh` bash script and execute it. This script is created from the task definition and also depends on your resource setting (which basically only contain the path to the workers directory, etc)\n",
"\n",
"The script is divided into 1 or 3 parts depending on which `Task` class you use. The main `Task` uses a single list of commands, while `PrePostTask` has the following structure\n",
"\n",
"1. **Pre-Exec**: Things to happen before the main command (optional)\n",
"\n",
"2. **Main**: the main commands are executed\n",
"\n",
"3. **Post-Exec**: Things to happen after the main command (optional)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Okay, lots of theory, now some real code for running a task that generated a trajectory"
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {},
"outputs": [],
"source": [
"task = engine.run(project.new_trajectory(pdb_file, 100))"
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"[Link('staging:///alanine.pdb' > 'worker://initial.pdb),\n",
" Link('staging:///system.xml' > 'worker://system.xml),\n",
" Link('staging:///integrator.xml' > 'worker://integrator.xml),\n",
" Link('staging:///openmmrun.py' > 'worker://openmmrun.py),\n",
" Touch('worker://traj/'),\n",
" '\\nj=0\\ntries=10\\nsleep=1\\n\\ntrajfile=traj/protein.dcd\\n\\nwhile [ $j -le $tries ]; do if ! [ -s $trajfile ]; then python openmmrun.py -r --report-interval 1 -p CPU --types=\"{\\'protein\\':{\\'stride\\':1,\\'selection\\':\\'protein\\',\\'name\\':null,\\'filename\\':\\'protein.dcd\\'},\\'master\\':{\\'stride\\':10,\\'selection\\':null,\\'name\\':null,\\'filename\\':\\'master.dcd\\'}}\" -s system.xml -i integrator.xml -t worker://initial.pdb --length 100 worker://traj/; fi; sleep 1; j=$((j+1)); done',\n",
" Move('worker://traj/' > 'sandbox:///{}/00000068/)]"
]
},
"execution_count": 6,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"task.script"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"We are linking a lot of files to the worker directory and change the name for the .pdb in the process. Then call the actual `python` script that runs openmm. And finally move the `output.dcd` and the restart file back tp the trajectory folder."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"There is a way to list lot's of things about tasks and we will use it a lot to see our modifications."
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Task: TrajectoryGenerationTask(OpenMMEngine) [created]\n",
"\n",
"Sources\n",
"-- Unstaged\n",
"-- Staged\n",
"- staging:///openmmrun.py \n",
"- staging:///system.xml \n",
"- staging:///integrator.xml \n",
"- staging:///alanine.pdb \n",
"\n",
"Targets\n",
"- sandbox:///{}/00000068/\n",
"\n",
"Modified\n",
"\n",
"Link('staging:///alanine.pdb' > 'worker://initial.pdb)\n",
"Link('staging:///system.xml' > 'worker://system.xml)\n",
"Link('staging:///integrator.xml' > 'worker://integrator.xml)\n",
"Link('staging:///openmmrun.py' > 'worker://openmmrun.py)\n",
"Touch('worker://traj/')\n",
"\n",
"j=0\n",
"tries=10\n",
"sleep=1\n",
"\n",
"trajfile=traj/protein.dcd\n",
"\n",
"while [ $j -le $tries ]; do if ! [ -s $trajfile ]; then python openmmrun.py -r --report-interval 1 -p CPU --types=\"{'protein':{'stride':1,'selection':'protein','name':null,'filename':'protein.dcd'},'master':{'stride':10,'selection':null,'name':null,'filename':'master.dcd'}}\" -s system.xml -i integrator.xml -t worker://initial.pdb --length 100 worker://traj/; fi; sleep 1; j=$((j+1)); done\n",
"Move('worker://traj/' > 'sandbox:///{}/00000068/)\n"
]
}
],
"source": [
"print task.description"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Modify a task"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"As long as a task is not saved and hence placed in the queue, it can be altered in any way. All of the 3 / 5 phases can be changed separately. You can add things to the staging phases or bash phases or change the command. So, let's do that now"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"#### Add a bash line"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"First, a `Task` is very similar to a list of bash commands and you can simply append (or prepend) a command. A text line will be interpreted as a bash command."
]
},
{
"cell_type": "code",
"execution_count": 8,
"metadata": {},
"outputs": [],
"source": [
"task.append('echo \"This new line is pointless\"')"
]
},
{
"cell_type": "code",
"execution_count": 9,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Task: TrajectoryGenerationTask(OpenMMEngine) [created]\n",
"\n",
"Sources\n",
"-- Unstaged\n",
"-- Staged\n",
"- staging:///openmmrun.py \n",
"- staging:///system.xml \n",
"- staging:///integrator.xml \n",
"- staging:///alanine.pdb \n",
"\n",
"Targets\n",
"- sandbox:///{}/00000068/\n",
"\n",
"Modified\n",
"\n",
"Link('staging:///alanine.pdb' > 'worker://initial.pdb)\n",
"Link('staging:///system.xml' > 'worker://system.xml)\n",
"Link('staging:///integrator.xml' > 'worker://integrator.xml)\n",
"Link('staging:///openmmrun.py' > 'worker://openmmrun.py)\n",
"Touch('worker://traj/')\n",
"\n",
"j=0\n",
"tries=10\n",
"sleep=1\n",
"\n",
"trajfile=traj/protein.dcd\n",
"\n",
"while [ $j -le $tries ]; do if ! [ -s $trajfile ]; then python openmmrun.py -r --report-interval 1 -p CPU --types=\"{'protein':{'stride':1,'selection':'protein','name':null,'filename':'protein.dcd'},'master':{'stride':10,'selection':null,'name':null,'filename':'master.dcd'}}\" -s system.xml -i integrator.xml -t worker://initial.pdb --length 100 worker://traj/; fi; sleep 1; j=$((j+1)); done\n",
"Move('worker://traj/' > 'sandbox:///{}/00000068/)\n",
"echo \"This new line is pointless\"\n"
]
}
],
"source": [
"print task.description"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"As expected this line was added to the end of the script."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"#### Add staging actions\n",
"\n",
"To set staging is more difficult. The reason is, that you normally have no idea where files are located and hence writing a copy or move is impossible. This is why the staging commands are not bash lines but objects that hold information about the actual file transaction to be done. There are some task methods that help you move files but also files itself can generate this commands for you."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Let's move one trajectory (directory) around a little more as an example"
]
},
{
"cell_type": "code",
"execution_count": 10,
"metadata": {},
"outputs": [],
"source": [
"traj = project.trajectories.one"
]
},
{
"cell_type": "code",
"execution_count": 11,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Copy('sandbox:///{}/00000067/' > 'worker://00000067)\n"
]
}
],
"source": [
"transaction = traj.copy()\n",
"print transaction"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"This looks like in the script. The default for a copy is to move a file or folder to the worker directory under the same name, but you can give it another name/location if you use that as an argument. Note that since trajectories are a directory you need to give a directory name (which end in a `/`)"
]
},
{
"cell_type": "code",
"execution_count": 12,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Copy('sandbox:///{}/00000067/' > 'worker://new_traj/)\n"
]
}
],
"source": [
"transaction = traj.copy('new_traj/')\n",
"print transaction"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"If you want to move it not to the worker directory you have to specify the location and you can do so with the prefixes (`shared://`, `sandbox://`, `staging://` as explained in the previous examples)"
]
},
{
"cell_type": "code",
"execution_count": 13,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Copy('sandbox:///{}/00000067/' > 'staging:///cached_trajs/)\n"
]
}
],
"source": [
"transaction = traj.copy('staging:///cached_trajs/')\n",
"print transaction"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Besides `.copy` you can also `.move` or `.link` files."
]
},
{
"cell_type": "code",
"execution_count": 14,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Copy('file://{}/alanine.pdb' > 'staging:///delete.pdb)\n",
"Move('file://{}/alanine.pdb' > 'staging:///delete.pdb)\n",
"Link('file://{}/alanine.pdb' > 'staging:///delete.pdb)\n"
]
}
],
"source": [
"transaction = pdb_file.copy('staging:///delete.pdb')\n",
"print transaction\n",
"transaction = pdb_file.move('staging:///delete.pdb')\n",
"print transaction\n",
"transaction = pdb_file.link('staging:///delete.pdb')\n",
"print transaction"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"#### Local files"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Let's mention these because they require special treatment. We cannot copy files to the HPC, we need to store them in the DB first."
]
},
{
"cell_type": "code",
"execution_count": 15,
"metadata": {},
"outputs": [],
"source": [
"new_pdb = File('file://../files/ntl9/ntl9.pdb').load()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Make sure you use `file://` to indicate that you are using a local file. The above example uses a relative path which will be replaced by an absolute one, otherwise we ran into trouble once we open the project at a different directory."
]
},
{
"cell_type": "code",
"execution_count": 16,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"file:///Users/osz/adaptivemd/examples/files/ntl9/ntl9.pdb\n"
]
}
],
"source": [
"print new_pdb.location"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Note that now there are 3 `/` in the filename, two from the `://` and one from the root directory of your machine"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"The `load()` at the end really loads the file and when you save this `File` now it will contain the content of the file. You can access this content as seen in the previous example."
]
},
{
"cell_type": "code",
"execution_count": 17,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"CRYST1 50.000 50.000 50.000 90.00 90.00 90.00 P 1 \n",
"ATOM 1 N MET 1 33.720 28.790 34.120 0.00 0.00 N \n",
"ATOM 2 H1 MET 1 33.620 29.790 33.900 0.00 0.00 H \n",
"ATOM 3 H2 MET 1 33.770 28.750 35.120 0.00 0.00 \n"
]
}
],
"source": [
"print new_pdb.get_file()[:300]"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"For local files you normally use `.transfer`, but `copy`, `move` or `link` work as well. Still, there is no difference since the file only exists in the DB now and copying from the DB to a place on the HPC results in a simple file creation."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Now, we want to add a command to the staging and see what happens."
]
},
{
"cell_type": "code",
"execution_count": 18,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Transfer('file://{}/ntl9.pdb' > 'worker://ntl9.pdb)\n"
]
}
],
"source": [
"transaction = new_pdb.transfer()\n",
"print transaction"
]
},
{
"cell_type": "code",
"execution_count": 19,
"metadata": {},
"outputs": [],
"source": [
"task.append(transaction)"
]
},
{
"cell_type": "code",
"execution_count": 20,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Task: TrajectoryGenerationTask(OpenMMEngine) [created]\n",
"\n",
"Sources\n",
"-- Unstaged\n",
"- file://{}/ntl9.pdb [exists]\n",
"-- Staged\n",
"- staging:///integrator.xml \n",
"- staging:///system.xml \n",
"- staging:///openmmrun.py \n",
"- staging:///alanine.pdb \n",
"\n",
"Targets\n",
"- sandbox:///{}/00000068/\n",
"\n",
"Modified\n",
"\n",
"Link('staging:///alanine.pdb' > 'worker://initial.pdb)\n",
"Link('staging:///system.xml' > 'worker://system.xml)\n",
"Link('staging:///integrator.xml' > 'worker://integrator.xml)\n",
"Link('staging:///openmmrun.py' > 'worker://openmmrun.py)\n",
"Touch('worker://traj/')\n",
"\n",
"j=0\n",
"tries=10\n",
"sleep=1\n",
"\n",
"trajfile=traj/protein.dcd\n",
"\n",
"while [ $j -le $tries ]; do if ! [ -s $trajfile ]; then python openmmrun.py -r --report-interval 1 -p CPU --types=\"{'protein':{'stride':1,'selection':'protein','name':null,'filename':'protein.dcd'},'master':{'stride':10,'selection':null,'name':null,'filename':'master.dcd'}}\" -s system.xml -i integrator.xml -t worker://initial.pdb --length 100 worker://traj/; fi; sleep 1; j=$((j+1)); done\n",
"Move('worker://traj/' > 'sandbox:///{}/00000068/)\n",
"echo \"This new line is pointless\"\n",
"Transfer('file://{}/ntl9.pdb' > 'worker://ntl9.pdb)\n"
]
}
],
"source": [
"print task.description"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"We now have one more transfer command. But something else has changed. There is one more files listed as required. So, the task can only run, if that file exists, but since we loaded it into the DB, it exists (for us). For example the newly created trajectory `25.dcd` does not exist yet. Would that be a requirement the task would fail. But let's check that it exists."
]
},
{
"cell_type": "code",
"execution_count": 21,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"True"
]
},
"execution_count": 21,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"new_pdb.exists"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Okay, we have now the PDB file staged and so any real bash commands could work with a file `ntl9.pdb`. Alright, so let's output its stats."
]
},
{
"cell_type": "code",
"execution_count": 22,
"metadata": {},
"outputs": [],
"source": [
"task.append('stat ntl9.pdb')"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Note that usually you place these stage commands at the top or your script."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Now we could run this task, as before and see, if it works. (Make sure you still have a worker running)"
]
},
{
"cell_type": "code",
"execution_count": 23,
"metadata": {},
"outputs": [],
"source": [
"project.queue(task)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"And check, that the task is running"
]
},
{
"cell_type": "code",
"execution_count": 24,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"u'created'"
]
},
"execution_count": 24,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"task.state"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"If we did not screw up the task, it should have succeeded and we can look at the STDOUT."
]
},
{
"cell_type": "code",
"execution_count": 25,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"None\n"
]
}
],
"source": [
"print task.stdout"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Well, great, we have the pointless output and the stats of the newly staged file `ntl9.pdb`"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### How does a real script look like"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Just for fun let's create the same scheduler that the `adaptivemdworker` uses, but from inside this notebook."
]
},
{
"cell_type": "code",
"execution_count": 26,
"metadata": {},
"outputs": [],
"source": [
"from adaptivemd import WorkerScheduler"
]
},
{
"cell_type": "code",
"execution_count": 27,
"metadata": {},
"outputs": [],
"source": [
"sc = WorkerScheduler(project._current_configuration)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"If you really wanted to use the worker you need to initialize it and it will create directories and stage files for the generators, etc. For that you need to call `sc.enter(project)`, but since we only want it to parse our tasks, we only set the project without invoking initialization. You should normally not do that."
]
},
{
"cell_type": "code",
"execution_count": 28,
"metadata": {},
"outputs": [],
"source": [
"sc.project = project"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Now we can use a function `.task_to_script` that will parse a task into a bash script. So this is really what would be run on your machine now."
]
},
{
"cell_type": "code",
"execution_count": 29,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"set -e\n",
"ln -s ../staging_area/alanine.pdb initial.pdb\n",
"ln -s ../staging_area/system.xml system.xml\n",
"ln -s ../staging_area/integrator.xml integrator.xml\n",
"ln -s ../staging_area/openmmrun.py openmmrun.py\n",
"mkdir -p traj/\n",
"\n",
"j=0\n",
"tries=10\n",
"sleep=1\n",
"\n",
"trajfile=traj/protein.dcd\n",
"\n",
"while [ $j -le $tries ]; do if ! [ -s $trajfile ]; then python openmmrun.py -r --report-interval 1 -p CPU --types=\"{'protein':{'stride':1,'selection':'protein','name':null,'filename':'protein.dcd'},'master':{'stride':10,'selection':null,'name':null,'filename':'master.dcd'}}\" -s system.xml -i integrator.xml -t initial.pdb --length 100 traj/; fi; sleep 1; j=$((j+1)); done\n",
"mkdir -p ../../projects/tutorial/trajs/00000068/\n",
"mv traj/* ../../projects/tutorial/trajs/00000068/\n",
"rm -r traj/\n",
"echo \"This new line is pointless\"\n",
"# write file `ntl9.pdb` from DB\n",
"stat ntl9.pdb\n"
]
}
],
"source": [
"print '\\n'.join(sc.task_to_script(task))"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Now you see that all file paths have been properly interpreted to work. See that there is a comment about a temporary file from the DB that is then renamed. This is a little trick to be compatible with RPs way of handling files. (TODO: We might change this to just write to the target file. Need to check if that is still consistent)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"#### A note on file locations\n",
"\n",
"One problem with bash scripts is that when you create the tasks you have no concept on where the files actually are located. To get around this the created bash script will be scanned for paths, that contain prefixed like we are used to and are interpreted in the context of the worker / scheduler. The worker is the only instance to know all that is necessary so this is the place to fix that problem."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Let's see that in a little example, where we create an empty file in the staging area."
]
},
{
"cell_type": "code",
"execution_count": 30,
"metadata": {},
"outputs": [],
"source": [
"task = Task()\n",
"task.append('touch staging:///my_file.txt')"
]
},
{
"cell_type": "code",
"execution_count": 31,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"set -e\n",
"touch ../staging_area/my_file.txt\n"
]
}
],
"source": [
"print '\\n'.join(sc.task_to_script(task))"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"And voila, the path has changed to a relative path from the working directory of the worker. Note that you see here the line we added in the very beginning of example 1 to our resource!"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### A Task from scratch"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"If you want to start a new task you can begin with"
]
},
{
"cell_type": "code",
"execution_count": 32,
"metadata": {},
"outputs": [],
"source": [
"task = Task()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"as we did before."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Just start adding staging and bash commands and you are done. When you create a task you can assign it a generator, then the system will assume that this task was generated by that generator, so don't do it for you custom tasks, unless you generated them in a generator. Setting this allows you to tell a worker only to run tasks of certain types."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## The Python RPC Task"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"The tasks so far a very powerful, but they lack the possibility to call a python function. Since we are using python here, it would be great to really pretend to call a python function from here and not taking the detour of writing a python bash executable with arguments, etc... An example for this is the PyEmma generator which uses this capability."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Let's do an example of this as well. Assume we have a python function in a file (you need to have your code in a file so far so that we can copy the file to the HPC if necessary). Let's create the `.py` file now."
]
},
{
"cell_type": "code",
"execution_count": 33,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Overwriting my_rpc_function.py\n"
]
}
],
"source": [
"%%file my_rpc_function.py\n",
"\n",
"def my_func(f):\n",
" import os\n",
" print f\n",
" return os.path.getsize(f)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Now create a PythonTask instead"
]
},
{
"cell_type": "code",
"execution_count": 35,
"metadata": {},
"outputs": [],
"source": [
"task = PythonTask(modeller)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"and the call function has changed. Note that also now you can still add all the bash and stage commands as before. A PythonTask is also a subclass of `PrePostTask` so we have a `.pre` and `.post` phase available."
]
},
{
"cell_type": "code",
"execution_count": 36,
"metadata": {},
"outputs": [],
"source": [
"from my_rpc_function import my_func"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"We call the function `my_func` with one argument"
]
},
{
"cell_type": "code",
"execution_count": 37,
"metadata": {},
"outputs": [],
"source": [
"task.call(my_func, f=project.trajectories.one)"
]
},
{
"cell_type": "code",
"execution_count": 38,
"metadata": {
"scrolled": true
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Task: PythonTask(PyEMMAAnalysis) [created]\n",
"\n",
"Sources\n",
"-- Unstaged\n",
"- file://{}/my_rpc_function.py [exists]\n",
"- file://{}/_rpc_input_0x1b095cb8a61811e8b1770000000002fcL.json \n",
"-- Staged\n",
"- staging:///_run_.py \n",
"\n",
"Targets\n",
"- file://{}/_rpc_output_0x1b095cb8a61811e8b1770000000002fcL.json\n",
"\n",
"Modified\n",
"\n",
"Transfer('file://{}/_rpc_input_0x1b095cb8a61811e8b1770000000002fcL.json' > 'worker://input.json)\n",
"Link('staging:///_run_.py' > 'worker://_run_.py)\n",
"Transfer('file://{}/my_rpc_function.py' > 'worker://my_rpc_function.py)\n",
"python _run_.py\n",
"Transfer('worker://output.json' > 'file://{}/_rpc_output_0x1b095cb8a61811e8b1770000000002fcL.json)\n"
]
}
],
"source": [
"print task.description"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Well, interesting. What this actually does is to write the input arguments to the function into a temporary `.json` file on the worker, (in RP on the local machine and then transfers it to remote), rename it to `input.json` and read it in the `_run_.py`. This is still a little clumsy, but needs to be this way to be RP compatible which only works with files! Look at the actual script."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"You see, that we really copy the `.py` file that contains the source code to the worker directory. All that is done automatically. A little caution on this. You can either write a function in a single file or use any installed package, but in this case the same package needs to be installed on the remote machine as well!"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Let's run it and see what happens."
]
},
{
"cell_type": "code",
"execution_count": 39,
"metadata": {},
"outputs": [],
"source": [
"project.queue(task)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"And wait until the task is done"
]
},
{
"cell_type": "code",
"execution_count": 42,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"project.wait_until(task.is_done)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"The default settings will automatically save the content from the resulting output.json in the DB an you can access the data that was returned from the task at `.output`. In our example the result was just the size of a the file in bytes"
]
},
{
"cell_type": "code",
"execution_count": 43,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"4096"
]
},
"execution_count": 43,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"task.output"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"And you can use this information in an adaptive script to make decisions."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"#### success callback"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"The last thing we did not talk about is the possibility to also call a function with the returned data automatically on successful execution. Since this function is executed on the worker we (so far) only support function calls with the following restrictions. "
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"1. you can call a function of the related generator class. for this you need to create the task using `PythonTask(generator)`\n",
"2. the function name you want to call is stored in `task.then_func_name`. So you can write a generator class with several possible outcomes and chose the function for each task.\n",
"3. The `Generator` needs to be part of `adaptivemd`"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"So in the case of `modeller.execute` we create a `PythonTask` that references the following functions"
]
},
{
"cell_type": "code",
"execution_count": 44,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"task = modeller.execute(project.trajectories)"
]
},
{
"cell_type": "code",
"execution_count": 45,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"'then_func'"
]
},
"execution_count": 45,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"task.then_func_name"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"So we will call the default `then_func` of modeller or the class modeller is of."
]
},
{
"cell_type": "code",
"execution_count": 46,
"metadata": {
"scrolled": true
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Help on function then_func in module adaptivemd.analysis.pyemma.emma:\n",
"\n",
"then_func(project, task, data, inputs)\n",
"\n"
]
}
],
"source": [
"help(modeller.then_func)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"These callbacks are called with the current project, the resulting data (which is in the modeller case a `Model` object) and array of initial inputs.\n",
"\n",
"This is the actual code of the callback\n",
"\n",
"```py\n",
"@staticmethod\n",
"def then_func(project, task, model, inputs):\n",
" # add the input arguments for later reference\n",
" model.data['input']['trajectories'] = inputs['kwargs']['files']\n",
" model.data['input']['pdb'] = inputs['kwargs']['topfile']\n",
" project.models.add(model)\n",
"```\n",
"\n",
"All it does is to add some of the input parameters to the model for later reference and then store the model in the project. You are free to define all sorts of actions here, even queue new tasks."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Next, we will talk about the factories for `Task` objects, called `generators`. There we will actually write a new class that does some stuff with the results."
]
},
{
"cell_type": "code",
"execution_count": 47,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"project.close()"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": []
}
],
"metadata": {
"anaconda-cloud": {},
"kernelspec": {
"display_name": "Python 2",
"language": "python",
"name": "python2"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 2
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython2",
"version": "2.7.15"
}
},
"nbformat": 4,
"nbformat_minor": 1
}
| lgpl-2.1 |
cristhro/Machine-Learning | ejercicio 5/Practica 5.ipynb | 1 | 96694 | {
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Sacar la lista de 250 Pelis"
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"[<Movie id:0111161[http] title:_The Shawshank Redemption (1994)_>,\n",
" <Movie id:0068646[http] title:_The Godfather (1972)_>,\n",
" <Movie id:0071562[http] title:_The Godfather: Part II (1974)_>,\n",
" <Movie id:0468569[http] title:_The Dark Knight (2008)_>,\n",
" <Movie id:0050083[http] title:_12 Angry Men (1957)_>,\n",
" <Movie id:0108052[http] title:_Schindler's List (1993)_>,\n",
" <Movie id:0110912[http] title:_Pulp Fiction (1994)_>,\n",
" <Movie id:0167260[http] title:_The Lord of the Rings: The Return of the King (2003)_>,\n",
" <Movie id:0060196[http] title:_The Good, the Bad and the Ugly (1966)_>,\n",
" <Movie id:0137523[http] title:_Fight Club (1999)_>,\n",
" <Movie id:0120737[http] title:_The Lord of the Rings: The Fellowship of the Ring (2001)_>,\n",
" <Movie id:0080684[http] title:_Star Wars: Episode V - The Empire Strikes Back (1980)_>,\n",
" <Movie id:0109830[http] title:_Forrest Gump (1994)_>,\n",
" <Movie id:1375666[http] title:_Inception (2010)_>,\n",
" <Movie id:0167261[http] title:_The Lord of the Rings: The Two Towers (2002)_>,\n",
" <Movie id:0073486[http] title:_One Flew Over the Cuckoo's Nest (1975)_>,\n",
" <Movie id:0099685[http] title:_Goodfellas (1990)_>,\n",
" <Movie id:0133093[http] title:_The Matrix (1999)_>,\n",
" <Movie id:0047478[http] title:_Seven Samurai (1954)_>,\n",
" <Movie id:0076759[http] title:_Star Wars: Episode IV - A New Hope (1977)_>,\n",
" <Movie id:0317248[http] title:_City of God (2002)_>,\n",
" <Movie id:0114369[http] title:_Se7en (1995)_>,\n",
" <Movie id:0102926[http] title:_The Silence of the Lambs (1991)_>,\n",
" <Movie id:0038650[http] title:_It's a Wonderful Life (1946)_>,\n",
" <Movie id:0118799[http] title:_Life Is Beautiful (1997)_>,\n",
" <Movie id:0114814[http] title:_The Usual Suspects (1995)_>,\n",
" <Movie id:0110413[http] title:_Léon: The Professional (1994)_>,\n",
" <Movie id:0245429[http] title:_Spirited Away (2001)_>,\n",
" <Movie id:0120815[http] title:_Saving Private Ryan (1998)_>,\n",
" <Movie id:0064116[http] title:_Once Upon a Time in the West (1968)_>,\n",
" <Movie id:0120586[http] title:_American History X (1998)_>,\n",
" <Movie id:0816692[http] title:_Interstellar (2014)_>,\n",
" <Movie id:0034583[http] title:_Casablanca (1942)_>,\n",
" <Movie id:0054215[http] title:_Psycho (1960)_>,\n",
" <Movie id:0021749[http] title:_City Lights (1931)_>,\n",
" <Movie id:0120689[http] title:_The Green Mile (1999)_>,\n",
" <Movie id:1675434[http] title:_The Intouchables (2011)_>,\n",
" <Movie id:0027977[http] title:_Modern Times (1936)_>,\n",
" <Movie id:0082971[http] title:_Raiders of the Lost Ark (1981)_>,\n",
" <Movie id:0047396[http] title:_Rear Window (1954)_>,\n",
" <Movie id:0253474[http] title:_The Pianist (2002)_>,\n",
" <Movie id:0407887[http] title:_The Departed (2006)_>,\n",
" <Movie id:0103064[http] title:_Terminator 2: Judgment Day (1991)_>,\n",
" <Movie id:0088763[http] title:_Back to the Future (1985)_>,\n",
" <Movie id:2582802[http] title:_Whiplash (2014)_>,\n",
" <Movie id:0172495[http] title:_Gladiator (2000)_>,\n",
" <Movie id:0209144[http] title:_Memento (2000)_>,\n",
" <Movie id:0482571[http] title:_The Prestige (2006)_>,\n",
" <Movie id:0110357[http] title:_The Lion King (1994)_>,\n",
" <Movie id:0078788[http] title:_Apocalypse Now (1979)_>,\n",
" <Movie id:0078748[http] title:_Alien (1979)_>,\n",
" <Movie id:0043014[http] title:_Sunset Boulevard (1950)_>,\n",
" <Movie id:0057012[http] title:_Dr. Strangelove or: How I Learned to Stop Worrying and Love the Bomb (1964)_>,\n",
" <Movie id:0032553[http] title:_The Great Dictator (1940)_>,\n",
" <Movie id:0095765[http] title:_Cinema Paradiso (1988)_>,\n",
" <Movie id:0405094[http] title:_The Lives of Others (2006)_>,\n",
" <Movie id:0095327[http] title:_Grave of the Fireflies (1988)_>,\n",
" <Movie id:0050825[http] title:_Paths of Glory (1957)_>,\n",
" <Movie id:1853728[http] title:_Django Unchained (2012)_>,\n",
" <Movie id:0081505[http] title:_The Shining (1980)_>,\n",
" <Movie id:0910970[http] title:_WALL·E (2008)_>,\n",
" <Movie id:0169547[http] title:_American Beauty (1999)_>,\n",
" <Movie id:1345836[http] title:_The Dark Knight Rises (2012)_>,\n",
" <Movie id:0119698[http] title:_Princess Mononoke (1997)_>,\n",
" <Movie id:0090605[http] title:_Aliens (1986)_>,\n",
" <Movie id:0364569[http] title:_Old Boy (2003)_>,\n",
" <Movie id:0087843[http] title:_Once Upon a Time in America (1984)_>,\n",
" <Movie id:0051201[http] title:_Witness for the Prosecution (1957)_>,\n",
" <Movie id:0082096[http] title:_Das Boot (1981)_>,\n",
" <Movie id:0033467[http] title:_Citizen Kane (1941)_>,\n",
" <Movie id:0053125[http] title:_North by Northwest (1959)_>,\n",
" <Movie id:0052357[http] title:_Vertigo (1958)_>,\n",
" <Movie id:0086190[http] title:_Star Wars: Episode VI - Return of the Jedi (1983)_>,\n",
" <Movie id:0105236[http] title:_Reservoir Dogs (1992)_>,\n",
" <Movie id:0112573[http] title:_Braveheart (1995)_>,\n",
" <Movie id:0022100[http] title:_M (1931)_>,\n",
" <Movie id:0180093[http] title:_Requiem for a Dream (2000)_>,\n",
" <Movie id:5074352[http] title:_Dangal (2016)_>,\n",
" <Movie id:0211915[http] title:_Amélie (2001)_>,\n",
" <Movie id:0066921[http] title:_A Clockwork Orange (1971)_>,\n",
" <Movie id:0986264[http] title:_Like Stars on Earth (2007)_>,\n",
" <Movie id:0056172[http] title:_Lawrence of Arabia (1962)_>,\n",
" <Movie id:0075314[http] title:_Taxi Driver (1976)_>,\n",
" <Movie id:0036775[http] title:_Double Indemnity (1944)_>,\n",
" <Movie id:0338013[http] title:_Eternal Sunshine of the Spotless Mind (2004)_>,\n",
" <Movie id:0086879[http] title:_Amadeus (1984)_>,\n",
" <Movie id:0056592[http] title:_To Kill a Mockingbird (1962)_>,\n",
" <Movie id:0435761[http] title:_Toy Story 3 (2010)_>,\n",
" <Movie id:0093058[http] title:_Full Metal Jacket (1987)_>,\n",
" <Movie id:0062622[http] title:_2001: A Space Odyssey (1968)_>,\n",
" <Movie id:0045152[http] title:_Singin' in the Rain (1952)_>,\n",
" <Movie id:0070735[http] title:_The Sting (1973)_>,\n",
" <Movie id:0114709[http] title:_Toy Story (1995)_>,\n",
" <Movie id:0040522[http] title:_Bicycle Thieves (1948)_>,\n",
" <Movie id:0012349[http] title:_The Kid (1921)_>,\n",
" <Movie id:0361748[http] title:_Inglourious Basterds (2009)_>,\n",
" <Movie id:0208092[http] title:_Snatch (2000)_>,\n",
" <Movie id:1187043[http] title:_3 Idiots (2009)_>,\n",
" <Movie id:5311514[http] title:_Your name (2016)_>,\n",
" <Movie id:0071853[http] title:_Monty Python and the Holy Grail (1975)_>,\n",
" <Movie id:0119488[http] title:_L.A. Confidential (1997)_>,\n",
" <Movie id:0476735[http] title:_Mi padre y mi hijo (2005)_>,\n",
" <Movie id:0059578[http] title:_For a Few Dollars More (1965)_>,\n",
" <Movie id:0086250[http] title:_Scarface (1983)_>,\n",
" <Movie id:2106476[http] title:_The Hunt (2012)_>,\n",
" <Movie id:0119217[http] title:_Good Will Hunting (1997)_>,\n",
" <Movie id:0053604[http] title:_The Apartment (1960)_>,\n",
" <Movie id:0042876[http] title:_Rashomon (1950)_>,\n",
" <Movie id:1832382[http] title:_A Separation (2011)_>,\n",
" <Movie id:0017136[http] title:_Metrópolis (1927)_>,\n",
" <Movie id:0097576[http] title:_Indiana Jones and the Last Crusade (1989)_>,\n",
" <Movie id:0042192[http] title:_All About Eve (1950)_>,\n",
" <Movie id:0055630[http] title:_Yojimbo (1961)_>,\n",
" <Movie id:0372784[http] title:_Batman Begins (2005)_>,\n",
" <Movie id:1049413[http] title:_Up (2009)_>,\n",
" <Movie id:0053291[http] title:_Some Like It Hot (1959)_>,\n",
" <Movie id:3315342[http] title:_Logan (2017)_>,\n",
" <Movie id:0040897[http] title:_The Treasure of the Sierra Madre (1948)_>,\n",
" <Movie id:0105695[http] title:_Unforgiven (1992)_>,\n",
" <Movie id:0363163[http] title:_Downfall (2004)_>,\n",
" <Movie id:0081398[http] title:_Raging Bull (1980)_>,\n",
" <Movie id:0095016[http] title:_Die Hard (1988)_>,\n",
" <Movie id:3783958[http] title:_La La Land (2016)_>,\n",
" <Movie id:0041959[http] title:_The Third Man (1949)_>,\n",
" <Movie id:0118849[http] title:_Children of Heaven (1997)_>,\n",
" <Movie id:0113277[http] title:_Heat (1995)_>,\n",
" <Movie id:0057115[http] title:_The Great Escape (1963)_>,\n",
" <Movie id:0071315[http] title:_Chinatown (1974)_>,\n",
" <Movie id:0044741[http] title:_Ikiru (1952)_>,\n",
" <Movie id:0457430[http] title:_Pan's Labyrinth (2006)_>,\n",
" <Movie id:0096283[http] title:_My Neighbor Totoro (1988)_>,\n",
" <Movie id:2096673[http] title:_Inside Out (2015)_>,\n",
" <Movie id:0089881[http] title:_Ran (1985)_>,\n",
" <Movie id:0015864[http] title:_The Gold Rush (1925)_>,\n",
" <Movie id:0047296[http] title:_On the Waterfront (1954)_>,\n",
" <Movie id:1305806[http] title:_The Secret in Their Eyes (2009)_>,\n",
" <Movie id:3170832[http] title:_La habitación (2015)_>,\n",
" <Movie id:0050212[http] title:_The Bridge on the River Kwai (1957)_>,\n",
" <Movie id:0083658[http] title:_Blade Runner (1982)_>,\n",
" <Movie id:0347149[http] title:_Howl's Moving Castle (2004)_>,\n",
" <Movie id:1255953[http] title:_Incendies (2010)_>,\n",
" <Movie id:0055031[http] title:_Judgment at Nuremberg (1961)_>,\n",
" <Movie id:0050976[http] title:_The Seventh Seal (1957)_>,\n",
" <Movie id:0120735[http] title:_Lock, Stock and Two Smoking Barrels (1998)_>,\n",
" <Movie id:0031679[http] title:_Mr. Smith Goes to Washington (1939)_>,\n",
" <Movie id:0112641[http] title:_Casino (1995)_>,\n",
" <Movie id:0060107[http] title:_Andrei Rublev (1966)_>,\n",
" <Movie id:0268978[http] title:_A Beautiful Mind (2001)_>,\n",
" <Movie id:0080678[http] title:_The Elephant Man (1980)_>,\n",
" <Movie id:0050986[http] title:_Wild Strawberries (1957)_>,\n",
" <Movie id:0434409[http] title:_V for Vendetta (2005)_>,\n",
" <Movie id:0993846[http] title:_The Wolf of Wall Street (2013)_>,\n",
" <Movie id:0017925[http] title:_The General (1926)_>,\n",
" <Movie id:1291584[http] title:_Warrior (2011)_>,\n",
" <Movie id:0116231[http] title:_The Bandit (1996)_>,\n",
" <Movie id:0117951[http] title:_Trainspotting (1996)_>,\n",
" <Movie id:0018455[http] title:_Sunrise (1927)_>,\n",
" <Movie id:1205489[http] title:_Gran Torino (2008)_>,\n",
" <Movie id:0046912[http] title:_Dial M for Murder (1954)_>,\n",
" <Movie id:2119532[http] title:_Hasta el último hombre (2016)_>,\n",
" <Movie id:0077416[http] title:_The Deer Hunter (1978)_>,\n",
" <Movie id:0118715[http] title:_The Big Lebowski (1998)_>,\n",
" <Movie id:0116282[http] title:_Fargo (1996)_>,\n",
" <Movie id:0031381[http] title:_Gone with the Wind (1939)_>,\n",
" <Movie id:0167404[http] title:_The Sixth Sense (1999)_>,\n",
" <Movie id:0084787[http] title:_The Thing (1982)_>,\n",
" <Movie id:0046438[http] title:_Tokyo Story (1953)_>,\n",
" <Movie id:0266543[http] title:_Finding Nemo (2003)_>,\n",
" <Movie id:0405508[http] title:_Rang De Basanti (2006)_>,\n",
" <Movie id:0477348[http] title:_No Country for Old Men (2007)_>,\n",
" <Movie id:0019254[http] title:_The Passion of Joan of Arc (1928)_>,\n",
" <Movie id:1280558[http] title:_A Wednesday (2008)_>,\n",
" <Movie id:0061512[http] title:_Cool Hand Luke (1967)_>,\n",
" <Movie id:0032976[http] title:_Rebecca (1940)_>,\n",
" <Movie id:0892769[http] title:_How to Train Your Dragon (2010)_>,\n",
" <Movie id:0469494[http] title:_There Will Be Blood (2007)_>,\n",
" <Movie id:0266697[http] title:_Kill Bill: Vol. 1 (2003)_>,\n",
" <Movie id:0091251[http] title:_Come and See (1985)_>,\n",
" <Movie id:0978762[http] title:_Mary and Max (2009)_>,\n",
" <Movie id:0758758[http] title:_Into the Wild (2007)_>,\n",
" <Movie id:2267998[http] title:_Gone Girl (2014)_>,\n",
" <Movie id:0079470[http] title:_Life of Brian (1979)_>,\n",
" <Movie id:0025316[http] title:_It Happened One Night (1934)_>,\n",
" <Movie id:1130884[http] title:_Shutter Island (2010)_>,\n",
" <Movie id:0091763[http] title:_Platoon (1986)_>,\n",
" <Movie id:0395169[http] title:_Hotel Rwanda (2004)_>,\n",
" <Movie id:1979320[http] title:_Rush (2013)_>,\n",
" <Movie id:3011894[http] title:_Relatos salvajes (2014)_>,\n",
" <Movie id:0074958[http] title:_Network (1976)_>,\n",
" <Movie id:0046268[http] title:_The Wages of Fear (1953)_>,\n",
" <Movie id:0107207[http] title:_En el nombre del padre (1993)_>,\n",
" <Movie id:0092005[http] title:_Stand by Me (1986)_>,\n",
" <Movie id:0053198[http] title:_The 400 Blows (1959)_>,\n",
" <Movie id:1895587[http] title:_Spotlight (2015)_>,\n",
" <Movie id:2278388[http] title:_The Grand Budapest Hotel (2014)_>,\n",
" <Movie id:1392190[http] title:_Mad Max: Fury Road (2015)_>,\n",
" <Movie id:2024544[http] title:_12 Years a Slave (2013)_>,\n",
" <Movie id:0374887[http] title:_Munna Bhai M.B.B.S. (2003)_>,\n",
" <Movie id:0052618[http] title:_Ben-Hur (1959)_>,\n",
" <Movie id:0060827[http] title:_Persona (1966)_>,\n",
" <Movie id:0064115[http] title:_Butch Cassidy and the Sundance Kid (1969)_>,\n",
" <Movie id:0405159[http] title:_Million Dollar Baby (2004)_>,\n",
" <Movie id:0245712[http] title:_Amores Perros (2000)_>,\n",
" <Movie id:0107290[http] title:_Jurassic Park (1993)_>,\n",
" <Movie id:0353969[http] title:_Memories of Murder (2003)_>,\n",
" <Movie id:0033870[http] title:_The Maltese Falcon (1941)_>,\n",
" <Movie id:0050783[http] title:_The Nights of Cabiria (1957)_>,\n",
" <Movie id:0079944[http] title:_Stalker (1979)_>,\n",
" <Movie id:0093779[http] title:_The Princess Bride (1987)_>,\n",
" <Movie id:0120382[http] title:_The Truman Show (1998)_>,\n",
" <Movie id:1028532[http] title:_Hachi: A Dog's Tale (2009)_>,\n",
" <Movie id:0087544[http] title:_Nausicaä of the Valley of the Wind (1984)_>,\n",
" <Movie id:0073707[http] title:_Sholay (1975)_>,\n",
" <Movie id:2488496[http] title:_Star Wars: The Force Awakens (2015)_>,\n",
" <Movie id:0112471[http] title:_Before Sunrise (1995)_>,\n",
" <Movie id:0242519[http] title:_Hera Pheri (2000)_>,\n",
" <Movie id:0032551[http] title:_The Grapes of Wrath (1940)_>,\n",
" <Movie id:1201607[http] title:_Harry Potter and the Deathly Hallows: Part 2 (2011)_>,\n",
" <Movie id:0075148[http] title:_Rocky (1976)_>,\n",
" <Movie id:0052311[http] title:_Touch of Evil (1958)_>,\n",
" <Movie id:1392214[http] title:_Prisoners (2013)_>,\n",
" <Movie id:0083987[http] title:_Gandhi (1982)_>,\n",
" <Movie id:0075686[http] title:_Annie Hall (1977)_>,\n",
" <Movie id:0046911[http] title:_Diabolique (1955)_>,\n",
" <Movie id:0246578[http] title:_Donnie Darko (2001)_>,\n",
" <Movie id:0198781[http] title:_Monsters, Inc. (2001)_>,\n",
" <Movie id:0264464[http] title:_Catch Me If You Can (2002)_>,\n",
" <Movie id:0440963[http] title:_The Bourne Ultimatum (2007)_>,\n",
" <Movie id:0088247[http] title:_The Terminator (1984)_>,\n",
" <Movie id:0032138[http] title:_The Wizard of Oz (1939)_>,\n",
" <Movie id:0056801[http] title:_8½ (1963)_>,\n",
" <Movie id:0107048[http] title:_Groundhog Day (1993)_>,\n",
" <Movie id:3896198[http] title:_Guardians of the Galaxy Vol. 2 (2017)_>,\n",
" <Movie id:0072684[http] title:_Barry Lyndon (1975)_>,\n",
" <Movie id:0113247[http] title:_La Haine (1995)_>,\n",
" <Movie id:0114746[http] title:_Twelve Monkeys (1995)_>,\n",
" <Movie id:0073195[http] title:_Jaws (1975)_>,\n",
" <Movie id:0338564[http] title:_Infernal Affairs (2002)_>,\n",
" <Movie id:0036868[http] title:_The Best Years of Our Lives (1946)_>,\n",
" <Movie id:0109117[http] title:_Andaz Apna Apna (1994)_>,\n",
" <Movie id:0072890[http] title:_Dog Day Afternoon (1975)_>,\n",
" <Movie id:0058946[http] title:_The Battle of Algiers (1966)_>,\n",
" <Movie id:1454029[http] title:_The Help (2011)_>,\n",
" <Movie id:1954470[http] title:_Gangs of Wasseypur (2012)_>,\n",
" <Movie id:0101414[http] title:_Beauty and the Beast (1991)_>,\n",
" <Movie id:0056687[http] title:_What Ever Happened to Baby Jane? (1962)_>,\n",
" <Movie id:0118694[http] title:_In the Mood for Love (2000)_>,\n",
" <Movie id:0325980[http] title:_Pirates of the Caribbean: The Curse of the Black Pearl (2003)_>,\n",
" <Movie id:2948356[http] title:_Zootopia (2016)_>,\n",
" <Movie id:0169102[http] title:_Lagaan: Érase una vez en la India (2001)_>]"
]
},
"execution_count": 7,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"from imdb import IMDb\n",
"from datetime import datetime\n",
"from elasticsearch import Elasticsearch\n",
"es = Elasticsearch()\n",
"\n",
"ia = IMDb()\n",
"listaPelis = ia.get_top250_movies()\n",
"listaPelis"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Sacar toda la info de una peli para poder meterla en un diccionario y usarla en ElasticSearch, indexandola (metodo todo en 1)\n",
"Tarda bastante en ejecutarse (5 a 15 min), mete 250 peliculas en elastic\n",
"\n",
"quitado parametro de es.index (, id=i)\n",
"\n",
"## Coge el sumario de cada peli de la lista, y guarda la info en elasticSearch"
]
},
{
"cell_type": "code",
"execution_count": 8,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"for i in range(10,250):\n",
" peli = listaPelis[i]\n",
" peli2 = ia.get_movie(peli.movieID)\n",
" string = peli2.summary()\n",
" separado = string.split('\\n')\n",
" solucion = {}\n",
" for i in range(2,len(separado)):\n",
" sep2 = separado[i].split(':')\n",
" #Forma de evitar que haya fallo al pasar el split a diccionario\n",
" #Caso del fallo en los 2 cuadros de abajo\n",
" sep2[1:len(sep2)] = [''.join(sep2[1:len(sep2)])]\n",
" solucion.update(dict([sep2]))\n",
" es.index(index='prueba-index', doc_type='text', body=solucion)"
]
},
{
"cell_type": "code",
"execution_count": 12,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"[u'Movie',\n",
" u'=====',\n",
" u'Title: Lagaan: Once Upon a Time in India (2001)',\n",
" u'Genres: Adventure, Drama, Musical, Romance, Sport.',\n",
" u'Director: Ashutosh Gowariker.',\n",
" u'Writer: Ashutosh Gowariker, Ashutosh Gowariker, Kumar Dave, Sanjay Dayma, K.P. Saxena.',\n",
" u'Cast: Aamir Khan (Bhuvan), Gracy Singh (Gauri), Rachel Shelley (Elizabeth Russell), Paul Blackthorne (Captain Andrew Russell), Suhasini Mulay (Yashodamai).',\n",
" u'Runtime: 224.',\n",
" u'Country: India.',\n",
" u'Language: Hindi, English, Awadhi, Urdu.',\n",
" u'Rating: 8.2 (80051 votes).',\n",
" u\"Plot: This is the story about the resilience shown by the Indians when they were under the British Rule. They are already taxed to the bone by the British and their cronies, but when Jack Russell announces that he will double the Lagaan (tax) from all villagers, they decide to oppose it. Leading the villagers is a handsome young man named Bhuvan, who challenges them to a game of cricket, a game that is to be played by veteran British cricket players, versus villagers, including Bhuvan himself, who have never played this game before, and do not even know a bat from a piece of wood. As the challenge is accepted, the interest grows and attracts Indians from all over the region, as well as the British from all over the country - as everyone gathers to see the 'fair play' that the British will display against their counter-parts, who are aided by none other than the sister, Elizabeth, of Captain Rusell.\"]"
]
},
"execution_count": 12,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"separado"
]
},
{
"cell_type": "code",
"execution_count": 9,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"u\" This is the story about the resilience shown by the Indians when they were under the British Rule. They are already taxed to the bone by the British and their cronies, but when Jack Russell announces that he will double the Lagaan (tax) from all villagers, they decide to oppose it. Leading the villagers is a handsome young man named Bhuvan, who challenges them to a game of cricket, a game that is to be played by veteran British cricket players, versus villagers, including Bhuvan himself, who have never played this game before, and do not even know a bat from a piece of wood. As the challenge is accepted, the interest grows and attracts Indians from all over the region, as well as the British from all over the country - as everyone gathers to see the 'fair play' that the British will display against their counter-parts, who are aided by none other than the sister, Elizabeth, of Captain Rusell.\""
]
},
"execution_count": 9,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"sep2[1]"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Pruebas"
]
},
{
"cell_type": "code",
"execution_count": 21,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"[[ u'Movie\\n=====\\nTitle: Tonto Kid, The (1934)\\nGenres: Action, Adventure, Crime, Drama, Romance, Western.\\nDirector: Harry L. Fraser.\\nWriter: Christopher Booth, Harry L. Fraser.\\nCast: Rex Bell (Skeets Slawson aka The Tonto Kid), Ruth Mix (Nancy Cahill), Buzz Barton (Wesley Fritch), Theodore Lorch (Lawyer Sam Creech), Joseph W. Girard (Rance Cartwright).\\nRuntime: 61.\\nCountry: USA.\\nLanguage: English.\\nRating: 5.8 (24 votes).\\nPlot: Lawyer Creech is after the ranch of the dying Cartwright. First he brings in Cahill to pose as the only living relative. Then when the Tonto Kid finds platinum on the ranch, Creech frames him for murder.']\n",
" [ u'Movie\\n=====\\nTitle: Torres Vedras (1933)\\nGenres: Documentary.\\nCountry: Portugal.\\n']\n",
" [ u'Movie\\n=====\\nTitle: Torres Vedras e o Carnaval de 1933 (1933)\\nGenres: Documentary.\\nCountry: Portugal.\\n']\n",
" [ u'Movie\\n=====\\nTitle: Tractores Citroen no Ex\\xe9rcito Portugu\\xeas (1933)\\nGenres: Documentary.\\nDirector: Mota da Costa.\\nCountry: Portugal.\\nLanguage: Portuguese.\\n']\n",
" [ u'Movie\\n=====\\nTitle: Hapax Legomena IV: Travelling Matte (1971)\\nDirector: Hollis Frampton.\\nCountry: USA.\\nRating: 6.7 (18 votes).\\n']\n",
" [ u'Movie\\n=====\\nTitle: tribunal de las Aguas, El (1960)\\nGenres: Documentary, Short.\\nDirector: Alberto Carles Blat.\\nWriter: Alberto Carles Blat.\\nCast: Juan Mart\\xedn Navas.\\nRuntime: 10.\\nCountry: Spain.\\n']\n",
" [ u'Movie\\n=====\\nTitle: True Story of Eskimo Nell, The (1975)\\nGenres: Comedy, Western.\\nDirector: Richard Franklin.\\nWriter: Richard Franklin, Alan Hopgood.\\nCast: Max Gillies (Deadeye Dick), Serge Lazareff (Mexico Pete), Butcher Vachon (The Alaskan Kid), Jerry Thomas (The Sprunker), Kurt Beimel (Waldo the Great).\\nRuntime: 103.\\nCountry: Australia.\\nLanguage: English.\\nRating: 4.9 (58 votes).\\n']\n",
" [ u'Movie\\n=====\\nTitle: Two Idiots in Hollywood (1988)\\nGenres: Musical, Comedy.\\nDirector: Stephen Tobolowsky.\\nWriter: Stephen Tobolowsky, Stephen Tobolowsky.\\nCast: Jim McGrath (Murphy Wegg), Jeff Doucette (Taylor Dup), Cheryl Anderson (Marianne Plambo), Kat Sawyer, Lisa Robins (NBA Casting Secretary).\\nRuntime: 85.\\nCountry: USA.\\nLanguage: English.\\nRating: 6.6 (92 votes).\\nPlot: Idiots Taylor Dupp and Murphy Wegg flee their humdrum existence in Dayton, Ohio for the glamour of Hollywood. Murphy turns his complete lack of talent into a career as a television producer (\"The Pac-Man Show\"), while Taylor is unjustly accused of murder.']]\n"
]
}
],
"source": [
"import pandas as pd\n",
"lista=[]\n",
"\n",
"for i in range(0400000,0400010,1):\n",
" peli = ia.get_movie(i)\n",
" lista.append(peli.summary())\n",
"\n",
" \n",
"datos = pd.DataFrame(lista)\n",
"print datos.values\n"
]
},
{
"cell_type": "code",
"execution_count": 22,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"[[<Movie id:0002560[http] title:_The Vicissitudes of a Top Hat (1912)_>]\n",
" [ {'titlesRefs': {}, 'data': {'plot': [u'A man throws away an old top hat and a tramp uses it to sole his boots.']}, 'charactersRefs': {}, 'namesRefs': {}}]\n",
" [<Movie id:0002560[http] title:_The Vicissitudes of a Top Hat (1912)_>]\n",
" [ {'titlesRefs': {}, 'data': {'plot': [u'A man throws away an old top hat and a tramp uses it to sole his boots.']}, 'charactersRefs': {}, 'namesRefs': {}}]\n",
" [<Movie id:0002561[http] title:_Victim of Circumstances (1912)_>]\n",
" [ {'titlesRefs': {}, 'data': {'plot': [u'Richard Worthington on the way to work sees a thug knock a man down and apparently rob him. Rushing to the aid of the victim he is arrested for the crime. He is tried before a jury, found guilty, and sentenced to the convict camp. While working in the turpentine woods, under charge of a keeper, he is seen by Meg of the Everglades, who shows sympathy for the poor convict. The following Sunday morning word is passed around between the convicts that an attempt will be made to escape. At the opportune time one of the keepers at the gate is assaulted and fifteen of the convicts, Worthington among the lot, escape to the Everglades. Worthington is successful in eluding the bloodhounds and reaches a lonely hut in the Everglades, which proves to be the home of Meg and her father, where he successfully hides for two weeks, resulting in a strong friendship springing up between the convict and Meg. Becoming careless, Worthington goes outside of the hut to enjoy a smoke. He is seen by some of the guards from the convict camp and is arrested. A few weeks later Red Lopers, for whose crime Worthington is arrested, confesses. Worthington is immediately discharged and exonerated from all blame. Although his good name is restored and he is back with his mother, Worthington finds something is missing. His happiness is not complete, and he soon realizes that unconsciously he has fallen in love with the girl of the Everglades, who showed such practical sympathy for the poor unfortunate. Leaving his home, he goes to the little cabin in the woods, where he tells Meg of his love, and they agree to start a new life together.']}, 'charactersRefs': {}, 'namesRefs': {}}]\n",
" [<Movie id:0002560[http] title:_The Vicissitudes of a Top Hat (1912)_>]\n",
" [ {'titlesRefs': {}, 'data': {'plot': [u'A man throws away an old top hat and a tramp uses it to sole his boots.']}, 'charactersRefs': {}, 'namesRefs': {}}]\n",
" [<Movie id:0002561[http] title:_Victim of Circumstances (1912)_>]\n",
" [ {'titlesRefs': {}, 'data': {'plot': [u'Richard Worthington on the way to work sees a thug knock a man down and apparently rob him. Rushing to the aid of the victim he is arrested for the crime. He is tried before a jury, found guilty, and sentenced to the convict camp. While working in the turpentine woods, under charge of a keeper, he is seen by Meg of the Everglades, who shows sympathy for the poor convict. The following Sunday morning word is passed around between the convicts that an attempt will be made to escape. At the opportune time one of the keepers at the gate is assaulted and fifteen of the convicts, Worthington among the lot, escape to the Everglades. Worthington is successful in eluding the bloodhounds and reaches a lonely hut in the Everglades, which proves to be the home of Meg and her father, where he successfully hides for two weeks, resulting in a strong friendship springing up between the convict and Meg. Becoming careless, Worthington goes outside of the hut to enjoy a smoke. He is seen by some of the guards from the convict camp and is arrested. A few weeks later Red Lopers, for whose crime Worthington is arrested, confesses. Worthington is immediately discharged and exonerated from all blame. Although his good name is restored and he is back with his mother, Worthington finds something is missing. His happiness is not complete, and he soon realizes that unconsciously he has fallen in love with the girl of the Everglades, who showed such practical sympathy for the poor unfortunate. Leaving his home, he goes to the little cabin in the woods, where he tells Meg of his love, and they agree to start a new life together.']}, 'charactersRefs': {}, 'namesRefs': {}}]\n",
" [<Movie id:0002562[http] title:_The Victoria Cross (1912)_>]\n",
" [ {'titlesRefs': {}, 'data': {'plot': [u'Just previous to the Charge of the Light Brigade, made famous by Tennyson, in the fall of 1854, young Lieutenant Cholmondeley, of the English Army, asks Colonel Carson for the hand of his daughter Ellen. The Colonel replies: \"When you have won your spurs, I will give my consent.\" Russia declares war against England and France and the Light Brigade is ordered to the front. After the departure of the Lieutenant and her father, Ellen decides to become a nurse under Florence Nightingale. She offers her services and though somewhat young, is accepted by Miss Nightingale. At her father\\'s encampment he recognizes Ellen as the nurses pass before him in review. At first he is displeased, but upon second thought is justly proud of her. She is first upon the battlefield to aid and comfort the wounded and it is there that her sweetheart, the Lieutenant, learns of her presence at the seat of action. During the charge of the Six Hundred, Lieutenant Cholmondeley saves the life of his Colonel, defending him against the combined attacks of three Cossacks, lifting one bodily above his head and casting him maimed and helpless to the ground. Ellen watches through her father\\'s field-glasses with palpitating heart the deeds of her sweetheart and the progress of the battle. After the war what is left of the few survivors of the Light Brigade are mustered before Queen Victoria and the young Lieutenant receives the Victoria Cross as a special mark of distinction for services rendered. The Colonel gives him his daughter Ellen, saying, \"He has fairly won her and his spurs.\"', u'Ellen Carson volunteers to serve with Florence Nightingale in the Crimean war and witnesses the charge of the Light Brigade.']}, 'charactersRefs': {}, 'namesRefs': {}}]]\n"
]
}
],
"source": [
"import pandas as pd\n",
"lista=[]\n",
"datos = pd.DataFrame([])\n",
"for i in range(0005000,0005003):\n",
" lista.append(ia.get_movie(i))\n",
" lista.append(ia.get_movie_plot(i))\n",
"\n",
" datos = datos.append(lista)\n",
"\n",
"print datos.values"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Elastic Seach (cabezera de ejemplo)"
]
},
{
"cell_type": "code",
"execution_count": 23,
"metadata": {
"collapsed": false
},
"outputs": [
{
"ename": "NotFoundError",
"evalue": "TransportError(404, u'index_not_found_exception', u'no such index')",
"output_type": "error",
"traceback": [
"\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
"\u001b[0;31mNotFoundError\u001b[0m Traceback (most recent call last)",
"\u001b[0;32m<ipython-input-23-b426fb989a9c>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m 11\u001b[0m '''\n\u001b[1;32m 12\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m---> 13\u001b[0;31m \u001b[0mres\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mes\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mget\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mindex\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;34m\"movies-index\"\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mdoc_type\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;34m'text'\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mid\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;36m6\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 14\u001b[0m \u001b[1;32mprint\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mres\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;34m'_source'\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m 15\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n",
"\u001b[0;32mC:\\Users\\cr\\Anaconda2\\lib\\site-packages\\elasticsearch\\client\\utils.pyc\u001b[0m in \u001b[0;36m_wrapped\u001b[0;34m(*args, **kwargs)\u001b[0m\n\u001b[1;32m 71\u001b[0m \u001b[1;32mif\u001b[0m \u001b[0mp\u001b[0m \u001b[1;32min\u001b[0m \u001b[0mkwargs\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m 72\u001b[0m \u001b[0mparams\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0mp\u001b[0m\u001b[1;33m]\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mkwargs\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mpop\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mp\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m---> 73\u001b[0;31m \u001b[1;32mreturn\u001b[0m \u001b[0mfunc\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m*\u001b[0m\u001b[0margs\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mparams\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mparams\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 74\u001b[0m \u001b[1;32mreturn\u001b[0m \u001b[0m_wrapped\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m 75\u001b[0m \u001b[1;32mreturn\u001b[0m \u001b[0m_wrapper\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
"\u001b[0;32mC:\\Users\\cr\\Anaconda2\\lib\\site-packages\\elasticsearch\\client\\__init__.pyc\u001b[0m in \u001b[0;36mget\u001b[0;34m(self, index, id, doc_type, params)\u001b[0m\n\u001b[1;32m 407\u001b[0m \u001b[1;32mraise\u001b[0m \u001b[0mValueError\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m\"Empty value passed for a required argument.\"\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m 408\u001b[0m return self.transport.perform_request('GET', _make_path(index,\n\u001b[0;32m--> 409\u001b[0;31m doc_type, id), params=params)\n\u001b[0m\u001b[1;32m 410\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m 411\u001b[0m @query_params('_source', '_source_exclude', '_source_include', 'parent',\n",
"\u001b[0;32mC:\\Users\\cr\\Anaconda2\\lib\\site-packages\\elasticsearch\\transport.pyc\u001b[0m in \u001b[0;36mperform_request\u001b[0;34m(self, method, url, params, body)\u001b[0m\n\u001b[1;32m 310\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m 311\u001b[0m \u001b[1;32mtry\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m--> 312\u001b[0;31m \u001b[0mstatus\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mheaders\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mdata\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mconnection\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mperform_request\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mmethod\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0murl\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mparams\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mbody\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mignore\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mignore\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mtimeout\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mtimeout\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 313\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m 314\u001b[0m \u001b[1;32mexcept\u001b[0m \u001b[0mTransportError\u001b[0m \u001b[1;32mas\u001b[0m \u001b[0me\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
"\u001b[0;32mC:\\Users\\cr\\Anaconda2\\lib\\site-packages\\elasticsearch\\connection\\http_urllib3.pyc\u001b[0m in \u001b[0;36mperform_request\u001b[0;34m(self, method, url, params, body, timeout, ignore)\u001b[0m\n\u001b[1;32m 126\u001b[0m \u001b[1;32mif\u001b[0m \u001b[1;32mnot\u001b[0m \u001b[1;33m(\u001b[0m\u001b[1;36m200\u001b[0m \u001b[1;33m<=\u001b[0m \u001b[0mresponse\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mstatus\u001b[0m \u001b[1;33m<\u001b[0m \u001b[1;36m300\u001b[0m\u001b[1;33m)\u001b[0m \u001b[1;32mand\u001b[0m \u001b[0mresponse\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mstatus\u001b[0m \u001b[1;32mnot\u001b[0m \u001b[1;32min\u001b[0m \u001b[0mignore\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m 127\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mlog_request_fail\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mmethod\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mfull_url\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0murl\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mbody\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mduration\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mresponse\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mstatus\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mraw_data\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m--> 128\u001b[0;31m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_raise_error\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mresponse\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mstatus\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mraw_data\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 129\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m 130\u001b[0m self.log_request_success(method, full_url, url, body, response.status,\n",
"\u001b[0;32mC:\\Users\\cr\\Anaconda2\\lib\\site-packages\\elasticsearch\\connection\\base.pyc\u001b[0m in \u001b[0;36m_raise_error\u001b[0;34m(self, status_code, raw_data)\u001b[0m\n\u001b[1;32m 123\u001b[0m \u001b[0mlogger\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mwarning\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m'Undecodable raw error response from server: %s'\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0merr\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m 124\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m--> 125\u001b[0;31m \u001b[1;32mraise\u001b[0m \u001b[0mHTTP_EXCEPTIONS\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mget\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mstatus_code\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mTransportError\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mstatus_code\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0merror_message\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0madditional_info\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 126\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m 127\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n",
"\u001b[0;31mNotFoundError\u001b[0m: TransportError(404, u'index_not_found_exception', u'no such index')"
]
}
],
"source": [
"from datetime import datetime\n",
"from elasticsearch import Elasticsearch\n",
"es = Elasticsearch()\n",
"'''\n",
"doc = {\n",
" 'prueba': 'Holi',\n",
" 'text': 'A man throws away an old top hat and a tramp uses it to sole his boots.',\n",
"}\n",
"res = es.index(index=\"movies-index\", doc_type='text', id=1, body=doc)\n",
"print(res['created'])\n",
"'''\n",
"\n",
"res = es.get(index=\"movies-index\", doc_type='text', id=6)\n",
"print(res['_source'])\n",
"\n",
"es.indices.refresh(index=\"movies-index\")\n",
"\n",
"res = es.search(index=\"movies-index\", body={\"query\": {\"match_all\": {}}})\n",
"print(\"Got %d Hits:\" % res['hits']['total'])\n",
"for hit in res['hits']['hits']:\n",
" print(\"%(text)s\" % hit[\"_source\"])"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Inicializacion real de Elastic Search (ejecutar)"
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"{\n",
" \"name\" : \"olxdfU7\",\n",
" \"cluster_name\" : \"elasticsearch\",\n",
" \"cluster_uuid\" : \"DQylXrS0QwCriq9AQDxkSQ\",\n",
" \"version\" : {\n",
" \"number\" : \"5.4.0\",\n",
" \"build_hash\" : \"780f8c4\",\n",
" \"build_date\" : \"2017-04-28T17:43:27.229Z\",\n",
" \"build_snapshot\" : false,\n",
" \"lucene_version\" : \"6.5.0\"\n",
" },\n",
" \"tagline\" : \"You Know, for Search\"\n",
"}\n",
"\n"
]
}
],
"source": [
"# make sure ES is up and running\n",
"import requests\n",
"res = requests.get('http://localhost:9200')\n",
"print(res.content)\n",
"\n",
"from elasticsearch import Elasticsearch\n",
"es = Elasticsearch([{'host': 'localhost', 'port': 9200}])"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Guardamos el top 250 dentro de elastic search (antiguo)"
]
},
{
"cell_type": "code",
"execution_count": 281,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"#Lista con el top 250 de peliculas\n",
"top = ia.get_top250_movies()\n",
"#Recorro la lista y saco los datos para indexarlos en elastic search, el id es el orden en la lista\n",
"for i in range(0,250):\n",
" es.index(index='films-index', doc_type='text', id=i, body=top[i].data)\n"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Buscamos los datos guardados (antiguo)"
]
},
{
"cell_type": "code",
"execution_count": 24,
"metadata": {
"collapsed": false
},
"outputs": [
{
"ename": "NotFoundError",
"evalue": "TransportError(404, u'index_not_found_exception', u'no such index')",
"output_type": "error",
"traceback": [
"\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
"\u001b[0;31mNotFoundError\u001b[0m Traceback (most recent call last)",
"\u001b[0;32m<ipython-input-24-716e325fcaba>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mres\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mes\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0msearch\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mindex\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;34m\"films-index\"\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mbody\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;33m{\u001b[0m\u001b[1;34m\"query\"\u001b[0m\u001b[1;33m:\u001b[0m \u001b[1;33m{\u001b[0m\u001b[1;34m\"match_all\"\u001b[0m\u001b[1;33m:\u001b[0m \u001b[1;33m{\u001b[0m\u001b[1;33m}\u001b[0m\u001b[1;33m}\u001b[0m\u001b[1;33m}\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 2\u001b[0m \u001b[1;32mprint\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m\"Got %d Hits:\"\u001b[0m \u001b[1;33m%\u001b[0m \u001b[0mres\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;34m'hits'\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;34m'total'\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m 3\u001b[0m \u001b[1;31m#Modificar para que funcione\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m 4\u001b[0m \u001b[1;32mfor\u001b[0m \u001b[0mhit\u001b[0m \u001b[1;32min\u001b[0m \u001b[0mres\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;34m'hits'\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;34m'hits'\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m 5\u001b[0m \u001b[1;32mprint\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m\"%(kind)s %(title)s %(year)s %(rating)s\"\u001b[0m \u001b[1;33m%\u001b[0m \u001b[0mhit\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;34m\"_source\"\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
"\u001b[0;32mC:\\Users\\cr\\Anaconda2\\lib\\site-packages\\elasticsearch\\client\\utils.pyc\u001b[0m in \u001b[0;36m_wrapped\u001b[0;34m(*args, **kwargs)\u001b[0m\n\u001b[1;32m 71\u001b[0m \u001b[1;32mif\u001b[0m \u001b[0mp\u001b[0m \u001b[1;32min\u001b[0m \u001b[0mkwargs\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m 72\u001b[0m \u001b[0mparams\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0mp\u001b[0m\u001b[1;33m]\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mkwargs\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mpop\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mp\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m---> 73\u001b[0;31m \u001b[1;32mreturn\u001b[0m \u001b[0mfunc\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m*\u001b[0m\u001b[0margs\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mparams\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mparams\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 74\u001b[0m \u001b[1;32mreturn\u001b[0m \u001b[0m_wrapped\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m 75\u001b[0m \u001b[1;32mreturn\u001b[0m \u001b[0m_wrapper\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
"\u001b[0;32mC:\\Users\\cr\\Anaconda2\\lib\\site-packages\\elasticsearch\\client\\__init__.pyc\u001b[0m in \u001b[0;36msearch\u001b[0;34m(self, index, doc_type, body, params)\u001b[0m\n\u001b[1;32m 621\u001b[0m \u001b[0mindex\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;34m'_all'\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m 622\u001b[0m return self.transport.perform_request('GET', _make_path(index,\n\u001b[0;32m--> 623\u001b[0;31m doc_type, '_search'), params=params, body=body)\n\u001b[0m\u001b[1;32m 624\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m 625\u001b[0m @query_params('_source', '_source_exclude', '_source_include',\n",
"\u001b[0;32mC:\\Users\\cr\\Anaconda2\\lib\\site-packages\\elasticsearch\\transport.pyc\u001b[0m in \u001b[0;36mperform_request\u001b[0;34m(self, method, url, params, body)\u001b[0m\n\u001b[1;32m 310\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m 311\u001b[0m \u001b[1;32mtry\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m--> 312\u001b[0;31m \u001b[0mstatus\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mheaders\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mdata\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mconnection\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mperform_request\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mmethod\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0murl\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mparams\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mbody\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mignore\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mignore\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mtimeout\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mtimeout\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 313\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m 314\u001b[0m \u001b[1;32mexcept\u001b[0m \u001b[0mTransportError\u001b[0m \u001b[1;32mas\u001b[0m \u001b[0me\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
"\u001b[0;32mC:\\Users\\cr\\Anaconda2\\lib\\site-packages\\elasticsearch\\connection\\http_urllib3.pyc\u001b[0m in \u001b[0;36mperform_request\u001b[0;34m(self, method, url, params, body, timeout, ignore)\u001b[0m\n\u001b[1;32m 126\u001b[0m \u001b[1;32mif\u001b[0m \u001b[1;32mnot\u001b[0m \u001b[1;33m(\u001b[0m\u001b[1;36m200\u001b[0m \u001b[1;33m<=\u001b[0m \u001b[0mresponse\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mstatus\u001b[0m \u001b[1;33m<\u001b[0m \u001b[1;36m300\u001b[0m\u001b[1;33m)\u001b[0m \u001b[1;32mand\u001b[0m \u001b[0mresponse\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mstatus\u001b[0m \u001b[1;32mnot\u001b[0m \u001b[1;32min\u001b[0m \u001b[0mignore\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m 127\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mlog_request_fail\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mmethod\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mfull_url\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0murl\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mbody\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mduration\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mresponse\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mstatus\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mraw_data\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m--> 128\u001b[0;31m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_raise_error\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mresponse\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mstatus\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mraw_data\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 129\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m 130\u001b[0m self.log_request_success(method, full_url, url, body, response.status,\n",
"\u001b[0;32mC:\\Users\\cr\\Anaconda2\\lib\\site-packages\\elasticsearch\\connection\\base.pyc\u001b[0m in \u001b[0;36m_raise_error\u001b[0;34m(self, status_code, raw_data)\u001b[0m\n\u001b[1;32m 123\u001b[0m \u001b[0mlogger\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mwarning\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m'Undecodable raw error response from server: %s'\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0merr\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m 124\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m--> 125\u001b[0;31m \u001b[1;32mraise\u001b[0m \u001b[0mHTTP_EXCEPTIONS\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mget\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mstatus_code\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mTransportError\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mstatus_code\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0merror_message\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0madditional_info\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 126\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m 127\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n",
"\u001b[0;31mNotFoundError\u001b[0m: TransportError(404, u'index_not_found_exception', u'no such index')"
]
}
],
"source": [
"res = es.search(index=\"films-index\", body={\"query\": {\"match_all\": {}}})\n",
"print(\"Got %d Hits:\" % res['hits']['total'])\n",
"#Modificar para que funcione\n",
"for hit in res['hits']['hits']:\n",
" print(\"%(kind)s %(title)s %(year)s %(rating)s\" % hit[\"_source\"])"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Sacar los hits e info de unos cuantos de ellos"
]
},
{
"cell_type": "code",
"execution_count": 28,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Got 250 Hits:\n",
" Leben der Anderen, Das (2006) Drama, Thriller. Florian Henckel von Donnersmarck. Martina Gedeck (Christa-Maria Sieland), Ulrich Mühe (Hauptmann Gerd Wiesler), Sebastian Koch (Georg Dreyman), Ulrich Tukur (Oberstleutnant Anton Grubitz), Thomas Thieme (Minister Bruno Hempf). Florian Henckel von Donnersmarck. Germany. German. 8.5 (278124 votes). Gerd Wiesler is an officer with the Stasi, the East German secret police. The film begins in 1984 when Wiesler attends a play written by Georg Dreyman, who is considered by many to be the ultimate example of the loyal citizen. Wiesler has a gut feeling that Dreyman can't be as ideal as he seems, and believes surveillance is called for. The Minister of Culture agrees but only later does Wiesler learn that the Minister sees Dreyman as a rival and lusts after his partner Christa-Maria. The more time he spends listening in on them, the more he comes to care about them. The once rigid Stasi officer begins to intervene in their lives, in a positive way, protecting them whenever possible. Eventually, Wiesler's activities catch up to him and while there is no proof of wrongdoing, he finds himself in menial jobs - until the unbelievable happens.\n",
" Nuovo Cinema Paradiso (1988) Drama. Giuseppe Tornatore. Antonella Attili (Maria Di Vita - Younger), Enzo Cannavale (Spaccafico), Isa Danieli (Anna), Leo Gullotta (Usher), Marco Leonardi (Salvatore 'Totò' Di Vita - Teenager). Giuseppe Tornatore, Giuseppe Tornatore, Vanna Paoli, Richard Epcar. Italy, France. Italian. 8.5 (165897 votes). A boy who grew up in a native Sicilian Village returns home as a famous director after receiving news about the death of an old friend. Told in a flashback, Salvatore reminiscences about his childhood and his relationship with Alfredo, a projectionist at Cinema Paradiso. Under the fatherly influence of Alfredo, Salvatore fell in love with film making, with the duo spending many hours discussing about films and Alfredo painstakingly teaching Salvatore the skills that became a stepping stone for the young boy into the world of film making. The film brings the audience through the changes in cinema and the dying trade of traditional film making, editing and screening. It also explores a young boy's dream of leaving his little town to foray into the world outside.\n",
" Shichinin no samurai (1954) Adventure, Drama. Akira Kurosawa. Toshirô Mifune (Kikuchiyo), Takashi Shimura (Kambei Shimada), Keiko Tsushima (Shino), Yukiko Shimazaki (Wife), Kamatari Fujiwara (Farmer Manzo). Akira Kurosawa, Shinobu Hashimoto, Hideo Oguni. Japan. Japanese. 8.7 (244938 votes). A veteran samurai, who has fallen on hard times, answers a village's request for protection from bandits. He gathers 6 other samurai to help him, and they teach the townspeople how to defend themselves, and they supply the samurai with three small meals a day. The film culminates in a giant battle when 40 bandits attack the village.\n",
" Se7en (1995) Crime, Drama, Mystery, Thriller. David Fincher. Morgan Freeman (Somerset), Andrew Kevin Walker (Dead Man at 1st Crime Scene), Kevin Spacey (John Doe), Daniel Zacapa (Detective Taylor), Brad Pitt (Mills). Andrew Kevin Walker. USA. English. 8.6 (1103064 votes). A film about two homicide detectives' (Morgan Freeman and (Brad Pitt desperate hunt for a serial killer who justifies his crimes as absolution for the world's ignorance of the Seven Deadly Sins. The movie takes us from the tortured remains of one victim to the next as the sociopathic \"John Doe\" (Kevin Spacey) sermonizes to Detectives Somerset and Mills -- one sin at a time. The sin of Gluttony comes first and the murderer's terrible capacity is graphically demonstrated in the dark and subdued tones characteristic of film noir. The seasoned and cultured but jaded Somerset researches the Seven Deadly Sins in an effort to understand the killer's modus operandi while the bright but green and impulsive Detective Mills (Pitt) scoffs at his efforts to get inside the mind of a killer...\n",
" Paths of Glory (1957) Drama, War. Stanley Kubrick. Kirk Douglas (Col. Dax), Ralph Meeker (Cpl. Philippe Paris), Adolphe Menjou (Gen. George Broulard), George Macready (Gen. Paul Mireau), Wayne Morris (Lt. Roget). Stanley Kubrick, Calder Willingham, Jim Thompson, Humphrey Cobb. USA. English, German, Latin. 8.5 (130703 votes). The futility and irony of the war in the trenches in WWI is shown as a unit commander in the French army must deal with the mutiny of his men and a glory-seeking general after part of his force falls back under fire in an impossible attack.\n",
" Pulp Fiction (1994) Crime, Drama. Quentin Tarantino. Tim Roth (Pumpkin), Amanda Plummer (Honey Bunny), Laura Lovelace (Waitress), John Travolta (Vincent Vega), Samuel L. Jackson (Jules Winnfield). Quentin Tarantino, Roger Avary, Quentin Tarantino. USA. English, Spanish, French. 8.9 (1418706 votes). Jules Winnfield (Samuel L. Jackson) and Vincent Vega (John Travolta) are two hit men who are out to retrieve a suitcase stolen from their employer, mob boss Marsellus Wallace (Ving Rhames). Wallace has also asked Vincent to take his wife Mia (Uma Thurman) out a few days later when Wallace himself will be out of town. Butch Coolidge (Bruce Willis) is an aging boxer who is paid by Wallace to lose his fight. The lives of these seemingly unrelated people are woven together comprising of a series of funny, bizarre and uncalled-for incidents.\n",
" Django Unchained (2012) Drama, Western. Quentin Tarantino. Jamie Foxx (Django), Christoph Waltz (Dr. King Schultz), Leonardo DiCaprio (Calvin Candie), Kerry Washington (Broomhilda von Shaft), Samuel L. Jackson (Stephen). Quentin Tarantino. USA. English, German, French, Italian. 8.4 (1039218 votes). A German dentist buys the freedom of a slave and trains him with the intent to make him his deputy bounty hunter. Instead, he is led to the site of the slave's wife who belongs to a ruthless plantation owner.\n",
" Pianist, The (2002) Biography, Drama, War. Roman Polanski. Adrien Brody (Wladyslaw Szpilman), Emilia Fox (Dorota), Michal Zebrowski (Jurek), Ed Stoppard (Henryk), Maureen Lipman (Mother). Ronald Harwood, Wladyslaw Szpilman. France, Poland, Germany, UK. English, German, Russian. 8.5 (541568 votes). In this adaptation of the autobiography \"The Pianist The Extraordinary True Story of One Man's Survival in Warsaw, 1939-1945,\" Wladyslaw Szpilman, a Polish Jewish radio station pianist, sees Warsaw change gradually as World War II begins. Szpilman is forced into the Warsaw Ghetto, but is later separated from his family during Operation Reinhard. From this time until the concentration camp prisoners are released, Szpilman hides in various locations among the ruins of Warsaw.\n",
" Star Wars (1977) Action, Adventure, Fantasy, Sci-Fi. George Lucas. Mark Hamill (Luke Skywalker), Harrison Ford (Han Solo), Carrie Fisher (Princess Leia Organa), Peter Cushing (Grand Moff Tarkin), Alec Guinness (Ben Obi-Wan Kenobi). George Lucas. USA. English. 8.7 (978421 votes). The Imperial Forces, under orders from cruel Darth Vader, hold Princess Leia hostage in their efforts to quell the rebellion against the Galactic Empire. Luke Skywalker and Han Solo, captain of the Millennium Falcon, work together with the companionable droid duo R2-D2 and C-3PO to rescue the beautiful princess, help the Rebel Alliance and restore freedom and justice to the Galaxy.\n",
" Back to the Future (1985) Adventure, Comedy, Sci-Fi. Robert Zemeckis. Michael J. Fox (Marty McFly), Christopher Lloyd (Dr. Emmett Brown), Lea Thompson (Lorraine Baines), Crispin Glover (George McFly), Thomas F. Wilson (Biff Tannen). Robert Zemeckis, Bob Gale. USA. English. 8.5 (792473 votes). Marty McFly, a typical American teenager of the Eighties, is accidentally sent back to 1955 in a plutonium-powered DeLorean \"time machine\" invented by a slightly mad scientist. During his often hysterical, always amazing trip back in time, Marty must make certain his teenage parents-to-be meet and fall in love - so he can get back to the future.\n"
]
}
],
"source": [
"res = es.search(index=\"prueba-index\", body={\"query\": {\"match_all\": {}}})\n",
"print(\"Got %d Hits:\" % res['hits']['total'])\n",
"\n",
"for hit in res['hits']['hits']:\n",
" print(\"%(Title)s %(Genres)s %(Director)s %(Cast)s %(Writer)s %(Country)s %(Language)s %(Rating)s %(Plot)s\" % hit[\"_source\"])"
]
},
{
"cell_type": "code",
"execution_count": 27,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Got 250 Hits:\n",
" Leben der Anderen, Das (2006)\n",
" Nuovo Cinema Paradiso (1988)\n",
" Shichinin no samurai (1954)\n",
" Se7en (1995)\n",
" Paths of Glory (1957)\n",
" Pulp Fiction (1994)\n",
" Django Unchained (2012)\n",
" Pianist, The (2002)\n",
" Star Wars (1977)\n",
" Back to the Future (1985)\n"
]
}
],
"source": [
"res = es.search(index=\"prueba-index\", body={\"query\": {\"match_all\": {}}})\n",
"print(\"Got %d Hits:\" % res['hits']['total'])\n",
"\n",
"for hit in res['hits']['hits']:\n",
" print(\"%(Title)s\" % hit[\"_source\"])"
]
},
{
"cell_type": "code",
"execution_count": 26,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"{u'_shards': {u'failed': 0, u'successful': 5, u'total': 5},\n",
" u'hits': {u'hits': [{u'_id': u'AVxB2MAL6RBRcVMNlbJ2',\n",
" u'_index': u'prueba-index',\n",
" u'_score': 1.0,\n",
" u'_source': {u'Cast': u' Martina Gedeck (Christa-Maria Sieland), Ulrich M\\xfche (Hauptmann Gerd Wiesler), Sebastian Koch (Georg Dreyman), Ulrich Tukur (Oberstleutnant Anton Grubitz), Thomas Thieme (Minister Bruno Hempf).',\n",
" u'Country': u' Germany.',\n",
" u'Director': u' Florian Henckel von Donnersmarck.',\n",
" u'Genres': u' Drama, Thriller.',\n",
" u'Language': u' German.',\n",
" u'Plot': u\" Gerd Wiesler is an officer with the Stasi, the East German secret police. The film begins in 1984 when Wiesler attends a play written by Georg Dreyman, who is considered by many to be the ultimate example of the loyal citizen. Wiesler has a gut feeling that Dreyman can't be as ideal as he seems, and believes surveillance is called for. The Minister of Culture agrees but only later does Wiesler learn that the Minister sees Dreyman as a rival and lusts after his partner Christa-Maria. The more time he spends listening in on them, the more he comes to care about them. The once rigid Stasi officer begins to intervene in their lives, in a positive way, protecting them whenever possible. Eventually, Wiesler's activities catch up to him and while there is no proof of wrongdoing, he finds himself in menial jobs - until the unbelievable happens.\",\n",
" u'Rating': u' 8.5 (278124 votes).',\n",
" u'Runtime': u' 137.',\n",
" u'Title': u' Leben der Anderen, Das (2006)',\n",
" u'Writer': u' Florian Henckel von Donnersmarck.'},\n",
" u'_type': u'text'},\n",
" {u'_id': u'AVxB2Lcr6RBRcVMNlbJ1',\n",
" u'_index': u'prueba-index',\n",
" u'_score': 1.0,\n",
" u'_source': {u'Cast': u\" Antonella Attili (Maria Di Vita - Younger), Enzo Cannavale (Spaccafico), Isa Danieli (Anna), Leo Gullotta (Usher), Marco Leonardi (Salvatore 'Tot\\xf2' Di Vita - Teenager).\",\n",
" u'Country': u' Italy, France.',\n",
" u'Director': u' Giuseppe Tornatore.',\n",
" u'Genres': u' Drama.',\n",
" u'Language': u' Italian.',\n",
" u'Plot': u\" A boy who grew up in a native Sicilian Village returns home as a famous director after receiving news about the death of an old friend. Told in a flashback, Salvatore reminiscences about his childhood and his relationship with Alfredo, a projectionist at Cinema Paradiso. Under the fatherly influence of Alfredo, Salvatore fell in love with film making, with the duo spending many hours discussing about films and Alfredo painstakingly teaching Salvatore the skills that became a stepping stone for the young boy into the world of film making. The film brings the audience through the changes in cinema and the dying trade of traditional film making, editing and screening. It also explores a young boy's dream of leaving his little town to foray into the world outside.\",\n",
" u'Rating': u' 8.5 (165897 votes).',\n",
" u'Runtime': u' 155, Italy173(Europa Cinema Festival), 124(cut theatrical version).',\n",
" u'Title': u' Nuovo Cinema Paradiso (1988)',\n",
" u'Writer': u' Giuseppe Tornatore, Giuseppe Tornatore, Vanna Paoli, Richard Epcar.'},\n",
" u'_type': u'text'},\n",
" {u'_id': u'AVxB1wNE6RBRcVMNlbJR',\n",
" u'_index': u'prueba-index',\n",
" u'_score': 1.0,\n",
" u'_source': {u'Cast': u' Toshir\\xf4 Mifune (Kikuchiyo), Takashi Shimura (Kambei Shimada), Keiko Tsushima (Shino), Yukiko Shimazaki (Wife), Kamatari Fujiwara (Farmer Manzo).',\n",
" u'Country': u' Japan.',\n",
" u'Director': u' Akira Kurosawa.',\n",
" u'Genres': u' Adventure, Drama.',\n",
" u'Language': u' Japanese.',\n",
" u'Plot': u\" A veteran samurai, who has fallen on hard times, answers a village's request for protection from bandits. He gathers 6 other samurai to help him, and they teach the townspeople how to defend themselves, and they supply the samurai with three small meals a day. The film culminates in a giant battle when 40 bandits attack the village.\",\n",
" u'Rating': u' 8.7 (244938 votes).',\n",
" u'Runtime': u' 207, 160(international version), Argentina163, Sweden202(2002 re-release), UK150(original version), UK190(1991 re-release), USA158(original version) (cut), USA203(re-release), USA207(restored version), Spain202(DVD edition).',\n",
" u'Title': u' Shichinin no samurai (1954)',\n",
" u'Writer': u' Akira Kurosawa, Shinobu Hashimoto, Hideo Oguni.'},\n",
" u'_type': u'text'},\n",
" {u'_id': u'AVxB1yad6RBRcVMNlbJU',\n",
" u'_index': u'prueba-index',\n",
" u'_score': 1.0,\n",
" u'_source': {u'Cast': u' Morgan Freeman (Somerset), Andrew Kevin Walker (Dead Man at 1st Crime Scene), Kevin Spacey (John Doe), Daniel Zacapa (Detective Taylor), Brad Pitt (Mills).',\n",
" u'Country': u' USA.',\n",
" u'Director': u' David Fincher.',\n",
" u'Genres': u' Crime, Drama, Mystery, Thriller.',\n",
" u'Language': u' English.',\n",
" u'Plot': u' A film about two homicide detectives\\' (Morgan Freeman and (Brad Pitt desperate hunt for a serial killer who justifies his crimes as absolution for the world\\'s ignorance of the Seven Deadly Sins. The movie takes us from the tortured remains of one victim to the next as the sociopathic \"John Doe\" (Kevin Spacey) sermonizes to Detectives Somerset and Mills -- one sin at a time. The sin of Gluttony comes first and the murderer\\'s terrible capacity is graphically demonstrated in the dark and subdued tones characteristic of film noir. The seasoned and cultured but jaded Somerset researches the Seven Deadly Sins in an effort to understand the killer\\'s modus operandi while the bright but green and impulsive Detective Mills (Pitt) scoffs at his efforts to get inside the mind of a killer...',\n",
" u'Rating': u' 8.6 (1103064 votes).',\n",
" u'Runtime': u' 127.',\n",
" u'Title': u' Se7en (1995)',\n",
" u'Writer': u' Andrew Kevin Walker.'},\n",
" u'_type': u'text'},\n",
" {u'_id': u'AVxB2PyN6RBRcVMNlbJ4',\n",
" u'_index': u'prueba-index',\n",
" u'_score': 1.0,\n",
" u'_source': {u'Cast': u' Kirk Douglas (Col. Dax), Ralph Meeker (Cpl. Philippe Paris), Adolphe Menjou (Gen. George Broulard), George Macready (Gen. Paul Mireau), Wayne Morris (Lt. Roget).',\n",
" u'Country': u' USA.',\n",
" u'Director': u' Stanley Kubrick.',\n",
" u'Genres': u' Drama, War.',\n",
" u'Language': u' English, German, Latin.',\n",
" u'Plot': u' The futility and irony of the war in the trenches in WWI is shown as a unit commander in the French army must deal with the mutiny of his men and a glory-seeking general after part of his force falls back under fire in an impossible attack.',\n",
" u'Rating': u' 8.5 (130703 votes).',\n",
" u'Runtime': u' 88.',\n",
" u'Title': u' Paths of Glory (1957)',\n",
" u'Writer': u' Stanley Kubrick, Calder Willingham, Jim Thompson, Humphrey Cobb.'},\n",
" u'_type': u'text'},\n",
" {u'_id': u'AVxB1YlE6RBRcVMNlbJF',\n",
" u'_index': u'prueba-index',\n",
" u'_score': 1.0,\n",
" u'_source': {u'Cast': u' Tim Roth (Pumpkin), Amanda Plummer (Honey Bunny), Laura Lovelace (Waitress), John Travolta (Vincent Vega), Samuel L. Jackson (Jules Winnfield).',\n",
" u'Country': u' USA.',\n",
" u'Director': u' Quentin Tarantino.',\n",
" u'Genres': u' Crime, Drama.',\n",
" u'Language': u' English, Spanish, French.',\n",
" u'Plot': u' Jules Winnfield (Samuel L. Jackson) and Vincent Vega (John Travolta) are two hit men who are out to retrieve a suitcase stolen from their employer, mob boss Marsellus Wallace (Ving Rhames). Wallace has also asked Vincent to take his wife Mia (Uma Thurman) out a few days later when Wallace himself will be out of town. Butch Coolidge (Bruce Willis) is an aging boxer who is paid by Wallace to lose his fight. The lives of these seemingly unrelated people are woven together comprising of a series of funny, bizarre and uncalled-for incidents.',\n",
" u'Rating': u' 8.9 (1418706 votes).',\n",
" u'Runtime': u' 154, 178(original cut).',\n",
" u'Title': u' Pulp Fiction (1994)',\n",
" u'Writer': u' Quentin Tarantino, Roger Avary, Quentin Tarantino.'},\n",
" u'_type': u'text'},\n",
" {u'_id': u'AVxB2QwF6RBRcVMNlbJ5',\n",
" u'_index': u'prueba-index',\n",
" u'_score': 1.0,\n",
" u'_source': {u'Cast': u' Jamie Foxx (Django), Christoph Waltz (Dr. King Schultz), Leonardo DiCaprio (Calvin Candie), Kerry Washington (Broomhilda von Shaft), Samuel L. Jackson (Stephen).',\n",
" u'Country': u' USA.',\n",
" u'Director': u' Quentin Tarantino.',\n",
" u'Genres': u' Drama, Western.',\n",
" u'Language': u' English, German, French, Italian.',\n",
" u'Plot': u\" A German dentist buys the freedom of a slave and trains him with the intent to make him his deputy bounty hunter. Instead, he is led to the site of the slave's wife who belongs to a ruthless plantation owner.\",\n",
" u'Rating': u' 8.4 (1039218 votes).',\n",
" u'Runtime': u' 165.',\n",
" u'Title': u' Django Unchained (2012)',\n",
" u'Writer': u' Quentin Tarantino.'},\n",
" u'_type': u'text'},\n",
" {u'_id': u'AVxB2APi6RBRcVMNlbJn',\n",
" u'_index': u'prueba-index',\n",
" u'_score': 1.0,\n",
" u'_source': {u'Cast': u' Adrien Brody (Wladyslaw Szpilman), Emilia Fox (Dorota), Michal Zebrowski (Jurek), Ed Stoppard (Henryk), Maureen Lipman (Mother).',\n",
" u'Country': u' France, Poland, Germany, UK.',\n",
" u'Director': u' Roman Polanski.',\n",
" u'Genres': u' Biography, Drama, War.',\n",
" u'Language': u' English, German, Russian.',\n",
" u'Plot': u' In this adaptation of the autobiography \"The Pianist The Extraordinary True Story of One Man\\'s Survival in Warsaw, 1939-1945,\" Wladyslaw Szpilman, a Polish Jewish radio station pianist, sees Warsaw change gradually as World War II begins. Szpilman is forced into the Warsaw Ghetto, but is later separated from his family during Operation Reinhard. From this time until the concentration camp prisoners are released, Szpilman hides in various locations among the ruins of Warsaw.',\n",
" u'Rating': u' 8.5 (541568 votes).',\n",
" u'Runtime': u' 150.',\n",
" u'Title': u' Pianist, The (2002)',\n",
" u'Writer': u' Ronald Harwood, Wladyslaw Szpilman.'},\n",
" u'_type': u'text'},\n",
" {u'_id': u'AVxB1xNo6RBRcVMNlbJS',\n",
" u'_index': u'prueba-index',\n",
" u'_score': 1.0,\n",
" u'_source': {u'Cast': u' Mark Hamill (Luke Skywalker), Harrison Ford (Han Solo), Carrie Fisher (Princess Leia Organa), Peter Cushing (Grand Moff Tarkin), Alec Guinness (Ben Obi-Wan Kenobi).',\n",
" u'Country': u' USA.',\n",
" u'Director': u' George Lucas.',\n",
" u'Genres': u' Action, Adventure, Fantasy, Sci-Fi.',\n",
" u'Language': u' English.',\n",
" u'Plot': u' The Imperial Forces, under orders from cruel Darth Vader, hold Princess Leia hostage in their efforts to quell the rebellion against the Galactic Empire. Luke Skywalker and Han Solo, captain of the Millennium Falcon, work together with the companionable droid duo R2-D2 and C-3PO to rescue the beautiful princess, help the Rebel Alliance and restore freedom and justice to the Galaxy.',\n",
" u'Rating': u' 8.7 (978421 votes).',\n",
" u'Runtime': u' 121, 125(special edition).',\n",
" u'Title': u' Star Wars (1977)',\n",
" u'Writer': u' George Lucas.'},\n",
" u'_type': u'text'},\n",
" {u'_id': u'AVxB2DIf6RBRcVMNlbJq',\n",
" u'_index': u'prueba-index',\n",
" u'_score': 1.0,\n",
" u'_source': {u'Cast': u' Michael J. Fox (Marty McFly), Christopher Lloyd (Dr. Emmett Brown), Lea Thompson (Lorraine Baines), Crispin Glover (George McFly), Thomas F. Wilson (Biff Tannen).',\n",
" u'Country': u' USA.',\n",
" u'Director': u' Robert Zemeckis.',\n",
" u'Genres': u' Adventure, Comedy, Sci-Fi.',\n",
" u'Language': u' English.',\n",
" u'Plot': u' Marty McFly, a typical American teenager of the Eighties, is accidentally sent back to 1955 in a plutonium-powered DeLorean \"time machine\" invented by a slightly mad scientist. During his often hysterical, always amazing trip back in time, Marty must make certain his teenage parents-to-be meet and fall in love - so he can get back to the future.',\n",
" u'Rating': u' 8.5 (792473 votes).',\n",
" u'Runtime': u' 116.',\n",
" u'Title': u' Back to the Future (1985)',\n",
" u'Writer': u' Robert Zemeckis, Bob Gale.'},\n",
" u'_type': u'text'}],\n",
" u'max_score': 1.0,\n",
" u'total': 250},\n",
" u'timed_out': False,\n",
" u'took': 399}"
]
},
"execution_count": 26,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"res = es.search(index=\"prueba-index\", body={\"query\": {\"match_all\": {}}})\n",
"res"
]
},
{
"cell_type": "code",
"execution_count": 56,
"metadata": {
"collapsed": false
},
"outputs": [
{
"ename": "SyntaxError",
"evalue": "invalid syntax (<ipython-input-56-6969bc6a6e45>, line 12)",
"output_type": "error",
"traceback": [
"\u001b[0;36m File \u001b[0;32m\"<ipython-input-56-6969bc6a6e45>\"\u001b[0;36m, line \u001b[0;32m12\u001b[0m\n\u001b[0;31m })\u001b[0m\n\u001b[0m ^\u001b[0m\n\u001b[0;31mSyntaxError\u001b[0m\u001b[0;31m:\u001b[0m invalid syntax\n"
]
}
],
"source": [
"res = es.search(index=\"prueba-index\", body={\n",
" \"query\": \n",
" {\"match\" : {'Director': 'Christopher Nolan'}\n",
" },\n",
" {\n",
" \"highlight\" : {\n",
" \"fields\" : {\n",
" \"Language\" : {}\n",
" }\n",
" }\n",
" }\n",
"})\n",
"res"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Query sin fuzziness\n",
"**No funciona si le quitas una letra, la query de abajo si al ser fuzzy**"
]
},
{
"cell_type": "code",
"execution_count": 57,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Got 0 Hits:\n"
]
}
],
"source": [
"res = es.search(index=\"prueba-index\", body={\"query\": {\"match\" : {'Director': 'Christophe Nola'}}})\n",
"print(\"Got %d Hits:\" % res['hits']['total'])\n",
"for hit in res['hits']['hits']:\n",
" print(\"%(Title)s\" % hit[\"_source\"])"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Query con fuzziness añadida"
]
},
{
"cell_type": "code",
"execution_count": 104,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
" Monsters, Inc. (2001)\n",
" Into the Wild (2007)\n",
" Some Like It Hot (1959)\n",
" It Happened One Night (1934)\n",
" Mou gaan dou (2002)\n",
" Dr. Strangelove or How I Learned to Stop Worrying and Love the Bomb (1964)\n",
" Per qualche dollaro in più (1965)\n",
" Lion King, The (1994)\n",
" sjunde inseglet, Det (1957)\n",
" Faa yeung nin wa (2000)\n"
]
}
],
"source": [
"bodyQuery = {\n",
" \"query\": {\n",
" \"multi_match\" : {\n",
" \"query\" : \"Int\",\n",
" \"fields\": [\"Plot\", \"Title\"],\n",
" \"fuzziness\": \"2\"\n",
" }\n",
" }\n",
"}\n",
"res = es.search(index=\"prueba-index\", body=bodyQuery)\n",
"#print res\n",
"#print(\"Got %d Hits:\" % res['hits']['total'])\n",
"for hit in res['hits']['hits']:\n",
" print(\"%(Title)s\" % hit[\"_source\"])"
]
},
{
"cell_type": "code",
"execution_count": 102,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
" It's a Wonderful Life (1946)\n"
]
}
],
"source": [
"bodyQuery = {\n",
" \"query\": {\n",
" \"regexp\":{\n",
" \"Title\": \"wonder.*\"\n",
" }\n",
" }\n",
"}\n",
"res = es.search(index=\"prueba-index\", body=bodyQuery)\n",
"#print res\n",
"#print(\"Got %d Hits:\" % res['hits']['total'])\n",
"for hit in res['hits']['hits']:\n",
" print(\"%(Title)s\" % hit[\"_source\"])"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Query 2 con highlight de distintos campos y la forma de mostrarlo"
]
},
{
"cell_type": "code",
"execution_count": 93,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Got 0 Hits:\n"
]
}
],
"source": [
"bodyQuery2 = {\n",
" \"query\": {\n",
" \"match\" : {\n",
" \"Title\" : {\n",
" \n",
" \"query\" : \"wond\",\n",
" \"operator\" : \"and\",\n",
" \"zero_terms_query\": \"all\"\n",
" }\n",
" }\n",
" },\n",
" \"highlight\" : {\n",
" \"fields\" : {\n",
" \"Title\" : {},\n",
" \"Plot\" : {\"fragment_size\" : 150, \"number_of_fragments\" : 3}\n",
" },\n",
" #Permite el hightlight sobre campos que no se han hecho query\n",
" #como Plot en este ejemplo\n",
" \"require_field_match\" : False\n",
" }\n",
"}\n",
"res = es.search(index=\"prueba-index\", body=bodyQuery2)\n",
"print(\"Got %d Hits:\" % res['hits']['total'])\n",
"# Uso el [0] porque solo hay 1 hit, si hubiese mas, pues habria mas campos\n",
"# de la lista, habria que usar el for de arriba para sacar el highlight de\n",
"# cada uno de la lista\n",
"#print res['hits']['hits'][0]['highlight']\n",
"for hit in res['hits']['hits']:\n",
" print(hit)\n"
]
},
{
"cell_type": "code",
"execution_count": 114,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Got 18 Hits:\n"
]
},
{
"ename": "IndexError",
"evalue": "list index out of range",
"output_type": "error",
"traceback": [
"\u001b[0;31m\u001b[0m",
"\u001b[0;31mIndexError\u001b[0mTraceback (most recent call last)",
"\u001b[0;32m<ipython-input-114-334dd3e62403>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m 136\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0mhit\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mres\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'hits'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'hits'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 137\u001b[0m \u001b[0mresultado\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mappend\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mhit\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 138\u001b[0;31m \u001b[0;32mprint\u001b[0m \u001b[0mresultado\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m10\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'_source'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'Title'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
"\u001b[0;31mIndexError\u001b[0m: list index out of range"
]
}
],
"source": [
"\n",
"bodyQuery2 = {\n",
" \"query\": {\n",
" \"bool\": {\n",
" \"should\": [\n",
" { \"match\": {\n",
" \"Title\": {\n",
" \"query\": \"wonder\" + \".*\",\n",
" \"fuzziness\": \"AUTO\",\n",
" \"prefix_length\" : 1,\n",
" \"operator\": \"and\"\n",
"\n",
" }\n",
" }},\n",
" { \"match\": {\n",
" \"Plot\": {\n",
" \"query\": \"wonder\" + \".*\",\n",
" \"fuzziness\": 2,\n",
" \"prefix_length\" : 1,\n",
" \"operator\": \"and\"\n",
" }\n",
" }\n",
" },\n",
" { \"match\": {\n",
" \"Genres\": {\n",
" \"query\": \"wonder\" + \".*\",\n",
" \"fuzziness\": \"AUTO\",\n",
" \"prefix_length\" : 1,\n",
" \"operator\": \"and\"\n",
"\n",
" }\n",
" }},\n",
" { \"match\": {\n",
" \"Director\": {\n",
" \"query\": \"wonder\" + \".*\",\n",
" \"fuzziness\": \"AUTO\",\n",
" \"prefix_length\" : 1,\n",
" \"operator\": \"and\"\n",
"\n",
" }\n",
" }},\n",
" { \"match\": {\n",
" \"Writer\": {\n",
" \"query\": \"wonder\" + \".*\",\n",
" \"fuzziness\": \"AUTO\",\n",
" \"prefix_length\" : 1,\n",
" \"operator\": \"and\"\n",
"\n",
" }\n",
" }},\n",
" { \"match\": {\n",
" \"Cast\": {\n",
" \"query\": \"wonder\" + \".*\",\n",
" \"fuzziness\": \"AUTO\",\n",
" \"prefix_length\" : 1,\n",
" \"operator\": \"and\"\n",
"\n",
" }\n",
" }},\n",
" { \"match\": {\n",
" \"Country\": {\n",
" \"query\": \"wonder\" + \".*\",\n",
" \"fuzziness\": \"AUTO\",\n",
" \"prefix_length\" : 1,\n",
" \"operator\": \"and\"\n",
"\n",
" }\n",
" }},\n",
" { \"match\": {\n",
" \"Language\": {\n",
" \"query\": \"wonder\" + \".*\",\n",
" \"fuzziness\": \"AUTO\",\n",
" \"prefix_length\" : 1,\n",
" \"operator\": \"and\"\n",
"\n",
" }\n",
" }},\n",
" { \"match\": {\n",
" \"Rating\": {\n",
" \"query\": \"wonder\" + \".*\",\n",
" \"fuzziness\": \"AUTO\",\n",
" \"prefix_length\" : 1,\n",
" \"operator\": \"and\"\n",
"\n",
" }\n",
" }},\n",
"\n",
" ]\n",
" }\n",
"},\n",
" \"highlight\": {\n",
" \"fields\": {\n",
" \"Title\": {},\n",
" \"Plot\": {},\n",
" \"Director\": {}\n",
" },\n",
" # Permite el hightlight sobre campos que no se han hecho query\n",
" # como Plot en este ejemplo\n",
" \"require_field_match\": False\n",
"}\n",
"}\n",
"'''\n",
" \"query\": {\n",
" \"match\": {\n",
" \"Title\": {\n",
" \"query\": buscado,\n",
" \"fuzziness\": \"AUTO\",\n",
" \"boost\" : 2.0,\n",
" \"prefix_length\" : 1,\n",
" \"max_expansions\": 100,\n",
" #\"minimum_should_match\" : 10,\n",
"\n",
" \"operator\": \"and\"\n",
" }\n",
"\n",
" }\n",
" },\n",
" \"highlight\": {\n",
" \"fields\": {\n",
" \"Title\": {},\n",
" \"Plot\": {\"fragment_size\": 300, \"number_of_fragments\": 3}\n",
" },\n",
" # Permite el hightlight sobre campos que no se han hecho query\n",
" # como Plot en este ejemplo\n",
" \"require_field_match\": False\n",
" }\n",
"'''\n",
"res = es.search(index=\"prueba-index\", body= bodyQuery2)\n",
"print(\"Got %d Hits:\" % res['hits']['total'])\n",
"# Uso el [0] porque solo hay 1 hit, si hubiese mas, pues habria mas campos\n",
"# de la lista, habria que usar el for de arriba para sacar el highlight de\n",
"# cada uno de la lista\n",
"# print res['hits']['hits'][0]['highlight']\n",
"\n",
"resultado = []\n",
"for hit in res['hits']['hits']:\n",
" resultado.append(hit)\n",
"print resultado[10]['_source']['Title']"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Borrar datos"
]
},
{
"cell_type": "code",
"execution_count": 25,
"metadata": {
"collapsed": false
},
"outputs": [
{
"ename": "NotFoundError",
"evalue": "TransportError(404, u'{\"found\":false,\"_index\":\"prueba-index\",\"_type\":\"text\",\"_id\":\"1\",\"_version\":1,\"result\":\"not_found\",\"_shards\":{\"total\":2,\"successful\":1,\"failed\":0}}')",
"output_type": "error",
"traceback": [
"\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
"\u001b[0;31mNotFoundError\u001b[0m Traceback (most recent call last)",
"\u001b[0;32m<ipython-input-25-9a812aed628e>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mes\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mdelete\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mindex\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;34m'prueba-index'\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mdoc_type\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;34m'text'\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mid\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;36m1\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m",
"\u001b[0;32mC:\\Users\\cr\\Anaconda2\\lib\\site-packages\\elasticsearch\\client\\utils.pyc\u001b[0m in \u001b[0;36m_wrapped\u001b[0;34m(*args, **kwargs)\u001b[0m\n\u001b[1;32m 71\u001b[0m \u001b[1;32mif\u001b[0m \u001b[0mp\u001b[0m \u001b[1;32min\u001b[0m \u001b[0mkwargs\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m 72\u001b[0m \u001b[0mparams\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0mp\u001b[0m\u001b[1;33m]\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mkwargs\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mpop\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mp\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m---> 73\u001b[0;31m \u001b[1;32mreturn\u001b[0m \u001b[0mfunc\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m*\u001b[0m\u001b[0margs\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mparams\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mparams\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 74\u001b[0m \u001b[1;32mreturn\u001b[0m \u001b[0m_wrapped\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m 75\u001b[0m \u001b[1;32mreturn\u001b[0m \u001b[0m_wrapper\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
"\u001b[0;32mC:\\Users\\cr\\Anaconda2\\lib\\site-packages\\elasticsearch\\client\\__init__.pyc\u001b[0m in \u001b[0;36mdelete\u001b[0;34m(self, index, doc_type, id, params)\u001b[0m\n\u001b[1;32m 1076\u001b[0m \u001b[1;32mraise\u001b[0m \u001b[0mValueError\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m\"Empty value passed for a required argument.\"\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m 1077\u001b[0m return self.transport.perform_request('DELETE', _make_path(index,\n\u001b[0;32m-> 1078\u001b[0;31m doc_type, id), params=params)\n\u001b[0m\u001b[1;32m 1079\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m 1080\u001b[0m @query_params('allow_no_indices', 'analyze_wildcard', 'analyzer',\n",
"\u001b[0;32mC:\\Users\\cr\\Anaconda2\\lib\\site-packages\\elasticsearch\\transport.pyc\u001b[0m in \u001b[0;36mperform_request\u001b[0;34m(self, method, url, params, body)\u001b[0m\n\u001b[1;32m 310\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m 311\u001b[0m \u001b[1;32mtry\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m--> 312\u001b[0;31m \u001b[0mstatus\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mheaders\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mdata\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mconnection\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mperform_request\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mmethod\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0murl\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mparams\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mbody\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mignore\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mignore\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mtimeout\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mtimeout\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 313\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m 314\u001b[0m \u001b[1;32mexcept\u001b[0m \u001b[0mTransportError\u001b[0m \u001b[1;32mas\u001b[0m \u001b[0me\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
"\u001b[0;32mC:\\Users\\cr\\Anaconda2\\lib\\site-packages\\elasticsearch\\connection\\http_urllib3.pyc\u001b[0m in \u001b[0;36mperform_request\u001b[0;34m(self, method, url, params, body, timeout, ignore)\u001b[0m\n\u001b[1;32m 126\u001b[0m \u001b[1;32mif\u001b[0m \u001b[1;32mnot\u001b[0m \u001b[1;33m(\u001b[0m\u001b[1;36m200\u001b[0m \u001b[1;33m<=\u001b[0m \u001b[0mresponse\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mstatus\u001b[0m \u001b[1;33m<\u001b[0m \u001b[1;36m300\u001b[0m\u001b[1;33m)\u001b[0m \u001b[1;32mand\u001b[0m \u001b[0mresponse\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mstatus\u001b[0m \u001b[1;32mnot\u001b[0m \u001b[1;32min\u001b[0m \u001b[0mignore\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m 127\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mlog_request_fail\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mmethod\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mfull_url\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0murl\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mbody\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mduration\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mresponse\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mstatus\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mraw_data\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m--> 128\u001b[0;31m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_raise_error\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mresponse\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mstatus\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mraw_data\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 129\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m 130\u001b[0m self.log_request_success(method, full_url, url, body, response.status,\n",
"\u001b[0;32mC:\\Users\\cr\\Anaconda2\\lib\\site-packages\\elasticsearch\\connection\\base.pyc\u001b[0m in \u001b[0;36m_raise_error\u001b[0;34m(self, status_code, raw_data)\u001b[0m\n\u001b[1;32m 123\u001b[0m \u001b[0mlogger\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mwarning\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m'Undecodable raw error response from server: %s'\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0merr\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m 124\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m--> 125\u001b[0;31m \u001b[1;32mraise\u001b[0m \u001b[0mHTTP_EXCEPTIONS\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mget\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mstatus_code\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mTransportError\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mstatus_code\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0merror_message\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0madditional_info\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 126\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m 127\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n",
"\u001b[0;31mNotFoundError\u001b[0m: TransportError(404, u'{\"found\":false,\"_index\":\"prueba-index\",\"_type\":\"text\",\"_id\":\"1\",\"_version\":1,\"result\":\"not_found\",\"_shards\":{\"total\":2,\"successful\":1,\"failed\":0}}')"
]
}
],
"source": [
"es.delete(index='prueba-index', doc_type='text', id=1)"
]
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 2",
"language": "python",
"name": "python2"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 2
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython2",
"version": "2.7.13"
}
},
"nbformat": 4,
"nbformat_minor": 2
}
| gpl-3.0 |
csaladenes/csaladenes.github.io | present/bi2/2020/ubb/az_en_jupyter2_mappam/sklearn_tutorial/05-Validation.ipynb | 1 | 264163 | {
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"<small><i>This notebook was put together by [Jake Vanderplas](http://www.vanderplas.com). Source and license info is on [GitHub](https://github.com/jakevdp/sklearn_tutorial/).</i></small>"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Validation and Model Selection"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"In this section, we'll look at *model evaluation* and the tuning of *hyperparameters*, which are parameters that define the model."
]
},
{
"cell_type": "code",
"execution_count": 1,
"metadata": {},
"outputs": [],
"source": [
"from __future__ import print_function, division\n",
"\n",
"%matplotlib inline\n",
"import numpy as np\n",
"import matplotlib.pyplot as plt\n",
"\n",
"plt.style.use('seaborn')"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Validating Models\n",
"\n",
"One of the most important pieces of machine learning is **model validation**: that is, checking how well your model fits a given dataset. But there are some pitfalls you need to watch out for.\n",
"\n",
"Consider the digits example we've been looking at previously. How might we check how well our model fits the data?"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {},
"outputs": [],
"source": [
"from sklearn.datasets import load_digits\n",
"digits = load_digits()\n",
"X = digits.data\n",
"y = digits.target"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Let's fit a K-neighbors classifier"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"KNeighborsClassifier(n_neighbors=1)"
]
},
"execution_count": 3,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"from sklearn.neighbors import KNeighborsClassifier\n",
"knn = KNeighborsClassifier(n_neighbors=1)\n",
"knn.fit(X, y)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Now we'll use this classifier to *predict* labels for the data"
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {},
"outputs": [],
"source": [
"y_pred = knn.predict(X)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Finally, we can check how well our prediction did:"
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"1797 / 1797 correct\n"
]
}
],
"source": [
"print(\"{0} / {1} correct\".format(np.sum(y == y_pred), len(y)))"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"It seems we have a perfect classifier!\n",
"\n",
"**Question: what's wrong with this?**"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Validation Sets\n",
"\n",
"Above we made the mistake of testing our data on the same set of data that was used for training. **This is not generally a good idea**. If we optimize our estimator this way, we will tend to **over-fit** the data: that is, we learn the noise.\n",
"\n",
"A better way to test a model is to use a hold-out set which doesn't enter the training. We've seen this before using scikit-learn's train/test split utility:"
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"((1347, 64), (450, 64))"
]
},
"execution_count": 6,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"from sklearn.model_selection import train_test_split\n",
"X_train, X_test, y_train, y_test = train_test_split(X, y)\n",
"X_train.shape, X_test.shape"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Now we train on the training data, and validate on the test data:"
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"440 / 450 correct\n"
]
}
],
"source": [
"knn = KNeighborsClassifier(n_neighbors=1)\n",
"knn.fit(X_train, y_train)\n",
"y_pred = knn.predict(X_test)\n",
"print(\"{0} / {1} correct\".format(np.sum(y_test == y_pred), len(y_test)))"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"This gives us a more reliable estimate of how our model is doing.\n",
"\n",
"The metric we're using here, comparing the number of matches to the total number of samples, is known as the **accuracy score**, and can be computed using the following routine:"
]
},
{
"cell_type": "code",
"execution_count": 8,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"0.9777777777777777"
]
},
"execution_count": 8,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"from sklearn.metrics import accuracy_score\n",
"accuracy_score(y_test, y_pred)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"This can also be computed directly from the ``model.score`` method:"
]
},
{
"cell_type": "code",
"execution_count": 9,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"0.9777777777777777"
]
},
"execution_count": 9,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"knn.score(X_test, y_test)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Using this, we can ask how this changes as we change the model parameters, in this case the number of neighbors:"
]
},
{
"cell_type": "code",
"execution_count": 10,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"1 0.9777777777777777\n",
"5 0.9711111111111111\n",
"10 0.9666666666666667\n",
"20 0.9488888888888889\n",
"30 0.9422222222222222\n"
]
}
],
"source": [
"for n_neighbors in [1, 5, 10, 20, 30]:\n",
" knn = KNeighborsClassifier(n_neighbors)\n",
" knn.fit(X_train, y_train)\n",
" print(n_neighbors, knn.score(X_test, y_test))"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"We see that in this case, a small number of neighbors seems to be the best option."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Cross-Validation\n",
"\n",
"One problem with validation sets is that you \"lose\" some of the data. Above, we've only used 3/4 of the data for the training, and used 1/4 for the validation. Another option is to use **2-fold cross-validation**, where we split the sample in half and perform the validation twice:"
]
},
{
"cell_type": "code",
"execution_count": 11,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"((898, 64), (899, 64))"
]
},
"execution_count": 11,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"X1, X2, y1, y2 = train_test_split(X, y, test_size=0.5, random_state=0)\n",
"X1.shape, X2.shape"
]
},
{
"cell_type": "code",
"execution_count": 12,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"0.9832962138084632\n",
"0.982202447163515\n"
]
}
],
"source": [
"print(KNeighborsClassifier(1).fit(X2, y2).score(X1, y1))\n",
"print(KNeighborsClassifier(1).fit(X1, y1).score(X2, y2))"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Thus a two-fold cross-validation gives us two estimates of the score for that parameter.\n",
"\n",
"Because this is a bit of a pain to do by hand, scikit-learn has a utility routine to help:"
]
},
{
"cell_type": "code",
"execution_count": 13,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"0.9749627560521414"
]
},
"execution_count": 13,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"from sklearn.model_selection import cross_val_score\n",
"cv = cross_val_score(KNeighborsClassifier(1), X, y, cv=10)\n",
"cv.mean()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### K-fold Cross-Validation\n",
"\n",
"Here we've used 2-fold cross-validation. This is just one specialization of $K$-fold cross-validation, where we split the data into $K$ chunks and perform $K$ fits, where each chunk gets a turn as the validation set.\n",
"We can do this by changing the ``cv`` parameter above. Let's do 10-fold cross-validation:"
]
},
{
"cell_type": "code",
"execution_count": 14,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"array([0.93333333, 0.99444444, 0.97222222, 0.97222222, 0.96666667,\n",
" 0.98333333, 0.99444444, 0.98882682, 0.97765363, 0.96648045])"
]
},
"execution_count": 14,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"cross_val_score(KNeighborsClassifier(1), X, y, cv=10)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"This gives us an even better idea of how well our model is doing."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Overfitting, Underfitting and Model Selection"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Now that we've gone over the basics of validation, and cross-validation, it's time to go into even more depth regarding model selection.\n",
"\n",
"The issues associated with validation and \n",
"cross-validation are some of the most important\n",
"aspects of the practice of machine learning. Selecting the optimal model\n",
"for your data is vital, and is a piece of the problem that is not often\n",
"appreciated by machine learning practitioners.\n",
"\n",
"Of core importance is the following question:\n",
"\n",
"**If our estimator is underperforming, how should we move forward?**\n",
"\n",
"- Use simpler or more complicated model?\n",
"- Add more features to each observed data point?\n",
"- Add more training samples?\n",
"\n",
"The answer is often counter-intuitive. In particular, **Sometimes using a\n",
"more complicated model will give _worse_ results.** Also, **Sometimes adding\n",
"training data will not improve your results.** The ability to determine\n",
"what steps will improve your model is what separates the successful machine\n",
"learning practitioners from the unsuccessful."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Illustration of the Bias-Variance Tradeoff\n",
"\n",
"For this section, we'll work with a simple 1D regression problem. This will help us to\n",
"easily visualize the data and the model, and the results generalize easily to higher-dimensional\n",
"datasets. We'll explore a simple **linear regression** problem.\n",
"This can be accomplished within scikit-learn with the `sklearn.linear_model` module.\n",
"\n",
"We'll create a simple nonlinear function that we'd like to fit"
]
},
{
"cell_type": "code",
"execution_count": 15,
"metadata": {},
"outputs": [],
"source": [
"def test_func(x, err=0.5):\n",
" y = 10 - 1. / (x + 0.1)\n",
" if err > 0:\n",
" y = np.random.normal(y, err)\n",
" return y"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Now let's create a realization of this dataset:"
]
},
{
"cell_type": "code",
"execution_count": 16,
"metadata": {},
"outputs": [],
"source": [
"def make_data(N=40, error=1.0, random_seed=1):\n",
" # randomly sample the data\n",
" np.random.seed(1)\n",
" X = np.random.random(N)[:, np.newaxis]\n",
" y = test_func(X.ravel(), error)\n",
" \n",
" return X, y"
]
},
{
"cell_type": "code",
"execution_count": 17,
"metadata": {},
"outputs": [
{
"data": {
"image/png": "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\n",
"text/plain": [
"<Figure size 576x396 with 1 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"X, y = make_data(40, error=1)\n",
"plt.scatter(X.ravel(), y);"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Now say we want to perform a regression on this data. Let's use the built-in linear regression function to compute a fit:"
]
},
{
"cell_type": "code",
"execution_count": 18,
"metadata": {},
"outputs": [
{
"data": {
"image/png": "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\n",
"text/plain": [
"<Figure size 576x396 with 1 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"X_test = np.linspace(-0.1, 1.1, 500)[:, None]\n",
"\n",
"from sklearn.linear_model import LinearRegression\n",
"from sklearn.metrics import mean_squared_error\n",
"model = LinearRegression()\n",
"model.fit(X, y)\n",
"y_test = model.predict(X_test)\n",
"\n",
"plt.scatter(X.ravel(), y)\n",
"plt.plot(X_test.ravel(), y_test)\n",
"plt.title(\"mean squared error: {0:.3g}\".format(mean_squared_error(model.predict(X), y)));"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"We have fit a straight line to the data, but clearly this model is not a good choice. We say that this model is **biased**, or that it **under-fits** the data.\n",
"\n",
"Let's try to improve this by creating a more complicated model. We can do this by adding degrees of freedom, and computing a polynomial regression over the inputs. Scikit-learn makes this easy with the ``PolynomialFeatures`` preprocessor, which can be pipelined with a linear regression.\n",
"\n",
"Let's make a convenience routine to do this:"
]
},
{
"cell_type": "code",
"execution_count": 19,
"metadata": {},
"outputs": [],
"source": [
"from sklearn.preprocessing import PolynomialFeatures\n",
"from sklearn.linear_model import LinearRegression\n",
"from sklearn.pipeline import make_pipeline\n",
"\n",
"def PolynomialRegression(degree=2, **kwargs):\n",
" return make_pipeline(PolynomialFeatures(degree),\n",
" LinearRegression(**kwargs))"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Now we'll use this to fit a quadratic curve to the data."
]
},
{
"cell_type": "code",
"execution_count": 20,
"metadata": {},
"outputs": [
{
"data": {
"image/png": "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\n",
"text/plain": [
"<Figure size 576x396 with 1 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"model = PolynomialRegression(2)\n",
"model.fit(X, y)\n",
"y_test = model.predict(X_test)\n",
"\n",
"plt.scatter(X.ravel(), y)\n",
"plt.plot(X_test.ravel(), y_test)\n",
"plt.title(\"mean squared error: {0:.3g}\".format(mean_squared_error(model.predict(X), y)));"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"This reduces the mean squared error, and makes a much better fit. What happens if we use an even higher-degree polynomial?"
]
},
{
"cell_type": "code",
"execution_count": 21,
"metadata": {},
"outputs": [
{
"data": {
"image/png": "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\n",
"text/plain": [
"<Figure size 576x396 with 1 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"model = PolynomialRegression(30)\n",
"model.fit(X, y)\n",
"y_test = model.predict(X_test)\n",
"\n",
"plt.scatter(X.ravel(), y)\n",
"plt.plot(X_test.ravel(), y_test)\n",
"plt.title(\"mean squared error: {0:.3g}\".format(mean_squared_error(model.predict(X), y)))\n",
"plt.ylim(-4, 14);"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"When we increase the degree to this extent, it's clear that the resulting fit is no longer reflecting the true underlying distribution, but is more sensitive to the noise in the training data. For this reason, we call it a **high-variance model**, and we say that it **over-fits** the data."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Just for fun, let's use IPython's interact capability (only in IPython 2.0+) to explore this interactively:"
]
},
{
"cell_type": "code",
"execution_count": 22,
"metadata": {},
"outputs": [
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "dfbda216ee3544aaa9b372d9714b100a",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"interactive(children=(Dropdown(description='degree', options=(1, 30), value=1), Dropdown(description='Npts', o…"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"from ipywidgets import interact\n",
"\n",
"def plot_fit(degree=1, Npts=50):\n",
" X, y = make_data(Npts, error=1)\n",
" X_test = np.linspace(-0.1, 1.1, 500)[:, None]\n",
" \n",
" model = PolynomialRegression(degree=degree)\n",
" model.fit(X, y)\n",
" y_test = model.predict(X_test)\n",
"\n",
" plt.scatter(X.ravel(), y)\n",
" plt.plot(X_test.ravel(), y_test)\n",
" plt.ylim(-4, 14)\n",
" plt.title(\"mean squared error: {0:.2f}\".format(mean_squared_error(model.predict(X), y)))\n",
" \n",
"interact(plot_fit, degree=[1, 30], Npts=[2, 100]);"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Detecting Over-fitting with Validation Curves\n",
"\n",
"Clearly, computing the error on the training data is not enough (we saw this previously). As above, we can use **cross-validation** to get a better handle on how the model fit is working.\n",
"\n",
"Let's do this here, again using the ``validation_curve`` utility. To make things more clear, we'll use a slightly larger dataset:"
]
},
{
"cell_type": "code",
"execution_count": 23,
"metadata": {},
"outputs": [
{
"data": {
"image/png": "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\n",
"text/plain": [
"<Figure size 576x396 with 1 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"X, y = make_data(120, error=1.0)\n",
"plt.scatter(X, y);"
]
},
{
"cell_type": "code",
"execution_count": 24,
"metadata": {},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"/opt/conda/lib/python3.8/site-packages/sklearn/utils/validation.py:70: FutureWarning: Pass param_name=polynomialfeatures__degree, param_range=[ 0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17] as keyword args. From version 1.0 (renaming of 0.25) passing these as positional arguments will result in an error\n",
" warnings.warn(f\"Pass {args_msg} as keyword args. From version \"\n"
]
}
],
"source": [
"from sklearn.model_selection import validation_curve\n",
"\n",
"def rms_error(model, X, y):\n",
" y_pred = model.predict(X)\n",
" return np.sqrt(np.mean((y - y_pred) ** 2))\n",
"\n",
"degree = np.arange(0, 18)\n",
"val_train, val_test = validation_curve(PolynomialRegression(), X, y,\n",
" 'polynomialfeatures__degree', degree, cv=7,\n",
" scoring=rms_error)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Now let's plot the validation curves:"
]
},
{
"cell_type": "code",
"execution_count": 25,
"metadata": {},
"outputs": [
{
"data": {
"image/png": "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\n",
"text/plain": [
"<Figure size 576x396 with 1 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"def plot_with_err(x, data, **kwargs):\n",
" mu, std = data.mean(1), data.std(1)\n",
" lines = plt.plot(x, mu, '-', **kwargs)\n",
" plt.fill_between(x, mu - std, mu + std, edgecolor='none',\n",
" facecolor=lines[0].get_color(), alpha=0.2)\n",
"\n",
"plot_with_err(degree, val_train, label='training scores')\n",
"plot_with_err(degree, val_test, label='validation scores')\n",
"plt.xlabel('degree'); plt.ylabel('rms error')\n",
"plt.legend();"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Notice the trend here, which is common for this type of plot.\n",
"\n",
"1. For a small model complexity, the training error and validation error are very similar. This indicates that the model is **under-fitting** the data: it doesn't have enough complexity to represent the data. Another way of putting it is that this is a **high-bias** model.\n",
"\n",
"2. As the model complexity grows, the training and validation scores diverge. This indicates that the model is **over-fitting** the data: it has so much flexibility, that it fits the noise rather than the underlying trend. Another way of putting it is that this is a **high-variance** model.\n",
"\n",
"3. Note that the training score (nearly) always improves with model complexity. This is because a more complicated model can fit the noise better, so the model improves. The validation data generally has a sweet spot, which here is around 5 terms.\n",
"\n",
"Here's our best-fit model according to the cross-validation:"
]
},
{
"cell_type": "code",
"execution_count": 26,
"metadata": {},
"outputs": [
{
"data": {
"image/png": "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\n",
"text/plain": [
"<Figure size 576x396 with 1 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"model = PolynomialRegression(4).fit(X, y)\n",
"plt.scatter(X, y)\n",
"plt.plot(X_test, model.predict(X_test));"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Detecting Data Sufficiency with Learning Curves\n",
"\n",
"As you might guess, the exact turning-point of the tradeoff between bias and variance is highly dependent on the number of training points used. Here we'll illustrate the use of *learning curves*, which display this property.\n",
"\n",
"The idea is to plot the mean-squared-error for the training and test set as a function of *Number of Training Points*"
]
},
{
"cell_type": "code",
"execution_count": 27,
"metadata": {},
"outputs": [],
"source": [
"from sklearn.model_selection import learning_curve\n",
"\n",
"def plot_learning_curve(degree=3):\n",
" train_sizes = np.linspace(0.05, 1, 120)\n",
" N_train, val_train, val_test = learning_curve(PolynomialRegression(degree),\n",
" X, y, train_sizes, cv=5,\n",
" scoring=rms_error)\n",
" plot_with_err(N_train, val_train, label='training scores')\n",
" plot_with_err(N_train, val_test, label='validation scores')\n",
" plt.xlabel('Training Set Size'); plt.ylabel('rms error')\n",
" plt.ylim(0, 3)\n",
" plt.xlim(5, 80)\n",
" plt.legend()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Let's see what the learning curves look like for a linear model:"
]
},
{
"cell_type": "code",
"execution_count": 28,
"metadata": {},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"/opt/conda/lib/python3.8/site-packages/sklearn/utils/validation.py:70: FutureWarning: Pass groups=[0.05 0.05798319 0.06596639 0.07394958 0.08193277 0.08991597\n",
" 0.09789916 0.10588235 0.11386555 0.12184874 0.12983193 0.13781513\n",
" 0.14579832 0.15378151 0.16176471 0.1697479 0.17773109 0.18571429\n",
" 0.19369748 0.20168067 0.20966387 0.21764706 0.22563025 0.23361345\n",
" 0.24159664 0.24957983 0.25756303 0.26554622 0.27352941 0.28151261\n",
" 0.2894958 0.29747899 0.30546218 0.31344538 0.32142857 0.32941176\n",
" 0.33739496 0.34537815 0.35336134 0.36134454 0.36932773 0.37731092\n",
" 0.38529412 0.39327731 0.4012605 0.4092437 0.41722689 0.42521008\n",
" 0.43319328 0.44117647 0.44915966 0.45714286 0.46512605 0.47310924\n",
" 0.48109244 0.48907563 0.49705882 0.50504202 0.51302521 0.5210084\n",
" 0.5289916 0.53697479 0.54495798 0.55294118 0.56092437 0.56890756\n",
" 0.57689076 0.58487395 0.59285714 0.60084034 0.60882353 0.61680672\n",
" 0.62478992 0.63277311 0.6407563 0.6487395 0.65672269 0.66470588\n",
" 0.67268908 0.68067227 0.68865546 0.69663866 0.70462185 0.71260504\n",
" 0.72058824 0.72857143 0.73655462 0.74453782 0.75252101 0.7605042\n",
" 0.76848739 0.77647059 0.78445378 0.79243697 0.80042017 0.80840336\n",
" 0.81638655 0.82436975 0.83235294 0.84033613 0.84831933 0.85630252\n",
" 0.86428571 0.87226891 0.8802521 0.88823529 0.89621849 0.90420168\n",
" 0.91218487 0.92016807 0.92815126 0.93613445 0.94411765 0.95210084\n",
" 0.96008403 0.96806723 0.97605042 0.98403361 0.99201681 1. ] as keyword args. From version 1.0 (renaming of 0.25) passing these as positional arguments will result in an error\n",
" warnings.warn(f\"Pass {args_msg} as keyword args. From version \"\n"
]
},
{
"data": {
"image/png": "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\n",
"text/plain": [
"<Figure size 576x396 with 1 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"plot_learning_curve(1)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"This shows a typical learning curve: for very few training points, there is a large separation between the training and test error, which indicates **over-fitting**. Given the same model, for a large number of training points, the training and testing errors converge, which indicates potential **under-fitting**.\n",
"\n",
"As you add more data points, the training error will never increase, and the testing error will never decrease (why do you think this is?)\n",
"\n",
"It is easy to see that, in this plot, if you'd like to reduce the MSE down to the nominal value of 1.0 (which is the magnitude of the scatter we put in when constructing the data), then adding more samples will *never* get you there. For $d=1$, the two curves have converged and cannot move lower. What about for a larger value of $d$?"
]
},
{
"cell_type": "code",
"execution_count": 29,
"metadata": {},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"/opt/conda/lib/python3.8/site-packages/sklearn/utils/validation.py:70: FutureWarning: Pass groups=[0.05 0.05798319 0.06596639 0.07394958 0.08193277 0.08991597\n",
" 0.09789916 0.10588235 0.11386555 0.12184874 0.12983193 0.13781513\n",
" 0.14579832 0.15378151 0.16176471 0.1697479 0.17773109 0.18571429\n",
" 0.19369748 0.20168067 0.20966387 0.21764706 0.22563025 0.23361345\n",
" 0.24159664 0.24957983 0.25756303 0.26554622 0.27352941 0.28151261\n",
" 0.2894958 0.29747899 0.30546218 0.31344538 0.32142857 0.32941176\n",
" 0.33739496 0.34537815 0.35336134 0.36134454 0.36932773 0.37731092\n",
" 0.38529412 0.39327731 0.4012605 0.4092437 0.41722689 0.42521008\n",
" 0.43319328 0.44117647 0.44915966 0.45714286 0.46512605 0.47310924\n",
" 0.48109244 0.48907563 0.49705882 0.50504202 0.51302521 0.5210084\n",
" 0.5289916 0.53697479 0.54495798 0.55294118 0.56092437 0.56890756\n",
" 0.57689076 0.58487395 0.59285714 0.60084034 0.60882353 0.61680672\n",
" 0.62478992 0.63277311 0.6407563 0.6487395 0.65672269 0.66470588\n",
" 0.67268908 0.68067227 0.68865546 0.69663866 0.70462185 0.71260504\n",
" 0.72058824 0.72857143 0.73655462 0.74453782 0.75252101 0.7605042\n",
" 0.76848739 0.77647059 0.78445378 0.79243697 0.80042017 0.80840336\n",
" 0.81638655 0.82436975 0.83235294 0.84033613 0.84831933 0.85630252\n",
" 0.86428571 0.87226891 0.8802521 0.88823529 0.89621849 0.90420168\n",
" 0.91218487 0.92016807 0.92815126 0.93613445 0.94411765 0.95210084\n",
" 0.96008403 0.96806723 0.97605042 0.98403361 0.99201681 1. ] as keyword args. From version 1.0 (renaming of 0.25) passing these as positional arguments will result in an error\n",
" warnings.warn(f\"Pass {args_msg} as keyword args. From version \"\n"
]
},
{
"data": {
"image/png": "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\n",
"text/plain": [
"<Figure size 576x396 with 1 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"plot_learning_curve(3)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Here we see that by adding more model complexity, we've managed to lower the level of convergence to an rms error of 1.0!\n",
"\n",
"What if we get even more complex?"
]
},
{
"cell_type": "code",
"execution_count": 30,
"metadata": {},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"/opt/conda/lib/python3.8/site-packages/sklearn/utils/validation.py:70: FutureWarning: Pass groups=[0.05 0.05798319 0.06596639 0.07394958 0.08193277 0.08991597\n",
" 0.09789916 0.10588235 0.11386555 0.12184874 0.12983193 0.13781513\n",
" 0.14579832 0.15378151 0.16176471 0.1697479 0.17773109 0.18571429\n",
" 0.19369748 0.20168067 0.20966387 0.21764706 0.22563025 0.23361345\n",
" 0.24159664 0.24957983 0.25756303 0.26554622 0.27352941 0.28151261\n",
" 0.2894958 0.29747899 0.30546218 0.31344538 0.32142857 0.32941176\n",
" 0.33739496 0.34537815 0.35336134 0.36134454 0.36932773 0.37731092\n",
" 0.38529412 0.39327731 0.4012605 0.4092437 0.41722689 0.42521008\n",
" 0.43319328 0.44117647 0.44915966 0.45714286 0.46512605 0.47310924\n",
" 0.48109244 0.48907563 0.49705882 0.50504202 0.51302521 0.5210084\n",
" 0.5289916 0.53697479 0.54495798 0.55294118 0.56092437 0.56890756\n",
" 0.57689076 0.58487395 0.59285714 0.60084034 0.60882353 0.61680672\n",
" 0.62478992 0.63277311 0.6407563 0.6487395 0.65672269 0.66470588\n",
" 0.67268908 0.68067227 0.68865546 0.69663866 0.70462185 0.71260504\n",
" 0.72058824 0.72857143 0.73655462 0.74453782 0.75252101 0.7605042\n",
" 0.76848739 0.77647059 0.78445378 0.79243697 0.80042017 0.80840336\n",
" 0.81638655 0.82436975 0.83235294 0.84033613 0.84831933 0.85630252\n",
" 0.86428571 0.87226891 0.8802521 0.88823529 0.89621849 0.90420168\n",
" 0.91218487 0.92016807 0.92815126 0.93613445 0.94411765 0.95210084\n",
" 0.96008403 0.96806723 0.97605042 0.98403361 0.99201681 1. ] as keyword args. From version 1.0 (renaming of 0.25) passing these as positional arguments will result in an error\n",
" warnings.warn(f\"Pass {args_msg} as keyword args. From version \"\n"
]
},
{
"data": {
"image/png": "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\n",
"text/plain": [
"<Figure size 576x396 with 1 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"plot_learning_curve(10)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"For an even more complex model, we still converge, but the convergence only happens for *large* amounts of training data.\n",
"\n",
"So we see the following:\n",
"\n",
"- you can **cause the lines to converge** by adding more points or by simplifying the model.\n",
"- you can **bring the convergence error down** only by increasing the complexity of the model.\n",
"\n",
"Thus these curves can give you hints about how you might improve a sub-optimal model. If the curves are already close together, you need more model complexity. If the curves are far apart, you might also improve the model by adding more data.\n",
"\n",
"To make this more concrete, imagine some telescope data in which the results are not robust enough. You must think about whether to spend your valuable telescope time observing *more objects* to get a larger training set, or *more attributes of each object* in order to improve the model. The answer to this question has real consequences, and can be addressed using these metrics."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Summary\n",
"\n",
"We've gone over several useful tools for model validation\n",
"\n",
"- The **Training Score** shows how well a model fits the data it was trained on. This is not a good indication of model effectiveness\n",
"- The **Validation Score** shows how well a model fits hold-out data. The most effective method is some form of cross-validation, where multiple hold-out sets are used.\n",
"- **Validation Curves** are a plot of validation score and training score as a function of **model complexity**:\n",
" + when the two curves are close, it indicates *underfitting*\n",
" + when the two curves are separated, it indicates *overfitting*\n",
" + the \"sweet spot\" is in the middle\n",
"- **Learning Curves** are a plot of the validation score and training score as a function of **Number of training samples**\n",
" + when the curves are close, it indicates *underfitting*, and adding more data will not generally improve the estimator.\n",
" + when the curves are far apart, it indicates *overfitting*, and adding more data may increase the effectiveness of the model.\n",
" \n",
"These tools are powerful means of evaluating your model on your data."
]
},
{
"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": "3.8.8"
}
},
"nbformat": 4,
"nbformat_minor": 4
}
| mit |
winpython/winpython_afterdoc | docs/seaborn_demo_from_jakevdp.ipynb | 2 | 5294 | {
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Seaborn demo per Jake VanderPlas below"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"from __future__ import print_function, division\n",
"\n",
"%matplotlib inline\n",
"import matplotlib.pyplot as plt\n",
"import numpy as np\n",
"import pandas as pd"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"plt.style.use('ggplot')\n",
"x = np.linspace(0, 10, 1000)\n",
"plt.plot(x, np.sin(x), x, np.cos(x));"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"import seaborn as sns\n",
"sns.set()\n",
"plt.plot(x, np.sin(x), x, np.cos(x));"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"data = np.random.multivariate_normal([0, 0], [[5, 2], [2, 2]], size=2000)\n",
"data = pd.DataFrame(data, columns=['x', 'y'])\n",
"\n",
"for col in 'xy':\n",
" plt.hist(data[col], density=True, alpha=0.5)\n",
" # old Matplotlib would be plt.hist(data[col], normed=True, alpha=0.5)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"for col in 'xy':\n",
" sns.kdeplot(data[col], shade=True)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"sns.distplot(data['x']);"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"sns.kdeplot(data.x, data.y); # formerly sns.kdeplot(data)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"with sns.axes_style('white'):\n",
" sns.jointplot(\"x\", \"y\", data, kind='kde');"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"with sns.axes_style('white'):\n",
" sns.jointplot(\"x\", \"y\", data, kind='hex')"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"iris = sns.load_dataset(\"iris\")\n",
"iris.head()"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"tips = sns.load_dataset('tips')\n",
"tips.head()"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"tips['tip_pct'] = 100 * tips['tip'] / tips['total_bill']\n",
"\n",
"grid = sns.FacetGrid(tips, row=\"sex\", col=\"time\", margin_titles=True)\n",
"grid.map(plt.hist, \"tip_pct\", bins=np.linspace(0, 40, 15));"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"with sns.axes_style(style='ticks'):\n",
" g = sns.factorplot(\"day\", \"total_bill\", \"sex\", data=tips, kind=\"box\")\n",
" g.set_axis_labels(\"Day\", \"Total Bill\");"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"with sns.axes_style('white'):\n",
" sns.jointplot(\"total_bill\", \"tip\", data=tips, kind='hex')"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"sns.jointplot(\"total_bill\", \"tip\", data=tips, kind='reg');"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"planets = sns.load_dataset('planets')\n",
"planets.head()"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"with sns.axes_style('white'):\n",
" g = sns.factorplot(\"year\", data=planets, aspect=1.5)\n",
" g.set_xticklabels(step=5)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"with sns.axes_style('white'):\n",
" g = sns.factorplot(\"year\", data=planets, aspect=4.0,\n",
" hue='method', order=range(2001, 2015), kind=\"count\")\n",
" g.set_ylabels('Number of Planets Discovered')"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Scikit-learn tutorial from pycon 2015 Jake VanderPlas [here](http://nbviewer.ipython.org/github/jakevdp/sklearn_pycon2015/blob/master/notebooks/Index.ipynb)"
]
},
{
"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": "3.5.4"
}
},
"nbformat": 4,
"nbformat_minor": 1
}
| mit |
wcmckee/wcmckee-notebook | url redirect.ipynb | 2 | 4769 | {
"metadata": {
"name": "",
"signature": "sha256:6e9a3815b6370735d0e028396b2ef96ef2019fe81d8bc43635bde69b81133b3c"
},
"nbformat": 3,
"nbformat_minor": 0,
"worksheets": [
{
"cells": [
{
"cell_type": "code",
"collapsed": false,
"input": [
"optxt = open('brief.txt', 'r')"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 10
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"filtxt = optxt.read()"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 11
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"filtxt"
],
"language": "python",
"metadata": {},
"outputs": [
{
"metadata": {},
"output_type": "pyout",
"prompt_number": 12,
"text": [
"'ok\\nwe go to website - basically - sorry@\\n\\n\\n\\nlocalhost/redirect.py? everythingelse.html\\nreferal header that was in the request\\nif referal header:\\nprint html \\nthen javascript function to refresh and go to original url.\\nif no referal header:\\ngenerate htm header 302 redirect to that url. \\n \\n\\n'"
]
}
],
"prompt_number": 12
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"filtxt."
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 12
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"mksit = open('index.html', 'w')"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 14
},
{
"cell_type": "code",
"collapsed": false,
"input": [],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 14
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"import requests"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 44
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"opartz = requests.get('http://artcontrol.me')"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 55
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"headon = opartz.headers"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 56
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"headon"
],
"language": "python",
"metadata": {},
"outputs": [
{
"metadata": {},
"output_type": "pyout",
"prompt_number": 57,
"text": [
"CaseInsensitiveDict({'content-encoding': 'gzip', 'transfer-encoding': 'chunked', 'set-cookie': 'PHPSESSID=bp0npmihkpt3qsc9fm7r8v3t95; path=/', 'expires': 'Thu, 19 Nov 1981 08:52:00 GMT', 'vary': 'Accept-Encoding', 'server': 'Apache', 'link': '<http://wp.me/1LQEk>; rel=shortlink', 'pragma': 'no-cache', 'cache-control': 'no-store, no-cache, must-revalidate, post-check=0, pre-check=0', 'date': 'Sun, 15 Jun 2014 08:31:52 GMT', 'content-type': 'text/html; charset=UTF-8', 'x-pingback': 'http://artcontrol.me/xmlrpc.php'})"
]
}
],
"prompt_number": 57
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"print headon['server']"
],
"language": "python",
"metadata": {},
"outputs": [
{
"output_type": "stream",
"stream": "stdout",
"text": [
"gws\n"
]
}
],
"prompt_number": 52
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"checkser = headon['server']"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 50
},
{
"cell_type": "code",
"collapsed": false,
"input": [],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 50
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"if checkser is None:\n",
" print ('it is none')\n",
" \n",
"else:\n",
" print ('You have a referal header')"
],
"language": "python",
"metadata": {},
"outputs": [
{
"output_type": "stream",
"stream": "stdout",
"text": [
"You have a referal header\n"
]
}
],
"prompt_number": 54
},
{
"cell_type": "code",
"collapsed": false,
"input": [],
"language": "python",
"metadata": {},
"outputs": []
}
],
"metadata": {}
}
]
} | gpl-2.0 |
oiertwo/vampyr | notebooks/.ipynb_checkpoints/Blots-checkpoint.ipynb | 1 | 66187 | {
"metadata": {
"name": "",
"signature": "sha256:9a4badaa97fc7bd2fc8e07c6961cd412752ad29095b5e00062ee92215f2204fd"
},
"nbformat": 3,
"nbformat_minor": 0,
"worksheets": [
{
"cells": [
{
"cell_type": "code",
"collapsed": false,
"input": [
"import seaborn as sns\n",
"import numpy as np\n",
"import pandas as pd\n",
"import os\n",
"from datetime import datetime\n",
"import matplotlib.pyplot as plt\n",
"from sklearn import linear_model\n",
"\n",
"%matplotlib inline"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 1
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"path = \"/docs/cancer/blood\"\n",
"path = \"/home/oier/GDrive/[email protected]/blood/csv/\"\n",
"output = \"/home/oier/Desktop\"\n",
"file = \"Blood Samples.csv\"\n",
"file = os.path.join(path,file)\n",
"data = pd.read_csv(file, sep =\";\")\n",
"data[\"Fecha\"] = [ datetime.strptime(i, \"%m/%d/%Y\") for i in data[\"Fecha\"] ]\n",
"data = data.sort(columns=\"Fecha\")"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 24
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"#choose a number\n",
"[(k,i) for k,i in enumerate(data.columns)]"
],
"language": "python",
"metadata": {},
"outputs": [
{
"metadata": {},
"output_type": "pyout",
"prompt_number": 25,
"text": [
"[(0, 'Fecha'),\n",
" (1, 'Diagnostico'),\n",
" (2, 'Servicio'),\n",
" (3, 'Medico'),\n",
" (4, 'glucosa (mg/dL)'),\n",
" (5, 'urea (mg/dl)'),\n",
" (6, 'creatinina (mg/dL)'),\n",
" (7, 'acido urico (mg/dl)'),\n",
" (8, 'Filtrado Glomerular estimado (MDRD-4 IDMS) (mL/ min/ 1.73m3)'),\n",
" (9, 'Trigliceridos (mg/dL)'),\n",
" (10, 'colesterol (mg/dL)'),\n",
" (11, 'hdl-colesterol (mg/dL)'),\n",
" (12, 'ldl-colesterol (mg/dL)'),\n",
" (13, 'sodio(mEq/L)'),\n",
" (14, 'potasio(mEq/L)'),\n",
" (15, 'cloro(mEq/L)'),\n",
" (16, 'calcio (mg/dl)'),\n",
" (17, 'PCR (mg/L)'),\n",
" (18, 'fosforo (mg/dl)'),\n",
" (19, 'proteinas (g/dl)'),\n",
" (20, 'albumia (g/dl)'),\n",
" (21, 'alb/glob '),\n",
" (22, 'alb/prot'),\n",
" (23, 'ast/got (U/L)'),\n",
" (24, 'alt/gpt (U/L)'),\n",
" (25, 'gamma-gt (U/L)'),\n",
" (26, 'fosf. Alcalina (U/L)'),\n",
" (27, 'bilirrubina total (mg/dL)'),\n",
" (28, 'CO2 total (mmol/L)'),\n",
" (29, 'Lactato (mg/dL)'),\n",
" (30, 'LDH (U/L)'),\n",
" (31, 'amilasa (U/L)'),\n",
" (32, 'hierro (microg/dl)'),\n",
" (33, 'cap. Fij. Hierro (microg/dl)'),\n",
" (34, 'transferrina (g/l)'),\n",
" (35, 'saturac. Transf (%)'),\n",
" (36, 'ferritina (ng/ml)'),\n",
" (37, 'albumina(%)'),\n",
" (38, 'alfa-1 (%)'),\n",
" (39, 'alfa-2 (%)'),\n",
" (40, 'beta (%)'),\n",
" (41, 'Gamma (%)'),\n",
" (42, 'A/G '),\n",
" (43, 'albumina (g/dl)'),\n",
" (44, 'alfa 1 (g/dl)'),\n",
" (45, 'alfa 2 (g/dl)'),\n",
" (46, 'beta (g/dl)'),\n",
" (47, 'gamma (g/dl)'),\n",
" (48, 'alfa-1-antitripsina (g/L)'),\n",
" (49, 'n\u00ba leucocitos (miles)'),\n",
" (50, 'neutrofilos (%)'),\n",
" (51, 'linfocitos (%)'),\n",
" (52, 'monocitos (%)'),\n",
" (53, 'eosinofilos (%)'),\n",
" (54, 'basofilos (%)'),\n",
" (55, 'n\u00ba hematies (millones)'),\n",
" (56, 'hematocrito (%)'),\n",
" (57, 'Granulocitos inmad (%)'),\n",
" (58, 'Eritroblastos (%)'),\n",
" (59, 'hemoglobina(g/dl)'),\n",
" (60, 'VCM (fl)'),\n",
" (61, 'n\u00ba plaquetas (miles)'),\n",
" (62, 'neutrofilos (miles)'),\n",
" (63, 'linfocitos (miles)'),\n",
" (64, 'monocitos (miles)'),\n",
" (65, 'eosinofilos (miles)'),\n",
" (66, 'basofilos (miles)'),\n",
" (67, 'Granulocitos inmad (miles)'),\n",
" (68, 'Eritroblastos (miles)'),\n",
" (69, 'HCM (pg)'),\n",
" (70, 'CHCM (g/dl)'),\n",
" (71, 'ADE (%)'),\n",
" (72, 'VPM (fl)'),\n",
" (73, 'VSG 1\u00ba Hora (mm)'),\n",
" (74, 'Reticulocitos (%)'),\n",
" (75, 'TSH (microUl/ml)'),\n",
" (76, 'T4 Libre (ng/dl)'),\n",
" (77, 'FSH (Ul/L)'),\n",
" (78, 'LH(Ul/L)'),\n",
" (79, 'PSA TOTAL (ng/ml)'),\n",
" (80, '17-Beta-Estradiol (pg/ml)'),\n",
" (81, 'prolactina (ng(ml)'),\n",
" (82, 'Testosterona total (ng/ml)'),\n",
" (83, 'Testosterona libre calculada (pg/ml)'),\n",
" (84, 'Vitamina D (ng/mL)'),\n",
" (85, 'dehidroepiandrosterona sulfato (microg/mL)'),\n",
" (86, 'cortisol (microg/dl)'),\n",
" (87, 'sex hormone binding globulin (nmol/L)'),\n",
" (88, 'Antitransglutamin (U/mL)'),\n",
" (89, 'beta-hcg(mUl/L)'),\n",
" (90, 'aPTT (seg)'),\n",
" (91, 'Tiempo protombina (%)'),\n",
" (92, 'INR'),\n",
" (93, 'ratio TTPA'),\n",
" (94, 'Fibrinogeno (d) (mg/dl)'),\n",
" (95, 'Alfa-fetoproteina (U/ml)'),\n",
" (96, 'CEA (ng/ml)'),\n",
" (97, 'Ca 19.9 (U/ml)'),\n",
" (98, 'ceruloplasmina (g/l)'),\n",
" (99, 'Test COOMBS Directo'),\n",
" (100, 'haptoglobina (g/l)'),\n",
" (101, 'vitamina b12 (pg/ml)'),\n",
" (102, 'folatos (ng/ml)'),\n",
" (103, 'pH '),\n",
" (104, 'pO2 (mmHg)'),\n",
" (105, 'pCO2 (mmHg)'),\n",
" (106, 'Bicarbonato (mmol/L)'),\n",
" (107, 'Exceso de bases (mmol/L)'),\n",
" (108, 'Saturacion de O2 (%)'),\n",
" (109, 'FIO2'),\n",
" (110, 'proteina C (mg/L)'),\n",
" (111, 'Notas')]"
]
}
],
"prompt_number": 25
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"[(k,i) for k,i in enumerate(data[\"Fecha\"])]"
],
"language": "python",
"metadata": {},
"outputs": [
{
"metadata": {},
"output_type": "pyout",
"prompt_number": 45,
"text": [
"[(0, datetime.datetime(2009, 12, 2, 0, 0)),\n",
" (1, datetime.datetime(2009, 12, 3, 0, 0)),\n",
" (2, datetime.datetime(2010, 4, 9, 0, 0)),\n",
" (3, datetime.datetime(2010, 4, 12, 0, 0)),\n",
" (4, datetime.datetime(2010, 6, 22, 0, 0)),\n",
" (5, datetime.datetime(2010, 6, 23, 0, 0)),\n",
" (6, datetime.datetime(2010, 8, 23, 0, 0)),\n",
" (7, datetime.datetime(2011, 6, 28, 0, 0)),\n",
" (8, datetime.datetime(2011, 6, 29, 0, 0)),\n",
" (9, datetime.datetime(2011, 6, 30, 0, 0)),\n",
" (10, datetime.datetime(2011, 7, 7, 0, 0)),\n",
" (11, datetime.datetime(2011, 7, 8, 0, 0)),\n",
" (12, datetime.datetime(2011, 8, 2, 0, 0)),\n",
" (13, datetime.datetime(2011, 8, 22, 0, 0)),\n",
" (14, datetime.datetime(2011, 8, 23, 0, 0)),\n",
" (15, datetime.datetime(2011, 8, 30, 0, 0)),\n",
" (16, datetime.datetime(2011, 8, 30, 0, 0)),\n",
" (17, datetime.datetime(2011, 8, 31, 0, 0)),\n",
" (18, datetime.datetime(2011, 12, 22, 0, 0)),\n",
" (19, datetime.datetime(2012, 2, 3, 0, 0)),\n",
" (20, datetime.datetime(2012, 5, 28, 0, 0)),\n",
" (21, datetime.datetime(2012, 6, 22, 0, 0)),\n",
" (22, datetime.datetime(2012, 7, 19, 0, 0)),\n",
" (23, datetime.datetime(2012, 10, 1, 0, 0)),\n",
" (24, datetime.datetime(2012, 12, 20, 0, 0)),\n",
" (25, datetime.datetime(2013, 1, 30, 0, 0)),\n",
" (26, datetime.datetime(2013, 6, 19, 0, 0)),\n",
" (27, datetime.datetime(2013, 7, 5, 0, 0)),\n",
" (28, datetime.datetime(2013, 9, 18, 0, 0)),\n",
" (29, datetime.datetime(2013, 10, 21, 0, 0)),\n",
" (30, datetime.datetime(2015, 4, 6, 0, 0))]"
]
}
],
"prompt_number": 45
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"colname = data.columns[96]"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 4
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"m = data[colname]\n",
"mask = (m.isnull() == False)\n",
"\n",
"y = data[colname][mask]\n",
"x = data[\"Fecha\"][mask]"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 5
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"#linear model\n",
"clf = linear_model.LinearRegression()\n",
"X = np.arange(len(x)).reshape(len(x),1)\n",
"Y = y.reshape(len(y),1)\n",
"line = clf.fit( X , Y ).predict(X)"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 6
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"plt.plot(y.values)\n",
"plt.plot(np.arange(len(x)), line, c=\"r\")\n",
"plt.title(colname)\n",
"labels = [ i.strftime(\"%Y/%m/%d\") for i in x ]\n",
"plt.xticks(range(y.size), labels, rotation=90)\n",
"plt.show()"
],
"language": "python",
"metadata": {},
"outputs": [
{
"metadata": {},
"output_type": "display_data",
"png": "iVBORw0KGgoAAAANSUhEUgAAAXYAAAEzCAYAAAAlyyi6AAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\n",
"text": [
"<matplotlib.figure.Figure at 0x7fcded3d2e10>"
]
}
],
"prompt_number": 7
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"plt.boxplot(y.values[:-1])\n",
"plt.title(colname)\n",
"plt.axhline(y.values[-1], color=\"g\")\n",
"plt.show()"
],
"language": "python",
"metadata": {},
"outputs": [
{
"metadata": {},
"output_type": "display_data",
"png": "iVBORw0KGgoAAAANSUhEUgAAAXEAAAEKCAYAAADkYmWmAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz\nAAALEgAACxIB0t1+/AAAEadJREFUeJzt3X+Q3HV9x/HnXY7SJmzi6SzaFCW2g+8JnVbk7GAtgkWt\nncIh/qgz1KaCFIJYTeEPxQiYjvhjZIYZf1DGCaHWqaKVqjTEAUYngyCtk6ZURlrfKVXUGipbeyZn\nQinhtn/sN7pc7nb3kt3cfrjnYyaT3e/nu9/ve2cur/vk/d3vZ0eazSaSpDKNLnYBkqTDZ4hLUsEM\ncUkqmCEuSQUzxCWpYIa4JBVsbLELkNpFxDJgA3A+rZ/PXwC2Atdk5v9FxAXAR4DvzHrpNzPzgrbj\nTAK3Aedn5uc6nO8U4B2Z+ZYjqPkZwJcz86U97v9y4GOZ+RsRcS7wwsx83+GeX0ubIa5hcyOwCjgr\nM6cjYjnwaeAm4E+qfe7OzHO7HOetwN8Afw7MGeIRMVodd/IIaz4buP1wXpiZfx8RfxYRL8zMbx5h\nHVqCDHENjYh4PvBHwHMy86cAmbk/Ii4Ffrtt15Eux/lV4OXAicC/RcRLMvMf59j1jcB3MvOR6nUP\nA38FvAJ4HvC5zHxXNXYl8BZgGrgHeE1mPr86zmuAv6hm2B8Efgj8OrAfeC/wDiCAv8vMK+aoY0u1\n3+s6vS9pLvbENUxOBR48GOAHZeaPMvNLbZteFhH3z/rz5rbx9cDtmdkAPktrNj6XN/DUGXQTWJGZ\nZwAvBd4eESdGxKuBNwMvzswJ4LhqXyLiWOCkzHywOsaLgfdl5lrgR8C7gT+o3tvbIuI5c9TxZeDV\n1bGkBXEmrmHyJL1NLO7JzDlbIFUQXkhr1gzwKeDeiDghM/9z9u7AQ7O23QaQmbsj4lHgWbRC+G8z\nc2+1zw20ZutUf3+17fXfbWuL/Afwk8w8APw4IvYCz5xdc9U22kvrfw67Orxv6RDOxDVMdgBrI+K4\n9o0R8SsRcXtE/GIPx/hDYBz4eER8l1Y/vAm8fY59Z4Bls7Y91va4Sat18wRP/bcy0/b4NUD7/xIe\nn3W8Az3UTFXHkz3uK/2MIa6hkZk/pHUR8+aIqAFExErgL4H/zsz/7eEwlwHXZuaazHx+1be+FLi4\nukjabhfwa12O1wS2Aa+vagG4iJ8H+UuAr/dQ17wiYhXwS8D3j+Q4Wppsp2jYXAZcDdwXEQeAY4Ev\n0rrwB61QfVlE3D/rdU8Afwr8JnDOrLFPAVfR6mvf2Lb9VuC1wCc7FZSZ2yNiM/APEbEfeBB4LCJO\nA/4pM9uXAp29LOhcY81Z238P2JqZT3SqQ5rLiEvRaqmqPmK4Ezg7M3d32G8CeGlmfqx6fgXwW5l5\nfp/q+CqwITO/1Y/jaWnpaSYeEf8M7KmeficzL2obm6Q1czoA3JyZN/W9SmkAMnMmIi4GPgBc0GHX\nXcC7IuISWjPo7wGX9KOGiDgP+JoBrsPVdSZeXUy6LzNPnWPsGOBfaX2saj+t3uA5mfnoAGqVJM3S\ny4XNFwLLI+LOiPhq1Qc8aC3wUGbuqfp59wJnDKJQSdKhegnxfcB1mflqWlf5P131EgFW8vM2C7Tu\nZlvV3xIlSfPppSe+i+qGiMz894j4MfDLtG4t3gPU2vatAVOdDnbgwJPNsbHZH82VJHUw71ITvYT4\nhbQ+tvW2iFhNa/b9X9XYt4GTImKc1oz9DOC6TgebmtrfS8HSUVev12g0phe7DOkQ9Xpt3rFe2ilb\ngJUR8TVa61BcCLwxIi6u+uBXAHcC9wFbDi4mJEkavKP+OfFGY9oPpmsoORPXsKrXa/O2U7ztXpIK\nZohLUsEMcUkqmCEuSQUzxCWpYIa4JBXMEJekgh31z4k/7/oT/Zy4htLo6AgzM/54avh8/4rv+Tlx\nSXo68o5NqeIdmxpW3rEpSU9ThrgkFcwQl6SCGeKSVDBDXJIKZohLUsEMcUkqmCEuSQUzxCWpYIa4\nJBXMEJekghniklQwQ1ySCmaIS1LBxnrZKSKOB3YCr8jMXW3bLwcuAhrVpvXt45Kkweoa4hFxDPAJ\nYN8cw6cC6zLz/n4XJknqrpd2ynXAjcAjc4xNABsj4p6IuLKvlUmSuuo4E4+IC4BGZt4VEe8GZn+7\nxC3ADcA08MWIODszt3U65vj4csbGlh1BydLg1Ou1xS5BWpCOX88WEXcDzerPKUAC52bmo9X4yszc\nWz1+K/CszLy20wn9ejYNK7+eTcOq09ezdZyJZ+aZBx9HxHZaFy4PBvgq4IGIOBnYD5wFbOlLxZKk\nnvT06ZQ2IxFxPnBcZm6u+uDbgceBr2TmHX2vUJI0L7/tXqrYTtGw8tvuJelpaqHtFGnobdp0FVu3\nfmnBrxsdHWFmZmH/UZycPI9Nmzpey5cGypm4JBXMnrhUsSeuYWVPXJKepgxxSSqYIS4BExMrWLNm\nsauQFs4Ql6SCGeKSVDBDXJIKZohLUsEMcUkqmCEuATt37uPhhxe7CmnhDHFJKpghLkkFM8QlqWCG\nuCQVzBCXpIIZ4hKunaJyGeKSVDBDXJIKZohLUsEMcUkqWE/fdh8RxwM7gVdk5q627ZPA1cAB4ObM\nvGkgVUqS5tR1Jh4RxwCfAPbNsf164FXAmcAlVdhLxXHtFJWql3bKdcCNwCOztq8FHsrMPZn5BHAv\ncEaf65MkddAxxCPiAqCRmXdVm0bahlcCe9qeTwOr+lqdJKmjbj3xC4FmRLwSOAX464g4NzMfpRXg\ntbZ9a8BUtxOOjy9nbGzZ4dYrDVS9Xuu+kzRERprNZk87RsR2YP3BC5tVT/xB4DRa/fL7gMnMnN12\neYpGY7q3E0pHWb1eo9GYXuwypEPU67WR+cZ6+nRKm5GIOB84LjM3R8QVwJ202jJbugW4JKm/ep6J\n94szcQ2jiYkVjI6OsmOHM3ENn04zcW/2kaSCGeKSVDBDXJIKZohLUsEMcUkqmCEu4dopKpchLkkF\nM8QlqWCGuCQVzBCXpIIZ4pJUMENcorV2ypo1i12FtHCGuCQVzBCXpIIZ4pJUMENckgpmiEtSwQxx\nCddOUbkMcUkqmCEuSQUzxCWpYIa4JBXMEJekghniEq6donKNddshIpYBm4EXAE3g0sx8sG38cuAi\noFFtWp+ZuwZQqyRplq4hDpwDzGTm6RFxJvB+4Ly28VOBdZl5/yAKlCTNr2s7JTNvA9ZXT9cAU7N2\nmQA2RsQ9EXFlf8uTJHXSU088M5+MiE8CHwU+M2v4FlohfxZwekSc3dcKJUnzGmk2mz3vHBHPBr4B\nrM3Mx6ptKzNzb/X4rcCzMvPa+Y5x4MCTzbGxZUdWtdRnBy9qeuu9htTIfAO9XNhcB5yQmR8EHgNm\naF3gJCJWAQ9ExMnAflqz8S2djjc1tb/3sqWjZMcOqNdrNBrTi12KdIh6vTbvWC/tlFuBUyLibuAO\nYAPw2oi4ODP3AFcC24GvAd/KzDuOvGRJUi8W1E7ph0Zj+uieUOqRM3ENq3q9Nm87xZt9JKlghrgk\nFcwQl6SCGeISrp2ichniklQwQ1ySCmaIS1LBDHFJKpghLkkFM8QlYOfOfS5+pSIZ4pJUMENckgpm\niEtSwQxxSSqYIS5JBTPEJVw7ReUyxCWpYIa4JBXMEJekghniklQwQ1ySCmaIS7h2ispliEtSwQxx\nSSrYWLcdImIZsBl4AdAELs3MB9vGJ4GrgQPAzZl504BqlSTN0stM/BxgJjNPB64C3n9wICKOAa4H\nXgWcCVwSEccPolBJ0qG6hnhm3gasr56uAabahtcCD2Xmnsx8ArgXOKPfRUqS5tZTTzwzn4yITwIf\nBT7TNrQS2NP2fBpY1bfqpKPEtVNUqq498YMy84KIeBfwjYhYm5mP0QrwWttuNZ46Uz/E+PhyxsaW\nHVax0qCMVtOZer3WeUdpyPRyYXMdcEJmfhB4DJihdYET4NvASRExDuyj1Uq5rtPxpqb2H1HB0iDM\nzKxgdHSURmN6sUuRDtFpctFLO+VW4JSIuBu4A9gAvDYiLq764FcAdwL3AVsy85EjL1mS1IuRZrPZ\nfa8+ajSmj+4JpR5MTLRm4jt2OBPX8KnXayPzjfXcE5cWy6ZNx7J162B/VHfvbv0bmZhYMdDzTE4e\nYNOmxwd6Di0t3rGpobd169jPQnZQVq9ucsIJAz0Fu3ePDPyXkZYef6JUhNWrm+zcuW+g56jXazQa\ngzvHoGf5WpqciUtSwQxxSSqYIS5JBTPEJalghrgkFcwQl6SCGeKSVDBDXJIKZohLUsEMcUkqmCEu\nSQUzxCWpYIa4JBXMEJekghniklQwQ1ySCmaIS1LBDHFJKpghLkkFM8QlqWCGuCQVrOO33UfEMcDN\nwInAscC1mbm1bfxy4CKgUW1an5m7BlSrJGmWjiEOvAloZOa6iBgH/gXY2jZ+KrAuM+8fVIGSpPl1\nC/HPA7dWj0eBA7PGJ4CNEfEcYFtmfqjP9UmSOujYE8/MfZn504io0Qr098za5RZgPXAWcHpEnD2Y\nMiVJc+k2Eycingt8AbghMz87a/gjmbm32m8b8CJgW6fjjY8vZ2xs2WGWq6VotJpq1Ou1gZ9rkOc4\nmu9DS0e3C5vPBu4CLsvM7bPGVgEPRMTJwH5as/Et3U44NbX/8KvVkjQzswKARmPfQM9Tr9doNKYH\ndvyj9T709NPpF3+3mfhGYBVwTURcU23bDKzIzM0RcSWwHXgc+Epm3tGHeiVJPeoY4pm5AdjQYfwW\nWn1xSdIi8GYfSSqYIS5JBTPEJalghrgkFcwQl6SCGeKSVDBDXJIKZohLUsEMcUkqmCEuSQUzxCWp\nYIa4JBXMEJekghniklQwQ1ySCmaIS1LBDHFJKpghLkkFM8QlqWCGuCQVzBCXpIIZ4pJUMENckgpm\niEtSwcY6DUbEMcDNwInAscC1mbm1bXwSuBo4ANycmTcNsFYtURt/8k7O3n8rz5xoDvZEoyM8c2Zw\n5/j67hG2LX8D8N6BnUNLT7eZ+JuARmaeAfw+8PGDA1XAXw+8CjgTuCQijh9UoZKkQ3WciQOfB26t\nHo/SmnEftBZ4KDP3AETEvcAZbftLffGBZ3yYDzzjw+zcuW+g56nXa/xPY3pgx/+diRUAvJ7Bvg8t\nLR1DPDP3AUREjVagv6dteCWwp+35NLCq3wVKkubXbSZORDwX+AJwQ2Z+tm1oD1Bre14Dprodb3x8\nOWNjyxZap5aw0arpV6/XOu/YB4M8x9F8H1o6ul3YfDZwF3BZZm6fNfxt4KSIGAf20WqlXNfthFNT\n+w+zVC1VMzOtNkSjMfh2SmOA7ZSj9T709NPpF3+3mfhGWi2SayLimmrbZmBFZm6OiCuAO2n1y7dk\n5iN9qFeS1KNuPfENwIYO47cDt/e7KElSb7zZR5IKZohLUsEMcUkqmCEuSQUzxCWpYIa4JBWs6x2b\n0mL7wQ9ac42Jau2RQRkd/fkNOYOwe/cIq1cPeCVGLTmGuEQrYAFWrx7cOVavbjI5eaD7jtICGOIa\neo8+Orhb4Q+amFjB6OgIO3Z4S7zKYk9ckgpmiEtSwQxxSSqYIS5JBTPEJWDnzn08/PBiVyEtnCEu\nSQUzxCWpYIa4JBXMEJekghniklQwQ1yiddv9mjWLXYW0cK6doqedTZuuYuvWLy3oNQcXwJqYWNgq\ng5OT57Fp07ULeo3UT4a4RGuFwdHREWZmFrsSaWFGms2ju75xozHtgsoaSvV6jUZj8CsmSgtVr9dG\n5huzJy5JBeupnRIRpwEfyszfnbX9cuAioFFtWp+Zu/pboiRpPl1DPCLeCfwx8NM5hk8F1mXm/f0u\nTJLUXS/tlIeA1wFz9WQmgI0RcU9EXNnXyiRJXXUN8cz8AjDfFwPeAqwHzgJOj4iz+1ibJKmLI/2I\n4Ucycy9ARGwDXgRs6/SCTldZpcVWr9cWuwRpQQ47xCNiFfBARJwM7Kc1G9/Sr8IkSd0tJMSbABFx\nPnBcZm6u+uDbgceBr2TmHQOoUZI0j6N+s48kqX+82UeSCmaIS1LBDHFJKpghLlUi4rSI2L7YdUgL\n4VK0El2Xl5CGljNxqaXT8hLS0DLEJbouLyENLUNckgpmiEtSwQxx6am8hVlF8bZ7SSqYM3FJKpgh\nLkkFM8QlqWCGuCQVzBCXpIIZ4pJUMENckgpmiEtSwf4fE+2b9TOd3mIAAAAASUVORK5CYII=\n",
"text": [
"<matplotlib.figure.Figure at 0x7f68850630f0>"
]
}
],
"prompt_number": 8
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"plt.violinplot(y.values[:-1])\n",
"plt.title(colname)\n",
"plt.axhline(y.values[-1], color=\"g\")\n",
"plt.show()"
],
"language": "python",
"metadata": {},
"outputs": [
{
"metadata": {},
"output_type": "display_data",
"png": "iVBORw0KGgoAAAANSUhEUgAAAXcAAAEKCAYAAADpfBXhAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nAAAAAElFTkSuQmCC\n",
"text": [
"<matplotlib.figure.Figure at 0x7f687ea214a8>"
]
}
],
"prompt_number": 9
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"print (x)\n",
"print (y)"
],
"language": "python",
"metadata": {},
"outputs": [
{
"output_type": "stream",
"stream": "stdout",
"text": [
"14 2011-06-29\n",
"23 2011-06-30\n",
"13 2011-07-07\n",
"24 2011-07-08\n",
"11 2011-12-22\n",
"9 2012-05-28\n",
"5 2012-12-20\n",
"2 2013-07-05\n",
"1 2013-09-18\n",
"Name: Fecha, dtype: datetime64[ns]\n",
"14 1.6\n",
"23 1.6\n",
"13 1.9\n",
"24 1.9\n",
"11 2.5\n",
"9 2.2\n",
"5 4.4\n",
"2 4.2\n",
"1 4.8\n",
"Name: CEA (ng/ml), dtype: float64\n"
]
}
],
"prompt_number": 10
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"folder = \"all_images\"\n",
"subfolder = \"boxplots\"\n",
"out_path = os.path.join(output, folder, subfolder)\n",
"\n",
"select = 30\n",
"print(out_path)\n",
"\n",
"for i in np.arange(4,110,1):\n",
"\n",
" if( i in [8, 18, 28]):\n",
" continue\n",
" try:\n",
" colname = data.columns[i]\n",
" plt.close()\n",
" #fl = \"\".join(colname, \".png\")\n",
" #out_plotname = os.path.join(out_path, fl) \n",
" \n",
" m = data[colname]\n",
" \n",
" ax = m[select]\n",
" if ax == None:\n",
" continue\n",
" \n",
" mask = (m.isnull() == False)\n",
" y = data[colname][mask]\n",
" x = data[\"Fecha\"][mask]\n",
" plt.boxplot(y.values[:-1])\n",
" plt.title(colname)\n",
" plt.axhline(ax, color=\"g\")\n",
" #plt.show()\n",
" \n",
" #save png\n",
" plt.savefig(\"{}/{}_{}.png\".format(out_path,colname,data[\"Fecha\"][select]), bbox_inches='tight')\n",
" \n",
" plt.close()\n",
" except:\n",
" pass"
],
"language": "python",
"metadata": {},
"outputs": [
{
"output_type": "stream",
"stream": "stdout",
"text": [
"/home/oier/Desktop/all_images/boxplots\n"
]
},
{
"metadata": {},
"output_type": "display_data",
"png": "iVBORw0KGgoAAAANSUhEUgAAAXcAAAECCAYAAAAFL5eMAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nVnkIAAAAAElFTkSuQmCC\n",
"text": [
"<matplotlib.figure.Figure at 0x7fcdecad8e48>"
]
}
],
"prompt_number": 50
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"folder = \"all_images\"\n",
"subfolder = \"violins\"\n",
"out_path = os.path.join(output, folder)\n",
"out_path = os.path.join(out_path, subfolder)\n",
"print(out_path)\n",
"\n",
"select = 30\n",
"\n",
"for i in np.arange(4,110,1):\n",
" try:\n",
" \n",
" \n",
" plt.close()\n",
" colname = data.columns[i]\n",
" m = data[colname]\n",
" \n",
" ax = m[select]\n",
" if ax == None:\n",
" continue\n",
" \n",
" \n",
" mask = (m.isnull() == False)\n",
" y = data[colname][mask]\n",
" x = data[\"Fecha\"][mask]\n",
" plt.violinplot(y.values[:-1])\n",
" plt.title(colname)\n",
" \n",
" plt.axhline(y.values[-1], color=\"g\")\n",
" \n",
" plt.savefig(\"{}/{}_{}.png\".format(out_path,colname,data[\"Fecha\"][select]), bbox_inches='tight')\n",
" #plt.show()\n",
" plt.close()\n",
" except:\n",
" pass"
],
"language": "python",
"metadata": {},
"outputs": [
{
"output_type": "stream",
"stream": "stdout",
"text": [
"/home/oier/Desktop/all_images/violins\n"
]
},
{
"metadata": {},
"output_type": "display_data",
"png": "iVBORw0KGgoAAAANSUhEUgAAAXcAAAECCAYAAAAFL5eMAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nVnkIAAAAAElFTkSuQmCC\n",
"text": [
"<matplotlib.figure.Figure at 0x7fcdeab3dcf8>"
]
}
],
"prompt_number": 51
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"folder = \"all_images\"\n",
"subfolder = \"history\"\n",
"out_path = os.path.join(output, folder)\n",
"out_path = os.path.join(out_path, subfolder)\n",
"print(out_path)\n",
"\n",
"for i in np.arange(4,110,1):\n",
" try:\n",
" plt.close()\n",
" \n",
" colname = data.columns[i]\n",
" m = data[colname]\n",
" \n",
" ax = m[select]\n",
" if ax == None:\n",
" continue\n",
" \n",
" mask = (m.isnull() == False)\n",
" y = data[colname][mask]\n",
" x = data[\"Fecha\"][mask]\n",
" \n",
" #linear model\n",
" clf = linear_model.LinearRegression()\n",
" X = np.arange(len(x)).reshape(len(x),1)\n",
" Y = y.reshape(len(y),1)\n",
" line = clf.fit( X , Y ).predict(X)\n",
" \n",
" \n",
" plt.plot(y.values)\n",
" plt.plot(np.arange(len(x)), line, c=\"r\")\n",
" plt.title(colname)\n",
" labels = [ i.strftime(\"%Y/%m/%d\") for i in x ]\n",
" plt.xticks(range(y.size), labels, rotation=90)\n",
" #plt.show()\n",
" plt.savefig(\"{}/{}_{}.png\".format(out_path,colname,data[\"Fecha\"][select]), bbox_inches='tight')\n",
" \n",
" plt.close()\n",
" except:\n",
" pass\n",
"\n"
],
"language": "python",
"metadata": {},
"outputs": [
{
"output_type": "stream",
"stream": "stdout",
"text": [
"/home/oier/Desktop/all_images/history\n"
]
}
],
"prompt_number": 43
},
{
"cell_type": "code",
"collapsed": false,
"input": [],
"language": "python",
"metadata": {},
"outputs": []
}
],
"metadata": {}
}
]
} | mit |
ioam/holoviews | examples/reference/elements/bokeh/Raster.ipynb | 1 | 1996 | {
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"<div class=\"contentcontainer med left\" style=\"margin-left: -50px;\">\n",
"<dl class=\"dl-horizontal\">\n",
" <dt>Title</dt> <dd> Raster Element</dd>\n",
" <dt>Dependencies</dt> <dd>Bokeh</dd>\n",
" <dt>Backends</dt>\n",
" <dd><a href='./Raster.ipynb'>Bokeh</a></dd>\n",
" <dd><a href='../matplotlib/Raster.ipynb'>Matplotlib</a></dd>\n",
" <dd><a href='../plotly/Raster.ipynb'>Plotly</a></dd>\n",
"</dl>\n",
"</div>"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"import numpy as np\n",
"import holoviews as hv\n",
"hv.extension('bokeh')"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"A ``Raster`` is the base class for image-like elements (namely [``Image``](./Image.ipynb), [``RGB``](./RGB.ipynb) and [``HSV``](./HSV.ipynb)), but may be used directly to visualize 2D arrays using a color map:"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"xvals = np.linspace(0,4,202)\n",
"ys,xs = np.meshgrid(xvals, -xvals[::-1])\n",
"hv.Raster(np.sin(((ys)**3)*xs))"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
" The coordinate system of a ``Raster`` is the raw indexes of the underlying array, with integer values always starting from (0,0) in the top left, with default extents corresponding to the shape of the array. For a similar element used to visualize arrays but defined in a continuous Cartesian coordinate system, use the [``Image``](./Image.ipynb) element.\n",
" \n",
"For full documentation and the available style and plot options, use ``hv.help(hv.Raster).``"
]
}
],
"metadata": {
"language_info": {
"name": "python",
"pygments_lexer": "ipython3"
}
},
"nbformat": 4,
"nbformat_minor": 2
}
| bsd-3-clause |
ajhenrikson/phys202-2015-work | assignments/assignment02/ProjectEuler59.ipynb | 1 | 3968 | {
"cells": [
{
"cell_type": "markdown",
"metadata": {
"nbgrader": {}
},
"source": [
"# Project Euler: Problem 59"
]
},
{
"cell_type": "markdown",
"metadata": {
"nbgrader": {}
},
"source": [
"https://projecteuler.net/problem=59\n",
"\n",
"Each character on a computer is assigned a unique code and the preferred standard is ASCII (American Standard Code for Information Interchange). For example, uppercase A = 65, asterisk (*) = 42, and lowercase k = 107.\n",
"\n",
"A modern encryption method is to take a text file, convert the bytes to ASCII, then XOR each byte with a given value, taken from a secret key. The advantage with the XOR function is that using the same encryption key on the cipher text, restores the plain text; for example, 65 XOR 42 = 107, then 107 XOR 42 = 65.\n",
"\n",
"For unbreakable encryption, the key is the same length as the plain text message, and the key is made up of random bytes. The user would keep the encrypted message and the encryption key in different locations, and without both \"halves\", it is impossible to decrypt the message.\n",
"\n",
"Unfortunately, this method is impractical for most users, so the modified method is to use a password as a key. If the password is shorter than the message, which is likely, the key is repeated cyclically throughout the message. The balance for this method is using a sufficiently long password key for security, but short enough to be memorable.\n",
"\n",
"Your task has been made easy, as the encryption key consists of three lower case characters. Using cipher.txt (in this directory), a file containing the encrypted ASCII codes, and the knowledge that the plain text must contain common English words, decrypt the message and find the sum of the ASCII values in the original text."
]
},
{
"cell_type": "markdown",
"metadata": {
"nbgrader": {}
},
"source": [
"The following cell shows examples of how to perform XOR in Python and how to go back and forth between characters and integers:"
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {
"collapsed": true,
"nbgrader": {}
},
"outputs": [],
"source": [
"assert 65 ^ 42 == 107\n",
"assert 107 ^ 42 == 65\n",
"assert ord('a') == 97\n",
"assert chr(97) == 'a'"
]
},
{
"cell_type": "markdown",
"metadata": {
"nbgrader": {}
},
"source": [
"Certain functions in the `itertools` module may be useful for computing permutations:"
]
},
{
"cell_type": "code",
"execution_count": 9,
"metadata": {
"collapsed": false,
"nbgrader": {}
},
"outputs": [],
"source": [
"from itertools import permutations"
]
},
{
"cell_type": "code",
"execution_count": 1,
"metadata": {
"collapsed": false,
"deletable": false,
"nbgrader": {
"checksum": "6cff4e8e53b15273846c3aecaea84a3d",
"solution": true
}
},
"outputs": [],
"source": [
"#im sorry i will continue to try and figure this out but was unable to in the time given"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true,
"deletable": false,
"nbgrader": {
"checksum": "dcdf6792a88c661545d3ca651212dba8",
"grade": true,
"grade_id": "projecteuler59",
"points": 10
}
},
"outputs": [],
"source": [
"# This cell will be used for grading, leave it at the end of the notebook."
]
}
],
"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": "3.4.0"
}
},
"nbformat": 4,
"nbformat_minor": 0
}
| mit |
DJCordhose/ai | notebooks/rl/berater-v4.ipynb | 1 | 44364 | {
"nbformat": 4,
"nbformat_minor": 0,
"metadata": {
"colab": {
"name": "berater-v4.ipynb",
"version": "0.3.2",
"provenance": [],
"include_colab_link": true
},
"kernelspec": {
"display_name": "Python 3",
"language": "python",
"name": "python3"
},
"accelerator": "GPU"
},
"cells": [
{
"cell_type": "markdown",
"metadata": {
"id": "view-in-github",
"colab_type": "text"
},
"source": [
"<a href=\"https://colab.research.google.com/github/DJCordhose/ai/blob/master/notebooks/rl/berater-v4.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
]
},
{
"metadata": {
"colab_type": "text",
"id": "eU7ylMh1kQ2y"
},
"cell_type": "markdown",
"source": [
"# Berater Environment v4\n",
"\n",
"## Changes from v3\n",
"1. clean up\n",
"1. plot performance\n",
"1. switched back to ppo2\n",
"\n",
"## Next Steps\n",
" 1. create a complete customer graph including costs of travel\n",
" 1. non existing connection has hightst penalty\n",
" 1. per episode set certain rewards to 0 to simulate different customers per consultant\n",
" 1. make sure things generalize well\n",
" \n",
"## Links\n",
"1. Visualizing progress: https://github.com/openai/baselines/blob/master/docs/viz/viz.ipynb"
]
},
{
"metadata": {
"colab_type": "text",
"id": "zpzHtN3-kQ26"
},
"cell_type": "markdown",
"source": [
"## Installation (required for colab)"
]
},
{
"metadata": {
"colab_type": "code",
"id": "0E567zPTkQ28",
"colab": {}
},
"cell_type": "code",
"source": [
"# !pip install git+https://github.com/openai/baselines >/dev/null\n",
"# !pip install gym >/dev/null"
],
"execution_count": 0,
"outputs": []
},
{
"metadata": {
"colab_type": "code",
"id": "-S4sZG5ZkQ3T",
"colab": {}
},
"cell_type": "code",
"source": [
"import numpy\n",
"import gym\n",
"from gym.utils import seeding\n",
"from gym import spaces\n",
"\n",
"def state_name_to_int(state):\n",
" state_name_map = {\n",
" 'S': 0,\n",
" 'A': 1,\n",
" 'B': 2,\n",
" 'C': 3,\n",
" }\n",
" return state_name_map[state]\n",
"\n",
"def int_to_state_name(state_as_int):\n",
" state_map = {\n",
" 0: 'S',\n",
" 1: 'A',\n",
" 2: 'B',\n",
" 3: 'C'\n",
" }\n",
" return state_map[state_as_int]\n",
" \n",
"class BeraterEnv(gym.Env):\n",
" \"\"\"\n",
" The Berater Problem\n",
"\n",
" Actions: \n",
" There are 3 discrete deterministic actions:\n",
" - 0: First Direction\n",
" - 1: Second Direction\n",
" - 2: Third Direction / Go home\n",
" \"\"\"\n",
" metadata = {'render.modes': ['ansi']}\n",
" \n",
" showStep = False\n",
" showDone = True\n",
" envEpisodeModulo = 100\n",
"\n",
" def __init__(self):\n",
" self.map = {\n",
" 'S': [('A', 100), ('B', 400), ('C', 200 )],\n",
" 'A': [('B', 250), ('C', 400), ('S', 100 )],\n",
" 'B': [('A', 250), ('C', 250), ('S', 400 )],\n",
" 'C': [('A', 400), ('B', 250), ('S', 200 )]\n",
" }\n",
" self.action_space = spaces.Discrete(3)\n",
" self.observation_space = spaces.Box(low=numpy.array([0,-1000,-1000,-1000,-1000,-1000,-1000]),\n",
" high=numpy.array([3,1000,1000,1000,1000,1000,1000]),\n",
" dtype=numpy.float32)\n",
" self.reward_range = (-1, 1)\n",
"\n",
" self.totalReward = 0\n",
" self.stepCount = 0\n",
" self.isDone = False\n",
"\n",
" self.envReward = 0\n",
" self.envEpisodeCount = 0\n",
" self.envStepCount = 0\n",
"\n",
" self.reset()\n",
" self.optimum = self.calculate_customers_reward()\n",
"\n",
" def seed(self, seed=None):\n",
" self.np_random, seed = seeding.np_random(seed)\n",
" return [seed]\n",
"\n",
" def step(self, actionArg):\n",
" paths = self.map[self.state]\n",
" action = actionArg\n",
" destination, cost = paths[action]\n",
" lastState = self.state\n",
" lastObState = state_name_to_int(lastState)\n",
" customerReward = self.customer_reward[destination]\n",
"\n",
" info = {\"from\": self.state, \"to\": destination}\n",
"\n",
" self.state = destination\n",
" reward = (-cost + self.customer_reward[destination]) / self.optimum\n",
" self.customer_visited(destination)\n",
" done = destination == 'S' and self.all_customers_visited()\n",
"\n",
" stateAsInt = state_name_to_int(self.state)\n",
" self.totalReward += reward\n",
" self.stepCount += 1\n",
" self.envReward += reward\n",
" self.envStepCount += 1\n",
"\n",
" if self.showStep:\n",
" print( \"Episode: \" + (\"%4.0f \" % self.envEpisodeCount) + \n",
" \" Step: \" + (\"%4.0f \" % self.stepCount) + \n",
" #lastState + ':' + str(lastObState) + ' --' + str(action) + '-> ' + self.state + ':' + str(stateAsInt) +\n",
" lastState + ' --' + str(action) + '-> ' + self.state + \n",
" ' R=' + (\"% 2.2f\" % reward) + ' totalR=' + (\"% 3.2f\" % self.totalReward) + \n",
" ' cost=' + (\"%4.0f\" % cost) + ' customerR=' + (\"%4.0f\" % customerReward) + ' optimum=' + (\"%4.0f\" % self.optimum) \n",
" )\n",
"\n",
" if done and not self.isDone:\n",
" self.envEpisodeCount += 1\n",
" if BeraterEnv.showDone:\n",
" episodes = BeraterEnv.envEpisodeModulo\n",
" if (self.envEpisodeCount % BeraterEnv.envEpisodeModulo != 0):\n",
" episodes = self.envEpisodeCount % BeraterEnv.envEpisodeModulo\n",
" print( \"Done: \" + \n",
" (\"episodes=%6.0f \" % self.envEpisodeCount) + \n",
" (\"avgSteps=%6.2f \" % (self.envStepCount/episodes)) + \n",
" (\"avgTotalReward=% 3.2f\" % (self.envReward/episodes) )\n",
" )\n",
" if (self.envEpisodeCount%BeraterEnv.envEpisodeModulo) == 0:\n",
" self.envReward = 0\n",
" self.envStepCount = 0\n",
"\n",
" self.isDone = done\n",
" observation = self.getObservation(stateAsInt)\n",
"\n",
" return observation, reward, done, info\n",
"\n",
" def getObservation(self, position):\n",
" result = numpy.array([ position, \n",
" self.getEdgeObservation('S','A'),\n",
" self.getEdgeObservation('S','B'),\n",
" self.getEdgeObservation('S','C'),\n",
" self.getEdgeObservation('A','B'),\n",
" self.getEdgeObservation('A','C'),\n",
" self.getEdgeObservation('B','C'),\n",
" ],\n",
" dtype=numpy.float32)\n",
" return result\n",
"\n",
" def getEdgeObservation(self, source, target):\n",
" reward = self.customer_reward[target] \n",
" cost = self.getCost(source,target)\n",
" result = reward - cost\n",
"\n",
" return result\n",
"\n",
" def getCost(self, source, target):\n",
" paths = self.map[source]\n",
" targetIndex=state_name_to_int(target)\n",
" for destination, cost in paths:\n",
" if destination == target:\n",
" result = cost\n",
" break\n",
"\n",
" return result\n",
"\n",
" def customer_visited(self, customer):\n",
" self.customer_reward[customer] = 0\n",
"\n",
" def all_customers_visited(self):\n",
" return self.calculate_customers_reward() == 0\n",
"\n",
" def calculate_customers_reward(self):\n",
" sum = 0\n",
" for value in self.customer_reward.values():\n",
" sum += value\n",
" return sum\n",
"\n",
" def reset(self):\n",
" self.totalReward = 0\n",
" self.stepCount = 0\n",
" self.isDone = False\n",
" reward_per_customer = 1000\n",
" self.customer_reward = {\n",
" 'S': 0,\n",
" 'A': reward_per_customer,\n",
" 'B': reward_per_customer,\n",
" 'C': reward_per_customer,\n",
" }\n",
"\n",
" self.state = 'S'\n",
" return self.getObservation(state_name_to_int(self.state))"
],
"execution_count": 0,
"outputs": []
},
{
"metadata": {
"colab_type": "text",
"id": "Usj9iWTskQ3t"
},
"cell_type": "markdown",
"source": [
"# Try out Environment"
]
},
{
"metadata": {
"colab_type": "code",
"id": "oTtUfeONkQ3w",
"outputId": "a5378135-839e-44e4-d66c-9e5bec9ac0aa",
"colab": {
"base_uri": "https://localhost:8080/",
"height": 442
}
},
"cell_type": "code",
"source": [
"BeraterEnv.showStep = True\n",
"BeraterEnv.showDone = True\n",
"\n",
"env = BeraterEnv()\n",
"print(env)\n",
"observation = env.reset()\n",
"print(observation)\n",
"\n",
"for t in range(1000):\n",
" action = env.action_space.sample()\n",
" observation, reward, done, info = env.step(action)\n",
" if done:\n",
" print(\"Episode finished after {} timesteps\".format(t+1))\n",
" break\n",
"env.close()\n",
"print(observation)"
],
"execution_count": 4,
"outputs": [
{
"output_type": "stream",
"text": [
"<BeraterEnv instance>\n",
"[ 0. 900. 600. 800. 750. 600. 750.]\n",
"Episode: 0 Step: 1 S --0-> A R= 0.30 totalR= 0.30 cost= 100 customerR=1000 optimum=3000\n",
"Episode: 0 Step: 2 A --2-> S R=-0.03 totalR= 0.27 cost= 100 customerR= 0 optimum=3000\n",
"Episode: 0 Step: 3 S --0-> A R=-0.03 totalR= 0.23 cost= 100 customerR= 0 optimum=3000\n",
"Episode: 0 Step: 4 A --0-> B R= 0.25 totalR= 0.48 cost= 250 customerR=1000 optimum=3000\n",
"Episode: 0 Step: 5 B --0-> A R=-0.08 totalR= 0.40 cost= 250 customerR= 0 optimum=3000\n",
"Episode: 0 Step: 6 A --2-> S R=-0.03 totalR= 0.37 cost= 100 customerR= 0 optimum=3000\n",
"Episode: 0 Step: 7 S --1-> B R=-0.13 totalR= 0.23 cost= 400 customerR= 0 optimum=3000\n",
"Episode: 0 Step: 8 B --2-> S R=-0.13 totalR= 0.10 cost= 400 customerR= 0 optimum=3000\n",
"Episode: 0 Step: 9 S --2-> C R= 0.27 totalR= 0.37 cost= 200 customerR=1000 optimum=3000\n",
"Episode: 0 Step: 10 C --0-> A R=-0.13 totalR= 0.23 cost= 400 customerR= 0 optimum=3000\n",
"Episode: 0 Step: 11 A --1-> C R=-0.13 totalR= 0.10 cost= 400 customerR= 0 optimum=3000\n",
"Episode: 0 Step: 12 C --1-> B R=-0.08 totalR= 0.02 cost= 250 customerR= 0 optimum=3000\n",
"Episode: 0 Step: 13 B --1-> C R=-0.08 totalR=-0.07 cost= 250 customerR= 0 optimum=3000\n",
"Episode: 0 Step: 14 C --1-> B R=-0.08 totalR=-0.15 cost= 250 customerR= 0 optimum=3000\n",
"Episode: 0 Step: 15 B --0-> A R=-0.08 totalR=-0.23 cost= 250 customerR= 0 optimum=3000\n",
"Episode: 0 Step: 16 A --1-> C R=-0.13 totalR=-0.37 cost= 400 customerR= 0 optimum=3000\n",
"Episode: 0 Step: 17 C --0-> A R=-0.13 totalR=-0.50 cost= 400 customerR= 0 optimum=3000\n",
"Episode: 0 Step: 18 A --0-> B R=-0.08 totalR=-0.58 cost= 250 customerR= 0 optimum=3000\n",
"Episode: 0 Step: 19 B --1-> C R=-0.08 totalR=-0.67 cost= 250 customerR= 0 optimum=3000\n",
"Episode: 0 Step: 20 C --2-> S R=-0.07 totalR=-0.73 cost= 200 customerR= 0 optimum=3000\n",
"Done: episodes= 1 avgSteps= 20.00 avgTotalReward=-0.73\n",
"Episode finished after 20 timesteps\n",
"[ 0. -100. -400. -200. -250. -400. -250.]\n"
],
"name": "stdout"
}
]
},
{
"metadata": {
"colab_type": "text",
"id": "4GlYjZ3xkQ38"
},
"cell_type": "markdown",
"source": [
"# Train model\n",
"\n",
"* 0.73 would be perfect total reward"
]
},
{
"metadata": {
"id": "-rAaTCL0r-ql",
"colab_type": "code",
"colab": {}
},
"cell_type": "code",
"source": [
"!rm -r logs\n",
"!mkdir logs\n",
"!mkdir logs/berater"
],
"execution_count": 0,
"outputs": []
},
{
"metadata": {
"colab_type": "code",
"id": "NzbylmYAkQ3-",
"outputId": "73009468-c9c1-4ccc-cfdf-04e765b0a505",
"colab": {
"base_uri": "https://localhost:8080/",
"height": 782
}
},
"cell_type": "code",
"source": [
"# https://github.com/openai/baselines/blob/master/baselines/deepq/experiments/train_pong.py\n",
"# log_dir = logger.get_dir()\n",
"log_dir = '/content/logs/berater/'\n",
"\n",
"import gym\n",
"from baselines import deepq\n",
"from baselines import bench\n",
"from baselines import logger\n",
"\n",
"from baselines.common.vec_env.dummy_vec_env import DummyVecEnv\n",
"from baselines.common.vec_env.vec_monitor import VecMonitor\n",
"from baselines.ppo2 import ppo2\n",
"\n",
"BeraterEnv.showStep = False\n",
"BeraterEnv.showDone = False\n",
"\n",
"env = BeraterEnv()\n",
"\n",
"wrapped_env = DummyVecEnv([lambda: BeraterEnv()])\n",
"monitored_env = VecMonitor(wrapped_env, log_dir)\n",
"model = ppo2.learn(network='mlp', env=monitored_env, total_timesteps=50000)\n",
"\n",
"# monitored_env = bench.Monitor(env, log_dir)\n",
"# https://en.wikipedia.org/wiki/Q-learning#Influence_of_variables\n",
"# %time model = deepq.learn(\\\n",
"# monitored_env,\\\n",
"# seed=42,\\\n",
"# network='mlp',\\\n",
"# lr=1e-3,\\\n",
"# gamma=0.99,\\\n",
"# total_timesteps=30000,\\\n",
"# buffer_size=50000,\\\n",
"# exploration_fraction=0.5,\\\n",
"# exploration_final_eps=0.02,\\\n",
"# print_freq=1000)\n",
"\n",
"model.save('berater-ppo-v4.pkl')\n",
"monitored_env.close()"
],
"execution_count": 6,
"outputs": [
{
"output_type": "stream",
"text": [
"--------------------------------------\n",
"| approxkl | 0.0014639212 |\n",
"| clipfrac | 0.0076904297 |\n",
"| eplenmean | 8.71 |\n",
"| eprewmean | 0.22116669 |\n",
"| explained_variance | -2.36 |\n",
"| fps | 217 |\n",
"| nupdates | 1 |\n",
"| policy_entropy | 1.0971643 |\n",
"| policy_loss | -0.0067208204 |\n",
"| serial_timesteps | 2048 |\n",
"| time_elapsed | 9.42 |\n",
"| total_timesteps | 2048 |\n",
"| value_loss | 0.106561884 |\n",
"--------------------------------------\n",
"-------------------------------------\n",
"| approxkl | 0.0018530977 |\n",
"| clipfrac | 0.010864258 |\n",
"| eplenmean | 4.78 |\n",
"| eprewmean | 0.57166666 |\n",
"| explained_variance | 0.803 |\n",
"| fps | 229 |\n",
"| nupdates | 10 |\n",
"| policy_entropy | 0.69755495 |\n",
"| policy_loss | -0.018101279 |\n",
"| serial_timesteps | 20480 |\n",
"| time_elapsed | 95.9 |\n",
"| total_timesteps | 20480 |\n",
"| value_loss | 0.006653257 |\n",
"-------------------------------------\n",
"--------------------------------------\n",
"| approxkl | 0.00031319028 |\n",
"| clipfrac | 0.005126953 |\n",
"| eplenmean | 4.05 |\n",
"| eprewmean | 0.71933335 |\n",
"| explained_variance | 0.984 |\n",
"| fps | 193 |\n",
"| nupdates | 20 |\n",
"| policy_entropy | 0.10268526 |\n",
"| policy_loss | -0.009290915 |\n",
"| serial_timesteps | 40960 |\n",
"| time_elapsed | 191 |\n",
"| total_timesteps | 40960 |\n",
"| value_loss | 0.0007002192 |\n",
"--------------------------------------\n"
],
"name": "stdout"
}
]
},
{
"metadata": {
"id": "0cfzto7W8Mpd",
"colab_type": "text"
},
"cell_type": "markdown",
"source": [
"### Visualizing Results\n",
"\n",
"https://github.com/openai/baselines/blob/master/docs/viz/viz.ipynb"
]
},
{
"metadata": {
"id": "yBzvtyVcvhkn",
"colab_type": "code",
"outputId": "010ae100-b356-41ad-f574-26d2a5d2f6ba",
"colab": {
"base_uri": "https://localhost:8080/",
"height": 51
}
},
"cell_type": "code",
"source": [
"!ls -l $log_dir"
],
"execution_count": 0,
"outputs": [
{
"output_type": "stream",
"text": [
"total 236\n",
"-rw-r--r-- 1 root root 237825 Dec 30 16:37 monitor.csv\n"
],
"name": "stdout"
}
]
},
{
"metadata": {
"id": "2ZWB88EVsRei",
"colab_type": "code",
"outputId": "279ad824-42be-4fda-cd9c-92c77379f745",
"colab": {
"base_uri": "https://localhost:8080/",
"height": 71
}
},
"cell_type": "code",
"source": [
"from baselines.common import plot_util as pu\n",
"results = pu.load_results(log_dir)"
],
"execution_count": 7,
"outputs": [
{
"output_type": "stream",
"text": [
"/usr/local/lib/python3.6/dist-packages/baselines/bench/monitor.py:164: UserWarning: Pandas doesn't allow columns to be created via a new attribute name - see https://pandas.pydata.org/pandas-docs/stable/indexing.html#attribute-access\n",
" df.headers = headers # HACK to preserve backwards compatibility\n"
],
"name": "stderr"
}
]
},
{
"metadata": {
"id": "M1G3TPcfsVzb",
"colab_type": "code",
"colab": {}
},
"cell_type": "code",
"source": [
"import matplotlib.pyplot as plt\n",
"import numpy as np\n",
"r = results[0]\n",
"# plt.ylim(-1, 1)\n",
"# plt.plot(np.cumsum(r.monitor.l), r.monitor.r)"
],
"execution_count": 0,
"outputs": []
},
{
"metadata": {
"id": "mZc2jIUr8wKQ",
"colab_type": "code",
"outputId": "d3eb6cf7-a1a4-431b-c94d-23ff539cbb8b",
"colab": {
"base_uri": "https://localhost:8080/",
"height": 364
}
},
"cell_type": "code",
"source": [
"plt.plot(np.cumsum(r.monitor.l), pu.smooth(r.monitor.r, radius=100))"
],
"execution_count": 9,
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/plain": [
"[<matplotlib.lines.Line2D at 0x7efbe0cbaeb8>]"
]
},
"metadata": {
"tags": []
},
"execution_count": 9
},
{
"output_type": "display_data",
"data": {
"image/png": "iVBORw0KGgoAAAANSUhEUgAAAeIAAAFKCAYAAADBplwgAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nEZEb/T8Zgj6BW2TbewAAAABJRU5ErkJggg==\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x7efc574013c8>"
]
},
"metadata": {
"tags": []
}
}
]
},
{
"metadata": {
"colab_type": "text",
"id": "TtBh4c6-kQ4K"
},
"cell_type": "markdown",
"source": [
"# Enjoy model"
]
},
{
"metadata": {
"colab_type": "code",
"id": "ucP0gNhhkQ4O",
"outputId": "4c14a15e-a3ff-4cf9-82e5-655032de498a",
"colab": {
"base_uri": "https://localhost:8080/",
"height": 102
}
},
"cell_type": "code",
"source": [
"import numpy as np \n",
"\n",
"observation = env.reset()\n",
"state = np.zeros((1, 2*128))\n",
"dones = np.zeros((1))\n",
"\n",
"BeraterEnv.showStep = True\n",
"BeraterEnv.showDone = False\n",
"\n",
"for t in range(1000):\n",
" actions, _, state, _ = model.step(observation, S=state, M=dones)\n",
" observation, reward, done, info = env.step(actions[0])\n",
" if done:\n",
" print(\"Episode finished after {} timesteps\".format(t+1))\n",
" break\n",
"env.close()"
],
"execution_count": 10,
"outputs": [
{
"output_type": "stream",
"text": [
"Episode: 0 Step: 1 S --0-> A R= 0.30 totalR= 0.30 cost= 100 customerR=1000 optimum=3000\n",
"Episode: 0 Step: 2 A --0-> B R= 0.25 totalR= 0.55 cost= 250 customerR=1000 optimum=3000\n",
"Episode: 0 Step: 3 B --1-> C R= 0.25 totalR= 0.80 cost= 250 customerR=1000 optimum=3000\n",
"Episode: 0 Step: 4 C --2-> S R=-0.07 totalR= 0.73 cost= 200 customerR= 0 optimum=3000\n",
"Episode finished after 4 timesteps\n"
],
"name": "stdout"
}
]
},
{
"metadata": {
"colab_type": "code",
"id": "5fY1da_0l15E",
"colab": {}
},
"cell_type": "code",
"source": [
""
],
"execution_count": 0,
"outputs": []
}
]
} | mit |
bryanmontesv/incubator-spot | spot-oa/oa/dns/ipynb_templates/Edge_Investigation_master.ipynb | 5 | 9631 | {
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Get Suspicious DNS"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"import urllib2\n",
"import json\n",
"import os\n",
"import csv\n",
"\n",
"# getting date from the parent path. \n",
"path = os.getcwd().split(\"/\") \n",
"date = path[len(path)-1] \n",
"dsource = path[len(path)-2] \n",
"dpath = '/'.join(['data' if var == 'ipynb' else var for var in path]) + '/'\n",
"\n",
"sconnect = dpath + 'dns_scores.csv'\n",
"sconnectbu = dpath + 'dns_scores_bu.csv'\n",
"score_tmp = dpath + 'score_tmp.csv' \n",
"score_fbk = dpath + 'dns_scores_fb.csv' "
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"def apply_css_to_select(select):\n",
" select._css = (\n",
" (None, 'height', '90%'),\n",
" (None, 'width', '90%'),\n",
" ('select', 'overflow-x', 'auto'),\n",
" ('select', 'width', '100%'),\n",
" ('select', 'margin', 0)\n",
" )\n",
"\n",
"try:\n",
" import ipywidgets as widgets # For jupyter/ipython >= 1.4\n",
"except ImportError:\n",
" from IPython.html import widgets\n",
"from IPython.display import display, HTML, clear_output, Javascript \n",
"\n",
"def fill_list(list_control,source):\n",
" options_list = ['--Select--'] \n",
" options_list.extend([s for s in source])\n",
" list_control.options = options_list\n",
"\n",
"# client panel\n",
"client_header = widgets.HTML(value=\"Client IP\")\n",
"client_select = widgets.Select(height='90%')\n",
"apply_css_to_select(client_select)\n",
"\n",
"client_box = widgets.Box(width='20%', height='100%')\n",
"client_box.children = [client_header, client_select]\n",
"\n",
"# query panel\n",
"query_header = widgets.HTML(value=\"Query\")\n",
"query_select = widgets.Select(height='90%')\n",
"apply_css_to_select(query_select)\n",
"\n",
"query_box = widgets.Box(width='60%', height='100%')\n",
"query_box.children = [query_header, query_select]\n",
"\n",
"# Actions Panel\n",
"actions_header = widgets.HTML(value=\" \")\n",
"quick_text = widgets.Text(value='', width='100%', placeholder='Quick scoring')\n",
"quick_text._css = (\n",
" (None, 'width', '100%'),\n",
")\n",
"rating_btn = widgets.RadioButtons(description='Rating:', options=['1', '2', '3'], width='100%')\n",
"assign_btn = widgets.Button(description='Score', width='45%')\n",
"assign_btn.button_style = 'primary'\n",
"save_btn = widgets.Button(description='Save', width='45%')\n",
"save_btn.button_style = 'primary'\n",
"save_btn._css = (\n",
" (None, 'margin-left', '10%'),\n",
")\n",
"actions_box = widgets.Box(width='20%', height='100%')\n",
"actions_box.children = [actions_header,quick_text,rating_btn, assign_btn,save_btn]\n",
"\n",
"scoring_form = widgets.HBox(width='90%', height=250)\n",
"scoring_form.children = [client_box,query_box,actions_box]\n",
"\n",
"\n",
"def data_loader(): \n",
" us_ips = []\n",
" us_dns = []\n",
"\n",
" with open(sconnect, 'r') as f:\n",
" reader = csv.DictReader(f, delimiter=',')\n",
" for row in reader: \n",
" if row['ip_dst'] not in us_ips and row['ip_sev'] == '0': \n",
" us_ips.append(row['ip_dst'])\n",
" if row['dns_qry_name'] not in us_dns and row['dns_sev'] == '0':\n",
" us_dns.append(row['dns_qry_name']) \n",
"\n",
" fill_list(client_select,us_ips)\n",
" fill_list(query_select,us_dns)\n",
" client_select.value = \"--Select--\"\n",
" query_select.value = \"--Select--\" \n",
"\n",
"\n",
"display(Javascript(\"$('.widget-area > .widget-subarea > *').remove();\"))\n",
"data_loader()\n",
"display(scoring_form)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Update Suspicious DNS"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"import csv\n",
"import datetime\n",
"import subprocess \n",
"\n",
"def assign_score(b):\n",
" score_values = []\n",
" scored_threats = []\n",
" ip_sev = int(rating_btn.selected_label) if not \"--Select--\" in client_select.value else \"\"\n",
" dns_sev = int(rating_btn.selected_label) if not \"--Select--\" in query_select.value else \"\" \n",
"\n",
" if quick_text.value: \n",
" ip = \"\"\n",
" dns = quick_text.value\n",
" dns_sev = int(rating_btn.selected_label) \n",
" # Loop over every element in query_select widget\n",
" score_values = []\n",
" for query in query_select.options:\n",
" if query.endswith(dns):\n",
" # Matching element, create one row\n",
" score_values.append((ip,query,ip_sev,dns_sev))\n",
" else: \n",
" ip = client_select.value if not \"--Select--\" in client_select.value else \"\"\n",
" dns = query_select.value if not \"--Select--\" in query_select.value else \"\"\n",
" score_values.append((ip,dns,ip_sev,dns_sev))\n",
"\n",
" with open(sconnect, 'r') as f:\n",
" reader = csv.DictReader(f, delimiter=',')\n",
" rowct = 0\n",
" with open(score_tmp, 'w') as score:\n",
" wr = csv.DictWriter(score, delimiter=',', quoting=csv.QUOTE_NONE, fieldnames=reader.fieldnames) \n",
" wr.writeheader()\n",
" for row in reader: \n",
" for value in score_values: \n",
" if row['ip_dst'] == value[0]: \n",
" row['ip_sev'] = value[2] \n",
" scored_threats.append(row) \n",
" rowct += 1 \n",
" break\n",
" if row['dns_qry_name'] == value[1]: \n",
" row['dns_sev'] = value[3] \n",
" scored_threats.append(row) \n",
" rowct += 1\n",
" break\n",
" wr.writerow(row) \n",
" \n",
" if not os.path.exists(score_fbk): \n",
" with open(score_fbk, 'w') as feedback:\n",
" wr = csv.DictWriter(feedback, delimiter='\\t', quoting=csv.QUOTE_NONE, fieldnames=reader.fieldnames) \n",
" wr.writeheader()\n",
"\n",
" with open(score_fbk, 'a') as feedback:\n",
" for row in scored_threats:\n",
" wr = csv.DictWriter(feedback, delimiter='\\t', quoting=csv.QUOTE_NONE, fieldnames=reader.fieldnames) \n",
" wr.writerow(row)\n",
"\n",
" clear_output()\n",
" print \"{0} matching connections scored\".format(rowct)\n",
" !mv $score_tmp $sconnect \n",
"\n",
" if ip != \"--Select--\":\n",
" display(Javascript(\"$(\\\"option[data-value='\" + ip +\"']\\\").remove();\"))\n",
" if quick_text.value:\n",
" display(Javascript(\"$(\\\"option[data-value$='\" + quick_text.value +\"']\\\").remove();\"))\n",
" elif dns != \"--Select--\":\n",
" display(Javascript(\"$(\\\"option[data-value='\" + dns +\"']\\\").remove();\"))\n",
"\n",
" client_select.value = \"--Select--\"\n",
" query_select.value = \"--Select--\"\n",
" quick_text.value = \"\"\n",
"\n",
"\n",
"def save(b): \n",
" clear_output() \n",
" display(Javascript(\"$('.widget-area > .widget-subarea > *').remove();\"))\n",
" data_loader() \n",
" display(scoring_form)\n",
" display(Javascript('reloadParentData();'))\n",
" ml_feedback() \n",
" print \"Suspicious connects successfully updated\"\n",
"\n",
"\n",
"assign_btn.on_click(assign_score)\n",
"save_btn.on_click(save)\n",
" \n",
"\n",
"def ml_feedback():\n",
" dst_name = os.path.basename(sconnect)\n",
" str_fb=\"DSOURCE={0} &&\\\n",
" FDATE={1} &&\\\n",
" source /etc/spot.conf &&\\\n",
" usr=$(echo $LUSER | cut -f3 -d'/') &&\\\n",
" mlnode=$MLNODE &&\\\n",
" lpath=$LPATH &&\\\n",
" scp {2} $usr@$mlnode:$lpath/{3}\".format(dsource,date,score_fbk,dst_name) \n",
"\n",
" subprocess.call(str_fb, shell=True)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"# !cp $sconnectbu $sconnect"
]
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 2",
"language": "python",
"name": "python2"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 2
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython2",
"version": "2.7.5"
}
},
"nbformat": 4,
"nbformat_minor": 0
}
| apache-2.0 |
donfaq/cnn-rnn | Architecture.ipynb | 1 | 14918 | {
"cells": [
{
"cell_type": "code",
"execution_count": 8,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"import tensorflow as tf\n",
"import tensorflow.contrib.slim as slim\n",
"import numpy as np"
]
},
{
"cell_type": "code",
"execution_count": 18,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"def get_nb_params_shape(shape):\n",
" nb_params = 1\n",
" for dim in shape:\n",
" nb_params = nb_params * int(dim)\n",
" return nb_params\n",
"\n",
"def count_number_trainable_params():\n",
" tot_nb_params = 0\n",
" for trainable_variable in slim.get_trainable_variables():\n",
" print(trainable_variable.name, trainable_variable.shape)\n",
" shape = trainable_variable.get_shape() # e.g [D,F] or [W,H,C]\n",
" current_nb_params = get_nb_params_shape(shape)\n",
" tot_nb_params = tot_nb_params + current_nb_params\n",
" print('Total number of trainable params: ', tot_nb_params)"
]
},
{
"cell_type": "code",
"execution_count": 11,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"is_training = True"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Текущая модель"
]
},
{
"cell_type": "code",
"execution_count": 19,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Convolution/Conv1/weights:0 (1, 1, 3, 32)\n",
"Convolution/Conv1/BatchNorm/beta:0 (32,)\n",
"Convolution/Conv2/weights:0 (3, 3, 32, 32)\n",
"Convolution/Conv2/biases:0 (32,)\n",
"Convolution/Conv3/weights:0 (3, 3, 32, 32)\n",
"Convolution/Conv3/biases:0 (32,)\n",
"GRU_RNN_cell/rnn/gru_cell/gates/weights:0 (144868, 200)\n",
"GRU_RNN_cell/rnn/gru_cell/gates/biases:0 (200,)\n",
"GRU_RNN_cell/rnn/gru_cell/candidate/weights:0 (144868, 100)\n",
"GRU_RNN_cell/rnn/gru_cell/candidate/biases:0 (100,)\n",
"Fully-connected/weights:0 (100, 6)\n",
"Fully-connected/biases:0 (6,)\n",
"Total number of trainable params: 43479930\n"
]
}
],
"source": [
"tf.reset_default_graph()\n",
"with tf.Graph().as_default():\n",
" x = tf.placeholder(dtype=tf.float32, shape=(50, 240, 320, 3), name=\"input\")\n",
" y = tf.placeholder(dtype=tf.int32, shape=(1,), name='labels')\n",
"\n",
" with slim.arg_scope([slim.conv2d], stride=1, weights_initializer=tf.contrib.layers.xavier_initializer_conv2d()):\n",
" with tf.variable_scope('Convolution', [input]):\n",
" conv1 = slim.conv2d(x, 32, [1, 1], stride=2, scope='Conv1', normalizer_fn=slim.batch_norm,\n",
" normalizer_params={'is_training': is_training})\n",
" pool1 = slim.max_pool2d(conv1, [3, 3], scope='Pool1', stride=1)\n",
" conv2 = slim.conv2d(pool1, 32, [3, 3], scope='Conv2')\n",
" pool2 = slim.max_pool2d(conv2, [3, 3], scope='Pool2', stride=1)\n",
" conv3 = slim.conv2d(pool2, 32, [3, 3], stride=2, scope='Conv3')\n",
" size = np.prod(conv3.get_shape().as_list()[1:])\n",
"\n",
" with tf.variable_scope('GRU_RNN_cell'):\n",
" rnn_inputs = tf.reshape(conv3, (-1, 50, size))\n",
" cell = tf.contrib.rnn.GRUCell(100)\n",
" init_state = cell.zero_state(1, dtype=tf.float32)\n",
" rnn_outputs, _ = tf.nn.dynamic_rnn(cell, rnn_inputs, initial_state=init_state)\n",
" output = tf.reduce_mean(rnn_outputs, axis=1)\n",
"\n",
" with tf.name_scope('Dense'):\n",
" logits = slim.fully_connected(output, 6, scope=\"Fully-connected\")\n",
" count_number_trainable_params()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Модель Inception"
]
},
{
"cell_type": "code",
"execution_count": 20,
"metadata": {
"scrolled": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Conv2d_1a_3x3/weights:0 (3, 3, 3, 32)\n",
"Conv2d_1a_3x3/biases:0 (32,)\n",
"Conv2d_2a_3x3/weights:0 (3, 3, 32, 32)\n",
"Conv2d_2a_3x3/biases:0 (32,)\n",
"Conv2d_2b_3x3/weights:0 (3, 3, 32, 64)\n",
"Conv2d_2b_3x3/biases:0 (64,)\n",
"BlockInceptionA/IBranch_0/IConv2d_0a_1x1/weights:0 (1, 1, 64, 96)\n",
"BlockInceptionA/IBranch_0/IConv2d_0a_1x1/biases:0 (96,)\n",
"BlockInceptionA/IBranch_1/IConv2d_0a_1x1/weights:0 (1, 1, 64, 64)\n",
"BlockInceptionA/IBranch_1/IConv2d_0a_1x1/biases:0 (64,)\n",
"BlockInceptionA/IBranch_1/IConv2d_0b_3x3/weights:0 (3, 3, 64, 96)\n",
"BlockInceptionA/IBranch_1/IConv2d_0b_3x3/biases:0 (96,)\n",
"BlockInceptionA/IBranch_2/IConv2d_0a_1x1/weights:0 (1, 1, 64, 64)\n",
"BlockInceptionA/IBranch_2/IConv2d_0a_1x1/biases:0 (64,)\n",
"BlockInceptionA/IBranch_2/IConv2d_0b_3x3/weights:0 (3, 3, 64, 96)\n",
"BlockInceptionA/IBranch_2/IConv2d_0b_3x3/biases:0 (96,)\n",
"BlockInceptionA/IBranch_2/IConv2d_0c_3x3/weights:0 (3, 3, 96, 96)\n",
"BlockInceptionA/IBranch_2/IConv2d_0c_3x3/biases:0 (96,)\n",
"BlockInceptionA/IBranch_3/IConv2d_0b_1x1/weights:0 (1, 1, 64, 96)\n",
"BlockInceptionA/IBranch_3/IConv2d_0b_1x1/biases:0 (96,)\n",
"BlockReductionA/RBranch_0/RConv2d_1a_3x3/weights:0 (3, 3, 384, 384)\n",
"BlockReductionA/RBranch_0/RConv2d_1a_3x3/biases:0 (384,)\n",
"BlockReductionA/RBranch_1/RConv2d_0a_1x1/weights:0 (1, 1, 384, 192)\n",
"BlockReductionA/RBranch_1/RConv2d_0a_1x1/biases:0 (192,)\n",
"BlockReductionA/RBranch_1/RConv2d_0b_3x3/weights:0 (3, 3, 192, 224)\n",
"BlockReductionA/RBranch_1/RConv2d_0b_3x3/biases:0 (224,)\n",
"BlockReductionA/RBranch_1/RConv2d_1a_3x3/weights:0 (3, 3, 224, 256)\n",
"BlockReductionA/RBranch_1/RConv2d_1a_3x3/biases:0 (256,)\n",
"GRU_RNN_cell/rnn/gru_cell/gates/weights:0 (4632676, 200)\n",
"GRU_RNN_cell/rnn/gru_cell/gates/biases:0 (200,)\n",
"GRU_RNN_cell/rnn/gru_cell/candidate/weights:0 (4632676, 100)\n",
"GRU_RNN_cell/rnn/gru_cell/candidate/biases:0 (100,)\n",
"Fully-connected/weights:0 (100, 6)\n",
"Fully-connected/biases:0 (6,)\n",
"Total number of trainable params: 1392352026\n"
]
}
],
"source": [
"tf.reset_default_graph()\n",
"with tf.Graph().as_default():\n",
"\n",
" inputs = tf.placeholder(dtype=tf.float32, shape=(50, 240, 320, 3), name=\"input\")\n",
" y = tf.placeholder(dtype=tf.int32, shape=(1,), name='labels')\n",
"\n",
" # inputs = slim.conv2d(x, 32, [3, 3], scope='Conv2d_0a_1x1', stride=2, padding='VALID')\n",
"\n",
" conv1 = slim.conv2d(inputs, 32, [3, 3], stride=2, padding='VALID', scope='Conv2d_1a_3x3')\n",
" conv2 = slim.conv2d(conv1, 32, [3, 3], padding='VALID', scope='Conv2d_2a_3x3')\n",
" inc_inputs = slim.conv2d(conv2, 64, [3, 3], scope='Conv2d_2b_3x3')\n",
"\n",
" with slim.arg_scope([slim.conv2d, slim.avg_pool2d, slim.max_pool2d], stride=1, padding='SAME'):\n",
" with tf.variable_scope('BlockInceptionA', [inc_inputs]):\n",
" with tf.variable_scope('IBranch_0'):\n",
" ibranch_0 = slim.conv2d(inc_inputs, 96, [1, 1], scope='IConv2d_0a_1x1')\n",
" with tf.variable_scope('IBranch_1'):\n",
" ibranch_1_conv1 = slim.conv2d(inc_inputs, 64, [1, 1], scope='IConv2d_0a_1x1')\n",
" ibranch_1 = slim.conv2d(ibranch_1_conv1, 96, [3, 3], scope='IConv2d_0b_3x3')\n",
" with tf.variable_scope('IBranch_2'):\n",
" ibranch_2_conv1 = slim.conv2d(inc_inputs, 64, [1, 1], scope='IConv2d_0a_1x1')\n",
" ibranch_2_conv2 = slim.conv2d(ibranch_2_conv1, 96, [3, 3], scope='IConv2d_0b_3x3')\n",
" ibranch_2 = slim.conv2d(ibranch_2_conv2, 96, [3, 3], scope='IConv2d_0c_3x3')\n",
" with tf.variable_scope('IBranch_3'):\n",
" ibranch_3_pool = slim.avg_pool2d(inc_inputs, [3, 3], scope='IAvgPool_0a_3x3')\n",
" ibranch_3 = slim.conv2d(ibranch_3_pool, 96, [1, 1], scope='IConv2d_0b_1x1')\n",
" inception = tf.concat(axis=3, values=[ibranch_0, ibranch_1, ibranch_2, ibranch_3])\n",
"\n",
" with tf.variable_scope('BlockReductionA', [inception]):\n",
" with tf.variable_scope('RBranch_0'):\n",
" rbranch_0 = slim.conv2d(inception, 384, [3, 3], stride=2, padding='VALID', scope='RConv2d_1a_3x3')\n",
" with tf.variable_scope('RBranch_1'):\n",
" rbranch_1_conv1 = slim.conv2d(inception, 192, [1, 1], scope='RConv2d_0a_1x1')\n",
" rbranch_1_conv2 = slim.conv2d(rbranch_1_conv1, 224, [3, 3], scope='RConv2d_0b_3x3')\n",
" rbranch_1 = slim.conv2d(rbranch_1_conv2, 256, [3, 3], stride=2, padding='VALID', scope='RConv2d_1a_3x3')\n",
" with tf.variable_scope('RBranch_2'):\n",
" rbranch_2 = slim.max_pool2d(inception, [3, 3], stride=2, padding='VALID', scope='RMaxPool_1a_3x3')\n",
" reduction = tf.concat(axis=3, values=[rbranch_0, rbranch_1, rbranch_2])\n",
"\n",
" size = np.prod(reduction.get_shape().as_list()[1:])\n",
"\n",
" with tf.variable_scope('GRU_RNN_cell'):\n",
" rnn_inputs = tf.reshape(reduction, (-1, 50, size))\n",
" cell = tf.contrib.rnn.GRUCell(100)\n",
" init_state = cell.zero_state(1, dtype=tf.float32)\n",
" rnn_outputs, _ = tf.nn.dynamic_rnn(cell, rnn_inputs, initial_state=init_state)\n",
" output = tf.reduce_mean(rnn_outputs, axis=1)\n",
"\n",
" with tf.name_scope('Dense'):\n",
" logits = slim.fully_connected(output, 6, scope=\"Fully-connected\")\n",
" count_number_trainable_params()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### VGG16"
]
},
{
"cell_type": "code",
"execution_count": 23,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"conv1/conv1_1/weights:0 (3, 3, 3, 64)\n",
"conv1/conv1_1/biases:0 (64,)\n",
"conv1/conv1_2/weights:0 (3, 3, 64, 64)\n",
"conv1/conv1_2/biases:0 (64,)\n",
"conv2/conv2_1/weights:0 (3, 3, 64, 128)\n",
"conv2/conv2_1/biases:0 (128,)\n",
"conv2/conv2_2/weights:0 (3, 3, 128, 128)\n",
"conv2/conv2_2/biases:0 (128,)\n",
"conv3/conv3_1/weights:0 (3, 3, 128, 256)\n",
"conv3/conv3_1/biases:0 (256,)\n",
"conv3/conv3_2/weights:0 (3, 3, 256, 256)\n",
"conv3/conv3_2/biases:0 (256,)\n",
"conv3/conv3_3/weights:0 (3, 3, 256, 256)\n",
"conv3/conv3_3/biases:0 (256,)\n",
"conv4/conv4_1/weights:0 (3, 3, 256, 512)\n",
"conv4/conv4_1/biases:0 (512,)\n",
"conv4/conv4_2/weights:0 (3, 3, 512, 512)\n",
"conv4/conv4_2/biases:0 (512,)\n",
"conv4/conv4_3/weights:0 (3, 3, 512, 512)\n",
"conv4/conv4_3/biases:0 (512,)\n",
"conv5/conv5_1/weights:0 (3, 3, 512, 512)\n",
"conv5/conv5_1/biases:0 (512,)\n",
"conv5/conv5_2/weights:0 (3, 3, 512, 512)\n",
"conv5/conv5_2/biases:0 (512,)\n",
"conv5/conv5_3/weights:0 (3, 3, 512, 512)\n",
"conv5/conv5_3/biases:0 (512,)\n",
"fc6/weights:0 (512, 4096)\n",
"fc6/biases:0 (4096,)\n",
"fc7/weights:0 (4096, 4096)\n",
"fc7/biases:0 (4096,)\n",
"fc8/weights:0 (4096, 1000)\n",
"fc8/biases:0 (1000,)\n",
"GRU_RNN_cell/rnn/gru_cell/gates/weights:0 (70100, 200)\n",
"GRU_RNN_cell/rnn/gru_cell/gates/biases:0 (200,)\n",
"GRU_RNN_cell/rnn/gru_cell/candidate/weights:0 (70100, 100)\n",
"GRU_RNN_cell/rnn/gru_cell/candidate/biases:0 (100,)\n",
"Fully-connected/weights:0 (100, 6)\n",
"Fully-connected/biases:0 (6,)\n",
"Total number of trainable params: 58725154\n"
]
}
],
"source": [
"tf.reset_default_graph()\n",
"with tf.Graph().as_default():\n",
" inputs = tf.placeholder(dtype=tf.float32, shape=(50, 240, 320, 3), name=\"input\")\n",
" y = tf.placeholder(dtype=tf.int32, shape=(1,), name='labels')\n",
" with slim.arg_scope([slim.conv2d, slim.fully_connected],\n",
" activation_fn=tf.nn.relu,\n",
" weights_initializer=tf.truncated_normal_initializer(0.0, 0.01),\n",
" weights_regularizer=slim.l2_regularizer(0.0005)):\n",
" net = slim.repeat(inputs, 2, slim.conv2d, 64, [3, 3], scope='conv1')\n",
" net = slim.max_pool2d(net, [2, 2], scope='pool1')\n",
" net = slim.repeat(net, 2, slim.conv2d, 128, [3, 3], scope='conv2')\n",
" net = slim.max_pool2d(net, [2, 2], scope='pool2')\n",
" net = slim.repeat(net, 3, slim.conv2d, 256, [3, 3], scope='conv3')\n",
" net = slim.max_pool2d(net, [2, 2], scope='pool3')\n",
" net = slim.repeat(net, 3, slim.conv2d, 512, [3, 3], scope='conv4')\n",
" net = slim.max_pool2d(net, [2, 2], scope='pool4')\n",
" net = slim.repeat(net, 3, slim.conv2d, 512, [3, 3], scope='conv5')\n",
" net = slim.max_pool2d(net, [2, 2], scope='pool5')\n",
" net = slim.fully_connected(net, 4096, scope='fc6')\n",
" net = slim.dropout(net, 0.5, scope='dropout6')\n",
" net = slim.fully_connected(net, 4096, scope='fc7')\n",
" net = slim.dropout(net, 0.5, scope='dropout7')\n",
" net = slim.fully_connected(net, 1000, activation_fn=None, scope='fc8')\n",
" \n",
" size = np.prod(net.get_shape().as_list()[1:])\n",
"\n",
" with tf.variable_scope('GRU_RNN_cell'):\n",
" rnn_inputs = tf.reshape(net, (-1, 50, size))\n",
" cell = tf.contrib.rnn.GRUCell(100)\n",
" init_state = cell.zero_state(1, dtype=tf.float32)\n",
" rnn_outputs, _ = tf.nn.dynamic_rnn(cell, rnn_inputs, initial_state=init_state)\n",
" output = tf.reduce_mean(rnn_outputs, axis=1)\n",
"\n",
" with tf.name_scope('Dense'):\n",
" logits = slim.fully_connected(output, 6, scope=\"Fully-connected\")\n",
" count_number_trainable_params()"
]
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3.0
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.5.2+"
}
},
"nbformat": 4,
"nbformat_minor": 0
} | mit |
ProfessorKazarinoff/staticsite | content/code/matplotlib_plots/3_quiver_plots_for_title_image.ipynb | 1 | 49135 | {
"cells": [
{
"cell_type": "code",
"execution_count": 4,
"metadata": {},
"outputs": [
{
"data": {
"image/png": "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\n",
"text/plain": [
"<Figure size 432x288 with 3 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"import numpy as np\n",
"import matplotlib.pyplot as plt\n",
"%matplotlib inline\n",
"\n",
"fig, [ax1,ax2,ax3] = plt.subplots(1,3)\n",
"\n",
"x = np.arange(0,2.2,0.2)\n",
"y = np.arange(0,2.2,0.2)\n",
"\n",
"X, Y = np.meshgrid(x, y)\n",
"u = np.cos(X)*Y\n",
"v = np.sin(y)*Y\n",
"\n",
"n = -2\n",
"R = np.sqrt(((v-n)/2)**2 + ((u-n)/2)**2)\n",
"\n",
"ax1.quiver(X,Y,u,v,R, alpha=0.8)\n",
"\n",
"ax1.xaxis.set_ticks([])\n",
"ax1.yaxis.set_ticks([])\n",
"ax1.axis([-0.2, 2.3, -0.2, 2.3])\n",
"ax1.set_aspect('equal')\n",
"\n",
"x = np.arange(-2,2.2,0.2)\n",
"y = np.arange(-2,2.2,0.2)\n",
"\n",
"\n",
"X, Y = np.meshgrid(x, y)\n",
"z = X*np.exp(-X**2 -Y**2)\n",
"dx, dy = np.gradient(z)\n",
"\n",
"n = -2\n",
"R = np.sqrt(((dx-n)/2)**2 + ((dy-n)/2)**2)\n",
"\n",
"\n",
"ax2.quiver(X,Y,dx,dy,R)\n",
"\n",
"ax2.xaxis.set_ticks([])\n",
"ax2.yaxis.set_ticks([])\n",
"ax2.set_aspect('equal')\n",
"\n",
"x = np.arange(0,2*np.pi+2*np.pi/20,2*np.pi/20)\n",
"y = np.arange(0,2*np.pi+2*np.pi/20,2*np.pi/20)\n",
"\n",
"X,Y = np.meshgrid(x,y)\n",
"\n",
"u = np.sin(X)*np.cos(Y)\n",
"v = -np.cos(X)*np.sin(Y)\n",
"\n",
"n = -1\n",
"R = np.sqrt(((dx-n)/2)**2 + ((dy-n)/2)**2)\n",
"\n",
"\n",
"\n",
"ax3.quiver(X,Y,u,v,R)\n",
"\n",
"ax3.xaxis.set_ticks([])\n",
"ax3.yaxis.set_ticks([])\n",
"ax3.axis([0,2*np.pi,0,2*np.pi])\n",
"ax3.set_aspect('equal')\n",
"\n",
"plt.tight_layout()\n",
"fig.savefig('3_quiver_plots.png', dpi=300, bbox_inches='tight')\n",
"\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": "3.6.5"
}
},
"nbformat": 4,
"nbformat_minor": 2
}
| gpl-3.0 |
wuafeing/Python3-Tutorial | 02 strings and text/02.08 regexp for multiline partterns.ipynb | 2 | 4064 | {
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"[Previous](02.07 specify regexp for shortest match.ipynb)\n",
"\n",
"\n",
"## 2.8 多行匹配模式\n",
"\n",
"\n",
"**问题**\n",
"\n",
"你正在试着使用正则表达式去匹配一大块的文本，而你需要跨越多行去匹配。\n",
"\n",
"\n",
"**解决方案**\n",
"\n",
"这个问题很典型的出现在当你用点 `(.)` 去匹配任意字符的时候，忘记了点 `(.)` 不能匹配换行符的事实。 比如，假设你想试着去匹配 `C` 语言分割的注释： "
]
},
{
"cell_type": "code",
"execution_count": 1,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"[' this is a comment ']"
]
},
"execution_count": 1,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"import re\n",
"comment = re.compile(r\"/\\*(.*?)\\*/\")\n",
"text1 = '/* this is a comment */'\n",
"text2 = '''/* this is a\n",
"multiline comment */\n",
"'''\n",
"comment.findall(text1)"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"[]"
]
},
"execution_count": 2,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"comment.findall(text2)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"为了修正这个问题，你可以修改模式字符串，增加对换行的支持。比如："
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"[' this is a\\nmultiline comment ']"
]
},
"execution_count": 3,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"comment = re.compile(r'/\\*((?:.|\\n)*?)\\*/')\n",
"comment.findall(text2)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"\n",
"在这个模式中， `(?:.|\\n)` 指定了一个非捕获组 (也就是它定义了一个仅仅用来做匹配，而不能通过单独捕获或者编号的组)。\n",
"\n",
"\n",
"**讨论**\n",
"\n",
"`re.compile()` 函数接受一个标志参数叫 `re.DOTALL` ，在这里非常有用。 它可以让正则表达式中的点 `(.)` 匹配包括换行符在内的任意字符。比如："
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"[' this is a\\nmultiline comment ']"
]
},
"execution_count": 4,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"comment = re.compile(r'/\\*(.*?)\\*/', re.DOTALL)\n",
"comment.findall(text2)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"对于简单的情况使用 `re.DOTALL` 标记参数工作的很好， 但是如果模式非常复杂或者是为了构造字符串令牌而将多个模式合并起来(2.18节有详细描述)， 这时候使用这个标记参数就可能出现一些问题。 如果让你选择的话，最好还是定义自己的正则表达式模式，这样它可以在不需要额外的标记参数下也能工作的很好。\n",
"\n",
"\n",
"[Next](02.09 normalize unicode text to regexp.ipynb)"
]
}
],
"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": "3.5.1"
}
},
"nbformat": 4,
"nbformat_minor": 0
}
| gpl-3.0 |
Leguark/GeMpy | Prototype Notebook/legacy/Kriging 6.ipynb | 2 | 215973 | {
"cells": [
{
"cell_type": "code",
"execution_count": 1,
"metadata": {
"ExecuteTime": {
"end_time": "2016-10-19T10:24:45.730304",
"start_time": "2016-10-19T10:24:43.193192"
},
"collapsed": false
},
"outputs": [],
"source": [
"import numpy as np\n",
"import matplotlib.pyplot as plt\n",
"from sklearn.metrics.pairwise import euclidean_distances\n",
"import sklearn.metrics as me\n",
"from __future__ import division\n",
"%matplotlib inline"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {
"ExecuteTime": {
"end_time": "2016-10-19T10:24:47.045986",
"start_time": "2016-10-19T10:24:46.575082"
},
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"[ 1.29289322 4.70710678] [ 6.70710678 3.70710678] [[1 7]\n",
" [5 7]\n",
" [6 7]\n",
" [9 8]]\n"
]
},
{
"data": {
"image/png": "iVBORw0KGgoAAAANSUhEUgAAAXQAAAEACAYAAACj0I2EAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nXZIKYaBLUiEMdEkqhIEuSYX4f1RIhYvCRweMAAAAAElFTkSuQmCC\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x7fc566b55048>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"layer_1 = np.array([[1,7],[5,7],[6,7], [9,8]])\n",
"layer_2 = np.array([[1,1],[5,1],[9,1]])\n",
"layer_3 = np.array([[1,1],[3,2],[7,4]])\n",
"\n",
"dip_pos_1 = np.array([2,4])\n",
"dip_angle_1 = 135\n",
"dip_pos_1_v = np.array([np.cos(np.deg2rad(dip_angle_1))*1,\n",
" np.sin(np.deg2rad(dip_angle_1))]) + dip_pos_1\n",
"\n",
"\n",
"\n",
"dip_pos_2 = np.array([6,3])\n",
"dip_angle_2 = 45\n",
"dip_pos_2_v = np.array([np.cos(np.deg2rad(dip_angle_2))*1, \n",
" np.sin(np.deg2rad(dip_angle_2))]) + dip_pos_2\n",
"\n",
"\n",
"plt.arrow(dip_pos_1[0],dip_pos_1[1], dip_pos_1_v[0]-dip_pos_1[0],\n",
" dip_pos_1_v[1]-dip_pos_1[1], head_width = 0.2)\n",
"plt.arrow(dip_pos_2[0],dip_pos_2[1],dip_pos_2_v[0]-dip_pos_2[0], \n",
" dip_pos_2_v[1]-dip_pos_2[1], head_width = 0.2)\n",
"plt.plot(layer_1[:,0],layer_1[:,1], \"o\")\n",
"plt.plot(layer_2[:,0],layer_2[:,1], \"o\")\n",
"plt.xlim(0,10)\n",
"plt.ylim(0,10)\n",
"print (dip_pos_1_v, dip_pos_2_v, layer_1)\n",
"layers = np.asarray([layer_1,layer_2])\n",
"dips = np.asarray([dip_pos_1,dip_pos_2])"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {
"ExecuteTime": {
"end_time": "2016-10-19T10:24:47.627252",
"start_time": "2016-10-19T10:24:47.623021"
},
"collapsed": true
},
"outputs": [],
"source": [
"#layers = [np.random.uniform(0,10,(100,2)) for i in range(2)]\n",
"#dips = np.random.uniform(0,10, (30,2))\n",
"#dips_angles = np.random.normal(90,10, 8)"
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {
"ExecuteTime": {
"end_time": "2016-10-19T10:24:48.752044",
"start_time": "2016-10-19T10:24:48.206406"
},
"collapsed": false
},
"outputs": [],
"source": [
"import pdb;"
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {
"ExecuteTime": {
"end_time": "2016-10-19T10:24:49.207763",
"start_time": "2016-10-19T10:24:48.955145"
},
"collapsed": false
},
"outputs": [],
"source": [
"# =================================0\n",
"# THE INCREMENTS OF POTENTIAL\n",
"\n",
"def cov_cubic_f(r,a = 6, c_o = 1):\n",
" ans_d0 = c_o*(1-7*(r/a)**2+35/4*(r/a)**3-7/2*(r/a)**5+3/4*(r/a)**7)\n",
" ans_d0[r>a] = 0\n",
" return ans_d0\n",
"\n",
"def cov_cubic_d1_f(r,a = 6., c_o = 1):\n",
" ans_d1 = (-7* (a - r)**3 *r* (8* a**2 + 9 *a* r + 3* r**2)* (c_o))/(4* a**7)\n",
" ans_d1[r>a] = 0.\n",
" return ans_d1\n",
" \n",
"def cov_cubic_d2_f(r, a = 6, c_o = 1):\n",
" ans_d2 = (-7 * (4.* a**5. - 15. *a**4. * r + 20. *( a**2)*(r**3) - 9* r**5) * \n",
" (c_o))/(2*a**7)\n",
" \n",
" ans_d2[r>a] = 0\n",
" return ans_d2\n",
"\n",
"def cov_cubic_layer(X,Y, a = 6., c_o = 1., verbose = 0):\n",
" \"\"\"x = Array: Position of the measured points\"\n",
" a = Range of the spherical semivariogram\n",
" C_o = Nugget, variance\n",
" \"\"\"\n",
" # Creation of r vector\n",
" r_m = np.asarray(euclidean_distances(X,Y))\n",
" \n",
" # Initializing\n",
" # Applying the functio\n",
" C_h = c_o*(1.-7.*(r_m/a)**2.+35./4.*(r_m/a)**3.\n",
" -7./2.*(r_m/a)**5.+3./4.*(r_m/a)**7.)\n",
" C_h[r_m>a] = 0\n",
" if verbose !=0:\n",
" print(r_m>a)\n",
" print (\"Our lag matrix is\", r_m)\n",
" print(\"Our covariance matrix is\",C_h)\n",
" return C_h\n",
"\n",
"def C_I(layers, a = 6.):\n",
" #print \"layers\", layers\n",
" layers = np.asarray(layers)\n",
" #print \"layers\", len(layers)\n",
" for r in range(len(layers)):\n",
" for s in range(len(layers)):\n",
" # print \"layers2\", layers[r][1:],layers[s][1:]\n",
" \n",
" # print \"nagnoagjja\", layers[s][0].reshape(1,-1),layers[r][1:],\n",
" a_p = cov_cubic_layer(layers[r][1:],layers[s][1:], a = a)\n",
" b_p = cov_cubic_layer(layers[s][0].reshape(1,-1),\n",
" layers[r][1:], a = a).transpose()\n",
" \n",
" test = cov_cubic_layer(layers[r][1:],layers[s][0].reshape(1,-1), a =a)\n",
" c_p = cov_cubic_layer(layers[s][1:],\n",
" layers[r][0].reshape(1,-1),a=a).transpose()\n",
" d_p = cov_cubic_layer(layers[r][0].reshape(1,-1),\n",
" layers[s][0].reshape(1,-1), a=a) \n",
" \n",
" #pdb.set_trace()\n",
" #print \"s\", s,\"r\", r \n",
" if s == 0:\n",
" \n",
" C_I_row = a_p-b_p-c_p+d_p\n",
" else:\n",
" C_I_row = np.hstack((C_I_row, a_p-b_p-c_p+d_p)) \n",
" \n",
" if r == 0:\n",
" C_I = C_I_row\n",
" else:\n",
" C_I = np.vstack((C_I,C_I_row))\n",
" # C_I += 0.00000001\n",
" return C_I"
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {
"ExecuteTime": {
"end_time": "2016-10-19T10:24:51.016617",
"start_time": "2016-10-19T10:24:50.895487"
},
"collapsed": false,
"scrolled": false
},
"outputs": [
{
"data": {
"text/plain": [
"array([[ 1.87105624, 1.77585948, 0.98731906, 0. , 0. ],\n",
" [ 1.77585948, 1.98942794, 1.19742208, 0. , 0. ],\n",
" [ 0.98731906, 1.19742208, 2. , 0. , 0. ],\n",
" [ 0. , 0. , 0. , 1.87105624, 1. ],\n",
" [ 0. , 0. , 0. , 1. , 2. ]])"
]
},
"execution_count": 6,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"C_I(layers)"
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {
"ExecuteTime": {
"end_time": "2016-10-19T10:24:51.373934",
"start_time": "2016-10-19T10:24:51.207589"
},
"collapsed": false
},
"outputs": [],
"source": [
"#=====================\n",
"# THE GRADIENTS\n",
"\n",
"def h_f(dips, direct):\n",
" #pdb.set_trace()\n",
" if direct == \"x\":\n",
" #print (np.subtract.outer(dips[:,0],dips[:,0]))\n",
" #print (dips[:,0] , dips[:,0].reshape((dips[:,0].shape[0],1)))\n",
" # print (\"hx\",dips[:,0] - dips[:,0].reshape((dips[:,0].shape[0],1)))\n",
" return (np.subtract.outer(dips[:,0],dips[:,0]))\n",
" if direct == \"y\":\n",
" # print (\"hy\",dips[:,1] - dips[:,1].reshape((dips[:,1].shape[0],1)))\n",
" return (np.subtract.outer(dips[:,1],dips[:,1]))\n",
"\n",
"def C_G(dips, sig_z = 1, a = 6., C_000 = -14*1/6**2-0.2):\n",
" dips = np.asarray(dips)\n",
" \n",
" \n",
" if len(dips) == 1:\n",
" lalolu = np.ones((2,2))*C_0\n",
" lalolu[0,1 ] = 0\n",
" lalolu[1,0] = 0\n",
" return lalolu\n",
" r = me.euclidean_distances(dips)\n",
" # print (\"R\",r)\n",
" C_0 = 1\n",
" \n",
" for i in \"xy\":\n",
" for j in \"xy\":\n",
" if i == j and j == \"x\":\n",
" h0 = h_f(dips, direct = i)\n",
" C_G_row = (C_0*(h0**2/r**3-1/r)*cov_cubic_d1_f(r, a = a) -\n",
" (h0/r)**2*cov_cubic_d2_f(r, a = a))\n",
" # print (\"teta\", C_0*(h0**2/r**3-1/r)*cov_cubic_d1_f(r, a = a) -\n",
" # (h0/r)**2*cov_cubic_d2_f(r, a = a)) \n",
" \n",
" h1 = h_f(dips, direct = i)\n",
" h2 = h_f(dips, direct = j)\n",
" \n",
" \n",
" # print (\"teta 3\" , ((C_0*(h0*h0/r**2)*\n",
" # ((1/r)*cov_cubic_d1_f(r, a = a)\n",
" # -cov_cubic_d2_f(r, a = a)))))\n",
" # pdb.set_trace()\n",
" # print \"0\"\n",
" elif i == j:\n",
" # print (\"a\",h0**2)\n",
" h0 = h_f(dips, direct = i)\n",
" C_G_row = np.hstack((C_G_row, (\n",
" C_0*(h0**2/r**3-1/r)\n",
" *cov_cubic_d1_f(r, a = a) - \n",
" (h0/r)**2*cov_cubic_d2_f(r, a = a))))\n",
" # pdb.set_trace()\n",
" else:\n",
" if j == \"x\":\n",
" \"\"\"\n",
" print (\"cov_d1\",cov_cubic_d1_f(r, a = a))\n",
" print (\"cov_d2\",cov_cubic_d2_f(r, a = a))\n",
" print (\"a\",h1*h2)\n",
" print (\"b\", C_0*(h1*h2/r**2) )\n",
" print (\"c\", r**2)\n",
" \"\"\"\n",
"\n",
" h1 = h_f(dips, direct = i)\n",
" h2 = h_f(dips, direct = j)\n",
" C_G_row = ((C_0*(h1*h2/r**2)*\n",
" ((1/r)*cov_cubic_d1_f(r, a = a)\n",
" -cov_cubic_d2_f(r, a = a))))\n",
" # pdb.set_trace()\n",
" # print \"2\"\n",
" else:\n",
" h1 = h_f(dips, direct = i)\n",
" h2 = h_f(dips, direct = j)\n",
" # print (\"a\",h1*h2)\n",
" C_G_row = np.hstack((C_G_row, (C_0*(h1*h2/r**2)*\n",
" ((1/r)*cov_cubic_d1_f(r, a = a)\n",
" -cov_cubic_d2_f(r, a = a)))))\n",
" # pdb.set_trace()\n",
" # print \"3\"\n",
" if i == \"x\":\n",
" C_G = C_G_row\n",
" else:\n",
" C_G = np.vstack((C_G, C_G_row))\n",
" \n",
"\n",
" # C_G[C_G == 0] = 0.0000000000000000000001 \n",
" sol_CG = np.nan_to_num(C_G)\n",
" #sol_CG = C_G\n",
" # sol_CG[sol_CG== 0] = C_0 \n",
" g,h = np.indices(np.shape(sol_CG))\n",
" sol_CG[g==h] = C_000\n",
" # sol_CG[g+2==h] = 0.01\n",
" # sol_CG[g-2==h] = 0.01\n",
" # sol_CG[sol_CG == 0] = C_0\n",
" # sol_CG[2,0] = -C_0\n",
" # sol_CG[3,1] = -C_0\n",
" # print (sol_CG)\n",
" return sol_CG"
]
},
{
"cell_type": "code",
"execution_count": 42,
"metadata": {
"collapsed": false,
"scrolled": false
},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"/home/bl3/anaconda3/lib/python3.5/site-packages/ipykernel/__main__.py:32: RuntimeWarning: invalid value encountered in true_divide\n",
"/home/bl3/anaconda3/lib/python3.5/site-packages/ipykernel/__main__.py:32: RuntimeWarning: divide by zero encountered in true_divide\n",
"/home/bl3/anaconda3/lib/python3.5/site-packages/ipykernel/__main__.py:33: RuntimeWarning: invalid value encountered in true_divide\n",
"/home/bl3/anaconda3/lib/python3.5/site-packages/ipykernel/__main__.py:75: RuntimeWarning: invalid value encountered in true_divide\n",
"/home/bl3/anaconda3/lib/python3.5/site-packages/ipykernel/__main__.py:76: RuntimeWarning: divide by zero encountered in true_divide\n",
"/home/bl3/anaconda3/lib/python3.5/site-packages/ipykernel/__main__.py:76: RuntimeWarning: invalid value encountered in multiply\n",
"/home/bl3/anaconda3/lib/python3.5/site-packages/ipykernel/__main__.py:66: RuntimeWarning: invalid value encountered in true_divide\n",
"/home/bl3/anaconda3/lib/python3.5/site-packages/ipykernel/__main__.py:67: RuntimeWarning: divide by zero encountered in true_divide\n",
"/home/bl3/anaconda3/lib/python3.5/site-packages/ipykernel/__main__.py:67: RuntimeWarning: invalid value encountered in multiply\n",
"/home/bl3/anaconda3/lib/python3.5/site-packages/ipykernel/__main__.py:51: RuntimeWarning: invalid value encountered in true_divide\n",
"/home/bl3/anaconda3/lib/python3.5/site-packages/ipykernel/__main__.py:51: RuntimeWarning: divide by zero encountered in true_divide\n",
"/home/bl3/anaconda3/lib/python3.5/site-packages/ipykernel/__main__.py:52: RuntimeWarning: invalid value encountered in true_divide\n"
]
},
{
"data": {
"text/plain": [
"array([[-0.59, -0.11, 0. , 0.03],\n",
" [-0.11, -0.59, 0.03, 0. ],\n",
" [ 0. , 0.03, -0.59, 0.02],\n",
" [ 0.03, 0. , 0.02, -0.59]])"
]
},
"execution_count": 42,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"C_G(dips)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": []
},
{
"cell_type": "code",
"execution_count": 21,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"#========================================\n",
"#THE INTERACTION GRADIENTS/INTERFACES\n",
"\n",
"def h_f_GI(dips, layers, direct):\n",
" if direct == \"x\":\n",
" return (np.subtract.outer(dips[:,0],layers[:,0]))\n",
" if direct == \"y\":\n",
" return (np.subtract.outer(dips[:,1],layers[:,1]))\n",
" \n",
"def C_GI(dips,layers, sig_z = 1., a = 6., C_01 = 1, verbose = 0):\n",
" dips = np.asarray(dips)\n",
" layers = np.asarray(layers)\n",
" C_00 = C_01\n",
" \n",
" for k in range(len(layers)):\n",
" for i in \"xy\":\n",
" r = me.euclidean_distances(dips,layers[k])\n",
" h1 = h_f_GI(dips,layers[k], i)\n",
" \n",
" Cov_d1 = cov_cubic_d1_f(r, a = a)\n",
" # pdb.set_trace()\n",
" if verbose != 0:\n",
" print (\"dips\", dips)\n",
" print (\"layers\", layers)\n",
" print (\"h1\", h1, h1[:,0])\n",
" print (\"\")\n",
" print (\"r\", r, r[:,0])\n",
" print (\"\")\n",
" print (\"Cov_d1\", Cov_d1)\n",
" if i == \"x\":\n",
" cov_1 = C_00* h1[:,0] / r[:,0] * Cov_d1[:,0]\n",
" cov_j = C_00* h1[:,1:] / r[:,1:] * Cov_d1[:,1:]\n",
" # C_GI_row = alpha * sig_z / a**2 * h1 / r * Cov_d1\n",
" # print \"cov_j, cov_1\", cov_j, cov_1.reshape(-1,1), \"h1\",h1\n",
" \n",
" C_GI_row = cov_j.transpose()-cov_1#.transpose()\n",
" # pdb.set_trace()\n",
" else:\n",
" cov_1 = C_00* h1[:,0] / r[:,0] * Cov_d1[:,0]\n",
" cov_j = C_00* h1[:,1:] / r[:,1:] * Cov_d1[:,1:]\n",
" #C_GI_row = np.hstack((C_GI_row,\n",
" # alpha * sig_z / a**2 * h1 / r * Cov_d1))\n",
" #pdb.set_trace()\n",
" C_GI_row = np.hstack((C_GI_row, cov_j.transpose()-cov_1))\n",
" # pdb.set_trace()\n",
" #.reshape(-1,1)))\n",
" if k==0:\n",
" C_GI = C_GI_row\n",
" else:\n",
" #pdb.set_trace()\n",
" C_GI = np.vstack((C_GI,C_GI_row))\n",
" \n",
" return C_GI"
]
},
{
"cell_type": "code",
"execution_count": 22,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"array([[ 1.28e-01, -2.32e-02, -1.51e-01, 9.29e-02],\n",
" [ 8.56e-02, -0.00e+00, -1.98e-01, 1.10e-01],\n",
" [ 6.98e-02, 6.39e-05, -2.09e-01, 1.06e-04],\n",
" [ 1.28e-01, -1.36e-01, 1.51e-01, -2.81e-01],\n",
" [ 6.98e-02, 1.39e-01, 2.09e-01, -8.76e-02]])"
]
},
"execution_count": 22,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"np.set_printoptions(precision=2)\n",
"a = C_GI(dips,layers, verbose =0)\n",
"a"
]
},
{
"cell_type": "code",
"execution_count": 23,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"#======================\n",
"# The condition of universality\n",
"# GRADIENTS\n",
"def U_G(dips):\n",
" dips = np.asarray(dips)\n",
" n = len(dips)\n",
" # x\n",
" U_G = np.array([np.ones(n), #x\n",
" np.zeros(n),]) #y\n",
" # dips[:,0]*2, #xx\n",
" # np.zeros(n), #yy\n",
" # dips[:,1],]) #xy\n",
" \n",
" # y\n",
" U_G = np.hstack((U_G,np.array([np.zeros(n),\n",
" np.ones(n),])))\n",
" # np.zeros(n),\n",
" # 2*dips[:,1]\n",
" # ,dips[:,0]])))\n",
" return U_G"
]
},
{
"cell_type": "code",
"execution_count": 24,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"array([[ 1., 1., 0., 0.],\n",
" [ 0., 0., 1., 1.]])"
]
},
"execution_count": 24,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"U_G(dips)"
]
},
{
"cell_type": "code",
"execution_count": 25,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"#======================\n",
"# The condition of universality\n",
"# Interfaces\n",
"\n",
"def U_I(layers):\n",
" layers = np.asarray(layers)\n",
" for e,l in enumerate(layers):\n",
" if e == 0:\n",
" U_I = np.array([(l[1:,0]-l[0,0]), # x\n",
" (l[1:,1]-l[0,1]),]) # y\n",
" # np.square(l[1:,0])- np.square(l[0,0]), # xx\n",
" # np.square(l[1:,1])- np.square(l[0,1]), # yy\n",
" #(l[1:,0]* l[1:,1])-(l[0,0]*l[0,1])]) #xy\n",
" else:\n",
" U_I = np.hstack((U_I, np.array([(l[1:,0]-l[0,0]), # x\n",
" (l[1:,1]-l[0,1]),]))) # y\n",
" # np.square(l[1:,0])- np.square(l[0,0]), # xx\n",
" # np.square(l[1:,1])- np.square(l[0,1]), # yy\n",
" # (l[1:,0]* l[1:,1])-(l[0,0]*l[0,1])]))) #xy\n",
"\n",
"\n",
" return U_I"
]
},
{
"cell_type": "code",
"execution_count": 26,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"U_I(layers);"
]
},
{
"cell_type": "code",
"execution_count": 27,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"array([[-0.59, -0.13, 0. , 0.03],\n",
" [-0.13, -0.59, 0.03, 0. ],\n",
" [ 0. , -0.06, -0.59, -0.1 ],\n",
" [ 0.03, 0. , -0.1 , -0.59]])"
]
},
"execution_count": 27,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"theano_CG = np.array([[-0.58888888, -0.13136305, 0. , 0.03284076],\n",
" [-0.13136305, -0.58888888, 0.03284076, 0. ],\n",
" [ 0. , -0.06287594, -0.58888888, -0.10392689],\n",
" [ 0.03284076, 0. , -0.10392689, -0.58888888]]\n",
")\n",
"theano_CG"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## A matrix"
]
},
{
"cell_type": "code",
"execution_count": 28,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"def A_matrix(layers,dips, sig_z = 1., a = 6., C_0 = -14*1/6**2-0.2,\n",
" C_01 = 1, verbose = 0):\n",
" \n",
" #CG = theano_CG\n",
" CG = C_G(dips)\n",
" CGI = C_GI(dips,layers,a = a, C_01=C_01)\n",
" CI = C_I(layers, a = a)\n",
" UG = U_G(dips)\n",
" UI = U_I(layers)\n",
" # print np.shape(UI)[0]\n",
" zeros = np.zeros((np.shape(UI)[0],np.shape(UI)[0]))\n",
" #print CG,CGI.transpose(),UG.transpose()\n",
" A1 = np.hstack((-CG,CGI.transpose(),UG.transpose()))\n",
" A2 = np.hstack((CGI,CI,UI.transpose()))\n",
" A3 = np.hstack((UG,UI,zeros))\n",
" A = np.vstack((A1,A2,A3))\n",
" return A"
]
},
{
"cell_type": "code",
"execution_count": 29,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"/home/bl3/anaconda3/lib/python3.5/site-packages/ipykernel/__main__.py:32: RuntimeWarning: invalid value encountered in true_divide\n",
"/home/bl3/anaconda3/lib/python3.5/site-packages/ipykernel/__main__.py:32: RuntimeWarning: divide by zero encountered in true_divide\n",
"/home/bl3/anaconda3/lib/python3.5/site-packages/ipykernel/__main__.py:33: RuntimeWarning: invalid value encountered in true_divide\n",
"/home/bl3/anaconda3/lib/python3.5/site-packages/ipykernel/__main__.py:75: RuntimeWarning: invalid value encountered in true_divide\n",
"/home/bl3/anaconda3/lib/python3.5/site-packages/ipykernel/__main__.py:76: RuntimeWarning: divide by zero encountered in true_divide\n",
"/home/bl3/anaconda3/lib/python3.5/site-packages/ipykernel/__main__.py:76: RuntimeWarning: invalid value encountered in multiply\n",
"/home/bl3/anaconda3/lib/python3.5/site-packages/ipykernel/__main__.py:66: RuntimeWarning: invalid value encountered in true_divide\n",
"/home/bl3/anaconda3/lib/python3.5/site-packages/ipykernel/__main__.py:67: RuntimeWarning: divide by zero encountered in true_divide\n",
"/home/bl3/anaconda3/lib/python3.5/site-packages/ipykernel/__main__.py:67: RuntimeWarning: invalid value encountered in multiply\n",
"/home/bl3/anaconda3/lib/python3.5/site-packages/ipykernel/__main__.py:51: RuntimeWarning: invalid value encountered in true_divide\n",
"/home/bl3/anaconda3/lib/python3.5/site-packages/ipykernel/__main__.py:51: RuntimeWarning: divide by zero encountered in true_divide\n",
"/home/bl3/anaconda3/lib/python3.5/site-packages/ipykernel/__main__.py:52: RuntimeWarning: invalid value encountered in true_divide\n"
]
},
{
"data": {
"text/plain": [
"(11, 11)"
]
},
"execution_count": 29,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"np.set_printoptions(precision = 2, linewidth= 130, suppress = True)\n",
"aa = A_matrix(layers, dips)\n",
"np.shape(aa)\n",
"#aa"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Dual Kriging"
]
},
{
"cell_type": "code",
"execution_count": 30,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"def G_f(dips, dips_v):\n",
" a_g = np.asarray(dips)\n",
" b_g = np.asarray(dips_v)\n",
" # print a, a[:,0]\n",
" # print b,b[:,0]\n",
" Gx = b_g[:,0] - a_g[:,0] # x\n",
" Gy = b_g[:,1] -a_g[:,1] # y\n",
" G = np.hstack((Gx,Gy))\n",
" # G = np.array([-0.71,0.34,0.71,0.93])\n",
" return G\n",
"\n",
"def b(dips,dips_v,n):\n",
" n -= len(dips)*2 # because x and y direction \n",
" G = G_f(dips,dips_v)\n",
" b = np.hstack((G, np.zeros(n)))\n",
" return b"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Estimator normal"
]
},
{
"cell_type": "code",
"execution_count": 31,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"[-0.71 0.71 0.71 0.71 0. 0. 0. 0. 0. 0. 0. ]\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"/home/bl3/anaconda3/lib/python3.5/site-packages/ipykernel/__main__.py:32: RuntimeWarning: invalid value encountered in true_divide\n",
"/home/bl3/anaconda3/lib/python3.5/site-packages/ipykernel/__main__.py:32: RuntimeWarning: divide by zero encountered in true_divide\n",
"/home/bl3/anaconda3/lib/python3.5/site-packages/ipykernel/__main__.py:33: RuntimeWarning: invalid value encountered in true_divide\n",
"/home/bl3/anaconda3/lib/python3.5/site-packages/ipykernel/__main__.py:75: RuntimeWarning: invalid value encountered in true_divide\n",
"/home/bl3/anaconda3/lib/python3.5/site-packages/ipykernel/__main__.py:76: RuntimeWarning: divide by zero encountered in true_divide\n",
"/home/bl3/anaconda3/lib/python3.5/site-packages/ipykernel/__main__.py:76: RuntimeWarning: invalid value encountered in multiply\n",
"/home/bl3/anaconda3/lib/python3.5/site-packages/ipykernel/__main__.py:66: RuntimeWarning: invalid value encountered in true_divide\n",
"/home/bl3/anaconda3/lib/python3.5/site-packages/ipykernel/__main__.py:67: RuntimeWarning: divide by zero encountered in true_divide\n",
"/home/bl3/anaconda3/lib/python3.5/site-packages/ipykernel/__main__.py:67: RuntimeWarning: invalid value encountered in multiply\n",
"/home/bl3/anaconda3/lib/python3.5/site-packages/ipykernel/__main__.py:51: RuntimeWarning: invalid value encountered in true_divide\n",
"/home/bl3/anaconda3/lib/python3.5/site-packages/ipykernel/__main__.py:51: RuntimeWarning: divide by zero encountered in true_divide\n",
"/home/bl3/anaconda3/lib/python3.5/site-packages/ipykernel/__main__.py:52: RuntimeWarning: invalid value encountered in true_divide\n"
]
}
],
"source": [
"aa = A_matrix(layers, dips)\n",
" \n",
"bb = b([dip_pos_1, dip_pos_2], \n",
" [dip_pos_1_v,dip_pos_2_v], len(aa))\n",
"# bb[1] = 0\n",
"print (bb)\n",
"sol = np.linalg.solve(aa,bb)"
]
},
{
"cell_type": "code",
"execution_count": 32,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"array([[ 0.59, 0.11, -0. , -0.03, 0.13, 0.09, 0.07, 0.13, 0.07, 1. , 0. ],\n",
" [ 0.11, 0.59, -0.03, -0. , -0.02, -0. , 0. , -0.14, 0.14, 1. , 0. ],\n",
" [-0. , -0.03, 0.59, -0.02, -0.15, -0.2 , -0.21, 0.15, 0.21, 0. , 1. ],\n",
" [-0.03, -0. , -0.02, 0.59, 0.09, 0.11, 0. , -0.28, -0.09, 0. , 1. ],\n",
" [ 0.13, -0.02, -0.15, 0.09, 1.87, 1.78, 0.99, 0. , 0. , 4. , 0. ],\n",
" [ 0.09, -0. , -0.2 , 0.11, 1.78, 1.99, 1.2 , 0. , 0. , 5. , 0. ],\n",
" [ 0.07, 0. , -0.21, 0. , 0.99, 1.2 , 2. , 0. , 0. , 8. , 1. ],\n",
" [ 0.13, -0.14, 0.15, -0.28, 0. , 0. , 0. , 1.87, 1. , 4. , 0. ],\n",
" [ 0.07, 0.14, 0.21, -0.09, 0. , 0. , 0. , 1. , 2. , 8. , 0. ],\n",
" [ 1. , 1. , 0. , 0. , 4. , 5. , 8. , 4. , 8. , 0. , 0. ],\n",
" [ 0. , 0. , 1. , 1. , 0. , 0. , 1. , 0. , 0. , 0. , 0. ]])"
]
},
"execution_count": 32,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"aa"
]
},
{
"cell_type": "code",
"execution_count": 33,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"array([-0.71, 0.71, 0.71, 0.71, 0. , 0. , 0. , 0. , 0. , 0. , 0. ])"
]
},
"execution_count": 33,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"bb"
]
},
{
"cell_type": "code",
"execution_count": 34,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"array([-1.54, 1.68, 0.16, 0.14, 0.26, 0.12, -0.3 , 0.38, -0.11, -0.04, 0.64])"
]
},
"execution_count": 34,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"sol"
]
},
{
"cell_type": "code",
"execution_count": 35,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"x = [1,1]\n",
"def estimator(x, dips, layers, sol, sig_z = 1., a = 6., C_01 = 1, verbose = 0):\n",
" x = np.asarray(x).reshape(1,-1)\n",
" dips = np.asarray(dips)\n",
" layers = np.asarray(layers)\n",
" C_01 = C_01\n",
" n = 0\n",
" m = len(dips)\n",
" # print layers\n",
" # print x.reshape(1,-1), dips\n",
" r_i = me.euclidean_distances(dips,x)\n",
" hx = h_f_GI(dips, x, \"x\")\n",
" Cov_d1 = cov_cubic_d1_f(r_i, a = a)\n",
" \n",
" KzGx = sol[:m] * np.squeeze( C_01*hx / r_i * Cov_d1)\n",
" hy = h_f_GI(dips, x, \"y\")\n",
" KzGy = sol[m:2*m] * np.squeeze( C_01 * hy / r_i * Cov_d1)\n",
" # KzGx[KzGx == 0] = -0.01\n",
" # KzGy[KzGy == 0] = -0.01\n",
" # print \"KzGx\", KzGx, sol[:m]\n",
" for s in range(len(layers)):\n",
" n += len(layers[s][1:])\n",
" a_l = cov_cubic_layer(x, layers[s][1:], a = a)\n",
" b_l = cov_cubic_layer(x, layers[s][0].reshape(1,-1), a = a)\n",
" aux = a_l-b_l\n",
" # aux[aux==0] = 0.000001\n",
" if s == 0:\n",
" L = np.array(sol[2*m:2*m+n]*(aux))\n",
" else:\n",
" L = np.hstack((L,sol[2*m+n2:2*m+n]*(aux)))\n",
" n2 = n \n",
" L = np.squeeze(L)\n",
" univ = (sol[2*m+n]*x[0,0] + # x\n",
" sol[2*m+n+1] * x[0,1] ) # y \n",
" # + sol[2*m+n+2]* x[0,0]**2 # xx\n",
" # + sol[2*m+n+3] * x[0,1]**2 # yy\n",
" # + sol[2*m+n+4] * x[0,0]*x[0,1]) #xy\n",
" \n",
" if verbose != 0:\n",
" print (KzGx, KzGy, L ,univ)\n",
" print (Cov_d1, r_i)\n",
" print (\"\")\n",
" print (hx, hx/r_i)\n",
" print (\"angaglkagm\",hy/r_i, sol[m:2*m])\n",
" z_star = np.sum(KzGx)+np.sum(KzGy)+np.sum(L)+univ\n",
" return z_star"
]
},
{
"cell_type": "code",
"execution_count": 36,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"/home/bl3/anaconda3/lib/python3.5/site-packages/ipykernel/__main__.py:15: RuntimeWarning: invalid value encountered in true_divide\n",
"/home/bl3/anaconda3/lib/python3.5/site-packages/ipykernel/__main__.py:17: RuntimeWarning: invalid value encountered in true_divide\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"[ 1.29 4.71] [ 6.71 3.71] [[1 7]\n",
" [5 7]\n",
" [6 7]\n",
" [9 8]]\n"
]
},
{
"data": {
"image/png": "iVBORw0KGgoAAAANSUhEUgAAAWEAAAEKCAYAAADDzOROAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nDBby/7MV4U0Vx0zBAAAAAElFTkSuQmCC\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x7f5e63728d30>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"pot = np.zeros((100,100))\n",
"for i in range(100):\n",
" for j in range(100):\n",
" pot[i,j] = estimator([i/10.,j/10.],[dip_pos_1, dip_pos_2], \n",
" [layer_1, layer_2]\n",
" , sol, verbose = 0, C_01 = 1,\n",
" a = 6.)\n",
"\n",
"\n",
"plt.arrow(dip_pos_1[0],dip_pos_1[1], dip_pos_1_v[0]-dip_pos_1[0],\n",
" dip_pos_1_v[1]-dip_pos_1[1], head_width = 0.2)\n",
"plt.arrow(dip_pos_2[0],dip_pos_2[1],dip_pos_2_v[0]-dip_pos_2[0], \n",
" dip_pos_2_v[1]-dip_pos_2[1], head_width = 0.2)\n",
"\n",
"plt.plot(layer_1[:,0],layer_1[:,1], \"o\")\n",
"plt.plot(layer_2[:,0],layer_2[:,1], \"o\")\n",
"plt.plot(layer_1[:,0],layer_1[:,1], )\n",
"plt.plot(layer_2[:,0],layer_2[:,1], )\n",
"plt.contour(pot.transpose(),30,extent = (0,10,0,10) )\n",
"plt.colorbar()\n",
"plt.xlim(0,10)\n",
"plt.ylim(0,10)\n",
"plt.title(\"GeoMigueller v 0.1\")\n",
"print (dip_pos_1_v, dip_pos_2_v, layer_1)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": []
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": []
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## La Buena"
]
},
{
"cell_type": "code",
"execution_count": 22,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"%matplotlib inline"
]
},
{
"cell_type": "code",
"execution_count": 35,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"def pla(angle1,angle2, C_0 = -14*1/6**2-0.2, C_01 = 1):\n",
" layer_1 = np.array([[1,7],[5,6], [6,8], [9,9] ])\n",
" layer_2 = np.array([[1,2],[5,3], [9,7]])\n",
" layer_3 = np.array([[1,1],[3,2],[7,4]])\n",
"\n",
" dip_pos_1 = np.array([3,4])\n",
" dip_angle_1 = angle1\n",
" dip_pos_1_v = np.array([np.cos(np.deg2rad(dip_angle_1))*1,\n",
" np.sin(np.deg2rad(dip_angle_1))]) + dip_pos_1\n",
"\n",
" \n",
" \n",
" dip_pos_2 = np.array([6,6])\n",
" dip_angle_2 = angle2\n",
" dip_pos_2_v = np.array([np.cos(np.deg2rad(dip_angle_2))*1, \n",
" np.sin(np.deg2rad(dip_angle_2))]) + dip_pos_2\n",
"\n",
" \n",
" dip_pos_3 = np.array([9,5])\n",
" dip_angle_3 = 90\n",
" dip_pos_3_v = np.array([np.cos(np.deg2rad(dip_angle_3))*1, \n",
" np.sin(np.deg2rad(dip_angle_3))]) + dip_pos_3\n",
" #print b([dip_pos_1,dip_pos_2], [dip_pos_1_v,dip_pos_2_v],13)\n",
"\n",
" aa = A_matrix([layer_1,layer_2, layer_3], \n",
" [dip_pos_1,dip_pos_2, dip_pos_3], a = 6.,\n",
" C_0= C_0,\n",
" C_01 = C_01)\n",
" bb = b([dip_pos_1, dip_pos_2, dip_pos_3], \n",
" [dip_pos_1_v,dip_pos_2_v, dip_pos_3_v], len(aa))\n",
"# bb[1] = 0\n",
" print (bb)\n",
" sol = np.linalg.solve(aa,bb)\n",
"\n",
" #sol[:-2] = 0\n",
" #print aa \n",
"\n",
" print( sol)\n",
" pot = np.zeros((50,50))\n",
" for i in range(50):\n",
" for j in range(50):\n",
" pot[i,j] = estimator([i/5.,j/5.],[dip_pos_1, dip_pos_2, dip_pos_3], \n",
" [layer_1, layer_2, layer_3]\n",
" , sol, verbose = 0, C_01 = C_01,\n",
" a = 6.)\n",
"\n",
" \n",
" plt.arrow(dip_pos_1[0],dip_pos_1[1], dip_pos_1_v[0]-dip_pos_1[0],\n",
" dip_pos_1_v[1]-dip_pos_1[1], head_width = 0.2)\n",
" plt.arrow(dip_pos_2[0],dip_pos_2[1],dip_pos_2_v[0]-dip_pos_2[0], \n",
" dip_pos_2_v[1]-dip_pos_2[1], head_width = 0.2)\n",
" plt.arrow(dip_pos_3[0],dip_pos_3[1],dip_pos_3_v[0]-dip_pos_3[0], \n",
" dip_pos_3_v[1]-dip_pos_3[1], head_width = 0.2)\n",
"\n",
" plt.plot(layer_1[:,0],layer_1[:,1], \"o\")\n",
" plt.plot(layer_2[:,0],layer_2[:,1], \"o\")\n",
" plt.plot(layer_3[:,0],layer_3[:,1], \"o\")\n",
" plt.plot(layer_1[:,0],layer_1[:,1], )\n",
" plt.plot(layer_2[:,0],layer_2[:,1], )\n",
" plt.plot(layer_3[:,0],layer_3[:,1], )\n",
" plt.contour(pot.transpose(),30,extent = (0,10,0,10) )\n",
" plt.colorbar()\n",
" plt.xlim(0,10)\n",
" plt.ylim(0,10)\n",
" plt.title(\"GeoMigueller v 0.1\")\n",
" print (dip_pos_1_v, dip_pos_2_v, layer_1)\n",
" \n",
" return pot"
]
},
{
"cell_type": "code",
"execution_count": 36,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"R [[ 0. 3.605551 6.082763]\n",
" [ 3.605551 0. 3.162278]\n",
" [ 6.082763 3.162278 0. ]]\n",
"hx [[ 0 3 6]\n",
" [-3 0 3]\n",
" [-6 -3 0]]\n",
"hx [[ 0 3 6]\n",
" [-3 0 3]\n",
" [-6 -3 0]]\n",
"hy [[ 0 2 1]\n",
" [-2 0 -1]\n",
" [-1 1 0]]\n",
"hy [[ 0 2 1]\n",
" [-2 0 -1]\n",
" [-1 1 0]]\n",
"hx [[ 0 3 6]\n",
" [-3 0 3]\n",
" [-6 -3 0]]\n",
"hy [[ 0 2 1]\n",
" [-2 0 -1]\n",
" [-1 1 0]]\n",
"[[-0.5 -0.099713 0. 0. 0.041274 -0. ]\n",
" [-0.099713 -0.5 -0.121062 0.041274 0. -0.030068]\n",
" [ 0. -0.121062 -0.5 -0. -0.030068 0. ]\n",
" [ 0. 0.041274 -0. -0.5 -0.056754 0. ]\n",
" [ 0.041274 0. -0.030068 -0.056754 -0.5 -0.044483]\n",
" [-0. -0.030068 0. 0. -0.044483 -0.5 ]]\n",
"[-0.5 -0.642788 0. 0.866025 0.766044 1. 0. 0. 0. 0. 0. 0. 0. 0.\n",
" 0. ]\n",
"[-1.774062 0.157252 0.664421 0.915743 1.972454 2.263119 0.850932 -0.949148 0.841094 1.301378 -1.51487 -0.429629 0.556393\n",
" -0.161104 0.325884]\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"/home/bl3/anaconda3/lib/python3.5/site-packages/ipykernel/__main__.py:32: RuntimeWarning: invalid value encountered in true_divide\n",
"/home/bl3/anaconda3/lib/python3.5/site-packages/ipykernel/__main__.py:32: RuntimeWarning: divide by zero encountered in true_divide\n",
"/home/bl3/anaconda3/lib/python3.5/site-packages/ipykernel/__main__.py:33: RuntimeWarning: invalid value encountered in true_divide\n",
"/home/bl3/anaconda3/lib/python3.5/site-packages/ipykernel/__main__.py:56: RuntimeWarning: invalid value encountered in true_divide\n",
"/home/bl3/anaconda3/lib/python3.5/site-packages/ipykernel/__main__.py:57: RuntimeWarning: divide by zero encountered in true_divide\n",
"/home/bl3/anaconda3/lib/python3.5/site-packages/ipykernel/__main__.py:57: RuntimeWarning: invalid value encountered in multiply\n",
"/home/bl3/anaconda3/lib/python3.5/site-packages/ipykernel/__main__.py:48: RuntimeWarning: invalid value encountered in true_divide\n",
"/home/bl3/anaconda3/lib/python3.5/site-packages/ipykernel/__main__.py:49: RuntimeWarning: divide by zero encountered in true_divide\n",
"/home/bl3/anaconda3/lib/python3.5/site-packages/ipykernel/__main__.py:49: RuntimeWarning: invalid value encountered in multiply\n",
"/home/bl3/anaconda3/lib/python3.5/site-packages/ipykernel/__main__.py:40: RuntimeWarning: invalid value encountered in true_divide\n",
"/home/bl3/anaconda3/lib/python3.5/site-packages/ipykernel/__main__.py:40: RuntimeWarning: divide by zero encountered in true_divide\n",
"/home/bl3/anaconda3/lib/python3.5/site-packages/ipykernel/__main__.py:41: RuntimeWarning: invalid value encountered in true_divide\n",
"/home/bl3/anaconda3/lib/python3.5/site-packages/ipykernel/__main__.py:15: RuntimeWarning: invalid value encountered in true_divide\n",
"/home/bl3/anaconda3/lib/python3.5/site-packages/ipykernel/__main__.py:17: RuntimeWarning: invalid value encountered in true_divide\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"[ 2.5 4.866025] [ 5.357212 6.766044] [[1 7]\n",
" [5 6]\n",
" [6 8]\n",
" [9 9]]\n"
]
},
{
"data": {
"image/png": "iVBORw0KGgoAAAANSUhEUgAAAWMAAAEKCAYAAADHOTRzAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nPj4JwP8DJst8ZNx3UBkAAAAASUVORK5CYII=\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x7f729153bef0>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"jhjs2 = pla(120,130,C_0=-0.5, C_01 = 1)"
]
},
{
"cell_type": "code",
"execution_count": 25,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"# jhjs = pla(120,-30, C_01 = 0.9)"
]
},
{
"cell_type": "code",
"execution_count": 26,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"R [[ 0. 3.605551 6.082763]\n",
" [ 3.605551 0. 3.162278]\n",
" [ 6.082763 3.162278 0. ]]\n",
"hx [[ 0 3 6]\n",
" [-3 0 3]\n",
" [-6 -3 0]]\n",
"hx [[ 0 3 6]\n",
" [-3 0 3]\n",
" [-6 -3 0]]\n",
"hy [[ 0 2 1]\n",
" [-2 0 -1]\n",
" [-1 1 0]]\n",
"hy [[ 0 2 1]\n",
" [-2 0 -1]\n",
" [-1 1 0]]\n",
"hx [[ 0 3 6]\n",
" [-3 0 3]\n",
" [-6 -3 0]]\n",
"hy [[ 0 2 1]\n",
" [-2 0 -1]\n",
" [-1 1 0]]\n",
"[[-0.588889 -0.100938 0. 0. 0.048612 -0. ]\n",
" [-0.100938 -0.588889 -0.121682 0.048612 0. -0.035414]\n",
" [ 0. -0.121682 -0.588889 -0. -0.035414 0. ]\n",
" [ 0. 0.048612 -0. -0.588889 -0.059509 0. ]\n",
" [ 0.048612 0. -0.035414 -0.059509 -0.588889 -0.050069]\n",
" [-0. -0.035414 0. 0. -0.050069 -0.588889]]\n",
"[-0.5 0.866025 0. 0.866025 -0.5 1. 0. 0. 0. 0. 0. 0. 0. 0.\n",
" 0. ]\n",
"[-1.98635 2.626914 -0.543753 2.454589 -1.308389 1.95855 0.222263 0.304127 0.156125 0.970274 -0.65084 0.548595 -0.587844\n",
" -0.029849 -0.066016]\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"/home/bl3/anaconda3/lib/python3.5/site-packages/ipykernel/__main__.py:32: RuntimeWarning: invalid value encountered in true_divide\n",
"/home/bl3/anaconda3/lib/python3.5/site-packages/ipykernel/__main__.py:32: RuntimeWarning: divide by zero encountered in true_divide\n",
"/home/bl3/anaconda3/lib/python3.5/site-packages/ipykernel/__main__.py:33: RuntimeWarning: invalid value encountered in true_divide\n",
"/home/bl3/anaconda3/lib/python3.5/site-packages/ipykernel/__main__.py:56: RuntimeWarning: invalid value encountered in true_divide\n",
"/home/bl3/anaconda3/lib/python3.5/site-packages/ipykernel/__main__.py:57: RuntimeWarning: divide by zero encountered in true_divide\n",
"/home/bl3/anaconda3/lib/python3.5/site-packages/ipykernel/__main__.py:57: RuntimeWarning: invalid value encountered in multiply\n",
"/home/bl3/anaconda3/lib/python3.5/site-packages/ipykernel/__main__.py:48: RuntimeWarning: invalid value encountered in true_divide\n",
"/home/bl3/anaconda3/lib/python3.5/site-packages/ipykernel/__main__.py:49: RuntimeWarning: divide by zero encountered in true_divide\n",
"/home/bl3/anaconda3/lib/python3.5/site-packages/ipykernel/__main__.py:49: RuntimeWarning: invalid value encountered in multiply\n",
"/home/bl3/anaconda3/lib/python3.5/site-packages/ipykernel/__main__.py:40: RuntimeWarning: invalid value encountered in true_divide\n",
"/home/bl3/anaconda3/lib/python3.5/site-packages/ipykernel/__main__.py:40: RuntimeWarning: divide by zero encountered in true_divide\n",
"/home/bl3/anaconda3/lib/python3.5/site-packages/ipykernel/__main__.py:41: RuntimeWarning: invalid value encountered in true_divide\n",
"/home/bl3/anaconda3/lib/python3.5/site-packages/ipykernel/__main__.py:15: RuntimeWarning: invalid value encountered in true_divide\n",
"/home/bl3/anaconda3/lib/python3.5/site-packages/ipykernel/__main__.py:17: RuntimeWarning: invalid value encountered in true_divide\n"
]
}
],
"source": [
"jhjs = pla(120,-30)"
]
},
{
"cell_type": "code",
"execution_count": 27,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"R [[ 0. 3.605551 6.082763]\n",
" [ 3.605551 0. 3.162278]\n",
" [ 6.082763 3.162278 0. ]]\n",
"hx [[ 0 3 6]\n",
" [-3 0 3]\n",
" [-6 -3 0]]\n",
"hx [[ 0 3 6]\n",
" [-3 0 3]\n",
" [-6 -3 0]]\n",
"hy [[ 0 2 1]\n",
" [-2 0 -1]\n",
" [-1 1 0]]\n",
"hy [[ 0 2 1]\n",
" [-2 0 -1]\n",
" [-1 1 0]]\n",
"hx [[ 0 3 6]\n",
" [-3 0 3]\n",
" [-6 -3 0]]\n",
"hy [[ 0 2 1]\n",
" [-2 0 -1]\n",
" [-1 1 0]]\n",
"[[-0.588889 -0.100938 0. 0. 0.048612 -0. ]\n",
" [-0.100938 -0.588889 -0.121682 0.048612 0. -0.035414]\n",
" [ 0. -0.121682 -0.588889 -0. -0.035414 0. ]\n",
" [ 0. 0.048612 -0. -0.588889 -0.059509 0. ]\n",
" [ 0.048612 0. -0.035414 -0.059509 -0.588889 -0.050069]\n",
" [-0. -0.035414 0. 0. -0.050069 -0.588889]]\n",
"[-0.5 1. 0. 0.866025 0. 1. 0. 0. 0. 0. 0. 0. 0. 0.\n",
" 0. ]\n",
"[-2.247577 3.126876 -0.640137 2.201872 0.069394 2.000452 0.637562 -0.297231 0.305559 1.237968 -0.836211 0.347865 -0.45094\n",
" -0.02636 -0.081672]\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"/home/bl3/anaconda3/lib/python3.5/site-packages/ipykernel/__main__.py:32: RuntimeWarning: invalid value encountered in true_divide\n",
"/home/bl3/anaconda3/lib/python3.5/site-packages/ipykernel/__main__.py:32: RuntimeWarning: divide by zero encountered in true_divide\n",
"/home/bl3/anaconda3/lib/python3.5/site-packages/ipykernel/__main__.py:33: RuntimeWarning: invalid value encountered in true_divide\n",
"/home/bl3/anaconda3/lib/python3.5/site-packages/ipykernel/__main__.py:56: RuntimeWarning: invalid value encountered in true_divide\n",
"/home/bl3/anaconda3/lib/python3.5/site-packages/ipykernel/__main__.py:57: RuntimeWarning: divide by zero encountered in true_divide\n",
"/home/bl3/anaconda3/lib/python3.5/site-packages/ipykernel/__main__.py:57: RuntimeWarning: invalid value encountered in multiply\n",
"/home/bl3/anaconda3/lib/python3.5/site-packages/ipykernel/__main__.py:48: RuntimeWarning: invalid value encountered in true_divide\n",
"/home/bl3/anaconda3/lib/python3.5/site-packages/ipykernel/__main__.py:49: RuntimeWarning: divide by zero encountered in true_divide\n",
"/home/bl3/anaconda3/lib/python3.5/site-packages/ipykernel/__main__.py:49: RuntimeWarning: invalid value encountered in multiply\n",
"/home/bl3/anaconda3/lib/python3.5/site-packages/ipykernel/__main__.py:40: RuntimeWarning: invalid value encountered in true_divide\n",
"/home/bl3/anaconda3/lib/python3.5/site-packages/ipykernel/__main__.py:40: RuntimeWarning: divide by zero encountered in true_divide\n",
"/home/bl3/anaconda3/lib/python3.5/site-packages/ipykernel/__main__.py:41: RuntimeWarning: invalid value encountered in true_divide\n",
"/home/bl3/anaconda3/lib/python3.5/site-packages/ipykernel/__main__.py:15: RuntimeWarning: invalid value encountered in true_divide\n",
"/home/bl3/anaconda3/lib/python3.5/site-packages/ipykernel/__main__.py:17: RuntimeWarning: invalid value encountered in true_divide\n"
]
}
],
"source": [
"jh = pla(120,0)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": []
},
{
"cell_type": "code",
"execution_count": 28,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"R [[ 0. 3.605551 6.082763]\n",
" [ 3.605551 0. 3.162278]\n",
" [ 6.082763 3.162278 0. ]]\n",
"hx [[ 0 3 6]\n",
" [-3 0 3]\n",
" [-6 -3 0]]\n",
"hx [[ 0 3 6]\n",
" [-3 0 3]\n",
" [-6 -3 0]]\n",
"hy [[ 0 2 1]\n",
" [-2 0 -1]\n",
" [-1 1 0]]\n",
"hy [[ 0 2 1]\n",
" [-2 0 -1]\n",
" [-1 1 0]]\n",
"hx [[ 0 3 6]\n",
" [-3 0 3]\n",
" [-6 -3 0]]\n",
"hy [[ 0 2 1]\n",
" [-2 0 -1]\n",
" [-1 1 0]]\n",
"[[-0.588889 -0.100938 0. 0. 0.048612 -0. ]\n",
" [-0.100938 -0.588889 -0.121682 0.048612 0. -0.035414]\n",
" [ 0. -0.121682 -0.588889 -0. -0.035414 0. ]\n",
" [ 0. 0.048612 -0. -0.588889 -0.059509 0. ]\n",
" [ 0.048612 0. -0.035414 -0.059509 -0.588889 -0.050069]\n",
" [-0. -0.035414 0. 0. -0.050069 -0.588889]]\n",
"[ 0.999391 1. 0. -0.034899 0. 1. 0. 0. 0. 0. 0. 0. 0. 0.\n",
" 0. ]\n",
"[ 1.745852 1.529886 -0.092427 0.181988 0.1693 1.989711 0.049389 -0.152759 0.168621 -0.2107 -0.703772 0.105986 0.382493\n",
" -0.045037 0.032994]\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"/home/bl3/anaconda3/lib/python3.5/site-packages/ipykernel/__main__.py:32: RuntimeWarning: invalid value encountered in true_divide\n",
"/home/bl3/anaconda3/lib/python3.5/site-packages/ipykernel/__main__.py:32: RuntimeWarning: divide by zero encountered in true_divide\n",
"/home/bl3/anaconda3/lib/python3.5/site-packages/ipykernel/__main__.py:33: RuntimeWarning: invalid value encountered in true_divide\n",
"/home/bl3/anaconda3/lib/python3.5/site-packages/ipykernel/__main__.py:56: RuntimeWarning: invalid value encountered in true_divide\n",
"/home/bl3/anaconda3/lib/python3.5/site-packages/ipykernel/__main__.py:57: RuntimeWarning: divide by zero encountered in true_divide\n",
"/home/bl3/anaconda3/lib/python3.5/site-packages/ipykernel/__main__.py:57: RuntimeWarning: invalid value encountered in multiply\n",
"/home/bl3/anaconda3/lib/python3.5/site-packages/ipykernel/__main__.py:48: RuntimeWarning: invalid value encountered in true_divide\n",
"/home/bl3/anaconda3/lib/python3.5/site-packages/ipykernel/__main__.py:49: RuntimeWarning: divide by zero encountered in true_divide\n",
"/home/bl3/anaconda3/lib/python3.5/site-packages/ipykernel/__main__.py:49: RuntimeWarning: invalid value encountered in multiply\n",
"/home/bl3/anaconda3/lib/python3.5/site-packages/ipykernel/__main__.py:40: RuntimeWarning: invalid value encountered in true_divide\n",
"/home/bl3/anaconda3/lib/python3.5/site-packages/ipykernel/__main__.py:40: RuntimeWarning: divide by zero encountered in true_divide\n",
"/home/bl3/anaconda3/lib/python3.5/site-packages/ipykernel/__main__.py:41: RuntimeWarning: invalid value encountered in true_divide\n",
"/home/bl3/anaconda3/lib/python3.5/site-packages/ipykernel/__main__.py:15: RuntimeWarning: invalid value encountered in true_divide\n",
"/home/bl3/anaconda3/lib/python3.5/site-packages/ipykernel/__main__.py:17: RuntimeWarning: invalid value encountered in true_divide\n"
]
}
],
"source": [
"jhjs = pla(-2,0)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": []
},
{
"cell_type": "code",
"execution_count": 29,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"(43.267331830682124, 36.088541950282064)"
]
},
"execution_count": 29,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"137.769228/3.184139677, -106.724083/-2.9572844241540727772132"
]
},
{
"cell_type": "code",
"execution_count": 30,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"21.06666666666667"
]
},
"execution_count": 30,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"3.16/0.15"
]
},
{
"cell_type": "code",
"execution_count": 31,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"(18.76825396825397, 22.17543859649123)"
]
},
"execution_count": 31,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"59.12/3.15, 3.16/0.1425"
]
},
{
"cell_type": "code",
"execution_count": 32,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"(16.225259682539683, 18.732133333333334)"
]
},
"execution_count": 32,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"51.109568/3.15, 2.669329/0.1425"
]
},
{
"cell_type": "code",
"execution_count": 33,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"(14.300934285714286, 16.083228070175437)"
]
},
"execution_count": 33,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"45.047943/3.15, 2.29186/0.1425"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": []
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": []
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": []
},
{
"cell_type": "code",
"execution_count": 34,
"metadata": {
"collapsed": false
},
"outputs": [
{
"ename": "SyntaxError",
"evalue": "Missing parentheses in call to 'print' (<ipython-input-34-f95248eef32c>, line 27)",
"output_type": "error",
"traceback": [
"\u001b[1;36m File \u001b[1;32m\"<ipython-input-34-f95248eef32c>\"\u001b[1;36m, line \u001b[1;32m27\u001b[0m\n\u001b[1;33m print bb\u001b[0m\n\u001b[1;37m ^\u001b[0m\n\u001b[1;31mSyntaxError\u001b[0m\u001b[1;31m:\u001b[0m Missing parentheses in call to 'print'\n"
]
}
],
"source": [
"layer_1 = np.array([[1,7],[5,7],[6,7], [9,8], ])\n",
"layer_2 = np.array([[1,1],[5,1],[9,1], ])\n",
"layer_3 = np.array([[1,1],[3,2],[7,4]])\n",
"\n",
"dip_pos_1 = np.array([2,4])\n",
"dip_angle_1 = 45\n",
"dip_pos_1_v = np.array([np.cos(np.deg2rad(dip_angle_1))*1,\n",
" np.sin(np.deg2rad(dip_angle_1))]) + dip_pos_1\n",
"\n",
"\n",
"\n",
"dip_pos_2 = np.array([9,7])\n",
"dip_angle_2 = 90\n",
"dip_pos_2_v = np.array([np.cos(np.deg2rad(dip_angle_2))*1, \n",
" np.sin(np.deg2rad(dip_angle_2))]) + dip_pos_2\n",
"\n",
"\n",
"dip_pos_3 = np.array([5,5])\n",
"dip_angle_3 = 90\n",
"dip_pos_3_v = np.array([np.cos(np.deg2rad(dip_angle_3))*1, \n",
" np.sin(np.deg2rad(dip_angle_3))]) + dip_pos_3\n",
"#print b([dip_pos_1,dip_pos_2], [dip_pos_1_v,dip_pos_2_v],13)\n",
"\n",
"aa = A_matrix([layer_1,layer_2], [dip_pos_1,dip_pos_2], a = 6., alpha = 14)\n",
"bb = b([dip_pos_1,dip_pos_2], [dip_pos_1_v,dip_pos_2_v], 11)\n",
"print bb\n",
"sol = np.linalg.solve(aa,bb)\n",
"\n",
"#sol[:-2] = 0\n",
"#print aa \n",
"\n",
"print sol\n",
"pot = np.zeros((50,50))\n",
"for i in range(50):\n",
" for j in range(50):\n",
" pot[i,j] = estimator([i/5.,j/5.],[dip_pos_1,dip_pos_2], \n",
" [layer_1,layer_2], sol, verbose = 0, alpha = 14,\n",
" a = 6.)\n",
"\n",
" \n",
"plt.arrow(dip_pos_1[0],dip_pos_1[1], dip_pos_1_v[0]-dip_pos_1[0],\n",
" dip_pos_1_v[1]-dip_pos_1[1], head_width = 0.2)\n",
"plt.arrow(dip_pos_2[0],dip_pos_2[1],dip_pos_2_v[0]-dip_pos_2[0], \n",
" dip_pos_2_v[1]-dip_pos_2[1], head_width = 0.2)\n",
"#plt.arrow(dip_pos_3[0],dip_pos_3[1],dip_pos_3_v[0]-dip_pos_3[0], \n",
"# dip_pos_3_v[1]-dip_pos_3[1], head_width = 0.2)\n",
"\n",
"plt.plot(layer_1[:,0],layer_1[:,1], \"o\")\n",
"plt.plot(layer_2[:,0],layer_2[:,1], \"o\")\n",
"#plt.plot(layer_3[:,0],layer_3[:,1], \"o\")\n",
"plt.plot(layer_1[:,0],layer_1[:,1], )\n",
"plt.plot(layer_2[:,0],layer_2[:,1], )\n",
"plt.contour(pot.transpose(),20,extent = (0,10,0,10) )\n",
"plt.colorbar()\n",
"plt.xlim(0,10)\n",
"plt.ylim(0,10)\n",
"print dip_pos_1_v, dip_pos_2_v, layer_1"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"np.cos(np.deg2rad(45))"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": []
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"layer_1 = np.array([[1,7],[5,7],[6,7], [9,7], ])\n",
"layer_2 = np.array([[1,1],[5,1],[9,1], ])\n",
"layer_3 = np.array([[1,1],[3,2],[7,4]])\n",
"\n",
"dip_pos_1 = np.array([2,4])\n",
"dip_angle_1 = 100\n",
"dip_pos_1_v = np.array([np.cos(np.deg2rad(dip_angle_1))*1,\n",
" np.sin(np.deg2rad(dip_angle_1))]) + dip_pos_1\n",
"\n",
"\n",
"\n",
"dip_pos_2 = np.array([8,5])\n",
"dip_angle_2 = 70\n",
"dip_pos_2_v = np.array([np.cos(np.deg2rad(dip_angle_2))*1, \n",
" np.sin(np.deg2rad(dip_angle_2))]) + dip_pos_2\n",
"\n",
"\n",
"dip_pos_3 = np.array([8,5])\n",
"dip_angle_3 = 90\n",
"dip_pos_3_v = np.array([np.cos(np.deg2rad(dip_angle_3))*1, \n",
" np.sin(np.deg2rad(dip_angle_3))]) + dip_pos_3\n",
"#print b([dip_pos_1,dip_pos_2], [dip_pos_1_v,dip_pos_2_v],13)\n",
"\n",
"aa = A_matrix([layer_1,layer_2], [dip_pos_1,dip_pos_2], a = 6., alpha = 14)\n",
"bb = b([dip_pos_1,dip_pos_2], [dip_pos_1_v,dip_pos_2_v], 11)\n",
"print bb\n",
"sol = np.linalg.solve(aa,bb)\n",
"\n",
"#sol[:-2] = 0\n",
"#print aa \n",
"\n",
"print sol\n",
"pot = np.zeros((50,50))\n",
"for i in range(50):\n",
" for j in range(50):\n",
" pot[i,j] = estimator([i/5.,j/5.],[dip_pos_1,dip_pos_2], \n",
" [layer_1,layer_2], sol, verbose = 0, alpha = 14,\n",
" a = 6.)\n",
"\n",
" \n",
"plt.arrow(dip_pos_1[0],dip_pos_1[1], dip_pos_1_v[0]-dip_pos_1[0],\n",
" dip_pos_1_v[1]-dip_pos_1[1], head_width = 0.2)\n",
"plt.arrow(dip_pos_2[0],dip_pos_2[1],dip_pos_2_v[0]-dip_pos_2[0], \n",
" dip_pos_2_v[1]-dip_pos_2[1], head_width = 0.2)\n",
"#plt.arrow(dip_pos_3[0],dip_pos_3[1],dip_pos_3_v[0]-dip_pos_3[0], \n",
"# dip_pos_3_v[1]-dip_pos_3[1], head_width = 0.2)\n",
"\n",
"plt.plot(layer_1[:,0],layer_1[:,1], \"o\")\n",
"plt.plot(layer_2[:,0],layer_2[:,1], \"o\")\n",
"#plt.plot(layer_3[:,0],layer_3[:,1], \"o\")\n",
"plt.plot(layer_1[:,0],layer_1[:,1], )\n",
"plt.plot(layer_2[:,0],layer_2[:,1], )\n",
"plt.contour(pot.transpose(),20,extent = (0,10,0,10) )\n",
"plt.colorbar()\n",
"plt.xlim(0,10)\n",
"plt.ylim(0,10)\n",
"print dip_pos_1_v, dip_pos_2_v, layer_1"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": []
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": []
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"%matplotlib inline"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"from mpl_toolkits.mplot3d import Axes3D\n",
"from matplotlib import cm\n",
"from matplotlib.ticker import LinearLocator, FormatStrFormatter\n",
"\n",
"\n",
"fig = plt.figure()\n",
"ax = fig.gca(projection='3d')\n",
"X = np.arange(0, 10, 0.1)\n",
"Y = np.arange(0, 10, 0.1)\n",
"X, Y = np.meshgrid(X, Y)\n",
"Z = pot.transpose()\n",
"surf = ax.plot_surface(X, Y, Z, rstride=1, cstride=1, cmap=cm.coolwarm,\n",
" linewidth=0, antialiased=False)\n",
"\n",
"ax.set_xlabel(\"x\")\n",
"ax.set_ylabel(\"y\")"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": []
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"print \"layer1\",(pot.transpose()[1,7],pot.transpose()[3,4],pot.transpose()[8,5],\n",
" pot.transpose()[9,7])\n",
"print \"layer2\",pot.transpose()[1,3],pot.transpose()[3,4]\n",
"print \"layer3\",pot.transpose()[1,1],pot.transpose()[3,1],pot.transpose()[7,4]"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"layer_1 = np.array([[5,5],[3,5]])\n",
"layer_2 = np.array([[1,3],[5,3],[7,3],[9,3]])\n",
"\n",
"\n",
"dip_pos_1 = np.array([2,4])\n",
"dip_angle_1 = 90\n",
"dip_pos_1_v = np.array([np.cos(np.deg2rad(dip_angle_1))*1,\n",
" np.sin(np.deg2rad(dip_angle_1))]) + dip_pos_1\n",
"\n",
"\n",
"\n",
"dip_pos_2 = np.array([6,4])\n",
"dip_angle_2 = 90\n",
"dip_pos_2_v = np.array([np.cos(np.deg2rad(dip_angle_2))*1, \n",
" np.sin(np.deg2rad(dip_angle_2))]) + dip_pos_2\n",
"#print b([dip_pos_1,dip_pos_2], [dip_pos_1_v,dip_pos_2_v],13)\n",
"bb = b([dip_pos_1], [dip_pos_1_v], 15 )\n",
"sol = np.linalg.solve(aa,bb)\n",
"print sol\n",
"pot = np.zeros((20,20))\n",
"for i in range(20):\n",
" for j in range(20):\n",
" pot[i,j] = estimator([i/2.,j/2.],[dip_pos_1,dip_pos_2], \n",
" [layer_1,], sol, verbose = 0)\n",
"\n",
" \n",
"plt.arrow(dip_pos_1[0],dip_pos_1[1], dip_pos_1_v[0]-dip_pos_1[0],\n",
" dip_pos_1_v[1]-dip_pos_1[1], head_width = 0.2)\n",
"plt.arrow(dip_pos_2[0],dip_pos_2[1],dip_pos_2_v[0]-dip_pos_2[0], \n",
" dip_pos_2_v[1]-dip_pos_2[1], head_width = 0.2)\n",
"plt.plot(layer_1[:,0],layer_1[:,1], \"o\")\n",
"plt.plot(layer_2[:,0],layer_2[:,1], \"o\")\n",
"plt.plot(layer_1[:,0],layer_1[:,1], )\n",
"plt.plot(layer_2[:,0],layer_2[:,1], )\n",
"plt.contour(pot,20, extent = (0,10,0,10) )\n",
"plt.colorbar()\n",
"plt.xlim(0,10)\n",
"plt.ylim(0,10)\n",
"print dip_pos_1_v, dip_pos_2_v, layer_1"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"plt.arrow?"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": []
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Normal Universal cookriging"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"def G_f(dips,x):\n",
" dips = np.asarray(dips)\n",
" \n",
" a = np.asarray(dips)\n",
" b = np.asarray(x)\n",
" # print a, a[:,0]\n",
" # print b,b[:,0]\n",
" Gx = b[0] - a[:,0]\n",
" Gy = b[1] -a[:,1] \n",
" G = np.hstack((Gx,Gy))\n",
" \n",
" return G\n",
"\n",
"def b(x, dips,n):\n",
" n -= len(dips)*2 # because x and y direction \n",
" G = G_f(dips,x)\n",
" b = np.hstack((G, np.zeros(n)))\n",
" return b,G"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"b([1,1],[dip_pos_1,dip_pos_2],13)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"bb,g = b([1,1],[dip_pos_1,dip_pos_2],13)\n",
"len(bb)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"sol = np.linalg.solve(aa,bb)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"sol"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"dip_pos_1, dip_pos_2"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"z1 = dip_pos_1_v - dip_pos_1\n",
"z2 = dip_pos_2_v - dip_pos_2\n",
"print z1, z2"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"g"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": []
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"#=====================\n",
"# THE GRADIENTS\n",
"\n",
"def h_f(dips, direct):\n",
"\n",
" if direct == \"x\":\n",
" return np.abs(np.subtract.outer(dips[:,0],dips[:,0]))\n",
" if direct == \"y\":\n",
" return np.abs(np.subtract.outer(dips[:,1],dips[:,1]))\n",
"\n",
"def C_G(dips, sig_z = 1., a = 6., nugget= 0.01):\n",
" dips = np.asarray(dips)\n",
" r = me.euclidean_distances(dips)\n",
" for i in \"xy\":\n",
" for j in \"xy\":\n",
" if j == \"x\":\n",
" h1 = h_f(dips, direct = i)\n",
" h2 = h_f(dips, direct = j)\n",
" # print h1,h2\n",
" C_G_row = (sig_z*h1*h2/a**2/r**2*\n",
" (1/r*cov_cubic_d1_f(r)-cov_cubic_d2_f(r)))\n",
" # print 1/r*cov_cubic_d1_f(r), cov_cubic_d2_f(r)\n",
" else:\n",
" h1 = h_f(dips, direct = i)\n",
" h2 = h_f(dips, direct = j)\n",
" C_G_row = np.hstack((C_G_row, (sig_z*h1*h2/a**2/r**2*\n",
" (1/r*cov_cubic_d1_f(r)-cov_cubic_d2_f(r)))))\n",
" \n",
" if i == \"x\":\n",
" C_G = C_G_row\n",
" else:\n",
" C_G = np.vstack((C_G, C_G_row))\n",
" return np.nan_to_num(C_G)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Estimator geomodeller (maybe)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"def estimator(x, dips, layers, sol, sig_z = 1., a = 6., alpha = 1, verbose = 0):\n",
" x = np.asarray(x).reshape(1,-1)\n",
" dips = np.asarray(dips)\n",
" layers = np.asarray(layers)\n",
" n = 0\n",
" m = len(dips)\n",
" # print layers\n",
" # print x.reshape(1,-1), dips\n",
" r_i = me.euclidean_distances(dips,x)\n",
" hx = h_f_GI(dips, x, \"x\")\n",
" Cov_d1 = cov_cubic_d1_f(r_i)\n",
" KzGx = sol[:m] * np.squeeze(alpha * sig_z / a**2 * hx / r_i * Cov_d1)\n",
" hy = h_f_GI(dips, x, \"y\")\n",
" KzGy = sol[m:2*m] * np.squeeze(alpha * sig_z / a**2 * hy / r_i * Cov_d1)\n",
" \n",
" for s in range(len(layers)):\n",
" n += len(layers[s][1:])\n",
" a = cov_cubic_layer(x, layers[s][1:])\n",
" b = cov_cubic_layer(x, layers[s][0].reshape(1,-1))\n",
" # print a,b\n",
" if s == 0:\n",
" L = np.array(sol[2*m:2*m+n]*(a-b))\n",
" else:\n",
" L = np.hstack((L,sol[2*m+n2:2*m+n]*(a-b)))\n",
" n2 = n \n",
" L = np.squeeze(L)\n",
" # print m,n\n",
" univ = (sol[2*m+n]*x[0,0]**2 + sol[2*m+n+1] * x[0,1]**2 \n",
" + sol[2*m+n+2]* x[0,0]*x[0,1] \n",
" + sol[2*m+n+3] * x[0,0]\n",
" + sol[2*m+n+4] * x[0,1])\n",
" \n",
" if verbose != 0:\n",
" print KzGx, KzGy, L, univ\n",
" z_star = np.sum(KzGx)+np.sum(KzGy)+np.sum(L)+univ\n",
" return z_star"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": []
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"#========================================\n",
"#THE INTERACTION GRADIENTS/INTERFACES\n",
"\n",
"def h_f_GI(dips, layers, direct):\n",
" if direct == \"x\":\n",
" return (np.subtract.outer(dips[:,0],layers[:,0]))\n",
" if direct == \"y\":\n",
" return (np.subtract.outer(dips[:,1],layers[:,1]))\n",
" \n",
"def C_GI(dips,layers, sig_z = 1., a = 6., alpha = 14, verbose = 0):\n",
" dips = np.asarray(dips)\n",
" layers = np.asarray(layers)\n",
" for k in range(len(layers)):\n",
" for i in \"xy\":\n",
" r = me.euclidean_distances(dips,layers[k])\n",
" h1 = h_f_GI(dips,layers[k], i)\n",
" \n",
" Cov_d1 = cov_cubic_d1_f(r)\n",
" if verbose != 0:\n",
" print \"dips\", dips\n",
" print \"layers\", layers\n",
" print \"h1\", h1, h1[:,0]\n",
" print \"\"\n",
" print \"r\", r, r[:,0]\n",
" print \"\"\n",
" print \"Cov_d1\", Cov_d1\n",
" if i == \"x\":\n",
" cov_1 = alpha * sig_z / a**2 * h1[:,0] / r[:,0] * Cov_d1[:,0]\n",
" cov_j = alpha * sig_z / a**2 * h1[:,1:] / r[:,1:] * Cov_d1[:,1:]\n",
" # C_GI_row = alpha * sig_z / a**2 * h1 / r * Cov_d1\n",
" #print \"cov_j, cov_1\", cov_j, cov_1.reshape(-1,1)\n",
" # pdb.set_trace()\n",
" C_GI_row = cov_j.transpose()-cov_1#.transpose()\n",
" else:\n",
" cov_1 = alpha * sig_z / a**2 * h1[:,0] / r[:,0] * Cov_d1[:,0]\n",
" cov_j = alpha * sig_z / a**2 * h1[:,1:] / r[:,1:] * Cov_d1[:,1:]\n",
" #C_GI_row = np.hstack((C_GI_row,\n",
" # alpha * sig_z / a**2 * h1 / r * Cov_d1))\n",
" #pdb.set_trace()\n",
" C_GI_row = np.hstack((C_GI_row, cov_j.transpose()-cov_1))\n",
" #.reshape(-1,1)))\n",
" if k==0:\n",
" C_GI = C_GI_row\n",
" else:\n",
" #pdb.set_trace()\n",
" C_GI = np.vstack((C_GI,C_GI_row))\n",
" \n",
" return C_GI"
]
}
],
"metadata": {
"anaconda-cloud": {},
"kernelspec": {
"display_name": "Python [Root]",
"language": "python",
"name": "Python [Root]"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.5.2"
},
"latex_envs": {
"bibliofile": "biblio.bib",
"cite_by": "apalike",
"current_citInitial": 1,
"eqLabelWithNumbers": true,
"eqNumInitial": 0
},
"nav_menu": {},
"toc": {
"navigate_menu": true,
"number_sections": true,
"sideBar": true,
"threshold": 6,
"toc_cell": false,
"toc_section_display": "block",
"toc_window_display": false
}
},
"nbformat": 4,
"nbformat_minor": 0
}
| mit |
a-mt/dev-roadmap | docs/!ml/notebooks/Decision Tree Regressor.ipynb | 1 | 459334 | {
"cells": [
{
"cell_type": "code",
"execution_count": 1,
"metadata": {},
"outputs": [],
"source": [
"import pandas as pd\n",
"import numpy as np"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Load data\n",
"\n",
"Using petrol consumption: https://www.kaggle.com/arpikr/petrol-consumption"
]
},
{
"cell_type": "code",
"execution_count": 2,
"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>Petrol_tax</th>\n",
" <th>Average_income</th>\n",
" <th>Paved_Highways</th>\n",
" <th>Population_Driver_licence(%)</th>\n",
" <th>Petrol_Consumption</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>9.0</td>\n",
" <td>3571</td>\n",
" <td>1976</td>\n",
" <td>0.525</td>\n",
" <td>541</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>9.0</td>\n",
" <td>4092</td>\n",
" <td>1250</td>\n",
" <td>0.572</td>\n",
" <td>524</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>9.0</td>\n",
" <td>3865</td>\n",
" <td>1586</td>\n",
" <td>0.580</td>\n",
" <td>561</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>7.5</td>\n",
" <td>4870</td>\n",
" <td>2351</td>\n",
" <td>0.529</td>\n",
" <td>414</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>8.0</td>\n",
" <td>4399</td>\n",
" <td>431</td>\n",
" <td>0.544</td>\n",
" <td>410</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" Petrol_tax Average_income Paved_Highways Population_Driver_licence(%) \\\n",
"0 9.0 3571 1976 0.525 \n",
"1 9.0 4092 1250 0.572 \n",
"2 9.0 3865 1586 0.580 \n",
"3 7.5 4870 2351 0.529 \n",
"4 8.0 4399 431 0.544 \n",
"\n",
" Petrol_Consumption \n",
"0 541 \n",
"1 524 \n",
"2 561 \n",
"3 414 \n",
"4 410 "
]
},
"execution_count": 2,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df = pd.read_csv('petrol_consumption.csv')\n",
"df.head()"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"(48, 5)"
]
},
"execution_count": 3,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df.shape"
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"Petrol_tax float64\n",
"Average_income int64\n",
"Paved_Highways int64\n",
"Population_Driver_licence(%) float64\n",
"Petrol_Consumption int64\n",
"dtype: object"
]
},
"execution_count": 4,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df.dtypes"
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"(48, 4) (48,)\n"
]
}
],
"source": [
"X = df.drop('Petrol_Consumption', axis=1)\n",
"y = df['Petrol_Consumption']\n",
"\n",
"print(X.shape, y.shape)"
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {},
"outputs": [],
"source": [
"from sklearn.model_selection import train_test_split"
]
},
{
"cell_type": "code",
"execution_count": 9,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"(43, 4) (5, 4)\n"
]
}
],
"source": [
"X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=.10, random_state=0)\n",
"\n",
"print(X_train.shape, X_test.shape)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Using sklearn"
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {},
"outputs": [],
"source": [
"from sklearn.tree import DecisionTreeRegressor"
]
},
{
"cell_type": "code",
"execution_count": 10,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"DecisionTreeRegressor(criterion='mse', max_depth=None, max_features=None,\n",
" max_leaf_nodes=None, min_impurity_decrease=0.0,\n",
" min_impurity_split=None, min_samples_leaf=1,\n",
" min_samples_split=2, min_weight_fraction_leaf=0.0,\n",
" presort=False, random_state=None, splitter='best')"
]
},
"execution_count": 10,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"regressor = DecisionTreeRegressor()\n",
"regressor.fit(X_train, y_train)"
]
},
{
"cell_type": "code",
"execution_count": 12,
"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>Actual</th>\n",
" <th>Predicted</th>\n",
" <th>Diff</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>29</th>\n",
" <td>534</td>\n",
" <td>487.0</td>\n",
" <td>47.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>410</td>\n",
" <td>524.0</td>\n",
" <td>-114.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>26</th>\n",
" <td>577</td>\n",
" <td>574.0</td>\n",
" <td>3.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>30</th>\n",
" <td>571</td>\n",
" <td>554.0</td>\n",
" <td>17.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>32</th>\n",
" <td>577</td>\n",
" <td>574.0</td>\n",
" <td>3.0</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" Actual Predicted Diff\n",
"29 534 487.0 47.0\n",
"4 410 524.0 -114.0\n",
"26 577 574.0 3.0\n",
"30 571 554.0 17.0\n",
"32 577 574.0 3.0"
]
},
"execution_count": 12,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"y_pred = regressor.predict(X_test)\n",
"\n",
"pd.DataFrame({\n",
" 'Actual': y_test,\n",
" 'Predicted': y_pred,\n",
" 'Diff': y_test - y_pred\n",
"})"
]
},
{
"cell_type": "code",
"execution_count": 14,
"metadata": {},
"outputs": [],
"source": [
"from sklearn.metrics import mean_squared_error"
]
},
{
"cell_type": "code",
"execution_count": 16,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"55.69919209467943"
]
},
"execution_count": 16,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"np.sqrt(mean_squared_error(y_test, y_pred))"
]
},
{
"cell_type": "code",
"execution_count": 20,
"metadata": {},
"outputs": [
{
"data": {
"image/png": "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\n",
"text/plain": [
"<IPython.core.display.Image object>"
]
},
"execution_count": 20,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"from IPython.display import Image \n",
"from sklearn.externals.six import StringIO\n",
"from sklearn.tree import export_graphviz\n",
"import pydotplus\n",
"\n",
"dot_data = StringIO() \n",
"export_graphviz(regressor, out_file=dot_data, \n",
" feature_names=X.columns) \n",
"graph = pydotplus.graph_from_dot_data(dot_data.getvalue()) \n",
"Image(graph.create_png())"
]
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.7.3"
}
},
"nbformat": 4,
"nbformat_minor": 2
}
| mit |
Zhang-O/small | tensor__cpu/Numba/getstarted.ipynb | 1 | 16036 | {
"cells": [
{
"cell_type": "code",
"execution_count": 24,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"from numba import jit\n",
"\n",
"\n",
"@jit\n",
"def f(x, y):\n",
" return x + y"
]
},
{
"cell_type": "code",
"execution_count": 25,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"The slowest run took 132984.30 times longer than the fastest. This could mean that an intermediate result is being cached.\n",
"1000000 loops, best of 3: 202 ns per loop\n"
]
}
],
"source": [
"%timeit f(1, 2)"
]
},
{
"cell_type": "code",
"execution_count": 8,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"(2+1j)"
]
},
"execution_count": 8,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"f(1j, 2)"
]
},
{
"cell_type": "code",
"execution_count": 20,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"from numba import int32\n",
"\n",
"@jit(int32(int32, int32))\n",
"def f1(x, y):\n",
" return x + y"
]
},
{
"cell_type": "code",
"execution_count": 22,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"The slowest run took 29.61 times longer than the fastest. This could mean that an intermediate result is being cached.\n",
"1000000 loops, best of 3: 213 ns per loop\n"
]
}
],
"source": [
"%timeit f1(1,2)"
]
},
{
"cell_type": "code",
"execution_count": 14,
"metadata": {},
"outputs": [
{
"ename": "TypeError",
"evalue": "No matching definition for argument type(s) complex128, int64",
"output_type": "error",
"traceback": [
"\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
"\u001b[1;31mTypeError\u001b[0m Traceback (most recent call last)",
"\u001b[1;32m<ipython-input-14-19300b83c20f>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m()\u001b[0m\n\u001b[1;32m----> 1\u001b[1;33m \u001b[0mf1\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;36m1j\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;36m2\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m",
"\u001b[1;32mC:\\ProgramData\\Anaconda3\\lib\\site-packages\\numba\\dispatcher.py\u001b[0m in \u001b[0;36m_explain_matching_error\u001b[1;34m(self, *args, **kws)\u001b[0m\n\u001b[0;32m 397\u001b[0m msg = (\"No matching definition for argument type(s) %s\"\n\u001b[0;32m 398\u001b[0m % ', '.join(map(str, args)))\n\u001b[1;32m--> 399\u001b[1;33m \u001b[1;32mraise\u001b[0m \u001b[0mTypeError\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mmsg\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 400\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 401\u001b[0m \u001b[1;32mdef\u001b[0m \u001b[0m_search_new_conversions\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m*\u001b[0m\u001b[0margs\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m**\u001b[0m\u001b[0mkws\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
"\u001b[1;31mTypeError\u001b[0m: No matching definition for argument type(s) complex128, int64"
]
}
],
"source": [
"f1(1j, 2)"
]
},
{
"cell_type": "code",
"execution_count": 23,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"The slowest run took 12.92 times longer than the fastest. This could mean that an intermediate result is being cached.\n",
"1000000 loops, best of 3: 275 ns per loop\n"
]
}
],
"source": [
"%timeit f(2**32, 2**31 + 1)"
]
},
{
"cell_type": "code",
"execution_count": 26,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"\n",
"正在 Ping www.bbc.net.uk [212.58.246.93] 具有 32 字节的数据:\n",
"来自 212.58.246.93 的回复: 字节=32 时间=280ms TTL=46\n",
"来自 212.58.246.93 的回复: 字节=32 时间=318ms TTL=46\n",
"来自 212.58.246.93 的回复: 字节=32 时间=311ms TTL=46\n",
"来自 212.58.246.93 的回复: 字节=32 时间=300ms TTL=46\n",
"\n",
"212.58.246.93 的 Ping 统计信息:\n",
" 数据包: 已发送 = 4，已接收 = 4，丢失 = 0 (0% 丢失)，\n",
"往返行程的估计时间(以毫秒为单位):\n",
" 最短 = 280ms，最长 = 318ms，平均 = 302ms\n"
]
}
],
"source": [
"!ping www.bbc.co.uk"
]
},
{
"cell_type": "code",
"execution_count": 27,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"from IPython.display import display"
]
},
{
"cell_type": "code",
"execution_count": 28,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"<function print>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"display(print)"
]
},
{
"cell_type": "code",
"execution_count": 31,
"metadata": {},
"outputs": [],
"source": [
"import numpy as np\n",
"from numba import generated_jit, types\n",
"\n",
"@ generated_jit(nopython=True)\n",
"def is_missing(x):\n",
" if isinstance(x, types.Float):\n",
" return lambda x: np.isnan(x)\n",
" elif isinstance(x, types.NPDatetime, types.NPTimedelta):\n",
" missing = x(NaT)\n",
" return lambda x: x == missing\n",
" else:\n",
" return lambda: False"
]
},
{
"cell_type": "code",
"execution_count": 34,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"True"
]
},
"execution_count": 34,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"is_missing(np.NaN)"
]
},
{
"cell_type": "code",
"execution_count": 36,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"from numba import vectorize, float64\n",
"\n",
"@vectorize([float64(float64, float64)])\n",
"def f3(x, y):\n",
" return x + y"
]
},
{
"cell_type": "code",
"execution_count": 38,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"5.3300000000000001"
]
},
"execution_count": 38,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"f3(2.0, 3.33)"
]
},
{
"cell_type": "code",
"execution_count": 42,
"metadata": {},
"outputs": [],
"source": [
"from numba import float64, int64, float32\n",
"\n",
"@vectorize([int32(int32, int32),\n",
" int64(int64, int64),\n",
" float32(float32, float32),\n",
" float64(float64, float64)])\n",
"def f(x, y):\n",
" return x + y"
]
},
{
"cell_type": "code",
"execution_count": 48,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"array([ 0., 1., 2., 3., 4., 5.])"
]
},
"execution_count": 48,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"a = np.arange(6,dtype='float64')\n",
"a"
]
},
{
"cell_type": "code",
"execution_count": 49,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"array([ 0., 2., 4., 6., 8., 10.])"
]
},
"execution_count": 49,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"f(a,a)"
]
},
{
"cell_type": "code",
"execution_count": 50,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"array([ 0.0+0.j , 0.2+0.2j, 0.4+0.4j, 0.6+0.6j, 0.8+0.8j, 1.0+1.j ])"
]
},
"execution_count": 50,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"a = np.linspace(0, 1+1j, 6)\n",
"a"
]
},
{
"cell_type": "code",
"execution_count": 51,
"metadata": {},
"outputs": [
{
"ename": "TypeError",
"evalue": "ufunc 'f' not supported for the input types, and the inputs could not be safely coerced to any supported types according to the casting rule ''safe''",
"output_type": "error",
"traceback": [
"\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
"\u001b[1;31mTypeError\u001b[0m Traceback (most recent call last)",
"\u001b[1;32m<ipython-input-51-12d30bce134a>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m()\u001b[0m\n\u001b[1;32m----> 1\u001b[1;33m \u001b[0mf\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0ma\u001b[0m\u001b[1;33m,\u001b[0m\u001b[0ma\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m",
"\u001b[1;31mTypeError\u001b[0m: ufunc 'f' not supported for the input types, and the inputs could not be safely coerced to any supported types according to the casting rule ''safe''"
]
}
],
"source": [
"f(a,a)"
]
},
{
"cell_type": "code",
"execution_count": 52,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"a = np.arange(12).reshape(3,4)"
]
},
{
"cell_type": "code",
"execution_count": 53,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"array([[ 0, 1, 2, 3],\n",
" [ 4, 5, 6, 7],\n",
" [ 8, 9, 10, 11]])"
]
},
"execution_count": 53,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"a"
]
},
{
"cell_type": "code",
"execution_count": 54,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"array([[ 0, 2, 4, 6],\n",
" [ 8, 10, 12, 14],\n",
" [16, 18, 20, 22]])"
]
},
"execution_count": 54,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"f(a, a)"
]
},
{
"cell_type": "code",
"execution_count": 55,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"array([12, 15, 18, 21])"
]
},
"execution_count": 55,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"f.reduce(a, axis=0) # 降维 "
]
},
{
"cell_type": "code",
"execution_count": 56,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"array([ 6, 22, 38])"
]
},
"execution_count": 56,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"f.reduce(a, axis=1)"
]
},
{
"cell_type": "code",
"execution_count": 58,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"array([[ 0, 1, 3, 6],\n",
" [ 4, 9, 15, 22],\n",
" [ 8, 17, 27, 38]])"
]
},
"execution_count": 58,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"f.accumulate(a, axis=1)"
]
},
{
"cell_type": "code",
"execution_count": 59,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"array([[ 0, 1, 2, 3],\n",
" [ 4, 6, 8, 10],\n",
" [12, 15, 18, 21]])"
]
},
"execution_count": 59,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"f.accumulate(a)"
]
},
{
"cell_type": "code",
"execution_count": 63,
"metadata": {},
"outputs": [],
"source": [
"from numba import guvectorize\n",
"\n",
"@guvectorize([(int64[:], int64[:], int64[:])], '(n),()->(n)', nopython=True)\n",
"def g(x, y, res):\n",
" for i in range(x.shape[0]):\n",
" res[i] = x[i] + y[0]"
]
},
{
"cell_type": "code",
"execution_count": 65,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"array([[10, 11, 12],\n",
" [13, 14, 15]], dtype=int64)"
]
},
"execution_count": 65,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"a = np.arange(6).reshape(2, 3)\n",
"g(a, 10)"
]
},
{
"cell_type": "code",
"execution_count": 66,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"from numba import cfunc\n",
"\n",
"@cfunc(\"float64(float64, float64)\")\n",
"def add(x, y):\n",
" return x + y"
]
},
{
"cell_type": "code",
"execution_count": 67,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"9.0\n"
]
}
],
"source": [
"@cfunc(\"float64(float64, float64)\")\n",
"def add(x, y):\n",
" return x + y\n",
"\n",
"print(add.ctypes(4.0, 5.0)) # prints \"9.0\""
]
},
{
"cell_type": "code",
"execution_count": 68,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"from numba.pycc import CC\n",
"\n",
"cc = CC('my_module')\n",
"# Uncomment the following line to print out the compilation steps\n",
"#cc.verbose = True\n",
"\n",
"@cc.export('multf', 'f8(f8, f8)')\n",
"@cc.export('multi', 'i4(i4, i4)')\n",
"def mult(a, b):\n",
" return a * b\n",
"\n",
"@cc.export('square', 'f8(f8)')\n",
"def square(a):\n",
" return a ** 2\n",
"cc.compile()"
]
},
{
"cell_type": "code",
"execution_count": 69,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"12"
]
},
"execution_count": 69,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"import my_module\n",
"my_module.multi(3, 4)"
]
},
{
"cell_type": "code",
"execution_count": 70,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"1.9993959999999997"
]
},
"execution_count": 70,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"my_module.square(1.414)"
]
},
{
"cell_type": "code",
"execution_count": 72,
"metadata": {},
"outputs": [
{
"ename": "AttributeError",
"evalue": "'DUFunc' object has no attribute 'inspect_types'",
"output_type": "error",
"traceback": [
"\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
"\u001b[1;31mAttributeError\u001b[0m Traceback (most recent call last)",
"\u001b[1;32m<ipython-input-72-d5cd027f6f78>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m()\u001b[0m\n\u001b[1;32m----> 1\u001b[1;33m \u001b[0mf\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0minspect_types\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m",
"\u001b[1;31mAttributeError\u001b[0m: 'DUFunc' object has no attribute 'inspect_types'"
]
}
],
"source": [
"f.inspect_types()"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"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
}
| mit |
utensil/julia-playground | dl/hello_captcha.ipynb | 1 | 1052496 | null | mit |
MarsUniversity/ece387 | website/block_1_basics/lsn3/lsn3.ipynb | 1 | 37425 | {
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Python\n",
"\n",
"Kevin J. Walchko\n",
"\n",
"created 16 Nov 2017\n",
"\n",
"----\n",
"\n",
"Here we will use python as our programming language. Python, like any other language, is really vast and complex. We will just cover the basics we need.\n",
"\n",
"## Objectives\n",
"\n",
"- Understand\n",
" - general syntax\n",
" - for/while loops\n",
" - if/elif/else\n",
" - functions\n",
" - data types: tuples, list, strings, etc\n",
" - intro to classes\n",
"\n",
"## References\n",
"\n",
"- [Python tutorialspoint](https://www.tutorialspoint.com/python/)\n",
"- [Python classes/objects](https://www.tutorialspoint.com/python/python_classes_objects.htm)\n",
"\n",
"## Setup"
]
},
{
"cell_type": "code",
"execution_count": 1,
"metadata": {},
"outputs": [],
"source": [
"from __future__ import print_function\n",
"from __future__ import division\n",
"import numpy as np"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Python\n",
"\n",
"Python is a widely used high-level programming language for general-purpose programming, created by Guido van Rossum and first released in 1991. An interpreted language, Python has a design philosophy which emphasizes code readability (notably using whitespace indentation to delimit code blocks rather than curly brackets or keywords), and a syntax which allows programmers to express concepts in fewer lines of code than might be used in languages such as C++ or Java. The language provides constructs intended to enable writing clear programs on both a small and large scale.\n",
"\n",
"<img src=\"rossum.png\" width=\"300px\">\n",
"\n",
"### Python’s Benevolent Dictator For Life!\n",
"\n",
" “Python is an experiment in how much freedom program-mers need. Too much freedom and nobody can read another's code; too little and expressive-ness is endangered.”\n",
" - Guido van Rossum \n",
"\n",
"## Why Use It?\n",
"\n",
"- Simple and easy to use and very efficient\n",
" - What you can do in a 100 lines of python could take you a 1000 in C++ … this is the reason many startups (e.g., Instagram) use python and keep using it\n",
"\n",
"- 90% of robotics uses either C++ or python\n",
" - Although C++ is faster in run-time, development (write, compile, link, etc) is much slower due to complex syntax, memory management, pointers (they can be fun!) and difficulty in debugging any sort of real program\n",
" - Java is dying (or dead)\n",
" - Microsoft is still struggling to get people outside of the Windows OS to embrace C#\n",
" - Apple's swift is too new and constantly making major changes ... maybe some day\n",
"\n",
"## Who Uses It?\n",
"\n",
"- Industrial Light & Magic (Stars Wars people): used in post production scripting to tie together outputs from other C++ programs\n",
"- Eve-Online (big MMORGP game): used for both client and server aspects of the game\n",
"- Instagram, Spotify, SurveyMonkey, The Onion, Bitbucket, Pinterest, and more use Django (python website template framework) to create/serve millions of users\n",
"- Dropbox, Paypal, Walmart and Google (YouTube)\n",
" - Note: Guido van Rossum worked for Google and now works for Dropbox\n",
"\n",
"## Running Programs on UNIX (or your robot)\n",
"\n",
"- Call python program via the python interpreter: `python my_program.py`\n",
" - This is kind of the stupid way\n",
"- Make a python file directly executable \n",
" - Add a shebang (it’s a Unix thing) to the top of your program: `#!/usr/bin/env python`\n",
" - Make the file executable: `chmod a+x my_program.py`\n",
" - Invoke file from Unix command line: `./my_program.py`\n",
"\n",
"## Enough to Understand Code (Short Version)\n",
"\n",
"- Indentation matters for functions, loops, classes, etc\n",
"- First assignment to a variable creates it\n",
"- Variable types (int, float, etc) don’t need to be declared. \n",
"- Assignment is = and comparison is ==\n",
"- For numbers + - * % are as expected\n",
" - modulas (%) returns the remainder: 5%3 => 2\n",
"- Logical operators are words (and, or, not) not symbols\n",
"- We are using `__future__` for python 2 / 3 compatibility\n",
" - The basic printing command is print(‘hello’)\n",
" - Division works like expected:\n",
" - Float division: 5/2 = 2.5\n",
" - Integer division: 5//2 = 2\n",
"- Start comments with #, rest of line is ignored\n",
"- Can include a “documentation string” as the first line of a new function or class you define\n",
"\n",
"```python\n",
"def my_function(n):\n",
" \"\"\"\n",
" my_function(n) takes a positive integer and returns n + 5\n",
" \"\"\"\n",
" # assert ... remember this from ECE281?\n",
" assert n>0, \"crap, n is 0 or negative!\"\n",
" \n",
"\treturn n+5\n",
"```"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Printing\n",
"\n",
"Again, to have Python 3 compatability and help you in the future, we are going to print things using the print function. Python 2 by default uses a print statement. Also, it is good form to use the newer `format()` function on strings rather than the old C style `%s` for a string or `%d` for an integer. There are lots of cool things you can do with `format()` but we won't dive too far into it ... just the basics.\n",
"\n",
"**WARNING:** Your homework with Code Academy uses the old way to `print`, just do it for that and get through it. For this class we are doing it this way!"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"from __future__ import division # fix division\n",
"from __future__ import print_function # use print function\n",
"\n",
"print('hello world') # single quotes\n",
"print(\"hello world\") # double quotes\n",
"print('3/4 is', 3/4) # this prints 0.75\n",
"print('I am {} ... for {} yrs I have been training Jedhi'.format(\"Yoda\", 853))\n",
"print('float: {:5.1f}'.format(3.1424567)) # prints float: 3.1"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Unicode\n",
"\n",
"Unicode sucks in python 2.7, but if you want to use it:\n",
"\n",
"- [alphabets](https://en.wikipedia.org/wiki/List_of_Unicode_characters)\n",
"- [arrows](https://en.wikipedia.org/wiki/List_of_Unicode_characters#Arrows)\n",
"- [emoji](https://en.wikipedia.org/wiki/Emoji#Unicode_blocks)"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"⇦ ⇧ ⇨ ⇩\n",
"☠\n",
"\n",
"You must go ⇧\n"
]
}
],
"source": [
"print(u'\\u21e6 \\u21e7 \\u21e8 \\u21e9')\n",
"print(u'\\u2620')\n",
"\n",
"# this is a dictionary, we will talk about it next ... sorry for the out of order\n",
"uni = {\n",
" 'left': u'\\u21e6',\n",
" 'up': u'\\u21e7',\n",
" 'right': u'\\u21e8',\n",
" 'down': u'\\u21e9',\n",
"}\n",
"print(u'\\nYou must go {}'.format(uni['up'])) # notice all strings have u on the front"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Data Types\n",
"\n",
"Python isn't typed, so you don't really need to keep track of variables and delare them as ints, floats, doubles, unsigned, etc. There are a few places where this isn't true, but we will deal with those as we encounter them."
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"z = 2.5\n"
]
}
],
"source": [
"# bool\n",
"z = True # or False\n",
"\n",
"# integers (default)\n",
"z = 3\n",
"\n",
"# floats\n",
"z = 3.124\n",
"z = 5/2\n",
"print('z =', z)"
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"bob[\"a\"]: 5\n",
"{'a': 5, 'c': 'this is a string!!', 'b': 6}\n",
"len(bob) = 3\n",
"bob.keys() = ['a', 'c', 'b']\n"
]
}
],
"source": [
"# dictionary or hash tables\n",
"bob = {'a': 5, 'b': 6}\n",
"print('bob[\"a\"]:', bob['a'])\n",
"\n",
"# you can assign a new key/values pair\n",
"bob['c'] = 'this is a string!!'\n",
"print(bob)\n",
"print('len(bob) =', len(bob))\n",
"\n",
"# you can also access what keys are in a dict\n",
"print('bob.keys() =', bob.keys())"
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"bob = {'a': True, 11: [1, 2, 3]}\n",
"bob[11] = [1, 2, 3]\n"
]
}
],
"source": [
"# let's get crazy and do different types and have a key that is an int\n",
"bob = {'a': True, 11: [1,2,3]}\n",
"print('bob = ', bob)\n",
"print('bob[11] = ', bob[11]) # don't do this, it is confusing!!"
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"bob list [1, 2, 'tom', 4, 5]\n",
"bob list[3]: 4\n",
"bob one-liner version 2: [1, 1, 1, 1, 1]\n",
"len(bob) = 5\n"
]
}
],
"source": [
"# arrays or lists are mutable (changable)\n",
"# the first element is 0 like all good programming languages\n",
"bob = [1,2,3,4,5]\n",
"bob[2] = 'tom'\n",
"print('bob list', bob)\n",
"print('bob list[3]:', bob[3]) # remember it is zero indexed\n",
"\n",
"# or ... tuple will do this too\n",
"bob = [1]*5\n",
"print('bob one-liner version 2:', bob)\n",
"print('len(bob) =', len(bob))"
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"my crazy string: hello world\n",
"formatting: 3.1234 6.67\n",
"len(z) = 11\n"
]
}
],
"source": [
"# strings\n",
"z = 'hello world!!'\n",
"z = 'hello' + ' world' # concatinate\n",
"z = 'hhhello world!@#$'[2:13] # strings are just an array of letters\n",
"print('my crazy string:', z)\n",
"print('{}: {} {:.2f}'.format('formatting', 3.1234, 6.6666))\n",
"print('len(z) =', len(z))"
]
},
{
"cell_type": "code",
"execution_count": 8,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"bob tuple (1, 2, 3, 4)\n",
"bob tuple*3 (1, 2, 3, 4, 1, 2, 3, 4, 1, 2, 3, 4)\n",
"len(bob) = 4\n"
]
}
],
"source": [
"# tuples are immutable (not changable which makes them faster/smaller)\n",
"bob = (1,2,3,4)\n",
"print('bob tuple', bob)\n",
"print('bob tuple*3', bob*3) # repeats tuple 3x\n",
"print('len(bob) =', len(bob))"
]
},
{
"cell_type": "code",
"execution_count": 9,
"metadata": {},
"outputs": [
{
"ename": "TypeError",
"evalue": "'tuple' object does not support item assignment",
"output_type": "error",
"traceback": [
"\u001b[1;31m\u001b[0m",
"\u001b[1;31mTypeError\u001b[0mTraceback (most recent call last)",
"\u001b[1;32m<ipython-input-9-dd7609bcd3d2>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m()\u001b[0m\n\u001b[0;32m 1\u001b[0m \u001b[1;31m# since tuples are immutable, this will throw an error\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m----> 2\u001b[1;33m \u001b[0mbob\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;36m1\u001b[0m\u001b[1;33m]\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;34m'tom'\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m",
"\u001b[1;31mTypeError\u001b[0m: 'tuple' object does not support item assignment"
]
}
],
"source": [
"# since tuples are immutable, this will throw an error\n",
"bob[1] = 'tom'"
]
},
{
"cell_type": "code",
"execution_count": 10,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"4 5\n",
"5 4\n"
]
}
],
"source": [
"# assign multiple variables at once\n",
"bob = (4,5,)\n",
"x,y = bob\n",
"print(x,y)\n",
"\n",
"# wait, I changed by mind ... easy to swap\n",
"x,y = y,x\n",
"print(x,y)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Flow Control\n",
"\n",
"## Logic Operators\n",
"\n",
"Flow control is generally done via some math operator or boolean logic operator.\n",
"\n",
"\n",
"\n",
"\n",
"\n",
"## For Loop"
]
},
{
"cell_type": "code",
"execution_count": 11,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"[3, 4, 5, 6, 7, 8, 9]"
]
},
"execution_count": 11,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# range(start, stop, step) # this only works for integer values\n",
"range(3,10) # jupyter cell will always print the last thing"
]
},
{
"cell_type": "code",
"execution_count": 12,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"0\n",
"1\n",
"2\n",
"3\n",
"4\n"
]
}
],
"source": [
"# iterates from start (default 0) to less than the highest number\n",
"for i in range(5):\n",
" print(i)"
]
},
{
"cell_type": "code",
"execution_count": 13,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"bob one-liner: [3, 5, 7, 9]\n"
]
}
],
"source": [
"# you can also create simple arrays like this:\n",
"bob = [2*x+3 for x in range(4)]\n",
"print('bob one-liner:', bob)"
]
},
{
"cell_type": "code",
"execution_count": 14,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"2\n",
"4\n",
"6\n"
]
}
],
"source": [
"for i in range(2,8,2): # start=2, stop<8, step=2, so notice the last value is 6 NOT 8\n",
" print(i)"
]
},
{
"cell_type": "code",
"execution_count": 15,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"d\n",
"e\n",
"f\n",
"c\n"
]
}
],
"source": [
"# I have a list of things ... maybe images or something else.\n",
"# A for loop can iterate through the list. Here, each time \n",
"# through, i is set to the next letter in my array 'dfec'\n",
"things = ['d', 'e', 'f', 'c']\n",
"for ltr in things:\n",
" print(ltr)"
]
},
{
"cell_type": "code",
"execution_count": 16,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"things[0]: d\n",
"things[1]: e\n",
"things[2]: f\n",
"things[3]: 3.14\n"
]
}
],
"source": [
"# enumerate()\n",
"# sometimes you need a counter in your for loop, use enumerate\n",
"things = ['d', 'e', 'f', 3.14] # LOOK! the last element is a float not a letter ... that's OK\n",
"for i, ltr in enumerate(things):\n",
" print('things[{}]: {}'.format(i, ltr))"
]
},
{
"cell_type": "code",
"execution_count": 17,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Bob is 10 yrs old and totally GOOD\n",
"Tom is 20 yrs old and totally EVIL\n",
"Sally is 15 yrs old and totally NICE\n"
]
}
],
"source": [
"# zip()\n",
"# somethimes you have a couple arrays that you want to work on at the same time, use zip\n",
"# to combine them together\n",
"# NOTE: all arrays have to have the SAME LENGTH\n",
"a = ['bob', 'tom', 'sally']\n",
"b = ['good', 'evil', 'nice']\n",
"c = [10, 20, 15]\n",
"\n",
"for name, age, status in zip(a, c, b): # notice I mixed up a, b, c\n",
" status = status.upper()\n",
" name = name[0].upper() + name[1:] # strings are immutable\n",
" print('{} is {} yrs old and totally {}'.format(name, age, status))"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## if / elif / else "
]
},
{
"cell_type": "code",
"execution_count": 18,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"this should print\n"
]
}
],
"source": [
"# classic if/then statements work the same as other languages.\n",
"# if the statement is True, then do something, if it is False, then skip over it.\n",
"if False:\n",
" print('should not get here')\n",
"elif True:\n",
" print('this should print')\n",
"else:\n",
" print('this is the default if all else fails')"
]
},
{
"cell_type": "code",
"execution_count": 19,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"4\n"
]
}
],
"source": [
"n = 5\n",
"n = 3 if n==1 else n-1 # one line if/then statement\n",
"print(n)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## While"
]
},
{
"cell_type": "code",
"execution_count": 20,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"3\n",
"2\n",
"1\n"
]
}
],
"source": [
"x = 3\n",
"while True: # while loop runs while value is True\n",
" if not x: # I will enter this if statement when x = False or 0\n",
" break # breaks me out of a loop\n",
" else:\n",
" print(x)\n",
" x -= 1\n",
" "
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Exception Handling\n",
"\n",
"When you write code you should think about how you could break it, then design it so you can't. Now, you don't necessary need to write bullet proof code ... that takes a lot of time (and time is money), but you should make an effort to reduce your debug time.\n",
"\n",
"A list of Python 2.7 exceptions is [here](https://docs.python.org/2/library/exceptions.html). **KeyboardInterrupt:** is a common one when a user pressed ctl-C to quit the program. Some others:\n",
"\n",
"```\n",
"BaseException\n",
" +-- SystemExit\n",
" +-- KeyboardInterrupt\n",
" +-- GeneratorExit\n",
" +-- Exception\n",
" +-- StopIteration\n",
" +-- StandardError\n",
" | +-- BufferError\n",
" | +-- ArithmeticError\n",
" | | +-- FloatingPointError\n",
" | | +-- OverflowError\n",
" | | +-- ZeroDivisionError\n",
" | +-- AssertionError\n",
" | +-- AttributeError\n",
" | +-- EnvironmentError\n",
" | | +-- IOError\n",
" | | +-- OSError\n",
" | | +-- WindowsError (Windows)\n",
" | | +-- VMSError (VMS)\n",
" | +-- EOFError\n",
" | +-- ImportError\n",
" | +-- LookupError\n",
" | | +-- IndexError\n",
" | | +-- KeyError\n",
" | +-- MemoryError\n",
" | +-- NameError\n",
" | | +-- UnboundLocalError\n",
" | +-- ReferenceError\n",
" | +-- RuntimeError\n",
" | | +-- NotImplementedError\n",
" | +-- SyntaxError\n",
" | | +-- IndentationError\n",
" | | +-- TabError\n",
" | +-- SystemError\n",
" | +-- TypeError\n",
" | +-- ValueError\n",
" | +-- UnicodeError\n",
" | +-- UnicodeDecodeError\n",
" | +-- UnicodeEncodeError\n",
" | +-- UnicodeTranslateError\n",
" +-- Warning\n",
" +-- DeprecationWarning\n",
" +-- PendingDeprecationWarning\n",
" +-- RuntimeWarning\n",
" +-- SyntaxWarning\n",
" +-- UserWarning\n",
" +-- FutureWarning\n",
"\t +-- ImportWarning\n",
"\t +-- UnicodeWarning\n",
"\t +-- BytesWarning\n",
"```"
]
},
{
"cell_type": "code",
"execution_count": 21,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"you idiot ... you cannot modify a tuple!!\n"
]
}
],
"source": [
"# exception handling ... use in your code in smart places\n",
"try:\n",
" a = (1,2,) # tupple ... notice the extra comma after the 2\n",
" a[0] = 1 # this won't work!\n",
"except: # this catches any exception thrown\n",
" print('you idiot ... you cannot modify a tuple!!')"
]
},
{
"cell_type": "code",
"execution_count": 22,
"metadata": {},
"outputs": [
{
"ename": "ZeroDivisionError",
"evalue": "division by zero",
"output_type": "error",
"traceback": [
"\u001b[1;31m\u001b[0m",
"\u001b[1;31mZeroDivisionError\u001b[0mTraceback (most recent call last)",
"\u001b[1;32m<ipython-input-22-e5cc2d71dba7>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m()\u001b[0m\n\u001b[0;32m 1\u001b[0m \u001b[1;31m# error\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m----> 2\u001b[1;33m \u001b[1;36m5\u001b[0m\u001b[1;33m/\u001b[0m\u001b[1;36m0\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m",
"\u001b[1;31mZeroDivisionError\u001b[0m: division by zero"
]
}
],
"source": [
"# error\n",
"5/0"
]
},
{
"cell_type": "code",
"execution_count": 23,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"division by zero\n"
]
}
],
"source": [
"try:\n",
" 5/0\n",
"except ZeroDivisionError as e:\n",
" print(e)\n",
"# raise # this rasies the error to the next \n",
" # level so i don't have to handle it here"
]
},
{
"cell_type": "code",
"execution_count": 24,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"division by zero\n"
]
},
{
"ename": "ZeroDivisionError",
"evalue": "division by zero",
"output_type": "error",
"traceback": [
"\u001b[1;31m\u001b[0m",
"\u001b[1;31mZeroDivisionError\u001b[0mTraceback (most recent call last)",
"\u001b[1;32m<ipython-input-24-0a673480edef>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m()\u001b[0m\n\u001b[0;32m 1\u001b[0m \u001b[1;32mtry\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m----> 2\u001b[1;33m \u001b[1;36m5\u001b[0m\u001b[1;33m/\u001b[0m\u001b[1;36m0\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 3\u001b[0m \u001b[1;32mexcept\u001b[0m \u001b[0mZeroDivisionError\u001b[0m \u001b[1;32mas\u001b[0m \u001b[0me\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 4\u001b[0m \u001b[1;32mprint\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0me\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 5\u001b[0m \u001b[1;32mraise\u001b[0m \u001b[1;31m# this rasies the error to the next (in this case, the Jupyter GUI handles it)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
"\u001b[1;31mZeroDivisionError\u001b[0m: division by zero"
]
}
],
"source": [
"try:\n",
" 5/0\n",
"except ZeroDivisionError as e:\n",
" print(e)\n",
" raise # this rasies the error to the next (in this case, the Jupyter GUI handles it)\n",
" # level so i don't have to handle it here"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"- When would you want to use `raise`?\n",
"- Why not *always* handle the error here?\n",
"- What is different when the `raise` command is used?"
]
},
{
"cell_type": "code",
"execution_count": 25,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"division by zero\n"
]
}
],
"source": [
"# Honestly, I generally just use Exception from which most other exceptions\n",
"# are derived from, but I am lazy and it works fine for what I do\n",
"try:\n",
" 5/0\n",
"except Exception as e:\n",
" print(e)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"# all is right with the world ... these will work, nothing will print\n",
"assert True\n",
"assert 3 > 1"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"# this will fail ... and we can add a message if we want to\n",
"assert 3 < 1, 'hello ... this should fail'"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Libraries\n",
"\n",
"We will need to import `math` to have access to trig and other functions. There will be other libraries like `numpy`, `cv2`, etc you will need to."
]
},
{
"cell_type": "code",
"execution_count": 26,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"messy 0.707106781187\n"
]
}
],
"source": [
"import math\n",
"\n",
"print('messy', math.cos(math.pi/4))"
]
},
{
"cell_type": "code",
"execution_count": 27,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"simpler math: 0.707106781187\n"
]
}
],
"source": [
"# that looks clumbsy ... let's do this instead\n",
"from math import cos, pi\n",
"print('simpler math:', cos(pi/4))\n",
"\n",
"# or we just want to shorten the name to reduce typing ... good programmers are lazy!\n",
"import numpy as np"
]
},
{
"cell_type": "code",
"execution_count": 28,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"['__doc__',\n",
" '__name__',\n",
" '__package__',\n",
" 'acos',\n",
" 'acosh',\n",
" 'asin',\n",
" 'asinh',\n",
" 'atan',\n",
" 'atan2',\n",
" 'atanh',\n",
" 'ceil',\n",
" 'copysign',\n",
" 'cos',\n",
" 'cosh',\n",
" 'degrees',\n",
" 'e',\n",
" 'erf',\n",
" 'erfc',\n",
" 'exp',\n",
" 'expm1',\n",
" 'fabs',\n",
" 'factorial',\n",
" 'floor',\n",
" 'fmod',\n",
" 'frexp',\n",
" 'fsum',\n",
" 'gamma',\n",
" 'hypot',\n",
" 'isinf',\n",
" 'isnan',\n",
" 'ldexp',\n",
" 'lgamma',\n",
" 'log',\n",
" 'log10',\n",
" 'log1p',\n",
" 'modf',\n",
" 'pi',\n",
" 'pow',\n",
" 'radians',\n",
" 'sin',\n",
" 'sinh',\n",
" 'sqrt',\n",
" 'tan',\n",
" 'tanh',\n",
" 'trunc']"
]
},
"execution_count": 28,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# well what is in the math library I might want to use????\n",
"dir(math)"
]
},
{
"cell_type": "code",
"execution_count": 29,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Help on built-in function tanh in module math:\n",
"\n",
"tanh(...)\n",
" tanh(x)\n",
" \n",
" Return the hyperbolic tangent of x.\n",
"\n"
]
}
],
"source": [
"# what is tanh???\n",
"help(math.tanh)"
]
},
{
"cell_type": "code",
"execution_count": 30,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"This module is always available. It provides access to the\n",
"mathematical functions defined by the C standard.\n"
]
}
],
"source": [
"print(math.__doc__) # print the doc string for the library ... what does it do?"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Functions\n",
"\n",
"There isn't too much that is special about python functions, just the format."
]
},
{
"cell_type": "code",
"execution_count": 31,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"4.0"
]
},
"execution_count": 31,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"def my_cool_function(x):\n",
" \"\"\"\n",
" This is my cool function which takes an argument x\n",
" and returns a value\n",
" \"\"\"\n",
" return 2*x/3\n",
"\n",
"my_cool_function(6) # 2*6/3 = 4"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Classes and Object Oriented Programming (OOP)\n",
"\n",
"Ok, we don't have time to really teach you how to do this. It would be better if your real programming classes did this. So we are just going to [kludge](https://www.merriam-webster.com/dictionary/kludge) this together here, because these could be useful in this class. In fact I generally (and 99% of the world) does OOP.\n",
"\n",
"Classes are awesome because of a few reasons. First, they help you reuse code instead of duplicating the code in other places all over your program. Classes will save your life when you realize you want to change a function and you will only change it in one spot instead of 10 different spots with slightly different code. You can also put a bunch of related functions together because they make sense. Another important part of Classes is that they allow you to create more flexible functions.\n",
"\n",
"We are going to keep it simple and basically show you how to do OOP in python very simply. This will be a little familar from ECE382 with structs (sort of)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"class ClassName(object):\n",
" \"\"\"\n",
" So this is my cool class\n",
" \"\"\"\n",
" def __init__(self, x):\n",
" \"\"\"\n",
" This is called a constructor in OOP. When I make an object\n",
" this function is called.\n",
" self = contains all of the objects values\n",
" x = an argument to pass something into the constructor\n",
" \"\"\"\n",
" self.x = x\n",
" print('> Constructor called', x)\n",
" \n",
" def my_cool_function(self, y):\n",
" \"\"\"\n",
" This is called a method (function) that works on\n",
" the class. It always needs self to access class\n",
" values, but can also have as many arguments as you want.\n",
" I only have 1 arg called y\"\"\"\n",
" self.x = y\n",
" print('> called function: {}'.format(self.x))\n",
" \n",
" def __del__(self):\n",
" \"\"\"\n",
" Destructor. This is called when the object goes out of scope\n",
" and is destoryed. It take NO arguments other than self.\n",
" \n",
" Note, this is hard to call in jupyter, because it will probably\n",
" get called with the program (notebook) ends (shutsdown)\n",
" \"\"\"\n",
" pass\n",
"\n",
"a = ClassName('bob')\n",
"a.my_cool_function(3.14)\n",
"\n",
"b = ClassName(28)\n",
"b.my_cool_function('tom')\n",
"\n",
"for i in range(3):\n",
" a = ClassName('bob')"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"There are tons of things you can do with objects. Here is one example. Say we have a ball class and for some reason we want to be able to add balls together."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"class Ball(object):\n",
" def __init__(self, color, radius):\n",
" # this ball always has this color and raduis below\n",
" self.radius = radius\n",
" self.color = color\n",
" \n",
" def __str__(self):\n",
" \"\"\"\n",
" When something tries to turn this object into a string,\n",
" this function gets called\n",
" \"\"\"\n",
" s = 'Ball {}, radius: {:.1f}'.format(self.color, self.radius)\n",
" return s\n",
" \n",
" def __add__(self, a):\n",
" c = Ball('gray', a.radius + self.radius)\n",
" return c\n",
"\n",
"r = Ball('red', 3)\n",
"g = Ball('green', radius=4)\n",
"b = Ball(radius=5, color='blue')\n",
"\n",
"print(r)\n",
"print(g)\n",
"print(b)\n",
"print('total size:', r.radius+b.radius+g.radius)\n",
"print('Add method:', r+b+g)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"# the base class of all objects in Python should be \n",
"# object. It comes with these methods already defined.\n",
"dir(object)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Now you can have classes with functions that make intuitive sense! If I want to calculate the area of a shape, call function `area()`. I don't need a function `areaCircle()` and `areaSquare()`. Or no, maybe the author named the function `area_circle()` or `AreaCircle()` or `areacircle()` or ...\n",
"\n",
"```python\n",
"from math import pi\n",
"\n",
"class Circle(object):\n",
" def __init__(self, radius):\n",
" self.radius = radius\n",
" def area(self):\n",
" return pi*self.radius**2\n",
" \n",
"class Square(object):\n",
" def __init__(self, length, width):\n",
" self.length = length\n",
" self.width = width\n",
" def area(self):\n",
" return length*width\n",
"```"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Exercises\n",
"\n",
"- Please run this notebook and change numbers/variables/etc so you understand how they work ... your grade depends on your understanding!\n",
"\n",
"# Questions\n",
"\n",
"1. What is the difference between `/` and `//`?\n",
"1. How do you use the `.format()` command on a string?\n",
"1. What does mutable/immutable mean for datatypes?\n",
"1. What is a hash table and how do you add new values and retrieve (or access) values in it?\n",
"1. On one line, how would I do a for loop that returns a new array of : [2,4,8,16]?\n",
"1. Write a function that takes a value between [5,-5] and returns the value divided by 2. Make sure to check the input meets the bounds and throws an error if it is wrong\n",
"1. Write a class for a `Circle`. Have the constructor take a radius value and if it is not given, have a default radius of 1.0. Also include 2 methods: area(), circumference(). Make sure it inherits from `object` (the base class)."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"\n",
"-----------\n",
"\n",
"<a rel=\"license\" href=\"http://creativecommons.org/licenses/by-sa/4.0/\"><img alt=\"Creative Commons License\" style=\"border-width:0\" src=\"https://i.creativecommons.org/l/by-sa/4.0/88x31.png\" /></a><br />This work is licensed under a <a rel=\"license\" href=\"http://creativecommons.org/licenses/by-sa/4.0/\">Creative Commons Attribution-ShareAlike 4.0 International License</a>."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": []
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 2",
"language": "python",
"name": "python2"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 2
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython2",
"version": "2.7.14"
}
},
"nbformat": 4,
"nbformat_minor": 2
}
| mit |
idan192/WebStem | iPython/CSE255 - Project 1.ipynb | 1 | 1237657 | null | apache-2.0 |
fmaschler/networkit | Doc/Notebooks/Tutorial_Solutions_Part_2.ipynb | 1 | 2532746 | null | mit |
eduardojvieira/Curso-Python-MEC-UCV | 4-matplotlib.ipynb | 1 | 557136 | {
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"<table width=\"100%\" border=\"0\">\n",
" <tr> \n",
" <td><img src=\"./images/ing.png\" alt=\"\" align=\"left\" /></td>\n",
" <td><img src=\"./images/ucv.png\" alt=\"\" align=\"center\" height=\"100\" width=\"100\" /></td>\n",
" <td><img src=\"./images/mec.png\" alt=\"\" align=\"right\"/></td>\n",
" </tr>\n",
"</table>\n",
"\n",
"<br>\n",
"\n",
"<h1 style=\"text-align: center;\"> Curso de Python para Ingenieros Mecánicos </h1> \n",
"<h3 style=\"text-align: center;\"> Por: Eduardo Vieira</h3>\n",
"<br>\n",
"<br>\n",
"<h1 style=\"text-align: center;\"> Visualización con matplotlib </h1> \n",
"<br>"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"_Después de estudiar la sintaxis de Python y empezar a manejar datos numéricos de manera un poco más profesional, ha llegado el momento de visualizarlos. Con la biblioteca **matplotlib** podemos crear gráficos de muy alta calidad y altamente personalizables._\n",
"\n",
"_matplotlib es una biblioteca muy potente que requiere tiempo de práctica para dominarla. Vamos a empezar por lo más sencillo._"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## ¿Qué es matplotlib?"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"* Estándar *de facto* para visualización en Python\n",
"* Pretende ser similar a las funciones de visualización de MATLAB\n",
"* Diferentes formas de usarla: interfaz `pyplot` y orientada a objetos"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Lo primero que vamos a hacer es activar el modo *inline* - de esta manera las figuras aparecerán automáticamente incrustadas en el notebook."
]
},
{
"cell_type": "code",
"execution_count": 1,
"metadata": {},
"outputs": [],
"source": [
"%matplotlib inline"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Importamos los paquetes necesarios:"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {},
"outputs": [],
"source": [
"import numpy as np\n",
"import matplotlib.pyplot as plt"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"La biblioteca matplotlib es gigantesca y es difícil hacerse una idea global de todas sus posibilidades en una primera toma de contacto. Es recomendable tener a mano la documentación y la galería (http://matplotlib.org/gallery.html#pylab_examples):"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Interfaz pyplot"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"La interfaz `pyplot` proporciona una serie de funciones que operan sobre un *estado global* - es decir, nosotros no especificamos sobre qué gráfica o ejes estamos actuando. Es una forma rápida y cómoda de crear gráficas pero perdemos parte del control."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Función `plot`"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"El paquete `pyplot` se suele importar bajo el alias `plt`, de modo que todas las funciones se acceden a través de `plt.<funcion>`. La función más básica es la función `plot`:"
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"<module 'matplotlib.pyplot' from '/home/juanlu/.local/lib/python3.4/site-packages/matplotlib/pyplot.py'>"
]
},
"execution_count": 5,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"plt"
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"[<matplotlib.lines.Line2D at 0x7f9ff97b70b8>]"
]
},
"execution_count": 6,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"image/png": "iVBORw0KGgoAAAANSUhEUgAAAXUAAAEACAYAAABMEua6AAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nrkJggg==\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x7f9ff9ab6710>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"plt.plot([0.0, 0.1, 0.2, 0.7, 0.9], [1, -2, 3, 4, 1])"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"La función `plot` recibe una sola lista (si queremos especificar los valores *y*) o dos listas (si especificamos *x* e *y*). Naturalmente si especificamos dos listas ambas tienen que tener la misma longitud."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"La tarea más habitual a la hora de trabajar con matplotlib es representar una función. Lo que tendremos que hacer es definir un dominio y evaluarla en dicho dominio. Por ejemplo:"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"$$ f(x) = e^{-x^2} $$"
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {},
"outputs": [],
"source": [
"def f(x):\n",
" return np.exp(-x ** 2)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Definimos el dominio con la función `np.linspace`, que crea un vector de puntos equiespaciados:"
]
},
{
"cell_type": "code",
"execution_count": 8,
"metadata": {},
"outputs": [],
"source": [
"x = np.linspace(-1, 3, 100)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Y representamos la función:"
]
},
{
"cell_type": "code",
"execution_count": 9,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"<matplotlib.text.Text at 0x7f9ff9732fd0>"
]
},
"execution_count": 9,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"image/png": "iVBORw0KGgoAAAANSUhEUgAAAYwAAAEgCAYAAACn50TfAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz\nAAALEgAACxIB0t1+/AAAIABJREFUeJzt3Xd4lFX6xvHvQ+hNQBARKSIgWBBEEXssKLIWbCiCFGVF\n/eHq4qrr2rIgoq6FRcUOiu4Ka8EVrIgGu8BShKUZFQVEEKWJoJCc3x9nYIeQMkkmc2Ym9+e65iIz\n8+adm1fMk3POe84x5xwiIiLFqRQ6gIiIpAYVDBERiYkKhoiIxEQFQ0REYqKCISIiMVHBEBGRmKhg\niIhITFQwREQkJioYIinIzPaL4ZgmZlYzEXmkYlDBECkDM1tgZscX8HpDM5tjZoeWw2e2ArrGcOgP\nwA3x/nypuExLg0g6MbNlwF5AbuQlB7R1zn2fwAyVgVeAO5xzn5bD+e92zt0Y47FHAO2dc+PjnUMq\nnsqhA4jEmQPOcM69GyyAc9uBM+J1PjP7I9AQ+Bb4BFhRgiwzzexqQAVDykxdUlIhmFlepCtnx/On\nzWx41PNlZnadmc0zs/VmNsHMqkW938zMXjazNWa21sxGR33fyVHHtTezbDNbF+muOjPWzygk9x5A\nL+DfwIfAmUBJi+EPZta6hN8jshsVDElHFsMxLvKIfn4BcBqwH9ABGABgZhnAFOBroAXQFJiQ/zxm\nVgWYDLwJNAKuBv5hZm2iPqfAzyjCkcBc59wM59x/gSOAhTH8/aLNAzqX8HtEdqOCIenGgFciv+Gv\nM7OXizk22mjn3PfOuXX4H/wdI693AZoA1zvntjjnfnXOfVzA+boCtZxzdznntjvn3sMXmosj77si\nPmP3cGZHAtcAlc3snMjLNV2+gUczO8vMfmdmd5lZHzN71szaRR2yDti3iOsgEhONYUi6ccDZpRzD\niB4Y3wLsE/m6GfCNcy6vmO/fB1ie77Vvos5T1Gfsxjn3mZltAUZFWhcAGdHHmFlzYKFzLsfMhgF3\nARvw4x3Rn1O1mOwixVLBkIriFyB6TkITdv/hXpjlQHMzy3DO5RZx3HdAMzOzqFZAC2BxIcfHcoti\ne3btgtq+ywmc+xbAzBoDm5xz6/Gtmmh7AD/F8FkiRVKXlFQUc4E+ZpZhZt2B3eZOFGEGsAq4y8xq\nmll1Mzu6gOM+wxemG8ysipll4u+WmlDAsVDMWEukCKzN1wX1vZnVjjqmXWSuRw/g/chrPfKdqgmQ\nU9RnicRCBUMqimvwdxitw48pTCrm+J2D2ZFWxZlAa3xXz3L8nUu7foNzv0WOOx0/ae4h4BLn3NLi\nPqMQRwIf5XttOn5MZYdT8UXJgOpm1hNYk+97OhZwHpESCzJxz8zGAr8D1jjnDinkmNH4//F+AQY4\n5+YkMKJIMGbWGfg9vhtponNuXtR79YA/OeduifFc1YE7nXNDyyWsVCihWhjjgO6FvRlpUrd2zrUB\nLgceSVQwkSSQi5+ctza6WABExijWmlnDGM91EfBYnPNJBRVk0Ns594GZtSzikLOAZyLHfmZm9cys\nsXNudSLyiYTknJuLH3MpzN+BQcATRZ3HzJoB65xzS+IYTyqwZL1Lqim73sGyAn8fuQqGVHiRQfAi\ni0XkuOXEfieYSLGSedA7/x0kWiVRRCSgZG1hrMRPltph38hruzAzFRERkVJwzsWyhM4ukrWF8SrQ\nD8DMugLrCxu/cM4l/eP2228P9tl5eY4pUxxHHeVo08Yxdqxj06bYcv76q+OVVxw9ezoaNXI89JBj\n27aKfT3TKaNyVtycpRWkhWFmzwMnAA3NbDlwO1AFwDn3mHPudTPrYWY5wGZgYIicqW7NGrj8cli6\nFG6/Hc4/HzIyiv++HapWhbPP9o/58+Gaa+Cxx2D0aMjMLLfYIpKkQt0l1TuGY4YkIku6euUVuPJK\nGDAAJk6EakUuol28Qw6BadPg5ZehXz+48EIYORIqJ2unpojEXbJ2SaWVzAT+Op6bC0OGwJ/+BC++\n6H+ox1osistpBuedB3PmwOefw6mnwg8/lD1zSSXyepZWKmQE5Yy3VMlZWim9Reuua7zJL79A796w\nZYsvFnXrlt9n5ebCbbfBc8/BpElw2GHl91kiEl9mhivFoLcKRppYuxbOPBPatIEnn/TjD4nw4otw\n1VXw6qvQtWtiPlMSw6zEP08kCRX0M7K0BUM90Glg9Wo44QQ491wYMcJ3HSXK+edDzZpw1ll+3OTo\ngtZwlZSlX8hSW7yLvsYwUtyGDdC9O1x0Edx5Z2KLxQ49esCzz0LPnvDhh4n/fBFJDHVJpbAtW+C0\n0+DQQ/2trqF7EN55By6+GKZO9ZkktUW6LULHkDIo7L+hxjAqmG3bfBdU3br+t/tKSdJWnDgRrr8e\nPvsMmjQJnUbKQgUj9cW7YCTJjxkpqeuug+3b4emnk6dYgJ+fMXiwH4DfvDl0GpH4qFOnDsuWLdvl\ntby8PM4++2zGjRtX6vMuWbKEjh07UrduXR566CEA3nrrLc4555yYvv/II49k4cKFxR8YJ2phpKBn\nn4Xhw2HGDKhXL3Sa3TnnJwxu2uTvokqmgiaxS+YWRsuWLVmzZg0ZkaULzIylS5ey9957JyzDzTff\nTJMmTRgypPRzjC+77DLq1avHfffdt/O1ww8/nDFjxtClS5civtN74YUXmDhxIi+++GKB76uFUcHN\nng1Dh/q5D8lYLMCPpTz+uL/V9447QqeRdGRmTJkyhU2bNrFp0yY2btyY0GIBMGLEiDIVC4BvvvmG\nAw88cOfzmTNnsnHjxpiKBcCZZ57Je++9x+rVidn5QQUjhaxd62dajxkDBx0UOk3RqlWDCRPgkUdg\n+vTQaaSiaNmyJdOmTdv5PCsri0suuQSAZcuWUalSJcaPH0+LFi1o1KgRd955585j8/LyuPPOO2nd\nujV169bl8MMPZ+VKv0h2pUqV+OqrrwDYsGED/fr1Y6+99qJly5aMGDFi52/xTz/9NMceeyzXX389\nDRo0oFWrVrz55psFZj3ppJPIzs5myJAh1K1bly+++II33nhjl9niH3/8MY0aNWLFihUAzJs3jwYN\nGrB0qd8mvnr16nTu3Jm33norTlewaCoYKcI5uOQSuOAC/0gF++wD48ZB375hlhCR9FZYV0v03IOC\n5iF89NFHLF26lGnTpjFs2DCWLPEbEt53331MmDCBN954g40bNzJ27Fhq1Kix2/dfffXVbNq0ia+/\n/prp06czfvz4XcYxZsyYQbt27fjxxx+54YYbuOyyywrM/+6773Lcccfx8MMPs3HjRtq0acOCBQs4\n4IADdh5z9NFHM3jwYPr378+WLVvo27cvd9xxB23btt15TPv27Zk3b15BHxF3KhgpYswY+OknPzEv\nlXTv7m+17d8f8vJCp5F4MovPozScc/Ts2ZP69etTv359zj333EKPy+/222+nWrVqdOjQgUMPPXTn\nD9snn3ySESNG0KZNGwA6dOhAgwYNdvne3NxcJk6cyMiRI6lVqxYtWrTguuuu49lnn915TIsWLbjs\nssswM/r168eqVatYs2ZNkX+XHdavX0+dOnV2eT8rK4sNGzbQpUsXmjVrxlVXXbXL+3Xq1GH9+vWF\nnj+eVDBSwOLFkJXlB7urVAmdpuTuuAPWrYMHHgidROLJufg8SsPM+Pe//826detYt24dL7/8cszf\nGz3WUbNmTX7++WcAVqxYwf7771/k965du5Zt27bRokWLna81b958Z9dVQecHdn5GYX+XHerXr8/G\njRt3eb9y5cr079+f//73v1x33XW7ff/GjRupX79+kbnjRQUjyW3b5rt0hg+HqFZoSqlSBZ5/3q+c\nu2hR6DSSzmrVqsXmqPu5v//++5i/t1mzZuTk5BR5TMOGDalSpcout9h+++237LvvviXOWpAOHTrs\nHJ/YYeXKlQwbNoxLL72UoUOH8ttvv+3y/qJFizg0QTNlVTCS3LBh0Lixn9uQylq29H+XSy/1K92K\nlIeOHTsyYcIEtm/fzqxZs3jppZdiXk9p0KBB3HrrreTk5OCc4/PPP+enn37a5ZiMjAx69erFzTff\nzM8//8w333zDAw88QN++fUudObpLqkePHkyPukvEOceAAQMYNGgQTz75JE2aNOHWW2/d+f7WrVuZ\nPXs23bp1K/Xnl4QKRhL7z3/87alPPRV+2Y94uOIKqF4dRo0KnUTS1fDhw/nyyy+pX78+WVlZ9OnT\nZ5f3iyoeQ4cOpVevXpx66qnsscce/P73v2fr1q27fd+DDz5IrVq1aNWqFccddxx9+vRh4MCBO4/L\n/xnFFazo9zt16sQee+zBjBkzABg9ejRr165l+PDhAIwbN45x48bx0UcfATB58mROPPHEhN1SrIl7\nSWr7dujSxW+L2r9/6DTx8+WXcOSR8PHHqdvFVlEk88S9dDZ16lTGjBnDpEmTij22a9eujB07dpe5\nHNG0llSUdC4Y998Pr73mF/RLh9ZFtNGj4V//gvff1yzwZKaCkfpUMKKka8H45hvo3Bk++cRviJRu\n8vLg2GP9eMagQaHTSGFUMFKfCkaUdCwYzvmF+7p2hVtuCZ2m/MyZA6efDgsXQr5b3SVJqGCkPq0l\nleZeegm+/hpuuCF0kvLVqZOfsX7zzaGTiEis1MJIIlu2QPv2fsnyqOVk0tb69f7vO3kyHH546DSS\nn1oYqU8tjDR2771wxBEVo1iAX2135Ei46iotGyKSCtTCSBIrVvhtTf/zHz/JraLIy4PjjoOBAzUA\nnmxinfAmyU2D3hHpVDD69oX99vNLgFQ0s2bBWWfB0qVQu3boNCLpTwUjhX3yCfTq5RcZrFUrdJow\n+vaF/feHv/41dBKR9KeCkaKc87fQXn21/6FZUX37rb9z6vPPoWnT0GlE0psGvVPUSy/5ZUAuvjh0\nkrCaN4fLL4eoddVEJMmohRHQtm1+q9UxY+CUU0KnCW/DBjjgAHjrLX8DgIiUD7UwUtBTT0GLFioW\nO+yxB9x2G1x/fegkIlIQtTAC2bzZrxM1ebJfN0q8bdv8ZL4nnoATTwydRiQ9qYWRYkaNguOPV7HI\nr0oVf6fUzTeXfvtOESkfamEEsHYttGsHn34KrVuHTpN8cnOhY0c/C/yMM0KnEUk/amGkkL/9Dc4/\nX8WiMBkZfgLjLbdoyRCRZKIWRoKtXu376OfNg2bNQqdJXs75nfmuuw4uvDB0GpH0klItDDPrbmaL\nzewLM7uxgPcbmtmbZjbXzBaY2YAAMcvFPfdAnz4qFsUxgxEj/F1T27eHTiMiEKCFYWYZwBLgFGAl\nMBPo7ZxbFHVMFlDNOXeTmTWMHN/YObc937lSqoWxapWfd7FgAeyzT+g0yc85f6fUwIHpta+5SGip\n1MLoAuQ455Y557YBE4Cz8x2zCqgb+bou8GP+YpGK7r7b/+BTsYiNGdx+u29pqJUhEl6IgtEUWB71\nfEXktWhPAAeZ2XfAPOCaBGUrNytXwvjxcONuHXBSlMxM2HtvmDAhdBIRqRzgM2PpQ/oLMNc5l2lm\n+wNTzexQ59ym/AdmZWXt/DozM5PMJN196K67fNfK3nuHTpJadrQy/u//oHdvfweViJRMdnY22dnZ\nZT5PiDGMrkCWc6575PlNQJ5z7u6oY14HRjjnPoo8nwbc6Jyble9cKTGGsWoVHHggLFqkglEazvlN\nlnYUDREpm1Qaw5gFtDGzlmZWFbgQeDXfMYvxg+KYWWPgAOCrhKaMo/vvh0suUbEoLTN/t9Tw4X5S\nn4iEEWQehpmdDowCMoCnnHMjzWwwgHPuscidUeOA5viiNtI5988CzpP0LYy1a6FtW827KCvn4Oij\n4dprNS9DpKy0gVKSuvVWP1nv8cdDJ0l9b7zhbxqYN8+3OkSkdFKpS6rCWL8eHnkE/vzn0EnSQ/fu\nftD7tddCJxGpmFQwytHDD0OPHtCqVegk6cEMbroJ7rxTK9mKhKAuqXKyeTPstx9Mn+7XjpL4yM39\n334ZJ5wQOo1IalKXVJJ56il/K6iKRXxlZPhxjJEjQycRqXjUwigH27b5pctfeAG6dAmdJv389hvs\nvz+88oo2oBIpDbUwksiECf4HmopF+aha1S97ftddoZOIVCxqYcRZXh506AD33QennRY6TfraMUb0\n4Yd+nouIxE4tjCTx+ut+X+pTTw2dJL3VqgVXXuln0YtIYqiFEWfHHgtDhsBFF4VOkv5++AEOOMCv\n0dW4ceg0IqlDLYwk8NFHfqHB888PnaRiaNTIL0b44IOhk4hUDGphxFHPntCtm19VVRLjyy+ha1f4\n+muoXTt0GpHUoBZGYEuWwMcf+z0vJHH2399v4/rUU6GTiKQ/tTDi5IorYK+9YNiw0EkqnpkzfTdg\nTo6/4UBEiqbVagNas8YPvi5Z4ouGJN6JJ8KgQdCnT+gkIslPXVIBjRkDvXqpWIT0pz/5uS9J8PuD\nSNpSC6OMfvkFWraEDz7wrQwJIy/Pb4P7yCO+tSEihVMLI5Dx4+Goo1QsQqtUCYYO9a0MESkfamGU\nQV4etGv3v5VpJawtW3xrb/p0/99FRAqmFkYAU6ZAvXp+dreEV6OGXy7kgQdCJxFJT2phlEFmpr+d\nVsuAJI81a3zrYskSPxNcRHanFkaCzZ4NX30F550XOolE22svPydjzJjQSUTSj1oYpXTJJXDoof52\nTkkuixb5O6WWLYPq1UOnEUk+amEk0MqV8NprfqKYJJ/27aFTJ/jnP0MnEUkvKhil8NBDvoVRr17o\nJFKYoUNh1ChN5BOJJxWMEtq8GZ54Aq65JnQSKcopp/hiMW1a6CQi6UMFo4SeeQaOPx5atQqdRIpi\nBtdeqx35ROJJg94loIl6qWXrVmjRArKz/biGiHga9E6AN96AunU1US9VVK/u58n8/e+hk4ikB7Uw\nSuCUU2DAAOjbN2EfKWW0erVvFebkwJ57hk4jkhzUwihn8+f7+/t79QqdREqicWO/de7jj4dOIpL6\n1MKI0WWX+YHum29OyMdJHM2bB7/7nd/3WzvyiaiFUa7WrIFJk2Dw4NBJpDQOPRTatoUXXwydRCS1\nqWDE4NFH/fpEDRuGTiKlde21fhXbFG5QiwSnLqli/Pqr32Nh2jS/o5ukptxcv8nV+PFw9NGh04iE\npS6pcjJxInTooGKR6jIy4A9/8MuFiEjpBCkYZtbdzBab2RdmdmMhx2Sa2RwzW2Bm2QmOCPjui1Gj\nfHeGpL6BA31L8ZtvQicRSU0JLxhmlgE8BHQHDgR6m1n7fMfUAx4GznTOHQycn+icAB984NeOOu20\nEJ8u8VanDvTv7xePFJGSC9HC6ALkOOeWOee2AROAs/MdczHwknNuBYBzbm2CMwK+dXHNNVBJHXdp\n4+qrYdw4+Pnn0ElEUk+IH4VNgeVRz1dEXovWBmhgZu+Z2SwzuyRh6SK++grefx/69Uv0J0t52m8/\nv3jk+PGhk4iknhAFI5bbmqoAhwE9gNOAW82sTbmmyuehh+DSS6F27UR+qiTCtdf69aXy8kInEUkt\nlQN85kqgWdTzZvhWRrTlwFrn3BZgi5m9DxwKfJH/ZFlZWTu/zszMJDMzs8wBN23yy5jPnl3mU0kS\nOu44qFUL3nwTevQInUak/GVnZ5OdnV3m8yR8HoaZVQaWACcD3wEzgN7OuUVRx7TDD4yfBlQDPgMu\ndM4tzHeucpmHMXo0fPgh/OtfcT+1JInx4+G55+Dtt0MnEUm8lJmH4ZzbDgwB3gIWAhOdc4vMbLCZ\nDY4csxh4E/gcXyyeyF8sykturi8YupU2vV14oV9Q8r//DZ1EJHVopnc+r74Kd9wBn33md22T9DVs\nGKxcCY89FjqJSGKVtoWhgpHPSSfBoEFw8cVxPa0koTVr/HIhX3yhdcKkYkmZLqlkNm8eLF0KF1wQ\nOokkwl57wTnnaK8MkViphRHl0kuhTRu46aa4nVKS3Pz50L273yujatXQaUQSQy2MMtqx58Xll4dO\nIol0yCHQvj288ELoJCLJTwUjYswYv/2q9n2ueLRXhkhs1CUFbN3q97x47z3/26ZULHl50K4djB0L\nxx4bOo1I+VOXVBn885/QqZOKRUVVqZJfZPKBB0InEUluFb6F4Zzf8/nee+HUU+MUTFLOzz/7VubM\nmX6BQpF0phZGKU2b5rskunULnURCql0bLrvMz/IXkYJV+BbG737n78UfNChOoSRlLV/uW5tffw17\n7BE6jUj5UQujFBYvhlmzoE+f0EkkGTRr5udkPPVU6CQiySnmFoaZ1QL6AAcDGUB1IA/4GfgUeME5\nl9AdBsrawrjySmjUyK8pJAL+F4jzzoMvv4TKIRb/F0mAcl1Lysy64fffnuKc+zLfe4bfq+IU4B3n\n3NyShiitshSMtWv9rO7Fi6Fx4zgHk5R2/PEwZIiflyOSjsqtYJhZdWBf51xODCEOcc7NL2mI0ipL\nwbjjDt9Xre4HyW/SJLjrLvj0U61YLOkpYavVmlkrYFVkN7ygSlswtm71t06+8w4cdFA5BJOUlpsL\nbdvCs8/C0UeHTiMSf4kc9L4OODLyocea2TGlOEdQzz/v74ZRsZCCZGT45ULuuy90EpHkUpqCMQPY\nz8z2c859COwV50zlyjm4/3647rrQSSSZDRwI06f7wW8R8UpTMJoBvwJDzew9oHN8I5WvqVN9v/Qp\np4ROIsmsdm34/e9h1KjQSUSSR2nGMC4GXnLO/WpmDYFznXNBtqApzRjGaadB794wYED5ZJL08d13\nvtvyyy+hQYPQaUTiJ5FjGBOBHb3/+wEpc1Pq55/7DXN69w6dRFLBPvtAz57w6KOhk4gkh1huq60G\n1HHOrS32ZGbNnXPfxitcDJ9XohZGv35+RVrtqCexmj/fL0q5bBlUqxY6jUh8lFsLwzn3K9DVzC42\nsxqFfHh9M7scaFHSAImyfDlMmQJXXBE6iaSSQw7xd9T94x+hk4iEF+tM757A98BJ+LuiqgNVgFzg\nF2AF8IRzbkP5RS0wV8wtjD/9yd9frz0PpKTeecfvlzF/vt87QyTVlffSIA8A/3DOzTKzs51z/y5N\nyHiLtWBs2ACtWsHs2dAiadtAkqyc8xtsjRjhVzcWSXWlLRixLq82Gbg5skxIDTNrA8wHFjjnVpb0\nQxPt8cf9KqQqFlIaZnDDDXDPPSoYUrGV5rba64BZ+DulDgb2wXdJPeicWxL3hEVnKbaF8dtvvnUx\nZQp07JigYJJ2tm+H1q1hwgTo2jV0GpGySdhaUoV8+EVAM+fc38p8spJ9brEF45ln/JpA77yToFCS\nth58ELKz4aWXQicRKZvQBeNcYJtzbnKZT1ayzy2yYOTlwcEH+203NbNbymrzZr9o5QcfwAEHhE4j\nUnpBd9xzzr2c6GIRi8mToUYNOPnk0EkkHdSq5Tfd0qKEUlGl7Z7ezvmlqYcOhQsuSHAwSVs//OBb\nFwsXwt57h04jUjra0zufDz7wu+qde27oJJJOGjWCiy/WooRSMaVtC6NHDzjnHL/iqEg8LVsGnTv7\nRQnr1QudRqTk1MKIMm8ezJ3r144SibeWLeGMM+Dhh0MnEUmstGxhXHyxn5l7/fUBQkmFsGgRZGb6\nfeFr1gydRqRkgt5WG0pBBSMnB446yncX1K0bKJhUCOee64vGH/4QOolIyahgRAwaBE2bwl//GiiU\nVBgzZ8J55/lfUqpWDZ1GJHYpNYZhZt3NbLGZfWFmNxZx3BFmtj0yMbBY334LkybpNz5JjCOOgHbt\ntPS5VBwJb2GYWQawBDgFWAnMBHo75xYVcNxU/PLp45xzuy3IkL+FcfXVfqLePfeU419AJEp2Ngwe\n7OdlZGSETiMSm1RqYXQBcpxzy5xz24AJwNkFHHc18CLwQywn/f57/5ve0KHxCypSnBNO8HMz/vWv\n0ElEyl+IgtEUWB71fEXktZ3MrCm+iDwSeanYZtD990OfPpp9K4llBrfdBsOH+7XLRNJZiIIRSx/Y\nKODPkf4mizwK9eOP8OSTuo1WwujWDerU0Sq2kv5i3UApnlYCzaKeN8O3MqJ1BiaYGUBD4HQz2+ac\nezX/ybKysnj3XT+Z6quvMmnePLN8UosUYkcr4y9/8XdNaRtXSTbZ2dlkZ2eX+TwhBr0r4we9Twa+\nA2ZQwKB31PHjgMnOuZcLeM+tXeto2xb+8x9fNERCcA4OPxxuvRV69gydRqRoKTPo7ZzbDgwB3gIW\nAhOdc4vMbLCZDS7p+R54wP9Wp2IhIZn5YjFsmC8eIuko5SfuNWjg1LqQpJCX55ekGT4czjordBqR\nwqVMCyPe1LqQZFGpkl9h4PbbdceUpKeULxh/+UvoBCL/c/bZvnC88kroJCLxl/JdUqmcX9LTa6/B\nn//sl9nXHVOSjCpsl5RIsunRw+///cILoZOIxJdaGCLl4O234ZprYMECrTElyUctDJEk0q0b7Lkn\n/POfoZOIxI9aGCLlZPp0GDgQFi/WfhmSXNTCEEkyJ5wABxwATzwROolIfKiFIVKO5szxg+BffAG1\na4dOI+KphSGShDp18vt+jxoVOolI2amFIVLOcnKga1c/ltGwYeg0IqVvYahgiCTAVVdBzZpw772h\nk4ioYIgktVWr4OCDtQy/JAeNYYgksSZN4OqrtfaZpDa1MEQSZPNmaNsWJk2CLl1Cp5GKTC0MkSRX\nq5bfK2PoUG2yJKlJBUMkgfr3h02b4OXdNhwWSX7qkhJJsKlT4corYeFCLRkiYahLSiRFdOvmlwx5\n8MHQSURKRi0MkQCWLIFjjvHLn++9d+g0UtFoHoZIirnhBlizBp5+OnQSqWhUMERSzKZN0K4dvPSS\nXzpEJFE0hiGSYurUgbvu8hP68vJCpxEpngqGSEB9+kCVKjB2bOgkIsVTl5RIYLNn+z0zFizQaraS\nGBrDEElh114LGzeqpSGJoYIhksI2bYIDD4TnnvNbu4qUJw16i6SwOnVg9Gi44gr49dfQaUQKpoIh\nkiR69oQ2beBvfwudRKRg6pISSSLffguHHQYffeSXDxEpD+qSEkkDzZtDVhZceink5oZOI7IrFQyR\nJHPVVVC5shYnlOSjLimRJJST45cL+fRTaN06dBpJN+qSEkkjrVvDLbf4riktGyLJQgVDJEntWGNK\nXVOSLNRj9RQKAAAM7klEQVQlJZLEcnLgqKPgvffg4INDp5F0kXJdUmbW3cwWm9kXZnZjAe/3MbN5\nZva5mX1kZh1C5BQJqXVrv6Jtnz6a0CfhBWlhmFkGsAQ4BVgJzAR6O+cWRR1zFLDQObfBzLoDWc65\nrvnOoxaGpD3n4LzzoFUruPfe0GkkHaRaC6MLkOOcW+ac2wZMAM6OPsA594lzbkPk6WfAvgnOKJIU\nzOCJJ2DCBJg2LXQaqchCFYymwPKo5ysirxXmMuD1ck0kksT23BPGjYP+/f22riIhVA70uTH3I5nZ\nicClwDEFvZ+VlbXz68zMTDIzM8sYTSQ5devmC0afPvDmm5CRETqRpIrs7Gyys7PLfJ5QYxhd8WMS\n3SPPbwLynHN35zuuA/Ay0N05l1PAeTSGIRXK9u1wyilw0klw222h00iqSqn9MMysMn7Q+2TgO2AG\nuw96NwfeBfo65z4t5DwqGFLhrFoFnTvDs8/CySeHTiOpKKUKBoCZnQ6MAjKAp5xzI81sMIBz7jEz\nexI4B/g28i3bnHNd8p1DBUMqpHffhb59YeZMaFrU6J9IAVKuYMSDCoZUZCNHwiuvwPTpUL166DSS\nSlQwRCoY56B3b6haFZ55xt9+KxKLVJuHISJlZAZjx8KCBXD//aHTSEUQ6rZaEYmDmjV9t1TXrnDQ\nQdC9e+hEks7UwhBJcc2bwwsvQL9+MHdu6DSSzlQwRNLAMcfAww/DGWf4fcFFyoO6pETSxAUXwIoV\ncPrp8OGHUL9+6ESSbnSXlEia+eMfYfZsv3xIjRqh00gy0m21IgL4Xfr69oUNG2DSJH/brUg0FQwR\n2WnbNujVCypVgokTobI6nyWK5mGIyE5Vqvj9M375BQYMgNzc0IkkHahgiKSpatXg5Zfhu+9g4EC/\n0q1IWahgiKSxGjVgyhRYvdovI/Lbb6ETSSpTwRBJczVrwquv+nGNc86BLVtCJ5JUpYIhUgFUq+Zn\ng9etCz16wPr1oRNJKlLBEKkgqlSB556DDh3g2GM1I1xKTgVDpALJyIC//x0GDYKjj4Y5c0InklSi\ngiFSAV17rS8cp53mJ/eJxEIT90QqsFmz4Lzz/MzwYcN8C0TSn2Z6i0iprFkDF17ot3n9xz+gQYPQ\niaS8aaa3iJTKXnvB1Klw4IFw2GHwwQehE0myUgtDRHZ67TU/IH755XDrrVqDKl2pS0pE4mLVKujf\nHzZtgnHjoF270Ikk3tQlJSJx0aSJ30ujTx8/X2PkSD9LXEQtDBEp1LJlMHiwHxh/7DHo0iV0IokH\ntTBEJO5atvStjT/+EXr29Kvefv996FQSigqGiBTJDPr1g8WLoVEjOPhguPtuv9eGVCwqGCISk7p1\n4Z574OOP/YS/Nm1gzBgtmV6RqGCISIm0betXvn31VZg8GQ44wBcOLZue/lQwRKRUOneGN97wK+C+\n+Sbstx+MGAE//RQ6mZQXFQwRKZNjjvGtjXfegaVLoVUruPRS320l6UW31YpIXK1ZA2PHwqOPQsOG\nMGAAXHSR/1qSg2Z6i0hSyc31a1Q9+6xfcuSEE/wih2ec4QfQJRwVDBFJWhs3wssvw4svwvvvw/HH\n+3kd3bvDvvuGTlfxqGCISErYuBGmTPF3WL39Nuyzjy8cJ53kx0PU+ih/KhgiknJyc2HmTHjrLZg+\n3X/dvr1fw+rII6FrV2je3E8elPhJqYJhZt2BUUAG8KRz7u4CjhkNnA78Agxwzu22+7AKhkh62boV\nZszwkwM/+ww++QTy8qBTJ//o2BEOOsjPBalWLXTa1JUya0mZWQbwENAdOBDobWbt8x3TA2jtnGsD\nXA48kuic8ZSdnR06QkyUM35SISMkX87q1f34xp//7PcaX7UKZs+GzMxsatf2YyAXXgh77OGLxpln\n+nWuHn7Yt1IWLw67ZEmyXc94C7E9Shcgxzm3DMDMJgBnA4uijjkLeAbAOfeZmdUzs8bOudWJDhsP\n2dnZZGZmho5RLOWMn1TICMmf08wPiv/6azZZWZk7X//tN/jiC//IyYH5832B+eYbWL7cj4Psu68f\nH2na1C/Z3rix312wcWN/i++ee/rtaOO5j3myX8+yClEwmgLLo56vAI6M4Zh9gZQsGCISX1Wr+q6p\ngw7a/b28PFi9GlauhO++83+uWgXz5vnXV6+GH3/0j/XroXZtqFcP6tf3LZe6daFOHf9n7dr/e9Sq\nBTVqQM2a/s/q1f/3Z7Vq/rF+vV/Nt2pVqFLF/1m5cnyLUkghCkasgw75+9c0WCEixapUybcomjQp\n/tjcXL+z4Lp1/of9+vX++caN/vHzz7B5sy88mzf79bJ++cU/tm71jy1b4Ndf/eOHH+D55/2GU9u2\n+ZbQjs2nKlf2RSQjw3+9o5Dkf1SqVPTD7H9/FveAgp+XVsIHvc2sK5DlnOseeX4TkBc98G1mjwLZ\nzrkJkeeLgRPyd0mZmYqIiEgplGbQO0QLYxbQxsxaAt8BFwK98x3zKjAEmBApMOsLGr8ozV9YRERK\nJ+EFwzm33cyGAG/hb6t9yjm3yMwGR95/zDn3upn1MLMcYDMwMNE5RURkVyk9cU9ERBInpZY3N7ML\nzOy/ZpZrZocVcVx3M1tsZl+Y2Y2JzBj5/AZmNtXMlprZ22ZWr5DjlpnZ52Y2x8xmJChbsdfGzEZH\n3p9nZp0SkauADEXmNLNMM9sQuXZzzOyWABnHmtlqM5tfxDHJcC2LzJkM1zKSo5mZvRf5f3yBmf2h\nkOOCXtNYcoa+pmZW3cw+M7O5ZrbQzEYWclzJrqVzLmUeQDugLfAecFghx2QAOUBLoAowF2if4Jz3\nADdEvr4RuKuQ474GGiQwV7HXBugBvB75+kjg0wD/nWPJmQm8GuLfYVSG44BOwPxC3g9+LWPMGfxa\nRnLsDXSMfF0bWJKk/z5jyRn8mgI1I39WBj4Fji3rtUypFoZzbrFzbmkxh+2cGOic2wbsmBiYSDsn\nHkb+7FnEsYkcuI/l2uwyaRKoZ2aNE5gRYv9vGPSmB+fcB8C6Ig5JhmsZS04IfC0BnHPfO+fmRr7+\nGT+Zd598hwW/pjHmhPD/PnfMea+K/yUs/16IJb6WKVUwYlTQpL+mCc4QPSt9NVDYfwQHvGNms8zs\n9wnIFcu1KWzSZCLFktMBR0ea0q+b2YEJSxe7ZLiWsUi6axm5i7IT8Fm+t5LqmhaRM/g1NbNKZjYX\n/zPoPefcwnyHlPhahrittkhmNhXf5MvvL865yTGcIiGj+EXkvHmXMM65IuaLHOOcW2VmjYCpZrY4\n8ttgeUmVSZOxfN5soJlz7hczOx14Bd9dmWxCX8tYJNW1NLPawIvANZHf4Hc7JN/zINe0mJzBr6lz\nLg/oaGZ7AG+ZWaZzLjvfYSW6lklXMJxz3cp4ipVAs6jnzfCVM66KyhkZYNzbOfe9mTUB1hRyjlWR\nP38ws0n4rpjyLBixXJv8x+wbeS2Ris3pnNsU9fUbZjbGzBo45/I3u0NKhmtZrGS6lmZWBXgJeM45\n90oBhyTFNS0uZzJdU+fcBjN7DTgcyI56q8TXMpW7pArrH9w5MdDMquInBr6auFgQ+bz+ka/743+7\n2IWZ1TSzOpGvawGnAoXebRMnsVybV4F+kVyFTposZ8XmNLPGZn6RAzPrgr9FPJmKBSTHtSxWslzL\nSIangIXOuVGFHBb8msaSM/Q1NbOGFrk708xqAN2A/FtElPxahhzFL8Wo/zn4PrctwPfAG5HX9wFe\nizrudPydCznATQFyNgDeAZYCbwP18ucEWuHv/pkLLEhUzoKuDTAYGBx1zEOR9+dRyN1ooXMC/xe5\nbnOBj4GuATI+j1+t4LfIv8tLk/RaFpkzGa5lJMexQF4kx5zI4/Rku6ax5Ax9TYFD8N1ic4HPgesj\nr5fpWmrinoiIxCSVu6RERCSBVDBERCQmKhgiIhITFQwREYmJCoaIiMREBUNERGKigiEiIjFRwRAR\nkZioYIiUgPnNu+ZEPW6IvP5R6Gwi5U0zvUVKwMw2OefqhM4hEoJaGCJxYGY/R33dN7I95hwze9TM\n9P+ZpAX9QxYpmRr5uqQuiLzuAMysPdALONo51wm/SF2f6BOYWYaZXWxmt5hZfzN72MxaJfRvIVIK\nSbcfhkiS2xIpBIU5GegMzIqsbl0Dv7JytEPxeymcB1QDXgBWxT+qSHypYIjE3zPOub8U9qZzbjaA\nmR0F3O+c+zphyUTKQF1SIvH1LnB+ZNtdzKyBmTWPPsDMjjCzhsDBzrmvzezYEEFFSkotDJGSqWFm\n0TuXvRFpTTgA59xCM7sFeDsy2L0NuAr4Nup7ugOrgY/M7BwK2cJXJNnotlqRMjKzPYH/OOdahs4i\nUp7UJSVSBma2D34Lzr+FziJS3tTCEBGRmKiFISIiMVHBEBGRmKhgiIhITFQwREQkJioYIiISExUM\nERGJiQqGiIjERAVDRERi8v83NICmWrTUzwAAAABJRU5ErkJggg==\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x7f9ff980ac18>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"plt.plot(x, f(x), label=\"Función f(x)\")\n",
"plt.xlabel(\"Eje $x$\")\n",
"plt.ylabel(\"$f(x)$\")\n",
"plt.legend()\n",
"plt.title(\"Función $f(x)$\")"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Notamos varias cosas:\n",
"\n",
"* Con diversas llamadas a funciones dentro de `plt.` se actualiza el gráfico *actual*. Esa es la forma de trabajar con la interfaz pyplot.\n",
"* Podemos añadir etiquetas, y escribir $\\LaTeX$ en ellas. Tan solo hay que encerrarlo entre signos de dólar $$.\n",
"* Añadiendo como argumento `label` podemos definir una leyenda."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Personalización"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"La función `plot` acepta una serie de argumentos para personalizar el aspecto de la función. Con una letra podemos especificar el color, y con un símbolo el tipo de línea."
]
},
{
"cell_type": "code",
"execution_count": 10,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"[<matplotlib.lines.Line2D at 0x7f9ff9708828>]"
]
},
"execution_count": 10,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"image/png": "iVBORw0KGgoAAAANSUhEUgAAAXcAAAEACAYAAABI5zaHAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nB9LkrpRSDvT/AZ2myroum23uAAAAAElFTkSuQmCC\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x7f9ff9708978>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"plt.plot(x, f(x), 'ro')\n",
"plt.plot(x, 1 - f(x), 'g--')"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Esto en realidad son códigos abreviados, que se corresponden con argumentos de la función `plot`:"
]
},
{
"cell_type": "code",
"execution_count": 11,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"[<matplotlib.lines.Line2D at 0x7f9ff9600400>]"
]
},
"execution_count": 11,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"image/png": "iVBORw0KGgoAAAANSUhEUgAAAXcAAAEACAYAAABI5zaHAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nB9LkrpRSDvT/AZ2myroum23uAAAAAElFTkSuQmCC\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x7f9ff96004e0>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"plt.plot(x, f(x), color='red', linestyle='', marker='o')\n",
"plt.plot(x, 1 - f(x), c='g', ls='--')"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"La lista de posibles argumentos y abreviaturas está disponible en la documentación de la función `plot` http://matplotlib.org/api/pyplot_api.html#matplotlib.pyplot.plot."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Más personalización, pero a lo loco"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Desde matplotlib 1.4 se puede manipular fácilmente la apariencia de la gráfica usando **estilos**. Para ver qué estilos hay disponibles, escribiríamos `plt.style.available`."
]
},
{
"cell_type": "code",
"execution_count": 12,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"['pybonacci',\n",
" 'grayscale',\n",
" 'ggplot',\n",
" 'dark_background',\n",
" 'fivethirtyeight',\n",
" 'bmh']"
]
},
"execution_count": 12,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"plt.style.available"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"No hay muchos pero podemos crear los nuestros. Para activar uno de ellos, usamos `plt.style.use`. ¡Aquí va el que uso yo! https://gist.github.com/Juanlu001/edb2bf7b583e7d56468a"
]
},
{
"cell_type": "code",
"execution_count": 13,
"metadata": {},
"outputs": [],
"source": [
"#plt.style.use(\"ggplot\") # Afecta a todos los plots"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"<div class=\"alert alert-warning\">No he sido capaz de encontrar una manera fácil de volver a la apariencia por defecto en el notebook. A ver qué dicen los desarrolladores (https://github.com/ipython/ipython/issues/6707) ¡pero de momento si quieres volver a como estaba antes toca reiniciar el notebook!</div>"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Para emplear un estilo solo a una porción del código, creamos un bloque `with plt.style.context(\"STYLE\")`:"
]
},
{
"cell_type": "code",
"execution_count": 14,
"metadata": {},
"outputs": [
{
"data": {
"image/png": "iVBORw0KGgoAAAANSUhEUgAAAXsAAAEECAYAAADJSpQfAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x7f9ff955cef0>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"with plt.style.context('ggplot'):\n",
" plt.plot(x, f(x))\n",
" plt.plot(x, 1 - f(x))"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Y hay otro tipo de personalización más loca todavía:"
]
},
{
"cell_type": "code",
"execution_count": 15,
"metadata": {},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"/home/juanlu/.local/lib/python3.4/site-packages/matplotlib/font_manager.py:1279: UserWarning: findfont: Font family ['Humor Sans', 'Comic Sans MS'] not found. Falling back to Bitstream Vera Sans\n",
" (prop.get_family(), self.defaultFamily[fontext]))\n"
]
},
{
"data": {
"image/png": "iVBORw0KGgoAAAANSUhEUgAAAXcAAAEPCAYAAAC5sYRSAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nKWVDPwERmxz1am53qAAAAABJRU5ErkJggg==\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x7f9ff95d6f98>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"with plt.xkcd():\n",
" plt.plot(x, f(x))\n",
" plt.plot(x, 1 - f(x))\n",
" plt.xlabel(\"Eje x\")"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"¡Nunca imitar a XKCD fue tan fácil! http://xkcd.com/353/"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Otros tipo de gráficas"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"La función `scatter` muestra una nube de puntos, con posibilidad de variar también el tamaño y el color."
]
},
{
"cell_type": "code",
"execution_count": 16,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"<matplotlib.collections.PathCollection at 0x7f9ff9466b70>"
]
},
"execution_count": 16,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"image/png": "iVBORw0KGgoAAAANSUhEUgAAAXAAAAEACAYAAACqOy3+AAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nSUVORK5CYII=\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x7f9ff95252e8>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"N = 100\n",
"x = np.random.randn(N)\n",
"y = np.random.randn(N)\n",
"\n",
"plt.scatter(x, y)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Con `s` y `c` podemos modificar el tamaño y el color respectivamente. Para el color, a cada valor numérico se le asigna un color a través de un *mapa de colores*; ese mapa se puede cambiar con el argumento `cmap`. Esa correspondencia se puede visualizar llamando a la función `colorbar`."
]
},
{
"cell_type": "code",
"execution_count": 17,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"<matplotlib.colorbar.Colorbar at 0x7f9ff93a94e0>"
]
},
"execution_count": 17,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"image/png": "iVBORw0KGgoAAAANSUhEUgAAAWEAAAEACAYAAABiV8coAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nRJ50iKwAAAAASUVORK5CYII=\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x7f9ff95817f0>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"s = np.abs(50 + 50 * np.random.randn(N))\n",
"c = np.random.randn(N)\n",
"\n",
"plt.scatter(x, y, s=s, c=c, cmap=plt.cm.Blues)\n",
"plt.colorbar()"
]
},
{
"cell_type": "code",
"execution_count": 18,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"<matplotlib.colorbar.Colorbar at 0x7f9ff92ef470>"
]
},
"execution_count": 18,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"image/png": "iVBORw0KGgoAAAANSUhEUgAAAWEAAAEACAYAAABiV8coAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\naZoF/T+k7Bu1Uk4p2QAAAABJRU5ErkJggg==\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x7f9ff94c1dd8>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"plt.scatter(x, y, s=s, c=c, cmap=plt.cm.Oranges)\n",
"plt.colorbar()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"matplotlib trae por defecto muchos mapas de colores. En las SciPy Lecture Notes dan una lista de todos ellos (http://scipy-lectures.github.io/intro/matplotlib/matplotlib.html#colormaps)\n",
"\n",
""
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"La función `contour` se utiliza para visualizar las curvas de nivel de funciones de dos variables y está muy ligada a la función `np.meshgrid`. Veamos un ejemplo:\n",
"\n",
"$$f(x) = x^2 - y^2$$"
]
},
{
"cell_type": "code",
"execution_count": 19,
"metadata": {},
"outputs": [],
"source": [
"def f(x, y):\n",
" return x ** 2 - y ** 2"
]
},
{
"cell_type": "code",
"execution_count": 20,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"<matplotlib.colorbar.Colorbar at 0x7f9ff9292048>"
]
},
"execution_count": 20,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"image/png": "iVBORw0KGgoAAAANSUhEUgAAAWEAAAEACAYAAABiV8coAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\n/sW9rJ8AAAAASUVORK5CYII=\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x7f9ff93e6ac8>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"x = np.linspace(-2, 2)\n",
"y = np.linspace(-2, 2)\n",
"xx, yy = np.meshgrid(x, y)\n",
"zz = f(xx, yy)\n",
"\n",
"plt.contour(xx, yy, zz)\n",
"plt.colorbar()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"La función `contourf` es casi idéntica pero rellena el espacio entre niveles. Podemos especificar manualmente estos niveles usando el cuarto argumento:"
]
},
{
"cell_type": "code",
"execution_count": 21,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"<matplotlib.colorbar.Colorbar at 0x7f9ff969be48>"
]
},
"execution_count": 21,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"image/png": "iVBORw0KGgoAAAANSUhEUgAAAXcAAAEACAYAAABI5zaHAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nYII=\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x7f9ff9305d30>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"plt.contourf(xx, yy, zz, np.linspace(-4, 4, 100))\n",
"plt.colorbar()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Para guardar las gráficas en archivos aparte podemos usar la función `plt.savefig`. matplotlib usará el tipo de archivo adecuado según la extensión que especifiquemos. Veremos esto con más detalle cuando hablemos de la interfaz orientada a objetos."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Varias figuras"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Podemos crear figuras con varios sistemas de ejes, pasando a `subplot` el número de filas y de columnas."
]
},
{
"cell_type": "code",
"execution_count": 22,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"<matplotlib.text.Text at 0x7f9ff90be080>"
]
},
"execution_count": 22,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"image/png": "iVBORw0KGgoAAAANSUhEUgAAAXoAAAEKCAYAAAAcgp5RAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nRU5ErkJggg==\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x7f9ff955c780>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"x = np.linspace(-1, 7, 1000)\n",
"\n",
"fig = plt.figure()\n",
"plt.subplot(211)\n",
"plt.plot(x, np.sin(x))\n",
"plt.grid(False)\n",
"plt.title(\"Función seno\")\n",
"\n",
"plt.subplot(212)\n",
"plt.plot(x, np.cos(x))\n",
"plt.grid(False)\n",
"plt.title(\"Función coseno\")"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"<div class=\"alert alert-info\">¿Cómo se ajusta el espacio entre gráficas para que no se solapen los textos? Buscamos en Google \"plt.subplot adjust\" en el primer resultado tenemos la respuesta http://stackoverflow.com/a/9827848</div>"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Como hemos guardado la figura en una variable, puedo recuperarla más adelate y seguir editándola."
]
},
{
"cell_type": "code",
"execution_count": 23,
"metadata": {},
"outputs": [
{
"data": {
"image/png": "iVBORw0KGgoAAAANSUhEUgAAAasAAAEaCAYAAACvh3i9AAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nnIu8/weowHyqYO5tjwAAAABJRU5ErkJggg==\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x7f9ff955c780>"
]
},
"execution_count": 23,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"fig.tight_layout()\n",
"fig"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"<div class=\"alert alert-warning\">Si queremos manipular la figura una vez hemos abandonado la celda donde la hemos definido, tendríamos que utilizar la interfaz orientada a objetos de matplotlib. Es un poco lioso porque algunas funciones cambian de nombre, así que en este curso no la vamos a ver. Si te interesa puedes ver los notebooks de la primera edición, donde sí la introdujimos.\n",
"\n",
"https://github.com/AeroPython/Curso_AeroPython/releases/tag/v1.0</div>"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"**Ejercicio**\n",
"\n",
"Crear una función que represente gráficamente esta expresión:\n",
"\n",
"$$\\sin(2 \\pi f_1 t) + \\sin(2 \\pi f_2 t)$$\n",
"\n",
"Siendo $f_1$ y $f_2$ argumentos de entrada (por defecto $10$ y $100$) y $t \\in [0, 0.5]$. Además, debe mostrar:\n",
"\n",
"* leyenda,\n",
"* título \"Dos frecuencias\",\n",
"* eje x \"Tiempo ($t$)\"\n",
"\n",
"y usar algún estilo de los disponibles."
]
},
{
"cell_type": "code",
"execution_count": 70,
"metadata": {},
"outputs": [
{
"data": {
"image/png": "iVBORw0KGgoAAAANSUhEUgAAAYcAAAElCAYAAAAPyi6bAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x7f9ff2b3fcf8>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"def frecuencias(f1=10.0, f2=100.0):\n",
" max_time = 0.5\n",
" times = np.linspace(0, max_time, 1000)\n",
" signal = np.sin(2 * np.pi * f1 * times) + np.sin(2 * np.pi * f2 * times)\n",
" with plt.style.context(\"ggplot\"):\n",
" plt.plot(signal, label=\"Señal\")\n",
" plt.xlabel(\"Tiempo ($t$)\")\n",
" plt.title(\"Dos frecuencias\")\n",
" plt.legend()\n",
"\n",
"frecuencias()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"**Ejercicio**\n",
"\n",
"Representar las curvas de nivel de esta función:\n",
"\n",
"$$g(x, y) = \\cos{x} + \\sin^2{y}$$\n",
"\n",
"Para obtener este resultado:\n",
"\n",
""
]
},
{
"cell_type": "code",
"execution_count": 68,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"<matplotlib.text.Text at 0x7f9ff2f7e710>"
]
},
"execution_count": 68,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"image/png": "iVBORw0KGgoAAAANSUhEUgAAAXUAAAGNCAYAAADuPOG8AAAABHNCSVQICAgIfAhkiAAAAAlwSFlz\nAAALEgAACxIB0t1+/AAAIABJREFUeJzsnXVYVcv3h9+hGxQBEcHu7u7u7u7uxrp2d9c177X72t3d\nrdgCiqDSfeb3xwG/6CX2Bryiv/M+z3mUc6b2ic+eWbPWGiGlRIcOHTp0/B7o/ewB6NChQ4eO5EMn\n6jp06NDxG6ETdR06dOj4jdCJug4dOnT8RuhEXYcOHTp+I3SirkOHDh2/ETpR16FDh47fCIOfPQAd\nOnSoRwiRDcgL5Af2Sylv/uQh6Ugh6GbqOn44QohMydSOoxDCLDna+g2oC7gDc4GhP3ksOlIQOlH/\nf4QQ4r4Qonwsz6cRQtwSQhT4AX1mBkomU3MfgeHJ1NYvjZRynpTyKuAMvPzZ49GRctCJ+k9ECPFK\nCBEkhPCPevgJIdL+qP6klHmllGe/G4MBsA7oJaW88wO67SGl3JwcDUkpI4ADQoj2ydFeSkQIoSeE\ncFOxumkETPmRY9Lxa6ET9Z+LBOpKKS2jHlZSyvf/6QCkjJBS1pVSXk7utqNm/u+Ss00p5TWganK2\n+aMRQvRRWlZKqQGGoeB9E0LUBxYCTokfnY7fDZ2op0CEEJoos0X03+uEEJNi/P1KCDFECHFHCPFF\nCLFFCGEc43VnIcQuIYSXEMJbCLEwRr0qMcrlEkKcFkJ8jjLN1FPah0LqAicT8x4kwEchRNYf0O6P\nIo2awlLK3VLK8PjKCCEaAWOBXUDzJIxNx2+Gzvvl5yMUlJFRj5h/NwNqAKHABaAjsEIIoQ/8AxwH\n2gAaoMj37QghDIH9wGq0M99ywF4hRBEp5bOo8rH2oeLaigFTVZRXyh201+QW/UTUTbBbPHUuSyn3\nRpXtDZgDQUColHK1EKI1kBrttWqklGuiVhrFAR+gg5SyQXJeRGztR9105wG9gCzAHKAlYAfUBMZL\nKV9LKXcDu5NzPDp+D3Si/nMRwB4hRETU36eklI3jKRuThdGmGiHEfqBg1PPFAUdgWNRSHuBiLO2V\nBMyllNOj+xZC/AO0BiagFf+4+vh2YNobyUjgMeAAFJNSdgLM5He5naNMBpFobyL30ArVFCnl4xhl\n0gElgOZSylZR7Z+QUlaMKvIZyB6zXSnlC2BUbOP7rv9yaE1etYUQhYDeQohbQCUpZbeoMvOiNpSb\nATOllG+FELZqrkEhXb9vX0p5Imo8+lLKDUKIroCRlHKLEMIeaIpW6BO6zoTeQx2/KTpR/7lIoIGU\nMjEmipi292AgXdT/nYHXMQQ9LtIBb7977nWMduLr43smA4+llDuFEG2Au1HP68csJIRwAR5KKd2E\nEBOB6YAv8Oa79nIC14BBUX8X/a5MMGAUx1gSojFwDkBKeQvoJoSYATyIUeYh0ArYDlwXQpxDO3tW\ndA1CiFxAzM3cskIIkxh/n5NSHkQ70/6m/ShifnaRaG+WRPWTUeF1JvQe6vhN0Yl6yiQIiOmP7ci/\nBTgu3gIuQgh9KWVkPOU8AGchhIgxm87A/wTke2I9TSXKe6YH/xP8isCCqP9HxCwrpXwTVccB8JdS\nfkFrKuK7cieFEGOATVFPVQGOxChiDXz6bhxKzS+Cf+8lGUc9ojECDIFXQG6gNrBKCFFZyTVIKR8R\nY9UghBgvpZwQy5i+b7+SlPJDLOXi+xxjRcF7qOM3RbdRmjK5DbQRQugLIWoC//Itj4ergCcwXQhh\nJoQwEUKUjqXcFbQ3j+FCCEMhREW0G5tb4mg3Ltu/OeAupQwRQhgB+aWU96Neey+EsPjagBA5o+zI\ntYGzUc/VjvF6NiFE9HeyBFo7PmgF6ViMPh2JYU8HrflFSjkqnsfeqKJ7gcox+qwH7AQKx2iuQNRz\nvYAAKeVGtDeqtFHXUDCua1DJ9+07Rg+Lb99v8d2/SonvPdTxm6KbqadMBgDrgT7AHhLeEPu6ASql\njIwSqoVol9sS+Ivv7OpSyrCockvRzirfAe2klE8T6uO7dnyFEHuFEM2APHw70z+D1sYfbV6qDlii\nvemYCCEa8q3r3j5gINoZ5W6grhCiEmArpfSKUa4g2g1e1UgpTwkh8kWZTp6jNRtdibqh9EU70bkj\npTwkhCgDdBVCfEG7/3BbCNE/gWtQQ2iM9i2i2q8FlALMhBBOQC5gpBBiBVqTkLUQ4oCU8rqC9uN7\nD3X8pgjdGaU6koLQBkt9iZqpjwCeSSl3Rb1mAwyVUo5R2JYRWo8ZY6CalHKUEGI82j2CdVFlTICp\nUsrBP+ByfghCiOFSypn/cZ9VgKqxvYc6fm9+mqhH/TjPoP0BGwF7pZQJei/oSFkIIVYDN4EvQDop\n5ezvXh8IbJJSeitoqw5wCMgHFCJqdSCl3BCjTEfgkpTySbJdxG9IlJkr1vdQx3+HEMIZ2ADYo/0s\nVkopF8ZSbiFQC61JtGPUJn7i+vyZM3UhhJmUMihqs+082lnd+Z82IB3JjhBCAF2llKuSoS1noHAM\n+7gOHSmaqJVs2ijTmgVwA2gYtZkeXaY20DfKzbYEsEBKmeh8ST/Vpi6lDIr6rxFa97dP8RTX8QsS\n5VmTZEGPaustyr2AdOj46UTFebyP+n+AEOIRWk+xRzGK1Ue7h0bU/o6NEMIhDk+oBPmp3i9Cm7zo\nNvABbeDNw585Hh06dOj4UQghMqI1iV357iUnvp2svAPSJ7afnyrqUkqNlLIg2gsoH+VWp0OHDh2/\nFVGmlx3AACllQGxFvvs70XbxFOHSGOUWdwBt1Nvp6OeFEDrXHB06dChGSqnWl/8ryaU3348hKs/S\nTrQOA3tiqeKONhI8mvRRzyWKnybqQog0QISU8osQwhSohjbnyDcEDq4Wa33j4o6xPh8fnwNCydR/\nNx/Pj8DYKPZL9wsIYcC0QzStnoc6FbTpRYRLLgDGT9mGc3pbunaoEmvd77ly7Rmd+i/n+u3/bXZP\nmbiF0eNaKh7z7p0XWbh4H+v2/TeH29y/9YrurZbQsHdDqrZWnuF258KdNOnfJNbXwkLCmNd7HlVb\nV6VI1SJfny+cJpg1C4/w5MFbKlbPT9W6hdi67iwP77xm6pJOaPdYk4eBHZdTqmIuhveLfYxxEfPz\nqlllDCP61qNOzcIJ1PqWlWuPc+7iIzaMVf5+SilJV3E258ZVI7ODpeJ6u6++ZuXxp+ypmjfOMlMu\nPmd06Sz/en7DfXfKO6fGxNNGcX+x0fHqFYqGO1DF7L/NCFzXM+kBs/W67UxS/f2rvv1+RTkKrEGb\nWmJ+HNX2AX2BLUKIkmhdhBNlT4efa35xBE5G2dSvoD1n8YSSipEayeD11wiPSCi9ybecvP+eMoVc\n4hR0ACsLExpVzcX8jZf4+8Ddb16rW7MwK/48Tnh4RBy1v2XH0ZvUrlNM1Ri/Z/nKgzRuUyZJbSjl\n3PH7dGm6kI7jO6oS9IQwMjFixJ8jMDYz5sK+C1+fX7zuFqfPv6RFpwpUqJEfI2NDMmdLi56eHpGR\nGpLTM6txmzLs2HAuSW3UqluMnUduqK5XrVJ+jp68i0aj/PsqhKBqycwcv+epqq9KeRy5/MyboHDV\nmQVon9eJjNamqut9T4eMGTkq3yTr5/cLUwZoC1QS2tPFbgkhagkhegghegBE5QF6IYRwQ5sFtXdS\nOvxpoi6lvCelLCylLCilzC+lnKW07hXPL5x64ImhgbrhH3rlS61y2RIsV79STvq0Ks6UFWfJXGMe\ni5Yf4sjx28xcsI9ypXNiaJjwAkdKye6dF2nYqJSqMcbk2VN3Ht59Q40GRRPdhlLmr7zG8N7rGLRs\nEEWr/Zj+8pbOi7mVOT7vfQgJDOHVg1eUrleaUMfsmFuY4O3lx9yJuyhQNDMGBvpoNJKwMGU30IQo\nUzkPfl+CuHo58e7tDRqWZP/eK4pv6tFkymhPGltLrt5Tt6KuVT4bh9w+q6pjY25Ekcy2nHjto6pe\nclLBzh5DPT08IoMSLvybI6U8L6XUi9K5QlGPQ1LKFVLKFTHK9ZVSZpVSFpBJPET8l8z9st8vkEbF\nM6iqExGp4Z8zT2lQOWecZTqP2cOLt1qvyoZVcvFgX19WLe3LlevPOHLiDsWLZGXciKaK+rt63Q0h\noFCRb5e55SrEvSz+nqmzttG8Q3mMTQwV11GLlJLxk46ye/FuRm8cTfbC2ROuFAu5SuRSVK5gxYLY\nprUlPCycN4/fkD5bekzMTbj8Tp8ZY7ZSqWYBGrUpzbvXH+nffilj+69n4ZTYzJDq0NfXo233ykyb\nuU1VvZifV8ZMDmTJ6sjRE3fjqRE7jeoWZ9cVdSvq2uWycfraK/yD4z0v41/MbV+McrXj/hzLOaeK\nt37anKGq+vsePSHYXqoMVYslzYyjI3H8cqIeodGw7dIrWpZWd0D9ifueZE6figzpYv+inbvxmntP\nP5DZOTUAL9995to9d1zSp2HT6v5MHd+KYQPqY2Njrqi/VVvO0KZdpX/ZhcsrFHVPj08c3nOdNt0r\nJ1w4kURGahjQbxtXDl1h3OZxpMsSV2bdhMldIreq8gFfAggOCEYTqSEsJIwFfRcQaZGamg2Lcv7E\nA+ZP2o1LJnuGjG/ChVMPObT7WqLHFk3jtmW5fukZTx4rT9Xy/efVpl0lVm85o7rvFk1Ks3XXRVUm\nGBsrU8oVdmH/DXWu+fkzpMLBJm4zSvmo7/iPRC/qe5+r0I+bkOiInV9O1I+98iGDnTnZ01mpqrf5\n3kda1c4X5+tz119kaCet7XrjvjsMm32EAdMPMm/pAbbvvoSJifL03f7+wezacZF2CjdUY2Pc5E00\nalUaWzt116mU0JBwOrdZjccLD8b8NYZUDvHP3pIbx0yOVGtbjfl957NixAosbCxoPqQ5l91COX/i\nPgWLZ2HE5ObYO9qQK78zPh/9ktynuYUJ7XpWYcz4xEfMN2tZjtMn7+L5Xp1ZJF8eF1LZWHDm+mtV\n9VrVyc/ft//TY2sBCFdx89GRsvjlRH2juw9dKiVsF4+Jb1AY+888pXWd/LG+/t7bnzPXX3H0ohtP\nX3mzcvt1RnUrz9GDUyhVLDt/bjzFO3flNsqV285RsXI+HNMlbkb06uUHDuy4SteBtRJVPyH8fINo\nWX8B+gb6DFs9DDNLs4Qr/QDKNijL2L/H0mVyF3rN7oWRsREX918k1Cw1bbtrb4gPbr8mOCiMUhXU\nrQTiom33Kly98JQ7t18mqr6VlRlNm5dl0QZFe/rf0LltJdYccku4YAwaVcnJlbvuvPUOVN1fYjzE\nAC65f6HzvYvJttGpm63/t/xyor6iWylalVVnevnLzY8apbNglzp200naNJY83t8PS3NjyrZbg56e\noEiedFhamtK+dQW8vH2JiFDmTRAZqWHp4n/o069ewoXjYNjINbTrUeWHzNI/eHymaY1ZZMyTkd5z\ne2No9HN/cDZ2Nl9vKteOXuPFvRc07NOQm96mvHnpxZVzj7FJZU6WHI5cv/iUgzuvMt11K18+xRa/\nkTDmFib0HFKHIUNXJVq0evWtw+qVRwgODlNVr13L8hw4epOPn5QLtJmpES1r52Xtg/9u47O4ozUf\ng8K46JM8fUopMcuu7r3SkXh+KVE3Lu5IGisTTONxSfwejUayZPNVerUsHmeZkNBw7G0tmD+yFq+O\nDmLZuLpffdOXrjpCpgz2ZMxgr6i/HXsuY2dnTakyyjYOv+fi+YfcvOxGxz7VE1U/Pp4/8aRZjZmU\nbVCWtq5t0dNLWR+/qYUpLjld0NPT4/XD12za9YjnTzzp3K8Gpw7fYcqIzbx7402kRkPnRnPx8vyS\nqH6adSjHB88vHNh/NVH1c+Zypljx7KzYelZVvdSpLWjaoCQrj6ozwfRuWZxVO24QpnBiEY2Ukod2\n6k/909cTjCyZmcVvHyXLbP1NUBBNL13gbUTibsQ61JGyftU/gEO33TE1NqB80di9ZXYde8i4xaeo\n3GktJ6+8wMzUiNxZtAIeEhLGvoPXGdBL2cE2Go2GSXN2M3yUMg+Z74mIiKRvv2UMndAUM3PjhCuo\n4PbV57StN4fG/RpTt1vdZA3sSS5yFM3B26dvmdd7HitGriDQN5AynRrx3v0TI3uuYfSMVnQfVJvR\n01vhksmeTz7+ierH0NAA12ktGTJ4NcHBifP0GD6yKXNn71btcjmwd22WrDpCSKhyj5Y8We3Jk8WO\nLRdeqeorICSCmlOP8+KLetfC5jnTEhwRyeH36vzkYyODuTlDc+RkVtBtAjTqPHl0qOeXEfXE2gdn\nn3jO0E5lYhUxDy8/xiw8QbkiGahdPjszVp/nracvoJ3lmJgYsWFlX8qVVjbr3rHnMsbGhtSoVSTh\nwrEwdc42bFJbUKtx0gKWvufMsXv0aLWEblO7Ub6xmpPx/lsMjQyZsmcKlVtUpufMnjTp3wQDAwP6\ndlpFp8ndKFpa66YX6B+S5L5KV8pN3kIZcf0jcZumxUpkJ2eu9CzbrM4TJm9uFwoXyMT6s+o2P4d2\nKsPso0/RaJTPnC1NDelZLQdzXqjfaNUTgukVcjD7xSNCItUHMn1PC2cXKtrZMy/8NmHxHp2rI6n8\nMqKeGM4++oCHlz/Na+SJ9fXpq89Tv3JO6lXMwdBOZSic25HxS04hXHIhhODM+YfY21kr6is8PIIx\nU7cxflKbRM2CX7/yYsXcg4yfnbj6cbFg1XVG9lnPkBVDKFixYLK1+yMpUKEALjldkFJy/dh1ytQv\nQ/GaxbnprXXTGztgPVIjyZnXOYGW4mfklBZsX3+W+/fUmUOiGfdHa2ZM205goLqbzOhhjZkxfy9h\nKoKYqpXOgpmJIXuvv1HV18Daudh/4x3PPqvfaK3okpqhxTKSOmvSb6IAI3PlxtbImKXyHhFS513z\no/glRH3Zx8889VDn0ialZMKBJ4zuUR4DA/1/vR4REYm1pfE3gj+ofSncP2j72XfwOkNGK5/FLdt8\nhnROtlSpWkDVOKPH2rXbAjr1rU7GrGlV14+LyTNOsWPBDlw3uJK1YNZka/e/QghBcGAwNnb/iy0Y\nOGA7L5+9Z8GGXgBJsvnaO9owcEwjunSZp3gjPCaFi2aldJlczFx5WFW90iVykDVzWtafVR6MJIRg\nbK+KTNz/WNVsPZWFMYPq5GbSw8Tlh+qUPz1WxsmTIkpfCOYULISTqSlOeVOe+e93IcWL+kPvAGbs\nvU8aK3U25mN3PfH09qdt3djdGA0M9OnetCiprLSzP41Gg11qc9JncebgkZv8ve08owY3VNSXr28Q\nUyZtZeqMjomaZc9avAs/30A69U2ezVEpJcNH7OX0jtOM2zwOp6z/bWKl5MTOyY7jfx9n3/J9rJ+4\nnme3njFkwzhA+5kldVXTtH05rKzNGTt5U6LqT5jcliUL96v2W588piWTZu4kOES5jbluheyYmRqy\n9aI6d8z+tXJx1e0jVzwSt7EMSY8yjcZIT49xufOS2shI5+r4g0jxoj7i5ktcG+UjtYVyUddoJK67\n7zO5f5VYZ+nRODtakym9NuhGCIFehtx061CV+i1n8vK1F00aKDtRatzc3dSsVYSChTIrHmM0L557\nsmDyHqYu6RzvWJUSERFJr+6beHT1EWP/Houto22S2/yZlG1YlhZDWhARHkGu4rm++tVf9zJOFu8d\nIQQTF7Zn04oT3L71QnX9TJnT0r5TVYZO3KyqXoli2SheJAuLDijvUwjBtIFVGbfnAaEqEnaZGRtw\n0LUqJesmLgVENMkl7DHRCXvyk+JF/aNfCL2q51BVZ+Nzf0yNDWlSLe6AlZjL9pgzvlIlstOkQQkG\n9amjqK+Hj9/x98ZT/DG5raoxglaA27WbQ88hdciaM/Eh+tGEhoTTqdUqPn/4zKh1o7BMpTxla0qm\nSNUiNO7XmOI1i2OV2gopJXp6el9t7EklXXpbhk9uTvt2sxPlDTPCtRmnTt7l4hV1ycKmjW/NrAX7\n8fJR7upXsXgm8mSxY/EtL1V95XKywchAP9EOBz+SXIUMdRkdk5EUL+qLu5TEQF/5MP2Cwhiz8ATz\nRtaMc2ke7U4WHQSip6f31S8dYNncbrRsmnC6WyklPYasZtSYFjg4qE9eNHLcOszMjWnbI+n5XQL8\ngmndaBGGJoYMWT4EE3OTJLeZUon5ud70NlWdNTE26jUrQY486ek3aLnqupaWpkyb2ZEeg9eoGkv2\nbOlo17IcY9eqS8o3a2gNZv55gfdfgtUONcnENltPDkE+YPac4xYvidRtoCaZFC/qZXIoC/qJZuq5\nt9Qok5Xi+WI/4u/Amad0cN1N/T5/M2PNeSavOMP1+//bRNq26yKBQcp2+5dvPUtQUBjdetZQNUaA\nc2fvs33DOaYt7ZRkM8Inb3+a15lLuizp6D2nNwYqgrN+ddzd3KldZjIe75IW/SiEYNyctpw9do99\ney6rrt+0eVkc0towQ+Wm6fiRzdh/6MY338GEyJEpDZ0aFWLUgadqhwkk3j04mrQ5Q78RciEEoUl0\ne2yXMSPXPn1ifMg1XofrgpSSQooXdbUMaFuSmUNiPy0JoNfE/XRvVpSB7UtRqqAzGo1k7bFX7N5/\nlYiISD59DsA5fZoE+3n/4QvjRm9k8fLe6Ours4V//OhLx3ZzmbKoI3Zpk5ae1PPdJ5rVmEWB8gXo\nOL5jiosS/dE4ZXWiQtMKtKg5i5fPkpb4ysrajNmrutG391LevFZn3hBCsGBxD+bP3o3bc+XjsLEx\nZ9ofrek94wSRkcpnqeN6VeD0tVecfpC4azYsmjQvK7sc2onP68BA9nm4M/zuHXrduI5HcOJWD2lN\nTFlXvARNnJwZ63eN/aZuBGmSJ5f+/zd+KwXQy5sFJwcr0qSKPcfLG88v5M/uQJWSmalSMjNNquWm\nY8OClC2Vk8UrD3P3/mt6dknYA0VKSc/hf9KxczUKFFSXh0aj0dCmzUzqNi9JuXiOHFPCK7f3tKg1\ni0otKtFsULMUGSX6X1C7c20a92tM67qzeXhXnR/39xQoloXO/WrSvPl0wsLURT9mypyWoSOa0HnA\nClUpdju0roCpqRHLjypPsWthZsxC19r03niDkDB1s+TgsAiKjfqHd4kI4jr84iNHXnpjoKcHzn7M\ne/qEp/7+1HZ0pHCqVPS/dZOgiMSJsZ4QtM6QgQNly/MhNITDpi90G6mJ4JcU9dhseHp5/33m4vek\ns7PE1saMKp3Xce6GNuAkQ4lStG5elmYNS/LnxlOK7IMb9l/hyeN3jBrTXPXYR41fT0hIOP1dG6iu\nG5NH997Quu4cGvVpRO3OytIY/M5UaFKBjuM60rnxAm5cfpaktjr2qUYaByv6JsK+3qd/XcLCIpi/\nTnkWRyEEKxZ0449p279GNCuhQeWc5M/uwKQz6oKnTI0MaFYqI93OPyVShc87gIuVKWPPPWPWlZdM\nOO9GbhdjWrm4UCOtI90yZ8HJ1JTIJNrY7U1MmFOgEKNyaR0dchUy1Im7Cn5JUVf7RYzGwECf9dMa\nU79SDtbuvsnI1Tc4cFi7SXXvgfZMxYRmux6enxg2eA0r1/RXlWMd4NjRW2xdd4Y5q7snyX3x9tXn\ndGq8gPZj2lOhaYVEt/O7UaxGMXrN6UXvNss4f+J+otsRQjBtaWfOHr3H0nUHVNXV19dnxZp+TJu8\nlWduyvOm5MqRngG9atFrlrKJRTSLXOuwdvctrj33VjXOYfXzIATMcf+ouI6UktxpLDjSvCgeASGk\ntTCmTe50OJlqs2zOfvKYz2FhWBomjwDrffdbjBb3V+H+Om+ZePilRH3yzruM3nyTYRuvc+yux9fn\nRR51/uE9WxSjW9OiONjbsGLtMXIXG0RQcBh/uMY/85ZS0rH/Crp2r0GRYupyur9+5UWXTvOZs7o7\n9o6Jt6NfOvOIHq2W0GNGD4rXjDvz5P9X8pXJx6Clgxjacy0r/kq8sFtZmzF/fU+mjtzCg/vqZsLZ\nsjsxcnRz2vRcoipSdeTghrh7fmLDeeX2fIc02uyinVZfJVhFcjF9PT029i3HimNPOf3mk6I6QgjC\nIjWkMjEknaUJ5ob6OJgbkTZnKMc+vOfOly+Mya2N0I6U8ocIr394OLOCbtPH7zx7TJ5xN9RHl0vm\nO34ZUd949jn7b7ylVHZ7cqe3ocuyC4z86wZ6ebMghFBlwzQ2MqB0/er071mLfVtHcHzfWFYu7I5d\nmvjzl89fdxwfb39GuDZTNfbg4FCaNJlC1/41vyalSgynDt9hUJfVDFg8gALl1acj+P9C9iLZGb5m\nOOsmrmPh6huJbidXPhdGTG5O0yZT+fJFXe6UHr1rYWllyh/z9yquY2howLplfRg2ZqMqM0zL2vko\nkCMtow4/VzXGdKnNWNenDN1PPiJIYTCTkb4eUkqefgokWyozLIwMuOrxhQ3v3Wjm7Ewmc+1+ln6M\nWfY93y+J3kD9HktDQ05UqMSiQkUw1ddna6QbbT+eYr3+w2Rp/3dApORljBBCRmxpD8C4rbewMjNk\naD3t5qLn5yAazjqFU4Y0bJvbHCND5W58MX3SlfLg0Vsq1pnA8TNTyZZdedi9lJJWbWei0WiYuaJr\nojczl66/w8YpGxm8fDBZ8ie8f5CcaCI1uD93583jN1y964GfhxeB3p8J/uJHWGAwkVG+2foGBhia\nmWBqY4W5XSqsHO0omteRDDkz4JTNCQMVn1Fy4O7mzozOM2jQqwHD+pVOdDtTRm7m7cuP/LN/vCpP\nJ0+PT5QuPoQ9m4ZSqoTym/mUWbs4de4BRxY2VuzN9Nk3mIJNlrG0Q1FqFVKXFuKVVwAZ7S0Ivarc\nXLT1kSeuZ5/SKV96tj32ZGDRjHTI68THp9r4iEgp0ReCx35+zHn6mBcBgbjmyk0VBwdVY1NCQEQE\nXiEhVDt7Gillor0FhBCyXredSRrL/lVNkjSG5OCXEfVDt9zZcfkVa3r9LyhIL28WOo3eTZlCLnRt\nqizdbWIEPTg4jGJVRtOnX106dK6qqu7EGZvZu/USfx0agalZ4nKkz195jW1ztzF89XBccrokqg01\nSCl58/gN/xy6x7sb93n/wA2z1DakyZaB1BmdsHayx8LeFhMbK4zMTdGP8ovXhEcQFhhM8Bd/Aj9+\nwtfDi88+THpZAAAgAElEQVSv3PF2e43/Bx8ccmbGqUge6tTMR6a8mf4T98sPbz4wveN0qrWtxpjh\nlRLVRnh4BF0bz6NQiazMm9FNVd39e68wYuif3D47Q/Gh5RERkZSvOZ5mDUsysL7yG/iZa69oNWw7\n1yfXIm08B0/Hhxphv/DuM59DwrEyNvh6mLVGSgTw4YkJn8LCmP3kMYVTpcJIT4+Vz5/TyCk9XTKr\nT6ehhCwH/9GJOpDio1SiNy8r5UnLhjNulBl7kIOjqpKquFacG1TOyelrrxS19eLtJz57P6doYXUz\n3QHjNpEzV3rad1J3kPTJE3dYs+gIm4+OSrSgz1p0kX3L9+G6wZV0mZOeSiAupJS8efSG7Vsv8vzU\nFYSeHhlKFSRv42pUn9APEyuLJLUfGhCE572nvLt+n3mDlxMeFELmisVo0qI0WfJn+WHumA4uDoz5\nawzTOkwjPDScCWPVJ00zNDRg3tqeNK8yhRx50tOzvfKzY+s1KMGtm8954OFDGYWibmCgz6ZV/ShR\n2ZVK2dtSIKcyn/IKxTLSrWkR2q+9zqEBZdBPxE3TuLijYmEvk/7fh5VHb26mzRnKgVM+3Pz8mWoO\nDlSyd6Bkalsu+ngTEhmJicrYDh3KSfEz9d7Vc5DR3oLiWdNQNqcDozffZMPF1yxyrU3+HA7MWnuB\nyEjJ6onxuwiGh0dSvttmWjQuxaC+dRWPYceeywz/4y8uXJ2DtbWyHyXAczdPKpUfwdw/e1CsjLrc\nNdFMnX2GIxuOMGrdKOxd1EXWKiUkMISNmy7wYN9JQv0DyV6tDFmrlMQ2i/MP9Xv//NoDt5OXeXLk\nAnr6euSuX5n27cthruI9VtWf12emdZhGsRrFmDqpTqKu7eHdN3RrMo8Dhyeqjk+IxixYue/7xs1n\nmTpnN9f/6oiZqTJPq8hIDdW6rqd80YyMq5D4VZ2aGTvAlkeePPQOYGK5/zkQSCmZe/Q9W96+YXb+\nguSw+nbPKtpEk1wkx0y93BJ1EcHfc65PzZ8+U0/xor5tUAXOP/ZCIyVWpoa4NsrPuWA9Vm2/TppU\n5rx778s/yxJOpjVqzU3u3n/DP9tHKF72P3/xnpLVxrBr7xhV3i5+fkGUKTWUtt0r07JzRcX1YjJx\n2glObj2J6wZX0qRLOMJVLZ/ef+LPZUd4dOAMToVykbdhFdIXyYP4jyNSpZR43n3C/T0neHPpNtmr\nl6FT71o/5Cbm6+PLjI4zyF8+PzOm10+UsB/afY3Z43dw4fKcROX7USPqAO26LcLY2JBVw5W7rr73\n9qdo8xWs6lKcGgUSl3Y5NlFPyOX3c0g4qUwMcfscSNYYAYA9dz4jr7U1TdNrDzW55/uFKz4+PPX3\np4WLC0VSpU7UGL9HJ+paUryoR2xpT2BIODdffuLYXQ+CLC2YMagaoeERWJgZExwSjqlJ/H6xh88/\no9vEg9w6PzNBD5doQkLCKFVjHG3aVaJ3P+Uz+8jISOrVn4BjelvGzW6juF5Mxk86yrk953Bd70rq\ntMnzhY/G28ObVfP24XbyCjlrlSN/s5pYOdolax+JJdD7M3d3HuXhvpMUblOf3gPjD6o69tcxzK3N\nKV1X+Sao/2d/ZnSeQc5iOZkzp3GihH3R1L1cOfeY4yemYmys3idbjbD7+wdTtMJIxo1oSutSym/u\nZ6+/ovngbVz4ozqZ7BOXrTNa2JXEb0SXex8YStM9t+lXJAMtczniHxbBlEvPSRtqTUuXDKx9+YJn\nAQEY6emR19qa2U8es7JIMfLbJC1dBuhEPZoUL+pnJ9SkdFRSrzv6Ziz++wpVS2WhVe18itp46+lL\n8TZr2LZ+EOXLxJ2K93u6DV2Dj48fGzcPU/XD7ztoGQ/vvGbljoEYJsLbY+wfh7n0zyVGbRhFKvt/\n2ywTi/8nf5bN2cuTI+fJU68SBVvVwdQmZabmDfUPJMQvEGsn7edeyv7f39HggGDunLnDmrFraNin\nIXW6KEuVDBDoG8iMzjPIWjAr8+Y3VS3sGo2GQR1XYG5pwqb1QxN1Y/he2H19g7C2Nou17J17r6ha\nfxJn13YgZ2blN+D5Gy+xYe9tzo6qjFkiTy8KvOyOgZ4eAWERPPQO4MRrH8I0kvFl4j5J67aXH/2O\nPaKwgxUmBno8+RTIzIo5eOMXwoLz7+icKTO5LK2wMTJi9pPHlE2ThpK2SV+N6kRdS4r3U195/Cl7\nr71BL28WCuVypEm13Ow58YiAoITzXoeHR9JqzAEG9KqtStDX7LrA6VN3Wbqyr6of7PwVezl+4Bbz\n1vZMlKCPHneQywcvM3rT6GQT9IiwCJbOP8igGsOQkZG03jSTUr1aplhBBzC2NP8q6ACXvASXvL79\nHEwtTMlRNAd26e1UHwRibm3OiLUjeH7nOQP7b1cdJKOnp8e0ZZ15fP8tf0z7W1XdaIJMDfn8OYBd\n+65QuNwIuvVfTsuO82MtWyBfRqaOb0WzEXsJDApT3MeAtiXJm9WeHlvvqb7G848/cObhe8xLOhGh\n0TD+vBt/P/IkTCPxCgyj+tZrhMWRgKygvRUHmhYhrbkRJdLZMLpUFtJbmrDxgTsdijlQwNoGGyMj\nPIKD2e/h/q/IUR1JI8WLeoXcDux/7suoeccICg7j+gMPNBqJhQJvkhGrrpPKxpwRg5TnWbn34A0j\nh67lr60jsLKKfeYUGxfPP2TOHztZ+nc/bFKr9xQZPfYg145cw3WjK9ZplB12nRD3LtxjYK1RuN96\nSONl4yk/uCNmqZOn7Z9BTGEP8g9i9ZjVFChfgJK1tSdUqREucyutsL+4/yJRwm5mbsziv/qwYflx\nDh24pqouaE+8WrzyMLv3X6Vfj5psWz+YDx99WbPhZKzlu3aoQpGCmeg156zisQohWPFHfZ699mHm\nZY+EK8TAytSI/n9eZfWJp0x5+YEIjaRbAWfGl8nKkuq5yWhtGqeoA1gZGzCqVBYaZ3egmKM1Tz8H\nYqSvR0F7SzLnjcQuewjTHz+iaXpniqe2xTM4mLlPn/DHg3u4BfirGquOb0nxol4ulwNdGhfm3Xs/\n6vb+iyt337F0bMI27m3XPrP34HU2ruyreGPU1zeIxu3nMG1WJ/Lmy6B4jK9efqBVy5lMX9aZLDnU\n56oeM/4QV49cZdSGUVjbJl10fX18mdB7CUtd/6R0n9bUnTWMVC4/zh3yv+SSl+CCh4adC3diYGBA\ni6EtgMSdV2pmacaIP7XCPmjgDtXCni69LQvW9aRbl4WqUgloNBpWLDuE55dAXIc2olNbrf989qyO\nhIbGbm8XQrBsXjfuPnjDsiPKM1Gamhiye2Erlm65xt5ryupJKcmfIRX/jKzC+cdeBIVG0LtNIXKm\n1m5+/vXQg9te/qo8V15+CSYsUvN1A3XIqcc42etR2zEd5z9+ZNS9uwRHRJDBzJy+N2/wJkhdBK+O\n/5HiRX3jo0+UKezCxhlN2Da3OTvnt8Audfxub/effaDv0DXs2jSUVKmUzZo1Gg1tei+hStWCtG5b\nUfH4/PyCaFB/It0G1qJsFfWpdMdNPMLlg5dx3eCaZEGXUrJu00WG1h6FhX1qWm2cQaayhZPUZkrC\n200rSk+OnOfm5acUHNID0Ea8xrxxv3zwkjM7zihqM1rY3W67MXjwTtXCXqBYFkZObUGjBpPw8lJ2\nsPPwwX/y9Ik7Yye0IkPBTISEhLH/0HV8fYNoWLdY3GM1M2bnxiFMmL6Di7eUC7uTgxW75rekx59X\nufky4cNEhBCERUTinMacfC6pcLY1x9nWDLOS6bj53pe1d92ZUj4bpob6ipPrZUtlxl0vf6Zeek6f\now957BPIgKIZCbf351K4B+XS2DE6dx46ZcqMo6kp70PUpwXWoSXFi/qZa6/oMWEfALY2Zpgk4G3w\nxS+YxkN2MXdqBwrmz6i4nwkL9uH90Y/pszsprhMZGUmLltMpUiobbburP5JuwtTjnN97XivoSTS5\n+Pr4Mq7rAm5u2kfdWcMo3bs1hiaJC3hKiYQHh3B+wQb2DpzGne2HqTqmJyZW5lzw0KAX47jDsJAw\nIiMieXT1EVPaTcHbI+HshdHC/uT6E4YN36Na2Os1K0ndZiVo0nhKnDPtr9cRHkFwcCjzFnbH2toc\nD3cf9py5x/FT9yhZLBt2aazizWOUNUta1i7tTYsRu/H8qNxMUSyfE0vH1qXx/HO880l4FmxkoI+U\nktuvPpHXxYZUFsa88Q5g4v23NM7uQPn0Wq8sfT3x9f3yjefa89pZsq1hQcI1kiJprdhcvwAZrU05\n/soHa2MDRtd2Im3OUK5+8sFUT5/sFil3zyelk+JFfffCVmg0koiIyASX15GRGtr+cYTa1QvRrlV5\nxX0cOnqLVSsO89fW4RgZKXdR6zd4BaGh4bhOb6l66T919hlObTuF63pXbOyS5s51+/RthtUdjY1z\nWpqvnox9zh8Thv0zMTQ1of68URhbmOHn4UXqTNrjCoW+3jcbqUYmRmQtkJWeM3uSp1Qets7eikbB\niULm1uaMXDuS+xfu4zpGXbpdgH6jGpDG3opOXefFe1MwNDTAyMiAQf1Xsmr5YZYs+oeTx+/gksOJ\nPt1rYGho8HXVEdd5p3VqFqZ7x6o0G7mfMBVnojatnoe+bUpQf8F5/BRsuAohyOeSiuEbb7Dp7HPq\nTT9Jqez2dO1QGGMDvW/KhUREMunCc/odizuxVrZU5owvk5XO+dOTysSQ/W5eHHvlTe/C2iCpN37B\nvDL2IY+LSbIGJf1/I8WL+t6Tj7l+30NR/vHxG+8SGBTK7CntFLf//MV7OvReyoa/huCYTrlP+Jwl\nuzl95E6iPF1mLbrIkQ1HcN2QND/08LBwZo7exPKxa6kxoS+le7dCX8VN6VdDz0CfmpMHULR9Qx7+\ncxr/997f3EwveX2brbNY9WJERkQSovDMWQsbC0auG8n1Y9cZP+mourHp6TF1aWeePXJn3JRN8Zad\nt6gHpUrn5OGD1xQqnIVmLcvRq2+db75HH739WLn2OH9ujH3jdMzwxtjbWdF/4SVV4xzWqQylCjrT\nYtU1wiMSvtkNb5CXXtVz4B8SzpB6eRjXtACWpob/Ouc0QiPpUUgbXNRk9y1CFKQcDgiLpEkOB9KY\nGvHWL5hTrz/x2CeA2pntyJZPE+sh1zoSJsX7qVcrlYV5I2uSO0v8EYY7bnxh2NhNXDs9TXGAUWBg\nCCWqjaVLt+r06K389KBzZ+/TpuVMNvwznEzZ1J31uGDVdbbP287oTaNxcEl8xjqvt15M670IC3tb\nKo/qjonVjwmvT6n4v/fG7eQVnIvnI03W/4XDayI15DcJwN3Nnb3L9mLraEvXKV3RRGrYu2wv9XrU\nSzBb5Gevz0xuM5lqbarhOlTdISTv3T/Rqvo0Fi7uSd36JRIs7+Pjh63t/76vpkFhvHj5gZ37rvDP\n4Zs8eebB9g2xx1j4+wdTsspo+navSc/qzorHGBERSeMBW0htbcqaVvmTlA4iOkBJI+VX18RGu27S\nv2gGKrnE72p64d1nWu67w9jSWfjn+UfyprGglJMN9bL++7f+/nHCpkSdn7qWFC/q788Mw942/s3O\n2489qd7jL47uGUOhAspyckgpadZ1Iaamxixfrdwf/cVzTyqWH8mM5V0oXVG57zvAir/us27iOlzX\nu+KUNXHh2wC3Tt1i6chVFGlXn/zNav6/PZs0LDAIfWMjPr14x4cHbgR/8ePluRvoGxli7eRAziy2\nNBuozX2/oN8Cnlx7glM2J9qPbY9z9vhF0NvDm8ltJtOwd0MG90pYnGNy7+ZLerZYyMEjE8kfz/fx\n40dfVi0/TK++db5u6Pv4+DF64Bqev/zAhWOTOHfxEUNcN3Bs79hYg5Pcnr+nTPWxbJ3ZmArFMioe\nY2BQGFW7rqdisYxMqabcXLfo0CNsLY1pXTbzN5Gm0eJ+0f0zK26/ZUjxTOS3syQ8UoOhftwGgSse\nXzj44iOZbcyon9WeVAlEh0dqJJvOfqa0bZp/nbCkE3UtKT5LY0KeLl4+ATQavJMlc7ooFnSAKUsP\n8PLFB46dnqJYFH19A2nYYBK9h9VVLegXTj5g7fi1DP9zeKIFXaPRsGjmHh7uP0WtqYNwzJf4Azd+\nB4zMtSL3+vIdrqzaTsnuzakxqT+mqawxMjOJKiXZvWQ3/p/9WXp5KRf2XWB2t9n0X9Q/3rz0adKl\nYeTakUxpOwUTcxN6t1d+KEm+wpkYPb0VTRpN4fyl2XHmiLGzs6Z5y3JYWpri7x+MpaUptrZWVKlb\nlHszdwFQrnQuBvety8PH72LNyZ41S1o2repHqx6LubihIxmdlAWtmZsZsX9Ja8q1/xOHNBb0L6Qs\n107rspkICNHa8T/4huAXFM5r7wAeefvidu8DL74Ekd/eEicLYwLCIlh55x32Zka0zRO7S22JdDYU\nd7RW/Bv0CQlnl88rht+7TZ40FpRJn4pskWnIZ/3rxl8kNyl+pq65PyHO10PDIqjaexuVyuVh0tiW\nits9euIOHXov5cyFmaR3VhaeHBkZSd16E0jnrD6ny+2rz+nRagkDlwwkR9HEZWwMCQxh6oDlBH32\npeakAZinSb4UAmoIDw4hwOsTQZ98CfULIDw4hMiISJASPQMDDE2NMbYyxyyVNRYOthiZJS6vt1oe\n/nOaRwdOU7BlHbJU0LoFSikJCwzm4h/zMDIxYujKoQB4vPBQnMb4zeM3TO80ndnLO6l2WV00dS+X\nzz7ixMlp8eaIefXyA7t3XWTQkEZoNFr3zKEDV5MviyO9uipLFbxg6UHWbDzJ+T/bYmmu3OvptccX\nyrVbw9SBVWmdSXnQ3KkH76k++SiFM9lSJoc9IeGRZHO0ojwGZLIxw9xQn+2P37Pk5hu8gkLpmM+J\n4SWSbwM/ODySy55fuPjuC9fe+2JjbMj2J+91M3V+YVGXUtJ1xmm++AaxY+NgxQFGL15+oGS1MWza\nMoyy5fIoHktic7o8e+ROu/pz6TG9BwUqJO4IOh9PHyZ2mYNd9kxUHNrpP9sM9X/vzfv7z3hw1p2A\ndy8J/vCGiKAAjGzSYGSVCkNzK/SMTdDTNwQBMiKCyLAQwgP9Cff7ROhnb/RNTDFL64y5U2Zyl3Uk\nbb4cWKWz+yEmo/f3n3Fy2kqqjOmJXbaM6EVtrmsiInm9YQtfPn6h88TOmJhrZ/EhQSGEBIZgYmby\n9bnYeHrzKfN6zWPllj4UKKY8F390jhgLK1M2rhsS7zXXqTGeGjUL0z8q+rlFk2l0al6OxjHs8tdv\nPsfB3hrn9P+eiEgp6d5/BR+9/dg5rY6qA0gePveiSuf1rOhcnLpF0iuuN2zjdS488eLshJoYxDCx\n+F9y59grb+Zde82iarnIZWtB4923qJPFji75lbevFvO5x3Sizi8s6vP2PWfD5jOcPzIJC4u4f5Ax\nCQwMoWT1cXTsXJVefZUngFq4ah9LZ/3D1mOuqlIAuL/1oUWNmbQY2oIy9cskXCEWXt5/yfQecynQ\nrBYFW9X+ofbzyLBw3t54wM19j/j86AaRIUFYZcmDZYbsmKfPipmjC8Y2aRSn55VSEubrQ5DnGwLf\nPcf/9RP8XjxE6OmTKlcRCtXNhXPxfMnqTx8RGoaBsRGB3p8Renpf0yKE+PpzZcpies7qiY2dDS/v\nv+TklpME+Abg+dKTzhM6k71I3Oas26dvs8p1FRv3DSZrTuXRuUGBobSpNZ2GLUszbkSrOMt5en6i\nYZ1JNG9VjiMHb6CnJ9i1fyyBb304e+ERS1cfwTa1Jc+ee7JuWZ9YTTFhYRFUrT+JsqVyMKVTIcVj\nBLh67x31+vzN5j5lqJhH+eb/rH332Xn5Nat7lSavs3b1GB6hYceWu/Q++oDtDQpSLupUpIfeAeRO\nk7TDVuJDJ+pafklRP3j2Kd0mHuTS8clkcFGWtU5KSfOo3NQr1vRTLI7XrjylccPJrN07hGy5lNvC\nv3wKoGn1WVRuWZmaHWsqrheT26dvs3j4CioN60LmCnFHGiYFqdHgfvsRl/++hs/dS5ildSF1/lKk\nzl0Es3SZkv0mIqUk+MM7Pj+8js+9KwS8fUbqPMUo0aIYzsXzfxNIlBSeHL3AjfV7qDGxH7ZZXHh0\n8Axvr96j+h99yWvox5hGY6jbtS6l65fm9unbnN15liErhmBkEvdhFOf3nGf7/O1sPzKctE7KXVHd\n3/rQqvo01qwdSNVqBeMsd/vWCx4/eovbMw9Gj2uJp8cn1q45hufLD9SsWpDmjUuzc+9ltu66yJa1\nA2OdjX/09qNEZVcmujanTWl1KZVPXnlBq2E72Du4AsWzKs+aeOyuB1JCtfyOhEVoMDbUrpBOP3jP\nhLXXWF4jDxmt/2eG8wkOw0BPYJ2ItMXxoRN1LSl+o/R7Hrh50WncfvZsHqZY0AGmLjvAczdPVRuj\nnp6faNFsGhMXtFcl6CHBYXRotpRClQslWtD/XHeWS8u3Umf6ENLmVX5Ah1JC/AI5ufIKnucPomdk\njEPJamSo1xFjG3UZD9UihMAsrTNmaZ1xqtyIMP8veN86x+nFewnzXYtj2VpU7lEKs1RJ2/jKUb0M\nevp6HBm3CNsszoQFhZCtSikAJnSZS5EqRajWthoALjldcMrqhH5UFGVc34+yDcvy5eMX2jdZxI4j\nw7CKI1Xu9zg52zJ3TXc6d5jLqbMzyJI19vxABQtlpmCh/9md587eTVhoOH2GNaZIJu3s+fFTDywt\nTNFoJLEtmOzSWLFvy3Aq151I5vnNKVVQuatj5RKZWTOxAQ3H7eXwiMrkz6Bs36Zafu3K5eR9T158\n8KdrFe0qomKetMyxM0Mj5ddc6+PPu/ElNILHPgEsrpb769mmOpKPX0rUP34KpP7A7cyZ0p7SJZRv\nOB4/dZdF8/dz5sIMTE2VLfVDQ8Np0ngKzTtWoHKtuGdX3xMZqaF7x7XYpbf7mmxKLUvnH+TerqM0\nWjwm2RNx+X/w4fjik3hdOU7qPMXJ0XEElhlz/DS3SCNLG9KVr0e68vUIePcCz7P72dh8OGkKl6NK\nv6rYpFcXBxCTbFVKYZ8zC5/feGCb2RlLB1vubDuMvpEh2Xp0ALSr1Ev/XCLANwB9BQFudbrW4dP7\nT3RutYK/d/fFSOFss2jp7PQZUZ9GDSdx4dIcLC3j30D+a+Mprl99xq79Y0id2pJPIWHcPHmPt+7e\n9OlWI95gvLy5XVi7tDdN+6/g0saOuDgqj1iuWzEHC11rU2fGYY6OrEQuJ+V18zrbMGbLLZ5/8Gda\n6yLM/ecBrz8GkK1KJm4cesa2R+/RSNhcrwCn3nxiwfXX5E1jSWrT3zdg7meQ4iNKowkNi6DJiH20\nbFJaVQqAV6+9aNN9MWs3DsJZhamme69F2Ke1oedQ5bZ3gGHDdhPoF0i3ad1UbVZF9ztv8nYeHThN\n46XjklXQA70/s2vUZv5u54rQ06fw6BXk6Dgcq0w5U4yfu0X6zGRrPYAi41djaJWKrV0msH3IWvw8\nvBLdprWTPRlLFcTSwRZNRCQBHz9RsJX2M73kJbhx/Aantp76egNOyBwphKCta1tMLUzp0+MvVXli\nWnSqQOESWWnbfla8+V0APvn4U7N2EVKntuTNay8OH7zBzn1XyJo5LYULZub+wze8eevNM7fYzxKt\nU7MwQ/vXo/6gXYrOHohJ85p5mTawKjVnnsbtvZ/ievbWppz+owZPPf1oOf8MK48/ZX2fsgSHRbI/\nIAi9tObMrpQDfT2Bsb7ALywCU4NfRoJ+GX4Jm7qUki7TT+Prp87TJTg4jNI1x9GiVfmvXgVKmLlo\nJ5tWnGDLUVfMLZVtwgJMn3eO438fZ/yW8aoPUJZSMnvc37jffEj9uSMxTaUsKjYhwoJCODz3OJ7n\n/sGhZHXSV2uGkWXSjw77L4gICsD95G48zu7DoURVag6tleTI2eOTl5OuYE5y163Ih4fPOThqLj0m\nd6JIlSLabI8KbfqhwaFMbTeV/OXzM2Wi8mjksNBwOtSbTYUa+Zn2R4c4yz16+JZ6tf6gcdMyvHnt\nRdq0qShVJhedGpZi+NhNXL/1nIwudly+9ozdfw8jR7Z/TwCklHTr9z+PGH2V+xWrdlxnyoqznBhV\nWfWReJ8CQhFAKgtjNp9/wa6rb5jRpgiZHSx5e/o12x57opHQv0iGb6JRk4LOpq7llxD12buf8ff2\n85w/MhHzeFzPYiKlpEP/FQQHh7Fu02DFs9Grl5/QpNEUNh0cTsasypf+Z4/dY0Tf9YzfMh57Z3WH\nJkspmem6kQ8P3ag3Z2SyhPxLKXl2/BJnFmzFOls+MtbvhEnq5D/M+b8gzO8zrw9sxOfORTLU60D1\nnkUTfUD2m6t3OTd/A6kypMPA2Ii0+bKTv0l1Stqpz8fu6+3L+GbjaT64OX1VeJt88PhMi2pTWbm6\nP9Wqx13v5nU3bt5wI0eu9OTK7YKxsSGN6k7ESE+PLWsHktbBhvlLDnDy7H32bR0RaxthYRFUbziZ\nEkWzMr1rEVXXB7B0y1Vm/XmBEyMrk9FevedKaHgk1ScfY0DtXDQukQG/oDCO3fXk+D0PalqaU1PF\n8XwJoRN1LSnepr7/9BPmLz3E5RNTFAs6wOJNp7h+/Rmnz89Q/GP18vpCq5YzmbigvSpBf/bIneG9\n1jFw6cBEC7rXo+dfsxAmFV93L/4Zv5Fw/y/k6uKKVWZ10a8pDSOrVGRr1R/HsnVw27qIjZeOUndi\nx6+ZGtXgUjw/DReOxtfdCxuXtF83ZC9/1Iv1LNT4sE5jzZDlQ5jafirl8lop9mF3SJeK2au70aXj\nPM5dnE2GjLF/ZwoXzUrhotqzQF++eE/TdvMoUSoHM2Z3/nrGqW1qS8qWyhlnX0ZGBuzcOIQSlV3J\nnSM97cupyzfUu2VxIiI0VJt5ihMjK+Gi0iVRTwgy2llgaqSVms0XXvLw3RcKZkxNg2o5vqYX+F0R\nQvwJ1AG8pJT/OlhZCFER2Au8iHpqp5RyclL6TPEGrS7j97P7r2GxBlzExbUbbkz84282bxuBhYWy\niLLPIesAACAASURBVMbIyEhatppB/RYlVW2MfvkUQLeWS2k1ohXZC6sL2482uXx46Ea9uSOTLOhS\no+Hwggts6Twem5yFKDRi0S8v6DGxcM5CgcFzsS9ehe29pnJg5jE0CrIBfo95mlSkK5DjXx4235+D\nqgTnHM50n96d3u1W8N79k+J6RUtlp+uAWjRrNpWQkITT4D55/I7SZXMxY3bnr889c/Nk5brjODtp\nPZbiWnXb2lqyb+sIho3dxPmbyk9oiqZ/25L0b1OCKtNP8cY7QFVdQwM92pTLzKD1V6k66ShX3bwp\nnystPappHR2Mizv+K+Pjb8ZaICEXuDNSykJRjyQJOvwCor5wZieKF4375PLv8fHxp2nHeSxc0pNs\n2ZW7IQ4b/SegzYutlIiISLq2XUXR6kUp16ic4nrRLJi+i3c3HlBvTtIFPdD7M393n4fXtZMUGDIH\n52rNEPoJe3P8agg9PdKVr0uhEYv4/Pg2mzpNS9JG6vckRtgLVSpE9fbV6dJqOSHByg+Gbt+rKk4u\naejVb0mCZY8fvY2p6f986P86eI2SVUZTu3ohWjUrCxDvijR3zvRsWNGX5sN28cr9s+IxRjOgXamv\nwv76ozphr14gHWcn1GRW2yKs6VWGJiW1R0XGvAkZF3dEFrSP96CNXxEp5TkgoTc8Wc01KV7Uo7+w\nStBoNLTutYSGjUvToFEpxfWOHrnJnr8vMntVN1WbSSNG7EXfUJ+WQ5XnnYlm+eIjuB2/TP15Sbeh\nv7lyl7/aj8UqUy4KDJ6DmYNy3+RfFZPU9uTrN5U0hcqypfMfnFj7IFnalVKyZd9d1fXqdquLQwYH\n+vfZrOpg6MkLO3Dl3GOWrI3/YI427Suxbcs5/hi7iYF9VzB65HpWbxjIqCGNFI+xZrWCjBzUkAaD\nd+EfqD5X+YB2pRjQtiRVpp/kpZe6w6HtrU0plOnbFcX3N6FTD95TYONlFn3wITBc/QrsF0UCpYUQ\nd4QQB4UQSV5ap3hRV8OkRfvx9wti4pS2iut4uPvQtfMCZq3qhq2dco+TJetuc+P4DfrM6aM6CnL9\npovc3nKQ+vNGJinIRmo07J/6f+yddXQVV9eHn7lxdxIggQiEENzdrThFCi0uxYpDcae4FCvutBT3\n4u5uSbBACBES4q73Zr4/0vTN19J2zr1pIZRnrawFZPY5k5DsObPlt49xctZGvPpMoGir7h/l6fzP\nkFQqnBt1oNTgWQQe3MSBKXvQqJVPAnoXGUkpXFm2nbWrxIZkSJJEv9n9CHoSxPylVxTbmVua8P3m\ngcydsJPnz0L/9LoKFT1YvOxrvEsVpXwFdy5dX0izzyqRbCyWFhs2qDnVKhejx8xTf1tW+U77btUZ\n3asmjeae42W4mGPP4c/eKJpXKMyZKU25/SKaMtuucVL1cZ3a/4R7gIssy+WAFcBBXRf84Ktf5ITd\niq8fN3c3/fo3o7DC+LtGo6Fhw4nUrF+SgWNaKd7H/0ko3VotZvzm8RT1LqrYDsD3mi/LRqyi7fKJ\n2Llrf6LOSE5h35gNqFOSKdl3IoZW/+3OvMyURJ5tWUBWRhodvv8GE2vtZ1wmhEWyf9AM6ozoQbcv\nxOQZ3ga9ZcYXM1j782Ah8a+dmy+wa/NFrl1fjPFfSBXkJkfRMSdpqpQcjZg6Nb34rpfy/FFu1u6+\nzey1lzg5rgElCuW97K1fcBxGBiqKOWUftJQkVPOi+uWbKz8J2YTee0zo/Se//f325v1/uAdJklyB\nI+9KlL7jHl4BlWRZVp6g+R0fzUk9xcSAaTO7KnboABOnb0NPX8XXI5XXGScnpTGoxzq6jO0i7NBD\n/ENYNmIVzWYN08mhJ4RH8lPv2Rha2VFm2Nz/vEMHMDC1oNTA6Vi4lWRHrxnEBP75qffvsCzoQMsF\nY7iwaBMvHrwQsnUs4kjf2X0Z0ms9sdHKT7Kde9WjqLsjw0avVWyT06+RItiRaWioz74fR7NjzxV2\n3xaPrwMM+KIKM4Y0pMm8c/gFx2m1xl9RysX6N4cO/z+h+iHF3QtX9KZq3w6/fYgiSZKj9OuriyRJ\nVck+aGvt0OEjceqiP9QAN6495edNF5i/uq9QHH3ksF14lPOgXgexMWcJMQnM6beYWkO+onCFkqK3\n+xuRzwPZ1e87nGo0o1iXoaj0P7VY5yCp9HBr24cin33J3kFzePPwqdZrOXi60mjiABYMWkpUaJSQ\nbaVGlajWohrffL1NKL4+Y2l3zh9/yIljd4T2C3z1lrY9Fv/poOp34WBvycEd3zJkzEbuP9GurLD3\n5xVYMLopzeaf4/6raK3WECXZ25aSm6/S6qQPa6JieRKdJNTV+28jSdLPwDWghCRJwZIk9ZEkaYAk\nSQN+vaQj4CNJ0gNgKSCeoPsd+cqpa3JNhc+JB2rj0BMSUujZYzHTl3SngIAuxrL1dwjwCaDn1D/v\nBHwX6gw1swYso3jjmpT4TLxKJofQe485MGwBHh0HUrjh5x9Me/+HhmONppToOZYj45bz6spdrddx\nrVmBCl+1Yla/JYqHV+fwxagvSIxNZPbCC4ptrKzNmLemDwP6r+TtW+Wn3yJFHUhJyWDyov1C91iu\njCurlvSj/eh9RMYkC9nm8FXLsqyY1JKWCy9w0z9SqzVEsDU3ImhVR4Z85sXjkDg6/PIIt41XmPAk\n5B/fWxtkWf5SluVCsiwbyrLsIsvyJlmW18qyvPbXz/8gy3JpWZbLy7JcU5blG7rumS9i6j5+QZw8\n+4C4uBRsbc0ZNeR/8W9tnHq3novQ11cxY2kPxTavAyL4osk8JmydQBGvIn9vkIt5Y7eQFBVDizkj\nte6EDLz+gFMz1uHVdyLWntoN29CF9LhokkNekBIeTNbTYNKSo8lIS0CdmYpGk/06rFLpo29ogqGR\nJcZmtqhKZKsxmjl7YGTzzwzG+CsSXz/Hb/U03Dv2p3HfslqtIcsy5+auQ52WzrTVymfZQvZw8Gkd\np7H5wAi8yyr/mVkycz8vn77hyOFpivd7+zaOmlVGsXPjcOrVFiugmDhjB9dv+XNyeQcMDLRLtP9y\n8Tl9phxk55Ba1PPWXoRNG4Kjknkbn0r1Scf+9Zj67/mhdtf33lGaL5x6zcaTqV3Di3Kli7Lt50v4\nB4SxcmEf6rcTGwgM8MuRW4wetYH9l6ZipnC4hlqtoX3TRVRvWZ3PeopJ6W7eepm72w7TacPM32Zq\nivLqyj1Of7cB74HTsXTTLnST+Po5cpYGPSMTzAq5AvylzCxAnP8jYo4eJTrMD406Ayt7dyxsnDGz\ndMLYzA4jYyv0DU2yJx8BWRo16swUMlITSEuJITkhnMTYEBKiXwFg61QS8xpVsSlV9R+X+M0h+U0g\nvisn4tq2D036iw2OyEGdnsGBb2ZRrFF1Bg9Xnn8BuHr4KodWH+LopUkYmyhLgGZkqOnceDbd+jdi\n1KB2ivc6eeIuw79Zy6MrC7C2Vl4mq9Fk0arTPLw8C7FksPjvVA7nbgbQZcwetgyowWfltR+sri36\nXbZ9cuq8R6cuSZILsA0oQHat5jpZlpf/7hr58smZjJ64jZvn5/z27z/tusyk2bv4oksdJk/7Ej09\nlaITTUxMIhXLDWXxxv5UrqG8+3PqzJM8vfWUsZvGCikvBj8LZkb3ObRbPhk7d+3GeL2+8ZCT09dS\natBMLFy1m28a9eAqrw5swKpYGTKT4jFzdsO1da8/vd76QfareNQbH5ITwrErWAozy4Jan7RlWSY1\nKZLoMF8iQh4QGfIQc+vCOBeri1m7phiYal+pooSU8CB8lo/HrX1/Gvf92wKEd5IQHsne/tMYs3KY\n0JxZWZb5YeQPWBewZskS5Ym0Z34h9Gm3mBu3v1c8Rxdg1LB1xMUls3PtEMU2ADExSVSpP4HZU7vQ\nuar2iffrD4L5fNjP/DClFZ8XzLuJVkr45NSzeZ8x9UxgpCzLpYDqwDeSJP3hGFqjqidVKnpw4fL/\nmku6dq7D8dOzCA+LJTwsVrGz+WbYKpq1qSzk0J/6BnNy60lhKd20lDQWDFlB7SFdtXbobx4+4+T0\ntXgPmKa1Q89SZxJx8wxun/fFs/soPL4YROTdSwQe3vL/rrN+kPzbRw72hcpQ1KsJ5laFdAqdSJKE\nqUUBXDwbUqnhKJp23UDx8h2IDvPjzqSevF6+kOQ3gVqv/3eYOhWh9DezCdi7hsBr97Vaw9LJgYbj\nv+b7EatIjFVe1SJJEj2n9eTmsZvcufZcsV2JUs50H9CIfv2XCSUCv5vXk3t3X7DnwHXFNgC2tubs\n+3E0Q7/dhN8L7Tt0a5R34cS67gybc4xtL5TL9n4i73hvTl2W5XBZlh/8+uck4AnwB/1QPT0VlSq4\n02vQKhYsPQRkx9GLuhYgI0PNIYU/vKdO3uPejReMmKK8A0+t1jBq0Da6fNsFu4Ji4YLFk3+kQEkP\nrROj0QEhHB2/PFvzXAf9FpW+AaaFiiLpZTepGNs5UW7kQiJunyP0wqE/OPJ/A5WeAY5FKlGp0Wga\nfrESE/MC+C0Zj/+CqSSHBvz9AlpgVtgN7wHTOTlzPeG+/lqt4VqzAh4NqrJgzEYhR2thY0GvGb0Y\nM3grqQLa5n2Hf0ZEeBwrN/11t2luTE2NWLdxGEPGbiYiMl6xHUD5sq4s+q47Hb89oFXH6W/reBXk\n7MaeTFt5nhX3807C4RPK+CCqX34tzq8A3Pz952JikujxZT1OH5zMgSO3KFdnHLt3Xmbr5jPcue1P\ni1Z/3xySnJzGkMGrmLa4G6Zmyl8J5yy8gLm1OXU7KB/KAXDn9B1C7z2m7iixKpkckqNiOTBiMe7t\n+2NTUlwuFSDa53/fSkMLW4KP/4ysyW69NrSyo0qdkSTfvEVqcvR7LQkzMrHCs2JHGnVZhW1Bb/y+\nn8irpfPIiM/7EjlLNy9KdB/N4bHLiA99q9UaNQZ0JjE8kk2bLwnZVWpUCfcy7kydflyxjYGBPjOX\n9mDh1D1ERys/9VatXoKu3RswcOwmoXsE6Nm1PnVqejFgwQWdfi683B24tK0Pq3beZtaloA+67PBj\n4707dUmSzIG9wPBfT+z/j/7D1zJ0zCb8ngZz/exsRoxux+WLvoSHxTJ2Qkfc3P8+0z5x2lbKV/Gg\nVsNSiu8r5HUkR9cfpc+MPkKhh4SYBNZN3ULjyQMxNFWmEJkbdXoG+4avwLFGMwpUbShsDxB0fAeB\nhzeTmZIdJihUvw0mBQrzYNGI/4VZHIqhb2CKSqX/QZRG6ukb4VGmNQ2+WIGxqTX3Zw4i9sc9yFq0\nsv8VtqWrUqT5V+wbvpT0pBTx+zQ0oPGUQVxfs5PIELESvu6TunN5/2We+gYrtilT0Y3P2lVm5Jj1\nQntNntYFP98g9h/+wznpb1m+oA+Pn4Wy/qz2DVwARQtZc2lrHw6efcroX/zJyvrk2P8N3mv1iyRJ\nBsBR4Lgsy0vf8Xn5654NiYpJIjg0muIlnVm3aRhZWfJfzmjMzbOnITSqP4GDV6bj4Ki8nblrx1UU\nK1eMtoOUqzYCTB+0EnMHW2oN6SpkB9lJtX3fbkWTkY5XnwlaOduAfWtJeRuCV+/x6JuYoUlPQ88o\nu8on4Ps5ZKQl4Or9GSmJbwl6epZqzSdjYvbvVKKIkBgXwsNLq5GQ8Bg8DmP7vC2Te7FzJelxkXT5\nYYhWZaZ3tx8m5K4fc38aJ/T/dG7nOS4fvMzB098qtktMSKF1jWns2j2eqtWV51auXnlMr25LeHxj\nMVYKh2Tn8Mz/DbWbTuHs+u6U8RTTYP89cQmptP5mB+7ONqzrXAaDPBphd8EvnIuPw3/7+6x9jz4l\nSnmPJ/VfW2M3Ao/f5dBzWLdiIDs2DmP+9/2wsDBh47pTih26LMsMHbaa/iNbCDn0i6d9CAsIo0Vf\nsfK1++fvE/H0FdW+7iRkl8OpVbdIDPLHs5vySU250WSkE+N3G7NCruibmBH96DpBx3/i5d61yKfu\nU7HBcBxdKhEfFUBE8AMqNx7zQTp0AAtrZ2q1noWTa1Uezh1Gyv7Tebq+e8cBqJMT+WXhWa3sK3zZ\nkvSEJDZvvSxkV79TfdQZalZsvKfYxsLSlFFT2zN02GohEa5atb1p2qwC4+bsErpHgBLFC7Hwu+58\nNekIqWm6teVbW5pwcl13ouJS+GLDbVIzdBNdy6F+KSemdSr/28cnsnmf4ZdaQDeggSRJ93/9+EMR\n+IuX4RgbG1K9phfdezbi4IHrBAcpe+09efwub0Ji+OrrBopvKjNTzczxe+g6oSsGhsobm9JT01k3\nbSv1x/RB30hZPXJuol8GEXhkKyX7TfrtZC1CnP8j9AyNKP3NbGIf38FvzTSCju/ACTcM36YR/PwC\n8dGvcCvdghKVOlO16TjMrf/9WmIRJEmFR9k2VPtsMk9ubefN2lXIWXkjyarSN8CrzwTeXDjEm4fP\ntLDXo/7YflxfvVOoGkalp6Ln1J7sWrSL5CTlXaqtOlVDT0+PFRuOCt3nrLk9OLD/OvceiCege35V\nj9IlXRi3Tky24F2YmhhycPmXWJgZ0mLZVeJTlOvOf0KM91n9ckWWZdWv7bE5Uz/+MCBw3vcHWbb1\nLLIsY2dvSWhIFHb2fy+Rq1Zr+PbbTXw7syMGBsrlSZf8cAO7QnaUry/25F+95AhOpYrjUqW0kB1k\nx9EPj1+De7u+WmmhP147A59l40gMfIaxnSNefSeiTkmmfPk+FHStRumafcjMSCIu4n9VHzkNQ/kB\nawcP6rRbQHzUK/wXTkOTLtay/2cY2ThQvOsIjk1Zo1V8vYCXG8UaVGPl3L1CdsXKF8OrqhdzF55X\nbKNSqRj33Rcsn32Q1FTllSm2thZMn9mVb8ZtEU5WSpLE6u/7cejYbU5dFRM2excGBnpsm9ueciWc\naDj/AuFxqTqv+Yk/8t4TpX9Ho3qluXPbn1KeAxkzcgP9BzbH1PTvK1iWrzuMnYMF9Zoqbw9PTkrj\n4KqDfPntl0Lhj6jQKHwOnKHmN18qtsnN0TlHMXVyoUD1JkJ2clYWj9fOwNDKjqKtehD/wpcsjRpT\nRxdq1ZmAjWN2Pb4kqdDTMyQ/p6kMjS2o1nwyBkbmPJk39rcksK7YlamGjXdljkxXLvGcm6r9OhFw\n8TZBT4OE7DqN7MSp7aeIjlRe1VK+qgdlK7kxZ7HYvXbv1ZD09Ex27r0qZAdgY2POph8G0W/mMeIS\ndHfCKpWKZROa075JSep+d0ZrTfZP/DkfvFNv26MBazcOZf/hyaxa9w2Dh/697nl6eiarFh5lxGQx\n0asF31+iZLWSwpK6q+buoWyHppgXEI9Pv338kohb5/DoLKYrImdpeLlnNcb2ThTrMgSTAoWJ8buN\nSk8f6wfJ6OlnP/hkOYv755eTkZZAUS+xh8aHhkqlT/l6Q7Au4MnjeWPJTM4bh+D++dfE+T8i6Kb4\nxCNjSzMq9/qc1d/9LGRXwKUANVrVYPZcsVzB0Int2PLDaeLjlfcWqFQq5i3qzfiZPyuah/p7Gjco\nS+vPKjF6lc5aU0D2G8CUgfUZ07sWDeac4UGgTkqzn/gdH7xTb9dqJuHhsXiVdMHBQVmyc+naQxTz\nKkSFaspnmyYnpnFiywnaD20vdH+vn7wm5K4f5b9sKWQHkKXWcHzmVtzb98PQQrlaJACSCsfqTXDv\nkK3g6VCxLrJGTcSmzdmfliSystREhjxElrOo2mziB1G6qCuSJFGqei/sC5Xm6cIJqNPEwya/R8/Y\nhGKdh3J6zlbU6eJOr1SbhsSHRvD4xmMhuzYD23D5wGUi3ypvEvIoUZDajUoxb8keob1q1ylFmbKu\nfL/ljJBdDvNnduXsRV9OX3uplf27GNi5CkvHN+frrXfR5HHp6n+ZD96pFyxoy/YtyisUMjPVbFh6\ngkECk4wAFq+8SqmapSjk/oem1r9kw6L9VOzaCkNT8eTmqVU30Dc1x6GKWD16jhCXRdHs8EpWZrYj\nKuHekvTUeDIzUpBlGZVKHwfn8lRsOEL43j5kJEnCu1pPLG1defH9TLI0uldT2JaqjHmRYhxfLF4N\no2egT9U+7dm8cK9Q3NqmgA01W9dk4ZILQvsNGNWSH9edIylJLBwyfVY3vl90gMRE8TCKhYUJq5f0\nY/DckzpXw+SmY9NSXN/RD4OyxVGVVj4p6hN/zgfv1Js1r4Sv72vF1+/dfQXnovaUr6r8ByQjQ83J\nrSdp3b+10L0FPQ3i7eOXlGrbSMgOIC0hmaDjO/DoOFAw7JL1h+tVBoZYP0jG3NqZmLfPiI14jiRJ\nf6vCmJ+RJIkytfsDEqFrVubJmm6ff82bi4dIihQPBxRvXJPUuESe3Hzy9xfnomXfllzYc4EkgXi1\nW3EnKtf0ZOnqQ0J7eZcqQv2GZVmyWbvy0BbNKlKhnBvz92g/fORdGOYqZPgzx+4bHPuvDeLI73zw\nTv2H5Ufo2Km2omtlWWbxkgP0HtJUaI8TB25TuFhhipYUi6VvXnGUcl98plUJ48mlZ7AtUw2zwm6K\nrk95G0xadDiSSvXOsj5ZljG3LkSREo148WA/mRkpH61Dz0Gl0qNSo5FEhj4icbdyfZQ/w9jOEaea\nzTm5UHwtlZ6KCl1b8eNKsZJD+8L2lK5VmmVrxTo/ew9pyrY1Z9BoxEo8x03oxA/Lj5AioEGTmyVz\nevDD+pO8CtFuDJ4Scjv2p6HxdFpygW+336HbistM3qmdINt/iQ/eqRd2sad1W2UazzeuPSUlOY06\njcXKCtevvkiT7mJJxJjwGIJuPKRUG/FW/pTYeMKvHqdoi26Kro/39+HB/GH4/jCF1KgwJJXe/3Ps\n2YJc2a/9tk5eeJRrh4Ghdtrt+Q0DQzMqNx7D45tbSXmr+/Qb56adiHp4TSttmBJNaxH1MogQf7H7\naNq9KWd2nBFqLCpX2R07B0uO/yJWQ17S24UaNb1Ys1NMuyYHF2d7hg9qzvi1eZM0/TNUpT24GxDN\nmtPPKOpgzvGJTfBZ1JY7L6MIePupYuav+OCd+vIfBiq+dtmqw3TuVU9IIvepbzBxkXFUqC82QGHb\npvN4NqmJkYXyYQQ5nFlxEYeK9TCycfjba2VZJvrRNVzb9aFgnZY82zKfjPiYbMeu0WB1P1suJ+vX\n6UNWdm44ulQUvqf8jKVtUTwrduLlmvk6NycZmFpQsG4rziwXD1HoGRpQqk1DflwvlowsXqE4RsZG\n3Lws1gT1Zd/6rFgl9mYAMGxEW35YcUToIZKb0UNbc+3mc275/HMj5GLiU9j7Ig4LEwNmd8n+3bz8\n9C3xKZnYW/y7Ou35jQ/eqVtaKjtxxscnc+HEQ9p2qSG0/tqNN6nTvg4qgeHTGrWGJ0cvUKqdeCw9\nIyWNsKvHKdxY2cAESZJwbdcX+wp1KFSvDVbFyvJk01wyUxKx8UlDkiTSUmJ5/fQM6aliUqsfE67e\nn6GnZ0jsDrFGoHdRuH5bou5dJiVG/Pvp3bo+/mevC800lSSJep3qsXGz2Om3aZtK+Nx7RUiw2GDs\n6jW9sLQ05eSZh0J2OZiaGjF1XEcmr/vnTus7fvHhaUAUo0a3wMhAj9CYFB69jqV9tSJYmhp+Egf7\nCz54p66Ug/uuU71uSWzslE/RUas13PjlBrXbKovZ5+B7zRczBxvs3MW7P89tvI9VsdKY2BdUbKPS\n08fQwhpJpaJo6x6YF3bj6cbsSVDpKXHEhD/BvmApjEyU69t8bEiSijK1+/P83l4yEnSL9xqYW2Ff\nsQ7n1okNmgAwL2CHY6li3DktFhap0aoGD84/IEVAx9zE1IimbSqxdqtyOV/Ifoj069+M1dvPCdnl\npne3+rwICOfqPbGmKyVkZmq4/iCYkT1qYGdtSoRTAc77huEbHEt51+ypTCrVx50v0oWPxqlv33GO\nFh2qCtncufYcWydbnFzFFAAP77mOZ9NaQjaQHUoJv3KMgnXFyi1zo9LTx7VdH+wMXTi/ZziXDo7F\nzKoglnauWq/5sWBh7Yxz8Xq83b5V57UK1mlF+NUTZGnEQxQlmtbi2D6xB4KlrSUe5T24eEqsAapl\nh6oc239byAagY+c6XLrgIzxIIwcDA33GjWjL/B15n7jU05MwNTFg35nHBIXFsXT7DW7HZdKgVEGa\nlP1fyXFoTApXn0Ww6IgvfsFxeX4f+ZWPwqnHxCTicy+Quk3EEqQ/7/WhcpPKQjbqDDWBV+9RrIH4\ngN6oF69RpyZh7ambopyeoTHm1i6kJcdQru5grOyUVdD8FyheoQOhL6+QFqPbxB1zFw/0zS0JvS/W\nUATgWrsiYY+ekZwgNlGqSpMq7Nkv5tQr1ShOZHgcrwLC//7iXFhYmNCseWV+OnpLyC43Pb+qz627\nL3n2Siz883eoVCpWTmpJSHgCA2ccwchAj26tyvHVgMa/XXPl6Vtm7n3AiuNPCItNpdnsvFXxzM98\nFE791Il7VKntiYkCTZgcZFnmwYUHVGwollR8evsp1kUKYmZvI3qbXNvxEIfK9bXS786N+e0o3ry8\nQrXmkyjg/ElyNDdGxpYUKdGIqF3icrO/p0DlhtzcrVwiNwdDUxMKlffi0SUxB12hQQV8rvigVitP\n9urpqajXtCw/7bsgeJfQ8YtaHNwvHmLKwcTEkH49G7LmWN51meZgZKjPgeVfsnvJF4zvV4cqpbNP\n6KrSHvgZmjP/kC9Vizkwq3MFFveowrdtlA/A+dj5KJz6oWM3hMsYg15FkpmRibOn2FDo4yceUrSG\ndo40+uFV7CtoN7M0N/oGJlRpNgFbRy+d1/oYcSvVkhD/izpLCNhXqEX0oxtahWCKVi/P2dNiTt3G\n0QYbRxv8HihvtgOo3ag0V8+Lv1E0bFyO+/deEhPzh4FjiunXoxE/7rpMeh5ppP8ec1MjbvmEsvyn\nm2g0WcQlpLLsxxs0alyatpVdKF4wW7H15MM3ebJfjQKyTh8fAvneqcuyzK3Lz6het6SQ3Z2rzyhZ\ntaRwg07ovcc4VxaX140LCUeTnoq5i3I9mr9CLx9J5/7bmJjbYetUkqh72tVi52Bs54ShpQ0RVBgZ\n8QAAIABJREFUT8RPoi6VSxNy109Y7ta7ujcHT4lpn1ev68Xd6/5CJ3wAExMjatXx5swFcSGzHNxc\nC1Da24Xjl7Ub5q2EelVcaVGnOHp6KqLiUsjKyqJRNXcK1Mw+nc/c+xD9T4nT38j3Tj0kOIrMTDVF\n3QsI2Z25/BrPSp5CNilJKcSFhFPAy13IDiDkji/WXhV17vLMbjT6xN/hXLwesee1m2qUGxuvitw5\nKj5gwsrFCY1aQ/QbsdZ2z4qe+N8Xc5DWtuY4FbbB51GgkB1A/QZlOXVV/JSfm87ta7Lv6j9Xsw7g\n6WoPwNOAKOIS0ijnlV3csPJBJL5BsUztWO4f3T8/ke+d+p3b/pSt5C7sLF/5vMK9jJhzDvQNxL5Y\nEfQEhm7k8PhCIFbFxE/4uZE1GjRq7dq7/2sUcKlIXOQLneV5rYqVIeGln7CdJEk4envw8pHYKd+9\njDuvfF4J71e2kjt3b4uflmvV9ub6Nd20XFo3r8Tx0w/QaBGmEqVoIWseB0Sybs8dZq6+wKb99xg9\nrGm+nhWQ1+R7p37l9mO8yojVi2dmqgkLDMPFU8zu9ZPXOBR3FbLJISnoORauusXA41/6cuPYLJ3W\n+K+gr2+ErVNJ4p490GkdC9cSJL5+JhxGAbAvXpRrd8XquB2cHUhLSSNOMM7tVcaFa3fFnXOZcq68\nCggnOVn7aVLOhe1wcrTm3uMwrddQShlPR7bM/px7j8PIyNSwY2FHapR34WJ03ilH5nfyvVN/+SyM\nYiXE5HJDAqOwcbTB0FhMiOu+7xtsXMXnemamppEeG6XVqLrcZFy599s0o0/8PXYFS5F+Q7c6akMr\nWyQ9A5IixJUbbV2diQ0MFbKRJIlCHoV4+VzMQRbzKsTLZ+JO1cBAH88Szvg90S18UrdWSa4+yPtG\npHdRvZwLq6e24rthjfD2KMDZGwEcOpu3ypH5mXzv1IMCIygiGE8PDozEsYij8F7xoW+xdhFrVILs\nJKmxQ0EkPT1h2/+3TuRLrAsU12mN/xLWDsWIi9S93M7UyYW4IHGHae3ipJUwmGMRR0Jfi9V+F3Ev\nQPAr7WrzPb0K88xft+qRyhU8uBf47+V7csKtsixz6U4gnT77VNKYQ7536uGhsRR0thW2sXUSswFI\niojG3FF8ZF3im0iM7cQfBn9YJzYYS5siOq/zX8HStiiJscFahU5yY2znREKYuMO0cLQj6a24Brit\nky1hoWJvBo4FrYmOTBSugAFwc3Pk+RvdGohKl3Th8bN/Nln6LiRJwtbKhGMXn//re3+o5Gunnpmp\nJjkxDWtbMaXEmKgErOzFdVJSYuIxtRG3S46KxdBK/GGQGzlLQ2pyNKYWYm8l/2UMjS1AAnWK9nXY\nAIbWdgT4iieojSzNyUxLJzNDLN5raWfJ02Cx6UT6+npYWpsSFaV8kHUOBQvaEvZGtzmhHu6OvArU\nrYtXW4Z3r4G7i/gh7WMlXzv12NgkzC1NhKR2AfzDMjBVqP6Yg0atQZ2WjqG5uE55WkISBuaWwna5\nyUxKwMDQFNWn+nQhjE1tyIjXbWKOgZkl6mRxZylJEkbmZqQkiDVBmVmJ2wBY2ZgRFyv+ALO1syBW\nC7vc2NlakJSc9o81If0da6aJTS37mMnXTj01JQMTU/GpQxlpGRiZiGkyZ6RloG9kpFWd+ZvXEnpG\nJsJ2uVGnJmFgKK7d/l/HwNAcjY6dpXrGJmjStasO0Tc2JD1V7JRvZGIkbANgYmoopPKYg7m5MUlJ\n2le/QPYDzMrSlPhE3db5hO7ka6euVmvQ1xdPPmrUGvQEk5YatUZIcz03WRqNzklSWa1GpSdeH/9f\nR6WnT5Zat3I3SU8fWcvh1ip9PTSCcW49PXGbHDttYur6BvpkZuo2XATAyMiA9Azd1/mEbuRrp65S\nSVpNb5FUknDyTBub32wlCXRM1qFSIcv/fHPHx0b2oG7dfszlrCzQUoRNzpKRBFvYZVkWDilm22Vp\npTOelaWd3e/RaLLQ0/vUrv++yddO3djYkPR08ROUgaEBmelipzcDIwPUggmvHFSGhmgydOsE1TMy\nQZ356dVWFLU6DT0jY53WyMpMR2UgHuYDUKdnYGAklgfJ0MIGID1NjZFg7wVAWmoGJibafX25SUpO\nw1yLcOgn8pZ87dQtLE1IShCrEgAwMTchNUnMzsAw+5dMnZ4hvJ+LuwpNqm6JKANzSzLSEnQuz/uv\nkZEaj4GFtU5rqFOS0Tc1127/lFRMBZPrqUmpmJiL52ASE1IVj3/MTWxsElZWuuVrUlMzyMzUYGH2\naX7o+yZfO3UzM2NkWRZODnm7GJMYK6YJIkkSJlYWpMaJa4mY2lnpPGJNz9AYfQMT0lM/TXhRikaT\nSUZaAoaWupW7ZSbE4OolfgJVp2cga7IwNhN7U0iMScTcRuwhIssysdGJ2DuIV1lFRMRTwFG3B9/r\n4EhcCtvpLFj3Cd3J105dkiQKFLQmIkzMYdo7WhH7VtzJmjnYkBwpXs9rXsCOdB0n8QCYWRUiKe7f\nb/DIryTHv8HEooDOSeq0mAjMHe2F7ZIiYjCzsxZ2dLERsZRzF3Pq8XEpGBrqYyb4AAEIfh2Bu6P4\n0JfcPH0eimcx5XN3P/HPka+dOkDhIvYEB4p1wzkXtSciRNzJWhZ0IOGNuJ21ixMpEWIaIO/Cys6V\n+ChxBb//KvFRAXky6i81IhRrZ3FZiYQ3EVgUEm8WiwyJpJCLWLNaSGAkhYuKP3gAXrwIo7iHbh3P\n9x8GUq5MUZ3W+ETekO+dulsxR175i81nLOrhyNvXb4UrZ6yLFCJWCw0QUztryNKQEa9b155R1XLE\nhD/RaY3/EjHhTzGuWEanNbIyM0iPeYu1i/gpNDboDTZa2IW9CqOoh9hDJMA/HFdBG8gO2/j5vqa0\nt27yE9duPaO6m3g8Py+47RNK+2E/v5e9P0TyvVOvUdGLZ35iIQlLK1NMzE2IChU74deo4EzUC7FR\nY5AdJjIvUpzEIN2mw1h7liM6zI+srPfTtZefkGWZyNCHWJfQbYZrUshLTBxd0DMUr0aJfhFElQpi\nypwpiSkkxSXhLHjqfu4XgmcpsdGMAKEh0dnKkAW1D7+kpWVw47Y/dSr++7pExy49p+XgH+lZ8VPo\nJ4d879QrVPTA70GgsJ1rKVcC/cTs3Eq7EfEkQKsKFEs3bxICxIct5MbQyhYzq4JEh+m2zn+BhJhA\nJEnCtKBuIYGEl35YuouNSswh4mkArqVchWwCHwdSpEQR9AQb3fweBNKghvhbyY3rT6lazVOnBOf5\nS36UK10Ua0vduqZF2bjvLn2nHOLgqHq0rqybrPXHRL536qXLFiUkKIpEQa2MYuWKCY8Nc3B2ACAx\nLFLIDqBic1edBzYAFHKvSeiLKzqv87ET+uIyhdxr6lyNEffsIWWbiI8vTE9MJv5NBEW9xB4qL+6/\nwKOch5BNZqYa3/uvqVJVXGv/0gUf6tTVbSLX3kM3aN+mmk5riCDLMjNWnWfO+sucn9SY6sUd/rW9\n8wP53qkbGhpQtpI7d66JOejWjV15eltMWF+SJAqV9yLknvhMR6cynqRGhJKRqFtJokXbZoQH3iQz\nQzc9k4+ZLE0mIf4XsWrTUqd1NBlpJAT44VxJ3Om9efgUJ+9i6BuKSTs8uf2EFg3Fkrt+91/j7GqP\njRZlkKdP3adlHe21yFNTMzh49DadqmqXpBVFrdbQf/phjlx4xpXJjfEspJtQ3sdIvnfqADXqleTq\nebGQRNlK7oS9ChOuV2/QqAxBN8Wnr+sZ6GPjVZHoR9eFbXNjaGWHfeGyBD8/p9M6HzNvAq5hYeOC\nqZNur+Sxj+9iXsQTY0vxxpygm49wqSoWDsnMyMT/nj+Va5UQsrt63o8a9byFbAB8fV4jSRKlSmr/\nfdp36AaVK3jg7CQuSS1KckoGnw/bSUh4AufG1sfR+t8N92iDJEmbJEl6K0mSz19cs1ySJH9Jkh5K\nklRB1z0/CqfepV1dLp58JBTrNjTUp2S1kvhc+dPv9TspV68cIbd90GSKJysrta9A5J2Lwna/x7Fj\nF14+OkKW5tNcxt8jy1m8eHgQx3Zf6LxW5N2LlG8j/jsmyzKBV+/TrrWY7ZObT3DxdMHaRuwhcuHk\nI75oV1vIBuDg/mu0aVddpxDVqg2nGNBGu5yDCJExyTTquwV7G1MODqqOuXG+kaDeDHz2Z5+UJKkF\nUEyW5eJAf2C1rht+FE69VOkiqPRUPPEJFrJr37YMd07fEbKxdrDG1s2Z4NtiDwMA15oVSA59SVq0\n+Iiz3Fi4lsDCxoXXT0/rtM7HyJuAa+jpG2LjXVmndTJTEol9cpdiDasL2759/BJ9Y0MKeYjNzr17\n5i4VG1UUsnkTEk1oUDS1aoud1GVZZvfOy3RvK/715XD95nPC3sbSuv4/Ozc3IDiG2t030qi6Oxu6\nlMFAP/+4LVmWLwN/1enYBtj667U3AWtJksRrU3ORf747f4EkSTRrW5nj+28J2TVsUR6fKz6kCU5S\nL9a4Bs9PXxOyAdA3MsShcgPCr50Qtv09zt364X9/H5np/95cyA8djTqDp7d/wuXLfjonSCNunsXW\nuzLGluKaL/6nr1G8UQ2he9CoNdw+dZveX5YV2uv4/js0bllBWIL6+tUnGBjoUbmiWFI2N3OXHGDM\n0NZayV8r5d7jN9TtuYnh3aszq5HbxyhDUBjIfRoNAcRrU3PxUTh1gP49m3F0z000GuUNRTZ2FnhW\n8uTOGbHT+pedq/H6+gPSk8STlfW/rk341RNkZYoLg+XG3MUDx6JVeHpnh07rfEy8eHgASzs3nWvT\n5awswi4doXafesK2mkw1/mev0/mrmkJ2vtd8cSjsQBE35R2osixzZPd1+vZsKnqbbNpwmp59mmjt\nJO89CODugwB61xd7GxHh9LWXNB/4I8u6VWRgad3GQX7g/P4/QSfVvo9m6kKp0kVxcLLi6jk/6jZR\nnqDq0aMaG9dfonZb5TFJS1tLXCqX5vmpq5Rp30ToPm1dC2PuUoy3N89QsHYLIdvfU6hPf+5P708h\nj9rYOf3zcc0PmYSY1wQ+PkH5yT/ovFb0o+vomZhRsKx4WCHg0h1sXQvjVFSs7f7SvkvU/lwsLu57\nP5CU5HRq1xELvURExHH8l9us+K67kF1uJs3cycTRn2PyD8W2d/zyiFHzT7B7aG3qlNQpGiFERXsx\n9dZbV55x68ozXbYMBXJnqp1//Tet+WhO6gCdetRl1xaxRGSjFuUJeR5CeKCY1MAXvRvhs/+0Vo1I\n9b/5jJDTe8jScppODgZmFnh0G8aDC8vJzPjvhmE06gzun19GyardMLLRrWZZlmWCT/xMnf4ttDrF\n+uw/xec9GwnZxEfH43PFh0G9xOLpuzZfpGOPOsIDNdavOcHnHWpiZ2chZJfDmfOPeP4ijH6NCmtl\n/3cs2XqNCd+f4dSEhv+qQ9eGqrVLMGR8m98+tOAw0ANAkqTqQJwsyzol3T4qpz6odwvu33xJyGvl\nzUGGRgbU7VCXMzvOCO3lXd0blZ5Kq/LGQuW8MLJ15O0N3ROdduVq4OBcnoeXVv9ntdb9rm/GzKoQ\nFl/oPnw4+uFVZFnGrU4lYduIpwEkhkVRuYlYkvbC7gtUaVYFSyvl2imx0YmcOXqf4QPFHElychrr\n1hxn3GDtavjVag2jJm5jwfD6GBrk7Yt+VlYW3y46ycZ997g0uTGlXXRTjvwQkCTpZ+AaUEKSpGBJ\nkvpIkjRAkqQBALIsHwMCJEl6AawFBuu650fl1M3MjGnftRbb15wVshsxtDaXD1wmJVF5jFySJDoN\naMn9n46I3iYATca0J+iXH7UeaJwb5wFDSEmM4MXDAzqvld94/fQMUWG+uH4zRuckWpZGTeDhLTQY\n3h5Ji3Fy9348QrnOzdEXcHaZGZmc+ekMI4bXF9pr1+aLNGpZgQIFxHTQN6w7SZ26pShRXLtY+Kr1\nJ7G3s+Dzxnkb7svM1NBr4gGuPwjm4sSGuNh/HEPWZVn+UpblQrIsG8qy7CLL8iZZltfKsrw21zVD\nZFkuJstyOVmW7+m65wfv1CMj44WunzC6E4d2XScuRvmkoULOdpSrW46zP4s9DKq3rE5ieBRvHorH\n1By9PbAqXobg07uFbX+PysCQEiNnEPj4BG9eXtV5vfxCZMhDnt7Zgdfwmeib6O4Ewi4dxcimAEWq\nlxO2jQ4I4c3Dp/TuU1/I7trhazh7OlNCQIwrNSWdnzacZ9K4zkJ7JSensWzJIaaPbi9kl8ObsBhm\nLdjHitH187QKJSklnbZDdxCflM6JkXWwNf80PUkXPninvmj+PqHrCxW2o1mbSmxdLRbaGDuuCSe2\nniA9VfkUJX0DfSr1bMetDXu0Cn20mNiesEtHSY14I2z7e4ys7Sk5bBY+1zYQEaK7xsyHTmzEc+6d\nX4rXwCmYOupUAQZARnwMQSd+pvmkzlo5rNub9lG+SwuhKUcatYYj644wemwzob32bL1MhaoelPQW\n6wRdteIotet4U7a0diJnw8Zupn/vxnh7iGvE/xlRsck07rsVJ3sL9nxdBVOjj6Z2473xwTv1HdvP\n8zpQbDDF9Mld2bnpotBp3dPbmeLliwuf1nv1qE1ydJxWsXXzAna4NO2M/8/L8yQebu7sjtegqdw/\nv4zI0Ic6r/ehEhvxnFsn51K89xisiukmRpXDyz2rcarZDFs38QfE2ycvCff1p99AsUqoa0euYWVv\nRZVayqtsUlPS2bjiBDOmdhPaKyoqgRVLDzN3gnadtvsP38THL4jJX2qvE/N7gsLiqNNjEw2qurG+\nc2n0BZUpP/FuPvjv4qAhrZg6abuQjaubI03bVGLDMrEmn8nTWvLLhl+EhlLr6evRa1xnrv2wgyy1\nRmg/gM+G10GTlkz41ePCtu/CqlhpvAZN5d65pYQF3syTNT8kokJ9fnXoo7EtXTVv1nxwheTQAFqO\nay5sK8syV1f+RNW+HTAyUR42UGeo2b9iPxOntRZ6M9ix4TwVqnhQvoKYcuScmTvp1LkOxbUYORcZ\nlcCQMZvYOLUFxkZ5U8L4+GUEdbpvYsAXlZndxP1jbCp6b3zwTn3CoBZcv/aEa1fElBHnzOjBvh+v\nEBaifNqQp7czpWuW5tjGY0J7VWpcCRMbS/wOiZ3yAVT6erT6rh+BR7bmSRgGsh279/DZ+Fxdzytf\nsa/lQybE/yJ3zy2hxIBJ2JbOO6nXxNfPaT6jH/pG4sOlX5y7QUZyKr171RWyO7vzLAXdClJFQLwr\nLjaZTStOMm9Ob6G9/Hxfs2/vVb77toOQHWQ/tAaNXE/XL2pTK4+GYNx4GEyjPluZPbwRwyrkXSjn\nE9l88E7dzMyYRTO7MWr4etQCJ+FChe3o0qceS78TqwiZPqs1p386TXR4tGIbSZIYMqM7tzcfICVW\nLLELYOfuTJHPvuTplnlkqfNGpMuiSHHKjvuewCcneXh5NRq1bh2s75OsLA2Pb27l2d2dlB69AGtP\n8UTmX+HWtg8Fy4g3GmWkpHLthx0MmtEDlUDoIDk+mUOrDzFz7udC+61ZeJQmrStSwkt5iEiWZcaM\n2MCESZ21qkvftuMiz/zfMLNn3nzPT159QZshO1jfrypfuYlLMHzi7/ngnTpA5w41sbE1Z+0qsVPn\njEnduHHpCQ/vBCi2KexiR8MuDdm1aJfQXi4lXCjRvA5XV/wkZJfDZ8NrY2hpw6uDm7SyfxfG9k6U\nmbKczPRkrhyeSFJc3rwJ/JukJkVx/eg0EqJfU3bySswKueb5HmXLKX+A5+bm+r04VyqFVxUvIbv9\nK/dTqXElPL2VO+dX/uEc3n2D+bPFTum7fr5EXFwyw3o0FLIDeBkQzpjJ2/lxVqs8CbvsOu5Lzwn7\n2TeiLi0q6J7c/sS7yRdOXZIk1i7qy4K5ewkJVj5X1MLChJFT2jNn/M9CQ6anTGzC01tPhYdoDBn3\nOeE+z3l9Xbz6RJIkPp/fh5hH14m4c0HY/s/QNzbFY/RUino15srhibzyO4Ysiw3cfh/Iskzw8/Nc\nOvAtBYpUpMT4uRiY6zYQ4V3JaG0derivP/5nrzN8+ldCdsHPgrl+5DqzZilvlJJlmbkTdvL1iOZC\ndekxMYlMHLeVdUv6CYtuZWSo+bLPMqaM7UDZEmKSB+9iza7bjF54ghPjGlKrxKeQyz9JvnDqAF6e\nhRkwuAUjh60TqhQZ2q8V+vp67Nl2WbGNmbkxk+d0Ysv0LagFdNONTY0ZPKcvFxZuIj1RvG3f2NKc\nNouG8nLPap2HVOdGkiSsv+pA2XFLCH15lauHJxMf/SrP1s9rEuNCuHF8JgG+v1BqxFwcenXXqhko\nh2ifm6RGviEzKTs0Jv/6gNfWoavTMzg3dx19p3bHwkZ5SCMrK4vN0zfTflh7bO2V250+co+w0Bgm\njRGrXJk4dguft69BtSrFhewAFiw9hJOjNUNauArb5kaWZeasu8SizVc5P7ExZYvm/y7RD51849QB\npgxtzatX4ezZpXxGp0qlYvXqIayYc4ioiATFds3aVsKuoB2/bPhF6B5L1yqNa80KXF66TcguB4fi\nrjQe34vHa2eQHis+C/WvMHV0odTkJbiUaMCN47N4cPEHUpOUv/n806SnxuN7bSPXjkyhgEtFyk5b\nibmL9tKwAM+2LSLol+2EnN6D/49LSIsKR1KpfnPs2nBj3W5s3V2o3kJMi/zC7gto1BpGf1NDsU1S\nQirzJu5i1epvMBDoVD1z+gEXzvuwYJJYg1IOQwc0Z8tk7VUcIduhj118ip3HfbgwsREeTtppzXxC\njHzl1I2MDNi6chBjR2/k7Vvlsz7LlHXl869qMnfCTsU2kiSxaHkXjm8+zpsAsVj0iOldePv4Jf5n\ntRtd51G/KoUbtMN31RQyU8TG7f0dkkqFVZd2VJq5ESMTKy7uH82jK+tIThATNMtLUpOi8buxlfN7\nhgFQYfo67Lp3QdLTTac7OfQV6TERVBi/Eo/O32Dh6oXvqsnIsky5CrFa9QYE3/bhxbkbjJnXS8gu\nJjyGPd/vYfEP3dATSKou/e4ANRt4U1tgjmhCQgpDBq5iw7L+WFhoN/LNMv41Nlbaj4vTaLLoP/0w\nV+4FcW5cAwrZKte1+YRu5CunDlClUjF69m7MsMFiAlbzZvXG7+Frzh1XHu8uXMSe9kPbs37CerIE\ndNqNTY0ZvWwwl7/fRsIbscapHJqPqo91ifI8Xj0NTYbu+jC/R9/UnIL9B1BxxgYMjMy4fHA8t07N\n423QXbKyxOvtRZHlLCJDH3H37GIu7h+FLGuoMGU1zoOHYWghpmfyF7tgZFuALHUmkiRRpPlXmBVy\nI+TnSQDCp9CUmHjOzl7L0IUDhMIusiyzaeomGndtLJQcvXvDn9NH77H8+4FC9zlu9CYaNylP00bK\nKlbyWgguI1NNt3H7eBUS+6nt/z2Q75w6wHdj2hP4KoLtW5UPXzY1NWLd+qHMGvMT8XHK491jh9dC\n31CfY5vEKm/cSrtRqUcbTk5dgSZDvExRkiTazeiAsUMhHq+dqfNQjT/D0MKagl/3p8rc7VjWqsnz\n+3s4veNrHl1ZR0Tw/TwthczSZBL1xgff65s58/MAHt/chq1jSSrP3orzoKE6y+YCxL/wJTEwW4vH\nxNGFtMg3hF87iaTKPvV3XjGQjOQU/M/eELz3LE7PXIVXi7qUrinWxXpp3yViwmOYNln5MIvUlHQm\nD93CsuUDsLFRXvr3y5FbXLroy7KZf95xuvnH8xw9fpcDR7InhUmS9P8KCeSgJ4r3+8N9p2XSfthO\nUtMzOfxNTSxM8s0s0Y8G6X3KtUqStAloCUTIsvyHyRaSJMlywrsFr3z8gmjYeiYXrs7HzV15dv7r\nwctJjE9l/pq+im1Cg6Jo32AO47eMp2hJ5boZsiwz7etlmNhaUX9MH8V2uclSa9gzeiOa1GRKfj0F\nPcN//tSTGvGGpGNneRt0l4SYQKztPbBxLIGVnTsWNs6YWjiip//XjToaTSapiREkxoYQHx1AbIQ/\ncRHPMbcujGORSph91iDPyxOfbJxDRlwUWRo11p7lKVClPpK+AQ8WjqD04FnU/jxbm/vej0ewKGhP\n8UbKY9s31+8h7NEz5vw0Hj2BSpKI4AimdZzG9iOjhE7p8ybuIioinj27Jiq2efs2jhqVR7F360hq\n13h3meX0Obs5fvoBLZtV5OLVx5iaGHJk93ggO5ErhWg/8CExOZ12Q3+moIM5G78s96/PEtXvsg1Z\nlrVOAkiSJD+OWa/TPXjbfq3TPeQF79up1wGSgG2iTh1g/trj7N97ldMX5igu2UpOTqNKpeGMnNKe\npm2Ua2YvXXebYxuPMXPfTAyNlXcepiSmMLbdNCp2a03JluLj0SDbse8dvZHMpHi8+09Dz1j7WKco\n6tRkEgIek3nDh/joVyTFhpCaFIm+oSmGxpYYGJqi0ss+jWVp1KgzU8hISyQzPQljMzvMrZ2xsnPF\noHoZLN1LYWD2zyTL0qLfErB3Dd4DppEeF030g6skhbygSPOuJAX58+bEeuqN6YND8aKcmr6SojUr\nUK7Tnw55/38EXL7D5e+3Mv/gLKzsrRTfk0at4btu31GlSRUmj2ug2O7m5aeMG7iRO/eXYWenrIxT\nlmXat/mOcuXdWDDx3cnRiMh42nZZwNY13+D5q/TuFz2X4Ps4mJMHJuGcpX1iPjY+lZaDf6R0cUd+\naF8SPR2qlbTlk1PP5r06dQBJklyBI9o49aysLJp2mkflKsWZOkN5vfCtG8/o2H42+y5MpUBBZfFb\nWZbp230TZtZm9J4u1gAS+iKU6V/NpsW8UTiVFi8vg+zX//3jt5MSFkSpQTN1rtnWBTkri8zEODKT\n4lGnpSD/2gUr6Rugb2yCvpkVhpbWv4U8/kmy1Jmo9A1ICQ/Gd+Ukyo1ZgpG1PWnRb4l6cIW06Ld8\nPqsTT45dIvplMOG+z3HwdKX+t8re1KIDQjg0bDbj1o+iWLliQve2d9leXjx4wc5DQxUdkFU5AAAg\nAElEQVRPJ4qPS6Z93ZmsWvMNTZspn4T0w/Ij7N55mWsnZvxllcyE6TuoVa0ErZr/70AzY+4erpy/\ny7Z57XESKLXMISo2maZfb6NeFVcWtSj+r+i4ZGXJ3H0VzVmfMLJkmYmfl/3k1H8lX8bUc1CpVPy4\najBbN5/h4gUfxXZVq5egS5/6TBi8SXFTkiRJLFv1JY8uPeLWyVtC91m4WGEGz/+aE5OXkRiuXQmh\nSk9FhwU9sCpehodLRpEW9f6qVSSVCkMrW8wKu2HlUQrrEuWxLlEeK49SmBV2x8ja7l9x6BG3zhJ2\n+ReyNGpMnVxwqFKf0HPZshDGdo5YFy+LvV0q0QEheLeqT53h3Wk2c5hih54am8Cx8YupNeQrYYfu\nd92PC3su8MOGXooduizLzBz9Ew2alxNy6A/uB7Bg7l52rh/6ToeeW17Ds1hBxkzezs3b2X0QWVlZ\nTB7bgWJF7YhLEE/Ih0clUr/XZprXKf6PO3RNVhZnfcIYuO46LoP20GfVVSLi0yiXqSL9Vtg/tm9+\nI187dQAnR2u2rhpMv17LhAZqzJnek7TUDDavVK67bmFpysotX7Nl2hbeBomNEazQoALlu7Tgl3GL\nyEhWPmEpN5Ik0XZaOwrVbcPDJaNICBATOfuYCNi3jrCrJ7AtVQWVXrYjsytTHU16Gm8uHgagZmtb\nMlLSCLnr95udZUFlyVh1egbHJ35PsUbV6dVDbCB0XGQca75dw6LVvXFwVB6uObDjGv5PQlm+eIBi\nm8TEVHp2XcTC7/vh8Se5peYd5nD6XLY0dO9uDRg1pBXN2s9m47ZzqFQqVKHPePwygjt+YqW7IeHx\n1Ou5mS4tyjCrkds/fkJPSdcwY+8DihW04EyHStzpXJW5JZ1p7Gr3j+6b3/jgFemnz/lf+KV+nVLU\nf0e9btNG5fiyaz369VrGgSOTFZ2M9PX12LFjLLWqj6FKLU/KVnJTdD9lKrrRbnA7lg9dzrRd04Ti\n6wOHfsb8kLccn7SMVgu/RU/LGY+fDa9FYFUzTs2YgWvbPjjVFBuykN8JOv4T4ddOUHPxfgAykxIw\nMLfE0t2bzOREIm6ewdE+Dsq1RJORgb6gbomclcWZ79Zg5mDDiEkdhWw1ag0rR66kfqf61Gzgrdgu\n4HkYi6fv5eSZ7zBRKOEryzJDB6+mdp1S9Gr77kao0RO3UcDeiiYNy/72b/17N6ZCWVcGjdrAlTM3\nCItMwtzEkG6tlYt2vX4TR6M+WxjYuQqjquguI6AEQ58oTrcs/9vfLwXHcDk49l/ZOz+Rr2PquVGr\nNdRrPZMmzSowdkInxfsfOnCdsd9uYu+FKYoH/8qyTN8emzAwMqD/3P5CJxSNWsO0/sswNDWh8eSB\nOrW/xwSGcnD0cqyKl8Wj40BUBuLSsfmRxNfPeHPhMLZlqiGp9Ai/egKVgQFWxUrTfGQ9Ip4GcHHx\nFoytzNEzMKDF3JGK15ZlmSvLthP14jXfbR+LoaAc7475Owh+FsyOA0MUNxmlpWbQpckcvurXkG+H\nKh81t3HdSdauPs7ts7MxMfnjfW7afo4p3+0i9Fn2OMwdu6/wIiAcJ0drmjYsS4H0cB4+C0ejkalS\nppDiQdIBwTE06ruVkT1qMKS87mWouZFlmTM+YRgZ6FG3ZHa1ktLQitmS059i6rz/6pefgXqAHRAB\nTJVleXOuzyt26gChb2KoVH8Cm7ePpF79Pzwj/pSvBy8nIiyOZVsHKXbQyUlpfN54Po2+bESTrmIT\nb9JT05ncbT4OXu7UHtZNp9fWjOQUDozfSmpUGF69J+TJaLcPnSyNmnj/R7zYuRJJpcKz2yhkjYbA\nPYuoOagLHvWrkpGSiiY9ExMbsYTyna0HeXHuJnN2TcLMUmzu6Y1jN9i1aBeHLkzE2lZ5bfnU4dtI\nSU5j984Jin8W7t97SbuWM7lycuafDpG+//AVX/T8nv69G2NtZcqWny7SrlUVXgdFokqJY+6Ixpi+\n42HwV7wIiqZx362M61ubAaXzNuxx7VkEE3++R2RCGvOqedDUzV7I/pNTz+a9n9T/CkmS5OAnq3Eu\nrPyH58z5R3Qf+ANXbiyiYCFbRTbp6ZnUrfMtLTtUo+dg5Q76dUAEnZvNZ9jyYcLyq8nxyUzsMhv3\nupWp2ld8eEFuZFnm5MobvP5lO66te+JUq/lHN0lG1mj+n2yAJiON+OcPMS/iiaGlDWXLReN74AzR\nAcHUHdVLq6//0b5TPNx9gtm7JmNTQEx46vWT18zrNY/NB0bgXVb5MInDu66zZvEv3Lj1veKW/tjY\nJOpUH8OM77rTvdW7pz9pNFno6al4GxFH+66Lef7iDZdPzsTLszBvwmLo8tVc5o9qQvVyyuec+r/O\nduiTBtSlX8m8E+Z6GZ7I+B13ufMyimmdyvOFsSl6KvH/v09OPZsPPlHapssCkpOVZ+UbNyjL1wM+\no8dXi8hUqLBoZGTA7t0T2bDsOPduvFC8V1H3Aixe24cVI1YQFSpW1WJmZcbM7ePwP3Odez8dEbL9\nPZIk8dnQGnRaPZHwK8fwWzWFtBjt5Ak+JDQZabw6uInM5EQkPT3kXPIFeobGWHtVpHKdrN/UFsN8\nnmPt4qSVQ3989AL3fzrKjO3jhR16fHQ8S79ZSo8pPYQc+vPHIcyf/H/snXVYVVkXh98D0oKkCKhg\nF2B35+iY46hjjY46dreOjd3dnZjYhd0tIhYootLSHfdyz/cH4jiOyjn3gjEf7/P4iHD33ge8/M4+\na6/1W3vZs3e8ZEFXqVT07rGUpj9X+qSgK5VpLFx+lCkz93LkxF2s85py7cx0juweS8nidgDYKELR\n1hJITJZe6fz8dQQNe25hUr+6WSroKpVI52WXqVDIAo/O1ehkaKSWoOfwN9+9qDuXKUi3vitk+aFP\nHdYakzyGTBgn3SnR3iEv6zYMYWSvtYSFSs+iqdmgDC3+bMGifotIlnHzAchjmYdpO8fx+MgFHuzR\nvEepeaH8dN0+CZPCpfGYO5jAC4cQ07LfxyU7SI2JwHPRSMLuXsBn+wIABK1/Cnu5irGo0lTEBL7l\n0JCZaOvkomwH+X1Gn528zO2N+5myfRxW+eXFiBWpCpYOWkqNljUY+Ee5zAe8Iy42kaHdVzN2Rgcc\nnaRXKS+Ye4CoyHiWTO3yya93+XMZnl6vsc6bh0GjNrJzT7rldPWq6Z2dxDdP+X3sAYoUNKdBVWl9\nTjNCLpP716Nnyazy5UlHS0vgQqvyjLCzxEAn+9Ng/x/47sMvSfcn0WjAXurVKsOMyR0lj42Kiqdi\n/fFMcelC+99qSx436q+N3Lnmw8aDwyVbnYqiyMB+u4iJiGH4yuGyWpsBhAeFM7nzLJzaNqZcx59l\njf0cUa+DOD5tB8qEOIq070+eYtLPGL4H0lJTCL50BJs6LfHetgA9M0uKtEs3tnJ0DHv/MxZFkRfn\nb5ESF49jm0ay13l64hK31u1jyvbx2Bb5dGz6c4iiyLpx60iKT2KLa2/J+egqlYoh3VZjbWPKhjVD\nJa93xt2Dfn8u597F2dja/Du0uGr9aU64e3BsX3rZ/669V3nuG8yUd4kDKb5e3HgQwOJt1zm0XFqx\n3kv/SOr32JLlIReQfgAqlawIvyQo5LW//Nc16PySE37JDD3dXLjNa83OfVfe7zqkYGaWG7dtIxk1\nbAOPvF5LHjdvRg8MDHVZMHm/5DGCILBk+W+kJKawa+4uyeMysLS1ZLrrBB4dOsu97Ydlj/8UZva2\ndNk4mpp9fsZ763yerHMhMeRNlsz9NdDW1cO2fhu09fQp9Esv4l49I/jKcZzLRrwX9GAvH8JfvKZo\ng6pqCfqjQ+e4vWE/U3bIF3SAI2uOEPA8gDWbpBcYAaxbdILIsDhWLRsgeczrV2/p03MZezYN/aSg\np6WpcChoxeSxf6dgFshvwf7DN4mPT0Z88xRtLS3qVnZg/2JpHuuvAqNo2Gsr4/6slSWCfupBIEmp\n6SHRnGKh7OO7F3UAK3Mjjixpz/DxW7lxy0fyuHLODsxZ0IPOHeYSLdGZUUtLC9ddY7no/pAje6U7\n+enq5mLjrj48vPwQ9+3uksdlYGFjwQzXiXifvsrNtXuzxA5VEASKNazOH/vnYlKoFA8Xj8Z7yzwS\nQ/w1nvtroJUrPb/cwNKGhiPaEXnrIOHP02/QQZ7eRL8JxjivhVoxdA/X49zfeZRpuyZgW1i+oF8/\ndp3ze86zec8ADI2km6xdcn/I7s2X2L//L3R1peXPJyWl0LHdHEaObkudmp/OfdfW1qJp43IUL2rz\n/nOVKxTByFAfo0g/7j8Jou3Q3SQlKyT5JAWExNCo11ZGdK+ucZZLfLKCPmuvM2TTLV5eeJUj6NnM\nDyHqAI7FrNk0rSW//r6QNzL6lPZqW5MmTSvQs9tiyXF5M7PcHHCbwNwJe3j8QPouP4+pEVsODOLI\n2iPcO3tP8rj361qbMXP3RN7cfsilhZtlebh/iVx6uvw8qgF/HFiAgXV+Hi4exZN1LsS8eJTlXtpZ\njXPZCJzLRmBXvhRV/2yH+7SV7O4+jjAfP0o1r4t+HnleJaIocnPtXp4cvcCM3ROxLmgt+5qe3n7K\njhk72LR3sGTvIIBXL0KYMGgLrrvHSs7MEkWRwf3XULJUAUb3/nyRmUqlQktLC1PTv9Mw9fV1KVnc\nFi+fUMYsdKdV/RIY6Gd+IwkJj6PRn1vp26ESA8tqlod+3y+CyuOOkaYSud6hMkXN5KWJ5iCfH0bU\nAZrXLc6oIS1p+dtc4uOlH0oundaFhPgkZrpI73xUxtGelasHMKTbKllt8PLbW7HOdSAbJ27kuYf8\nPqMm5ibMdB1P9Jtg3KcsR5mSdX7mukaGtBj3Ez0OLsK0eDme71yMx+wBBF08giJe+veY1SiTEgi5\ndpJwj7/bFGaI+YcYWpgSG/QWh5oVJDssfohKmcbFeRvwv+PFrL2TsLCRvwP19/Fn+dDlLN74J8VK\n20keFxebyKAuKxk6oQ3VakhPf1294jiPvF6xeUnvTz6RhEek/79lfC3jJp3xd0xIKJU6rKVuZQd6\n/Zq5K2lEdCJNem+jc3NnRlaxyfT1n0MURVaeekbz2eeY2qEcqyoWxlj3uy9g/0/w3R+Uqh5N+8fn\nRFGkz/zLhEfE4rZzlORYZujbaCrV/4sFi/+kZeuqkq9h1PiN3Lr6jE2HRqIr40156YwX4wZuZcL2\nCWrFaxWpCmYOWUNCRDQ/zx6Bvon0YhapiCoVAfeecGPHdSIf3yFPMScsy9fGvEyVbHeBVCYlEPn4\nDhEPrhL19D6mJcpRp3cDbMuV+uTrVWkq3KeuoHCdShRvXEP2eqmJybhPXY6oVDF57RD0jfRlzxER\nHIFLRxc6jOzA4J7SDbfS0lQM7rqSfHbmbFwr/WD0yuVHdOu8kJtnZlDIIe+/vj5wxAZevnpLPmtT\nqlQsSrPG5XCwz4soioiiiBDgzfKdN/F6/pZ1U1tlul5sfDKNem2lftVCzGpcWOP+pFP3efJbHmOK\nmH2dVnY5B6Xp/HCiDuntspoM2k+NqiWYPVWG5e7dFzTvMIfT52dQspS0oguVSkWbttMxMzdm2pLf\nZb3Rl6y7g9tyNya7TsY8n7TH7Y/XXjjFldc3HtBi/mjy2MkPFUglNSERvyv38DjiSbSPJ4b5CmJa\nohwmRcpgbF9CY5FXJsYT98aHWN/HRHt7khDgi0kRR8q1LEvhOpUkhVFUyjS0ZDSoyCAhPIrjYxZg\nUdSecfN7kEsNz524qDimd55OvXb1ZHmjAyxyccPzji9nzsyUnFEV4B9OnRpj2L5m4D98WzJYs9Gd\nzTsucu7oZHbtu0pgUCRvw2Po3qku1aoUR3zzlKiYJAwNdNCTsBlJSlbQrN92ShfJy/I2JbOkeO1r\nx85zRD2dH1LUAcIiE6jWbQsuf3Wga8c6kudcs+cyi+a7cen6PPLkkRbfi4tLonbN0bTrVpvf+zaU\nvBbAtFlnuXb4GhN3TiS3qXq77fVrz3F7sxtNpw/Btqy8ylV1UKakEuzlg8epV8S+fEL8m+fkMjDC\nMF9B9PPaoW9mhW4ec3IZGqOtZ4Dw7kBTTFOSlpKEMiGO1NgoUqLekhQWRGLIGxRxMeQuUASTwmUo\n95MDtuVKoqOf/V2cwrz9ODF+MWXaNGTQyJZqiVVyYjJz/phDiUolmD+vjayxx/bfYtnMQ1y7uRBL\nS2k3xqSkFBrXm8CvHWoyYUCLT75m686L+AdGMHFMejXyw0evcT/vSXBINGPalyI6LpkDZ54wonuN\nTEVdoUij7dDdmBrrs7lLWbSyoPjnWxyG5oh6Oj+EqCsUaeh8ojDh0fNQGvy5nWN7x1G1svTmE/3G\nbubNmzD2uo2XHL555RdK3dpjmbOqpyz3PVEUGT36ED73fRi3eZxaj/0AXle9WDZyNVV7t6dMqwZq\nzaEuokpFbHAYUa8CiQkMxe9RIqmxUSgT40hLSUL1rkmGlnYutPT00TE0RsfEDIfShuTJb425vR0m\ndtay8/c15fm5m1xevIW6I3vw+2+fLqfPDEWqgkX9F2FqZcqa9fKe1B7e86N/x2WcOjNDcoGRKIr0\n7bWclBQFe9cP/ux6Dx+9pmOPJUwa8yud2qdbAz95FsCMqZtpXrc4P9UsSlxCCg52X05FVKlU/PHX\nQaJik9nfp4paLehEUSQ2SUEeQ121xDw1TYWumu+N4Phk7gTHolCp6HbcK0fU+QFEvXNzJ/T1dMhv\nbcLUgf9+7D160ZsBs05x6/ws7CRmFCgUSuq1mk7d+k5MnNJJ8vVcvfKYzr/NZduxMRQqJt1uVBRF\nBvTdSURQBCPXjURHYirbxwT7BTOrz2Jsy5ei9pDf0VZznv86KmUat9bv4/m5G4xbMxz70tIrNv8x\nT5qKFSNWkKZIY/Ou3pJbJgKEBEbSqclslq/sT/OW0m8oq1ccZ8ums9x0d8Eokw3AoWO3OXz8LtWr\nFKdPj/Q8/WPbD3PgzBPWT2uVqUukKIqMmHeKu4+CODmsFoZ68sNSyjQVw7bcJiIuhS01issa6xUW\nx5ybLzHS0aaHU35KmBthbqCTfh7wmZtZapqKky/DOPMqgotvIolJUVIpnwk/FbJk5AXvHFHnB8h+\nsbbMzfg/a7Pv9GN2HX/4r6+3rFeCgb1/ok2n+SQlScsU0dHJhdvW4Wzbco5jR25JvpZatcswdMIv\nDOi8nBiJee+QnpmwfFUnDIwNWDFsBWlK9Ur3bQrZMPfgVBIjYnAbOF3tLkr/ZRKjYjg6ci5vn71k\n3qHp6gu6SsXGSRtJiE5gw7ZesgQ9KTGFQV1X0rl3A1mCfuXyI+bO3sfhHSMzFXSA5j9VoHXzStz1\n8KXrn8t4dP4Sq3bfxtLMUJLt7/zN1zh38yWHBlZXS9DjkxW0XXAB39A4llWUZjmQQXhSKr1PPaJp\nYSsqWJvg+jSIlfdfE5eqRBCEL6ba7nwSTEkLI1Y6V+ZW/SasLl2dNgbqtYn8L/Ld79QzYuqz119G\nEATG9Kz5r5CJKIp0mepOrlzabFs3SPIj8u27L2j+2xzcz8+kREnplrUDhqzE+0kga/cOkXzwBZCa\nqqRb+9XkNstNv3n9ZFUhfogoiqxacpwHe07SYFxvHGqUV2ue/xpBD55yxmUVJX6qzdC/flU73COK\nIttnbsfvkR+7Dg3BKLf0kJlKpWJEz3XoG+iwa/sYye/FAP9w6tYcw9ZVA2jSUHqzClEUefU6jFWL\nXQkNjyePsT7L/srcamLHUU8mLjvHlUmNsTOXn50SFptMy7nncCxgypKyDujI/Fl7RyYw/NwzTrRP\nT7O84h/JMd8wbHLrMaySwyfHhDz78hlMkRPHcnbq/AA7dYCLt/24cMuPBlULfVIIBUFgw7j6PH4W\nwOKVxyXPW6VSUVxm/E7HdnOIjZXeYm754n7o6Ggze/weyWMgvep0s2sfIkMi2Tx5s9qFP4IgMHB4\nC0atGMKlBZu5unwHaanSHff+a6iUadzedIDTU1bQf2Yvhk9qr5Gg7124F597Pmw/MEiWoAOsnHOU\nt8HRbN4wXLKgJyen0rnDXAYOaSlL0CH9veCgFcG8kU3Y4NJakqCfu/mSUfNPc2xkXbUEPTohlbpT\nTtHIyZYV5QtJFvQkRdr793wJcyNUosgWrwAAqtuZUqeAGQGxyXiE/rNmIuSZXqaCnsPffPeifvaG\nL+7XfalftRDlS9p8VggNDXRxm9+G+UuPcPbCv8M0n6N/p7rUqetI7x5LJVecamtrs3fPeO5c82bn\n+vOS1wIwMNRj+76B+Pv4s33Gdo0qOktUKsH8ozOIDQpjf98pRLwMUHuuH5WYwLccHDydYE9v5h6a\nTtm68kTxYw6uOIjHRQ92HhoiuRNWBscP3OLwnhu4HZyIvsQ2h6IoMnzwOuwd8jJhQHPZ1yu+efr+\n408lE3yMl08oncfsx3VQTUrnV89xMY+hDqt7V2NSYek2x6dehlHX9TZDzj5l7s2XAPQrV4DrgdF4\nhMaSS0uL8nlNCEtKJfxdGDX4qW6OmKvBdy/qh84/QyeXFn+0KY+OjvYX4232tqbsnN2Grr2X8+q1\ndD/xlbO6E/Y2hrWrpNvfmpgYcvjIZNYuOsG1848zH/ABRsb67Dg4mOcez3Gd66qRsBubGeOyYShO\nbRtzaMgMPHYdzzJ7ge8ZURR5dOgc+/tOpkjdKszaORYza81Mp46sPcKNYzfYdWQ4Zhby7Ac8775k\n9vg9uB2aiLW1dLHctN6dO3d82Lqsn2SBfF81+oGgSyE4LI5Wg3axeGxT6pZWv6+oIAhUi5f+nn0W\nEc/cW34sqF+CfuULss87hG2PAilhYURpy9ys9/QnOD4FW2N9rJTG3HqSTMgzvf9co5evxXcv6g2q\nFGLaoAbYWP39S6Z4d9CYnPLvkEP9KoUYO6w1bbsulHxwqqubi0PbR9KxS11Z11aocD527R7D2H4b\n8fWWl8plkseQnYeH8vjGY/Ys2KORsAuCQK9e9Zi1fxqvrnvgNmDaf3rXHhP4liPDZvP02EWm7pxA\n/yHN1D6fyOD4xuNc2n8J12MjsMwrr9AqOCCSod1Xs3b9YJycHSSPu3PLB5epuzi0bSS5JYZ5lMo0\nWrSfg4enn6xrTExKpfWgXfRsW56O9pr5r8hNW8ytmwtDHW0cLY0pY5mbFY1L4+YTSlB8Cq2L5sVU\nT4f2hz1wORHAgQB/HPPk0ej6/t/57kW9beN/5oSv3n2bGWsv02HEHgbPOvHJMUNbFqZEURsGjNgg\nWSzzWZtip0YxTM1apRkx5VcGdF5OdGS8rLGmZkbsOjKUh5cfsm/xPo3NtfIWzMuc3eMp2bQ2h4bM\n4Nb6fVnqHfOtSVMqub/zKPv7TKJAFSfmu00hfzHNe7Ke3HySc67ncD02QpZBF0BiQgoDu6ygW79G\n/NyisuRxb99G07XTfDYs60vxz/QY/RTjp+4iLU2Fk4n0MyCVSkWPiYcoWciSv2rK/3l9+L6UKuhe\nYXHcDY5BFEW0BShmZsjTiHiUKhXVbE1pVTQvC2+/Ir+xPkOsnehkXRgBWFuxMtUs5PUmzeGfZCrq\ngiDcEwRhoCAIWeuQLxHVI18A3gRH03PiQe4+DqKEgyVDu1YnJUVJn6lH/jVGEATWj63Pnfu+rN9y\nLtuvcXi/1jRuUYFhf6yR3EIvAzMLY3YdHcb98/fZv2S/xsKupaVF774NmXdkJlGvg3DtOoaXV+5+\n926MmeF/9xF7/viLwPtPmHlgGgOHt0BbDcuAjzm5+SRndpzB9dhI8tnJs3JQqVSM7beBUk4FmTpe\nul1FWloaf3RdRKcudWndXPqN4MDhm+w7dJMd05pKSlnMwGX1JQJCYlnTwUl2SOOxfzR1ppwiVZkm\nWdC3eAXQ5agnkckKBEHAJrc+pno6HPQJJSQhfZPR0zk/YrI2s04FAvBr/gL0LlwEZ9Os7az0/0im\nKY2CIBQDegAdgLvAZsBd/AoqIQiCGLOlE9GJCmZc8MPWypguLZyxsTTGyFCXm57+HLngzaxhn26Q\n4PMqnFrdt3DKbQIVy0vPo000+HdRj0KhJDlZwUNPPwoXyYfNR40K0tLSaNXaBat8pkxd1FX2L09k\neBwdWyyiQoMKtB/ePsviiV7XvFg3bTtGFmbUGNQZq2IOWTLv1yLqdRDXV7sS6etPz4ldqNioYpb9\nbDIEfdfxEdjml+/YuGTGQe5e9+Hs2Vno6UkvBJsycQd37zzn7IG/JIvz8xfB1Gg8kRMrO1HJUbo7\npNuZJwyfe4obU5uQz1RaH9T3awbH0tDlNHO7VKStrrSx8269xPVJMDtalqWMZW4UaSp0tLVIUKQx\nwP0xzlbGNClkiVWEJatePMfeyIjmNvIN7z5FTkpjOpm+o0RRfC6K4l9AcWAXsAl4IwjCNEEQ5LtU\nycRIX4eHr6NIDo2i089OFC1ogZGhLi/eRNDf5RimJp+PRRZ3sGTVoj/p0H2x5CYZAIZJf8fqVSoV\nd2/7sHjBQZo2nMjSRYdo/bMLr/xC/zFGW1ub3a7jeHDblx3r5GXEAJhbGrP72AgeXHjA3oVZ0yQD\nwKmmE4uPz6Jw3cocHTkP92krifYPyZK5s5O4kHAuzN2A20AXbJyKs8R9LpUaV8oyQT++8Thndp7B\n9fhItQT96L6bnDhwGze3ibIE/eTxO7juvMie9YMlC3pSUirtui3CZcJvsgT98Yu39HM5yr7BNWUL\nemBkIs1mnWVyu7KSBR0gNU2koYMFRUwN8AqLY8YNX7Z6BeIXnci8eiV4Hahi7Clfxng+YOeb19gb\n5virZzWSio8EQShL+m69GXCadHGvBXQVRVF6t125FycIYtKOrgzfehvHAmb0a1ICLcciuJ15wuo9\nd2hcowhjeqb7XiiVaZ+t+huy7AZBIZHs3z5SligkGuhwyO0GZ909MLcwpm27mpQrX5iF890I9A9n\n0bI+/xqT4REze2UPajYoI/t7joqIo1PLJTjWdKTTmE5ZmgGQFJ/EhlWn8dx/miK49D4AACAASURB\nVIJVnanQpRUWhTWPSWclMYGh3N95DN+LtynTqj59hjRX2wjtcxxZe+T9oajckAuke7oM6LSck+7T\nZTWNfvP6LXVqjOHgjpHUrCbdmK3PkLXExSWxc2oTye+H6NgkqnZaz4Q+dehaRF4mT1R8CvWmnqZr\nncIMySfthqcSRbQEgbhUJVOuvCAwPpkn4fF0c7LjbUIqL6ISGGRXmlImJjyKicEzOpqWtraY6kpL\n/ZRCzk49HSnhl3tADLABOCCKYsoHXzsoiuIv2XZxgiAqd3dj4/nnrDz1jPX9arDqViD+ITH071iF\nyo62hIYnsGT7DUxy62Fva8q4P//dZDolVUnNnjvo0aU+g/pKb66w2vUS69acZPCwVnTsnJ4Zk5Ki\nYNSwDdjlt2DM+HafzLrI8IjZfnwMDkXlp45FRyXQpfVSilUoxu8T5JlISSExLpGNa8/ycP9prIo7\n4NzuJwpWcULQMINEXURRJOjBMx7uP03Qg6c4tmlEr/4/YWwuT4ykrHNo1SGuH7mu1qEoQGhQFB0b\nz5Lt6ZKaqqBxvQn80q4Gf/WXno++e/81Js3Ywz3XnhhLbJsniiJth+7GztqEZa3lu3puOv+cxwHR\nzJJQZf2hT0uGsD+NiGfdA396OefH0cqYuFQlk0+8wcHIiN8KFJR9PVLJEfV0pIh6EVEUfb/S9Xy8\ntqjc3Q2A6Qc8SUhWEh6XzMwpvxAdm8z52y+5+SAACzND+rSvSOtBrswd0fhfGTMAz19HULPbZs4e\nmURZJ4dM11YolAwYsYHf/mhIjVrp8715/ZZrV59w9fJjJrt0+WI+8oIVbmxZdYbdZ8ZjbCK/ai82\nJpEubZbhUNqB7lO6a5yy9ylSU1K5fuQ6h7acITUhiVI/16H4T7UwsdGshZlUEsKj8HG/ztPjFwFw\natuYbt1qq+1k+SVEUWT/kv3cO3uPnUeGYWUtP20uOSmVbi3m0ahFBWZO7iZr7LjRm/F9EcyxnaMk\n36Rf+oVStcFfnFrdmQqlpced52+6itvZp1wYUxddNQ+Tk28FffE641KVxKQoya2jjam+zntBzxD5\n2BQlJnq53hcPjfJ8QC1LS9rYZd+TYY6op/Pde79kiPrHuFx6Q+DbWDo2c6JhtfRDUJfVFzEy0GFE\n9xqffEPuuBbG7EUHuXtpDoaGme96KtQey7S/2lO9aQWuXH6E18NXvHoZSt36TrTrUAtRFL8otr37\nLyPgdTgrdw2Sla2QQXxsEl1/XYG1vTW9Z/bONutaURR56fWSfTuu8OLiLfLY5qVQ7Uo41CiHeeEC\nWfakIIoi0f7BvL7+AL+r94jw9adwnUr82rk2JSqVyLZiE1EU2TVnF49vPGbXkWGYW8p/AhBFkXH9\nN5GmTGPv7vGyrvXUibsMHbSWB5fnYiGxqEmhUFL7pyn81rY6w1oVkbzWdY83tB26m5suP1HQUr2w\nVWZZLjeDoul27CF1CpjjE5nA5p+d3nc3+nDnHvJMj+jUVJY898YnLp7F5cpjrZ/1N+wMvldRFwSh\nKbAE0AY2iKI496Ov1wMOAy/ffeqAKIoz1L2GH1LUR2y9w4NXkWxe3IlC+dMzLS/c9mP66ovMHdGE\nyk6fPkwSRZGu085gZmrEykV/Zrr+xSuPcZm7n4jIeGrWdcQotz6tWlelokTvdoVCSZMmEylbuTDD\nJ7WVNOZjEhNS6N5hNcbmxvSb10+trj1yUCqUPL31lONH7/HmpieKlFRsnUtgXboolsXtMXeww9DC\nNFNRE0WRpKhYol4FEv7iNaFPfAl+6A1AwarONGtREceajmrbEEtFpVKx1WUrfl5+7Dg0BFM1Gx9v\nWenO0X23uHxlnqQNQQbBQZHUqDKS/VuHU7vGp1v1fYopM/dy6+5zji9uI/kpLSomifLtVrO0SwVa\nVpLW2etjMhP0JEUa/dwf07JoXtqVyMf0ay+4FxrLrDrFKf3BTSTkmR6iKDLxkRcqUWS2s2b2DVL4\nHkVdEARtwBtoBAQCd4BOoig+/eA19YARoihm3nNQAj+cqKcq0xi44RZT2pelYF1nomOTuPs4CPfr\nvliYGjC2V+0vHppGxyZR7rcNrFncm2ZNMnc3DAqORFtbC5WpIYaGev9wZRRFkZCQKO7deU6LVp/u\nexoWFkONqiMZNa0dTdtUkvHd/01KsoIendehra3NoKWDsl0IPyQsIAzve95cu/WSiBdviHoVhCIp\nGSMrMwxMTdA1Mnjv656mUKJISCIpOo748Ei0dXQws7fBsog91asWokTFEljbW3+18u80ZRobJmwg\n9E0oOw4MIreJvAyQDK5ffMK4/pu4cm0eBe3/3Sv0c6hUKlo2m0bNWqWZMUr6Tf3mbR/adJ7P/T29\n/1FJ/SVEUeS3kXuxsTJmccsSktfKGCsIguQ89BHnnlHO2phu7zJxxl70RpEmMqNOMQx1tAl5pseL\n+DiK5jYmTqHAWOfrvF+/U1GvDkwRRbHpu3+PAxBFcc4Hr6kHjBRFsaVGi78j022fIAhGwAigoCiK\nvd/lrZcQRfFYVlyAXHRzaRMRn8Lcw49o+jqKZ0ptXvhHUqSAOaN71OTYRW9ueQWk+1M45+fnOv80\n7jc1MWDztBb8PngNXjcWYm7+5UdUWxtzzl30omB+S/I4FSQtLQ1tbW2Sk1Px8nzF0SO3WL3iOL37\nNWXW3D/+Nd7KKg/7DvxFi2ZTKFLcRlYH+gz09HXYsrsvff7YzMK+Cxm2chj6htn3GPshVvmtsMpv\nRa3Wtd5/LjkhmcjQSOIi40iKT0LxziEyl04uDHIbYGxmjFk+Mwxzf52Gw59Cmapk1ehVJMYmsuvQ\nEAwlHjJ+TOCbcMb228iOXaNlCTrA8qVHSUlOZeqw1pLHJCam0K3vSlaMbypZ0AG2HHqAt18EW7rK\ns2GOiEum7YKLHB7TACm3PJUokt9En6hkBeFJqVga6DK1ZlFaud1n77NgmuoUISgpiUXe3sx1Lkvu\nXNn3ZBmclMSV8DBuRUSg840O+SVgB/h/8O8A4OMdoAjUEATBk/Td/ChRFJ+ou6CUn8RmIBXIaOEe\nBMxUd8GsYM+wugjA+nM+aIdF8XvLsozpWYtekw4zeqE7DramWJoaMmLeKfyDY/41vn6VQrRrXY3B\nozdJWs/cLDf3Pf0wTFKgra2NUpnG1StP6N1zKeYWxly+MZ+rlx/j9fDVJ8eXK1+YsTM6MLjbKmJj\npJd3f4iubi42bOuJWV4z5vWaR2KcevNkBfpG+tgWtqVEpRKUq1eOyk0qU7lJZcrXL0/JyiWxK2r3\nTQU9NTmVxQMXk5aaxvZ9A9QW9OSkVIZ0W82fQ5pSp66jrLFeD1+xaJ4bO9cMktVgY/zUXVSpWIRf\nP3HY/zleBUYxdpE7W/+sgr6u9LVUKpE/Vl2jclFLDB5HZPp68d1haP2C5lzyj+JWUDSxKUoMdLQZ\nU7UQrh5hJKelYWtgwIoKFTHW0cm2p7I4hYI2165yLTyc/Al5aJiiXjMUTbl86REzXXa///MJpIRC\n7gMFRFEsCywHDmlyTZJSGkVRrCgIgocoiuXffc7z3QVkK186KE1RpKH3zmo00MKStkN3Y5vXmEPL\n/87t7jx6Hz/XKU7Xlv++1MSkVMr9tpEFM3+XXKodHBKFlaUJqcb6xMYmUqvqKNZvGkLV6iXZtuUc\nL54H4TLz98+O791/GcGBkSzfPkDtbBaVSsXwYft57vGcMRvHYGIuz3zqv05ifCKL+i3C3Nqc1Rt+\nl9XE5ENEUWTi4K2kJKfKPhhNSVFQp/oYBg1tSd8O/06x/RxXrj+lY48lPNzfB3OJtr8qlYpGvbbS\ntHYxRlWxkbwWwPwjjzhy15+TzZwle6JnVIge9AnltF84DezN6VDShh2Xojj/9i2znJxlXYO6PPVQ\nvM+4yaBF8GmNwy9i7F6Nrksw6fBx+KUaMPWD8Mt4QPXxYelH1+EHVBRFMVKda5DyP5kiCML7JzNB\nEIoAKV94/VchQ9AVShULF52kRb3iHF7R+f0vX3BYHLo62tSq8Om8WEMDXdZPasbAkRuJkbB7TklR\nMH3uAZ56p3tVmJgYMm5CB9auPokoinT7oyHde3zariCDlUv7ExkWx6blp+V8q/9AS0uLJUvb41zb\nmRmdZxARkvkO6/+FuMg4ZnebjV0RO9Zt7q62oAO47bjKw3sv2bxRerOLDObM3Iu9gxV92tfK/MXv\nSE5O5c9Ba1g+9ifJgg6wdu9dklKUDK9kLesab78IZ9GxJ2yuU0JW1yIdbS1EUeS0Xzi62gI3A2No\nvNWD2U+f0iivvGvIDFEUOR0SzP2oqPefe+qh4KnHu0bnP4Y1712gmCAIDoIg6AK/Af8wrBIEwVp4\n9yYTBKEK6ZtttQQdpIn6VOAUkF8QhF3AeWCsugtmNdGJqfhHJNDL8W9nt4feITTqtRVTE4MvdlOv\nW9mB5j9VYNzUnZmuo6enQ5lS+Rk7eed7G4HAgHBKlsr//pe+SNEv75R0dXXYu3c8W1ef5e51Hynf\n3icRBIE5s1pSp10dZnSeQcjr77/sP7uJDIlkRtcZlKlehmUrftMor//ZI38Wubixd994cueWd7jq\ncc+XzRvPsGFRb1k3g5nz3XAsXYBfGknPkPEPjmHKygus71YBbRnfr0KpovuKKyyqU5wCMg+Pk5Vp\nDD//DINcWixrVJrRts4MK16c/TVq0sA660T9TmQEba9fZeWL57z0UfxDzH8kRFFUAoNIr8R/AuwR\nRfGpIAh9BUHo++5l7QAvQRAekJ762FGTNaXaBFgC1d7986Yoil+l4/GXwi8ZKJQqqk88ztCfS1Oz\nRUWOXfJh+upLTOhbhxHda3xxLKRnw5Rpuxa3HSOpViXzbujN2s6iZHFbnvuG4OMXwrqNQ3AuV4g3\nr8N44PESzwcvqVqtBK3aVPvsHO6n79Ov93L2X5yEhZVm4ZOFK2/ittyN0RtGU7Bk9lXrfc+EvA5h\nbo+5NOjYgCl/fflpKTMS4pJp12A6A8e2YmAPeZ2IFAoltaqOZtjI1vRqW1PyuGc+gdRqMpkH+/pg\nZy39/dB2iCtlS+ZjUh35/+9eb6IoHpIsexzAq5gkHPIYZEtXovCUFGY9fcLtyAi66Bejjr6N5B35\n9xh++RZ8VtQFQSj17o5SkfRgf8aFiu/+RIqi+DpbL06CqAPcfB7GBNf7lLLLQ3CaFpP716N8qfRd\n84fFEJ9j141wFq44xp2LszMtEgp9G839B35cu+lN/zG/YGiox97dV7h905vg4Eh+alqRtWtOMHhI\nK7r3/LzADB29jmde/qzZM1jjatFV2zzZNn0bw1YMo3jFzG9M/yVeP33Ngt4L+GXQL4waVF2juURR\nZGy/jejr67J10wjZ4+fPPcC1K485vXecrC5GjVvPoGXTigxpUUjyWkcvejN6wWk8pjd9H4qUg9xG\nFx8T8kzvXzFtTVGJIi2vXqaWpRVNEwuhryUvfJYj6u+u4Quivv5dCuNFPn2CawE8FEWxa7ZdnERR\nB0hIVqClJWCgmwstxyKoVCrJYimKIvX77aFTu5r069VE1jVOXnQQ3xfBNGhUlo6d6yIIAmfcPXDb\nd41V6wZ+9pdboVBSt85YfmpdkT8GylvzU1w994iRfTbRb14/jft0/ih43/Vm6eCldJvUjQHdNP+e\nD+++zvolp7h1Z7GsAiOAl77B1Ks5lrsXZ+MgI/Vx/6GbuMzdz72df0jOkklOUeDYeiUrfq9Ik7Ly\nbWuzQtCzi/v3kjCQKeYZ5Ih6Op/96Ymi2Pvd3/U+9xpBENyz4ZrUwkhf5+/ejZmU73+MIAgsGV6X\npv1d6fhrTUxNM686FEWRsZN3cuW2DyvXDqBkqfQKPu9nASxfcoSfmlb44m5NRycXu3aNoVb1UVSt\nU5JSTpqFTmo1dGSt60D6dVlF17+6UqNl5qGnHxmPCx6sG7eORet7qeWG+TH+r8KYN2kfJ067yBZ0\nURQZNXwjQ0e2kSXoycmpjJ60nY1TmstKe1y87QbOxa3VEvTvmaceCrUFPYe/+azyCYIw5oOP23/0\ntVkAoihqvsXMhO2XfbnrKy2EnyGi4uOXmbzy35QraUPLZhWZvUhaRVlKioKg4Ej2bx5GyVIFiI9P\n4vZNb44dvY1z2UIMHJJ5cZhDIWvGTG/P2L4bSUnW/BCoXJUibD88gt3zd+O+7bu532Y5Vw9fZf1f\n61m3Z1CWCHpamopx/TbSZ/jPOJeVHgLJ4MSxO7zyC2Fsn2ayxi1fe4qyjvbUryJ9zbcR8SzaeoM5\nreS5L6pU6Rue73WX/iMegn6vfGk72+mDj//66Gvy3r0aoExTMWzLHdlNI95GxLNq921ZY6b9UZ4N\nW8/jH5D5TURfXxelUoXL3P0c23mJbZvP4brzImFvYxg4pAXR0QncuvGMN6/ffnGewX+2pHCxfCyb\npVG9wXuKlbZjz6nRuO9wZ98Szfuefm+c2nKKfYv2sePoSMpWkt7N6ktsXnEaXb1cTBz9m+yxqakK\nxo/ZwrLZf6CrK32XGRUVz7wlh5nd9/MH6p9i+tpLdG7uRNF88g7YOyy+xDmv70fQ3UNCWOv74ofN\navme+W5razPoaGhEiiKNA7fkncnq+wUwY80lHnpLT/ezzWtCnx6NmD7vgKTXb18/CNM8huw+cJ1U\nhZKfW1ZhzvweHDt8m3o1x7B54xn69FyGy5Rdn51DEATWrR3C0X238LyTNQ7HdgUt2Xd6NA8vP2Tz\nlM2o0lRZMu+3RBRF9izYwznXc+w5PZqiJbMm9ODrHczmFe5s3jRCrQPrtatOUrSYLU0ayovpz1t6\nhDYtKlOysHSb49dB0bge92K8zGyX84+Cefg6kspx8t8HaSqRtgc9eBElvXNYZhwLCmLSIy9sI3L6\nkWYH372oawkCLhUdmLjbA4VS+psyt74Oo3rUxGX1RVnrjfq1BAcO38Lv1Zd32JAeF58xqSNuO0fx\nV//mFCtmS8M649m4/jRbd45izYbBbHMdxf69V3np+/ldkpVVHhYt7s2EwVtJTcmaXYuFlQl7j48g\n9HUoy4cuJzUlNUvm/RZkGHM9vvGY/e6j1Wo/98l501RMGrKVweNbY+8gz9cFIDo6gQXzDrBwqvSm\n0wDhEbGs23yWiV3l3QhmrrtM3w6VyJtHem65KIr85XqfyZUKoZdL/q/7ek9/ElKVGIZkTd/50yHB\nTH/ymKkmFSmqI9/TPofM+dL/srMgCHGCIMQBThkfZ/z7K10fAA3sLchvYcSOK/J2sn0cLbjm4c/j\nF5kLdAbmeQzp16sxc5dIC4dkVC0GBkUyZuRGmrWoxM17iylbLj1OmpqipHKV4hhm0vihbbsa2BfJ\ny4alpyRfa2YYGeuz020gWtpa39wvRl1Sk1NZOngpkaGR7Dk+HDOJfuRS2LvlElpaAqMHq2eLvGzx\nYZo2q0SZUvJsbpesPEG7NtUoKKPzUkBIDAfcnzBMphXA0Xv+KJQqfi0hvzAoNCGF2TdfMrFw2SxJ\nXbwdGcHER15MNKmAg07WdrXK4W8+K+qiKGqLomj87k+uDz42FkXxqx9R/1Umv+yYoKFeLgZ3qcr8\nTVdljRvaqhh73G4QEhotecyjJ28okNeUUWN+JS0tDYCkpBSmTNyBdT5T8ubN88X4tiAIrF4xkB3r\nzvPGT/pNKDN09XTYsK0nBYoVYEbnGUS9jcp80HdCQkwCc/6Yg76RPtv3DcAod9Y5U4aFxrBizhHW\nrlWvTiAyMo71a07iIsNSFyAuLok1m84wpoO8A97F22/QvU05LIyl/wxEUWTGgYeMcy6olijPuO5L\nx1I2FDPWXIBFUWS+9zOG53aiqE6OX1F28t2HXzKomd+M7YOlmyNl0MfZkiMXvQkOi5M8xsrciM7t\narJirfRdc0hoNE+eBbz/987tFyhVtC9aWgJTp3dBS0sr04KUAgWt6Dm4CXP+2iN5XSloa2uxZFl7\nqv5cFZeOLgT7aXZg9jWICIlgeufpFClbhHWbuss6hJTCYhc3fulck9Jl1EslXbX8GC1bV6OQzLDN\nxm3naVjPkcIFpDe8jo1PZuuhBwyRuUtPSk2jWfn8tCgqvz1hXKqS64HR9LSQ3+P0UwiCgGvV6pTX\ns8z8xTloxHffJCNhRON/fE5P5hsbYKDbE/JZ5mbKgPqSx3j7hVO35zZeP16Jvr60jufN283G0FAP\nhQAvfUOYPrsbjd814pBaDJWSoqCc8yAmzeucJel6H7No9S32LdnHiFUjKFJWepu0r0ngi0Dm/TmP\nxl0bM2lsgyy3b33k8YoBnVfg9WQVJmr0j42PT6JMsX5cd59OsUz8fj5EpVJRvPxQtk1vSfVy0kM2\nK3bd4vLdV+zuKb/JiiYpjCpR5K131j0dZXeWS07xUTo/zE5dE/pWtGHDgfsolWmSx5QoZImzY0EO\nHL4lecyO9YNZNq8HQ4a15ua9xTRuUh5RFGUVQ+np6TBnzh8smLIflSrrs1ZG9K/KrKW/s7DvQjwv\neWb5/Jric8+Hmb/PpP2w9kwe1zDLBV0URRZM2c+gsa3UEnSAndsuUKNWKVmCDnDm/ENMTAypVlZ6\n82VRFFm37y59q2Zfw+bP8SMJeg5/838h6s72ZtjlNebMDXlFSb1blGLDtvOSX29mlhubfGY0KJ++\nA1apVAiCIFuYWrWphp6+DicP3pE1Tir1m5Zl9c7+rBu/jstul7NlDXW4d/YeiwcsZsGaHgz5s2K2\nrHH9whPCQmMY3l96N6IPEUWRtatPMLKv/FKNDdvO07u7vBvV3UdBJCUrqVc6a21tvyY5gv51+eFE\nXd3Hyd9blWXHUXk701b1S+D1+A1v/NUzpVTXqEsQBGbM6MaqecdIy6Yc8/JVi7Lj6EjclrtxZO2R\nb16kdG73OTZP3czG/UOo1VBelyGpiKLIyrlHmTy5s6yy/A+5cvkxWlpasppIQ3qxkft5TzpWkxdT\n3n7Uk64tndV6YtEk9JIVhUYX3oaSoFRqPE8O8vjhRB1gwzkfDt1+I2tMO/vcnLjynCQZ5fh6urn4\ntXVVdh+4JvcS33uuq0uDhmUxNjHg7DEPjeb5EkVK2LDv9BhuHrvJNpdt36RISRRF9i/dz/ENx3E9\nMRrH8g7Zttadaz5ER8Xza3v1fXG2bzlH9x6NZIvswWO3aVjXCVMZ/uVpaSr2uz/mt6LyinSU30Gx\n2auEBEZ7PuDhA/XsfXNQnx9S1LX949h66YWsMVYm+lQoZYP7dXm57u1q2MqKq39MRnqjXARBYNzY\n9mxZmb0eLta2Zuw7PZJA38CvXqSUUVTkecmTA2fGYl9YfgGQHLasdKfHwCZoa6u3S09MTOH40dv8\n8Yt8i98Dh2/Rvra8TJubngFYmRlR3FZeCmD/9TfZcVl+dbIoinQ//pBHWXDUsvy5D90dCmGqnX2O\njjl8mh9S1JsVtuLik1ASU+Q92rWoV5xjl7xljalbyQEf32CCQ+Tnd48dtYktG8/KHpdBi1ZVCAuN\n4ZHHK7XnkIKxiSG7Dg5CO5c2c3vMJT46PlvXA0hOTGZR/0VEhUax98QIjZuFZIb/qzA8775kYC95\njS8+xP3UfcpXLIp1Xnk754SEZC5ff0qz2sVkjTt2yZsW9eT546epVBy7H0CVFPnhGq/weG4Hx2Ch\nKy3b63P4JyZyKewtNWLlFWXlkDX8kKJupq9DOQdzLj6R18btJ2t9zlz3lRU/1tHRplE9J9zPyd++\n1C5fhGNH5ZmKfYi2tjbtu9dm37Yras8hlfQipR4UcizE9M7TCQ/KvuZWMRExzPp9FiYWJmzf3z9L\ni4o+x4EdV2nZvhoGBurvHI8evkV7iU3KP+TilSdUKl+EPDIKhwDO3PDlJzt5GTr3XkaS10QfexlW\nAhkcef6WNsWsNc442vbKj/YFCmKYY6P7TfghRR2gkZON7ArTErYmKJQq/ALk7bobOFly/vIjWWMA\nmjQoy/WrT0hKUr9P94CezXE/ci/LPGG+hJaWFosW/Uq99vVw6ejCm2fyzi2kEPI6BJeOLjjVdmLN\n+t81ag4tFZVKxdG9NxnQW/1dukql4uwZD5r/VEH22HOXvGhYV97hb3RsEj6vIqhSVN7B6jmvYBo6\nya/lADj5MozqOpoZpaWqVBwKCqRK3H/L6/1H4ocV9erk4pq3vHJ6QRCoVaEg1zz8ZY2rU8meKzee\nyRoDYGpqRBlHe27ekBfy+ZD8BSwpUsKGG5eeqj2HXCaMrkfnsZ2Z88ccHt94nGXz+j70ZXrn6fzc\n62dmz2iR5Tnon8Prnh8Ghno4OTuoPccjr9eYmhphX1B+debVG8+oUyzzxisfcsMzgMqOdujKzNK5\n+iyU6rnkh0/Ck1J5GZ1EeVPNjLt0BIG91WuQL5d6NQA5aM4PK+qVbfJwdGxD+eMc7bjzKFDWmJKF\nLImMiicsPFb2ejVrl+bGNc0EuVHz8pw/+XULhQZ0L8uyLX1YOXwl149e13g+jwseLOi9gJlLujJ6\n0NftynTh9EMaNi+n0Rw3rj2lZi35Fb4pKQoePfWnYml5u+c7jwKp7GQne72ohFQq28h3P7wVFE1V\n2zzoaNgvVxAEkn1yDke/JT+sqOtqa2FlIj8W66yv4qGPvFi8lpYW5ZwcePDwlez1apUrwt07z2WP\n+5D2LWtx/ULW7ZilUqVWCbYfGcELzxf/OIf4sG2gFC7svcCGiRtYt3sgDZppJq7qcP3CY35todmN\n5O6d59QsL78px5NnARR2sMbQQN7u+aF3CGWN5D/JXJ/xMza55Yvqz4Wt2Nr8q5qv5pBN/LCiri5l\n8pvyxDdM9jjHUgV4/FRe2AagvLMDXmrcDD6kVOkCJCakEBIYqdE86lCstB1LlrT/R6hEEARUaaq/\n2wd+RtxFUeTA0gMcXXsU1+OjKFv563vNJMQn4+sdTJWqJTSax+vhK8o7y2919/ipP44yrXkBnviG\nUTr/12siIQgCSX65v9p6OWQf/3fH0/lMDUhKVhIbn4yJjKyLouYCL17KPpXkOgAAIABJREFU2+ED\nFCxgSXRUPLGxiWp7jQiCgFOFQjx68Jp8dtLd/bKSCpZJ3A834MjaIyhTlbz1f0uZ6mWo/UvtdJH/\nyLBMqVCyafIm/L392X9mLJZ5v43d6rNH/hQtaYeeno7ac6hUKp77BFGyuPxwyIuXIRQrkk/2en6B\n0RSxzvEcz0E+31TUBUFoCiwBtIENoijOlTM+5XawbNdGQRAokM8E/5BYyhSVLur2tqac8/SRtRak\nh27sC+XllV+oWk2NMyhe2o4XTwNp1Ly82nNoSqLnHW4cu0G7Ye2wLWzLwZUHOb/nPH3n9iWf/d/C\nlZyQzLIhyxC0BPaeGPFVUhY/x/OngRQvLV+MPyQkOAqTPIbkVuP7eO0fTo1i8m5obyMTMDbSxVBP\n/q+npo2lNSFeqcRQzcKu7wXxzddLSMguvln4RRAEbWAF0BQoDXQSBEGeoQbS47ofks8yN6ER8gps\nbKyMCX0bI3stADs7S4KCNAudlC9VmDd+8sNGWcntaz40a+5ExYYVqda8GnNPzKVM9TLM6zWPO+7p\n5mNRb6OY0XUG5vnM2b7v6+Sgfwl/vzCcSzloNEdQYAR2duq10AsJjcbGSt6OOzQ8gXyWP14oZJSn\nB5tuBGT+whyylW8ZU68CvBBF8ZUoigpgNyDLOs8jNJbak+W3fzMzMSAyJknWGPM8BkREqldpaWFh\nTIQamTMfYmNnTliI9E5M2UHTNpV4GxxNkVx/Fya1G9qODiM68NLrJUEvg3Dp6ELFhhVZuUZ906ys\n5G1INLa2mvU0DY+Iw8JSvfBRZFQ85jILgaJik2SPAfAJiiUuVb6BlkoUUWWBodvb5BTMtb/tTTyH\nbyvqdsCHJ48B7z4nGT1tLWIS5XuV5DbUJUGm4VZuQ10SEtUzJzIxMSQ+Tt5N5GPMzYyJjfm2PUYL\nF8uHSR5D2tWbTtTtG1SwTP+eytYpy+1Tt3Hp5EKbgW2YPrXpV8tBz4zY6ETMzTXb9cbHJWEiw4jr\nQxISU8htKC/zJSFJgZHMbBmAPuuu8yBU/ubhemA0TffelT3uY6IVqRhrqX92kUPW8C1j6hpvDXS1\nBVKV8h3pdHS0SVXI29Ho6miTqsYuCEBHN5faYzPQ09chRYbDZFby4LYvKSkK7ApaMG7Wb9Ru5MiU\n4ds5vPs6DX8ux959Dwh9HcravUOp3Sh7bHPVJTVFgZ7EzlWfnSNVqXbla2qqEl0deU8sqYo02WMA\nUpUqdLXl79NS01Tk0jA/HdKrSXX//xLqvju+pagHAh/mehUgfbf+D2Z+4KpYu4AZdT7o7SgC6mwI\nRVFEQN5AURTV3n1qMvbvOfgmu99tq89y4bQnilQlJR0LoFKJtO5YnbOec9i66gweLxKwL2XPq6ev\nOHXpDbUalvludukACILGXvGCkP7zV3+svMHqjHk/TvaoH5eHKZF4pX79NN/vnW8p6neBYoIgOABB\nwG9Ap49fNKHG53ObU5Qq9NSI26ampqEns5FxSmqa2mlxKckK9PU1eyxNTEhGX41Hck1ITVHgtvMq\nq3cPwSa/OV73/Xjy8A0Htl/ltW8oTh1aUSZNhZa2Fo27NmZBnwUM6BvJspUdv4qnixQMDHRJSlTf\newfSWwymqGlJrKenQ0qqPPtlXR1t2WMA9HJpk6LGk6tBLm2SZbR6/BxWenoo+Hpe7s565jjr/b3J\nc42Xbzf8X+SbPSuJoqgEBgGngSfAHlEUZeUTxSvSMDaQL5ZxiSkYG8kTyNj4FIzVzOSIjU3EWM0c\n9QwiImIxNZfnH6IpgpZASacC3LmW7l3jVKEQTdtUok5jR3bvuY+/jz9a7x7381jmYcL2CUSHRfN7\nu9UkxH0fzRFMzY0I1/CQ2iSPETFqnmcY5zYgTuZNxdhIj7gE+TciYwMd4mWGFQGMdbWJ1TA8CHCw\nZm3scn3d92gO/+abBsBEUTwpimIJURSLiqI4W+74qjZ5ODe5iex1I6KTsDCVJ7IRMYlYWqiXAfH2\nbTRWeeX7cXxIYEDEVy880tHJRfNfq3Jw1zU2LjtNTHQCeUyNMK9WkyLORbh18tY/uiXpG+kzYvUI\nzPOZ0/7nhYSFqpcCmpVY25rx6IVmbpNWVnkIUzOd1cI8N+FR8m4IFqYGRMjMzoL0XrzaaoS+rAx1\niU7OaTv3X+GHPtUQBAE9NQ6UgsLiZOcBB4bGYZNPvbLtAP9w8ueXZ6H6Mfce+2Z7Z6APUSiUJCak\nULuRI31HNicyPJZFUw+wYnN6e73AF+mmaFofHcxp59Km14xeVGxYkXaN5+L3XH4VblZiX9ia176h\nGs1hl9+CAH/1agRsbcwIfCvvScHG0pigt3Gy15r+W3lad3SWPS6voS4+fWrLHvcpSpXPyX751vzQ\noi63mhTS+zcGvY2jQD55u+5XQVE4qGG7qlAoCQyIwN5BM0F+5uVP8TL5NZpDKivnHmHG6F10aDCD\nDUtPkjefKU3bVMK0VEnOuZ5jVrdZKFIVtBva7pPjBUHgl0G/0GZgGzo3X4DHLXmtB7OSEo75efZI\nvmfPh1hYpBcPhUfID+M4FMzL6zh5Gw/Td0Z1UfGanQVIRRAEtL6nw+0cNOL7OM36irwOS8Dawgh9\nmYeePmFKqlWWb/zv8yKYAgUtNfIeUSiUPPV6k61NmTPw9Q7mkOt1dp4cR1hoDJuWn+aJ5xvKNKtL\n3V/T/0SERGBinvlNse6vdTG1MqVf51XMXNqVRi3kN5jQlKIlbQkOjCQ6OgFTU/XivYIgULJUAR4/\nDaBurdKyxhYvasOWnfL89AVBoJi9OT7BsVQtJn8jkcP/Nz/0Tl0dHgdEUaao/F2z12N/HEvLd9u7\n5/GSsuXkW7Z+yN3bzylYKC8mebK/8cAbv7cUK2VHXhtTypSzp8vcoRSuXYV9i/axa84uACzyWaCj\nK+0mVbZOWcZsHMOUUbuZu+Rqdl76J9HRyUXZioW5qkbnqg8pW64Q9x68lD3OsVQBvJ7Ij+mXLmzF\nY/+vW0Gcr6TmTwYhyUlEp32dJ4wcPs0PI+qf8vNWhwcJULaEPNe8VIWSJ94BlHW0l73eVQ9fKleR\n1zz4Y/YeuUqtBvIbNKhDzQZlsLY148ieG1x6nn54VqNlDUauH0lYQBiRIfLzggs5FmKS6yRObz/N\nmLGHUam+XtobpH9P+49o1uijUpXiXLsvP4xUtEg+IiLjiYiWd1harpQND+I0TzP82mx4+ZI7eYK+\n9WX8X/PDiLogCCjfiYEmxS23HgZQ1VlebPr+k2CKFcmHkZH8lMYrlx9Rq7b6giyKImeP3qfhz9nf\nXEKlUqGrm4vajRw5cNCLK25XCHoZRFxUHBb5LAgLDCPIV71f2LwF8jLFdQred73p23ObxhW2cmjY\nvDznTz4gLU19kaxdpwxXLj+WvaHQ0tKiSsWi3HggL65f1Sk/tzy/rjlWUFwyyRr8jACa2dhwLChI\n44KvHNTnhxD1XU+C6HPqEXNu+jH92guO+75F6Sw/m0SZpuKGpz81y8sLo1x+kUidGrINJPEPCCfs\nbQzO5Rxkj83A84EfKSkKnCtpFsKRQoYfumnl6rTq14o3z95wfMNxds/fzbIhy8idJzeONdW3ATA2\nN2bclnEkJyTTte1K4mM188ORin3hvOSzNePiBS+15yhonxcTE0M8vV7LHlunRiku+8jLZqlYxoZn\nfuHEquFtpE4CAUDv04+5HKaZE2gFUzOUoopnim+fzvr/yncv6jcCo1l69zUN7C3Ib6xHHn0dzr6K\nYNZBLwIj5T3S3n4RjoOtKZZm8g7MzlzwolF9+alix07dp1GT8mhr4DG9cv0xWv1WPdtL75XvKgrv\nhekjiiKFHAvRb34/qjevTrXm1aj2czX6ze+n8Tp6BnoMXTEUa3tr2jdb+NWcJ1t3rM6a9Sc0muOn\nZhVxO+she1zDeo6cufBQ1hh9PR2qOttx6an66ZgZu2WpDozNC1txLUWz0IkgCHQuaM8VPc0yjnJQ\nn+9e1K8GRNGssBUdS9nwh1N+upS24bdSNihVIlP2ehAnw23xdFAiP9UqKmv92Phkbt39H3tnGVbF\n+rXx36a7QyxUROwWFQu7O1AxsBMLG7sTu7vj2N3d2CgqitIo3bVhz/uBA8fjQZgBPEf/r/d17Q/i\ns2Zmw973rFnPWvf9nkb1pWeoh888ol2HWpLjMhAXl8jZI4/o3Lturo8hBinJclRUlHkapomgEDLt\n6gDK1ylPhToVsG1hi4Fp/tirKSkr4TTLCdsWtnRptoSPXj/e2KFtt1rcufqaL19yfxNp16EmJ47d\nlxxXs7o1/gHhBHyWlr02r1OSC77iM/yU1DQuvkifH1B8pTcktl2xvbUZ57xDSc5jCaZbkaLcCg3l\nS+q/8yT2G3/HT0/qzYqbcNY7lL2vg5CnKTDWVKNBhzIs6lmVqHg5Nz3FZzKnrr2jrb00r8pzt95T\nr3YZya43wZ8jef7Um+Ytc9/Gt2rTKWrWtaFg4bzpgWeHhVMO0tZuJut2PgfSCffrKdHAD4G8upu3\nzpGsIJPJaD+sPR1HdsSxzXKeP/qxuh36Btq06FidpauO5voYdnXKEBYWw5t30mrdKirKtG5elZPP\npN1Q2trbcPrGOxQKcZm2mooymy57cf5ZIJo1C+IZFsdOjwD2eQaxzzOIRHn2ZF1IV4PypjpcC8nb\nsJaeqirzy1fEpvzvQaT/Aj89qVcy02VOvZLc8o9gwo137HoViE9IHDKZjOc+EZjpiyPbd0HRhEUl\nULuStHr64Vu+dO0oPdvecfwebdvXRFNTurM7pPem71x3mX7OzXMVLwaf3n/mxsWXNHFqw9UDV1k9\nejWhAaEoKSuhpKxEWmoaH158wDiPJhPZoX6n+gxeOJghPdZx7fzzH3YeAKfhTTm04yZxcbnLIJWV\nlXHoUZ+th29Lju3asRaHjknrwCldwhRDPU3uv8+5zp32ZxPBkKalWH7mNfOPvWTa7fcExCbz9HMM\n571DGX895375vuULcSzCR9J1ZoUWFhZYaOZOg/438oafntQFQaBlCVNGVLWkoqkuL0Niab/kGs3n\nX6Z11ULYlhS3YXrQK4quzcuhLEFvOiI6gWu3XtGpbU3J17xz22X69msiKe5rrN58GssSZlT6gRuk\nnzUK0npEZxo6NMR5tTPmRcxZPnQ5pzefBuDx5ccYWxhjUTx3G29iUalBJcZvGY/rmL0sX/fgh52n\nWMkC1KxXmkUrjuT6GH2cGrN393XJ3TvNG1fmzbtAPgVESopzaFmeg2/Cc1yn/Ocmd+PyFtQsaUKy\nPI0JtsUZVKkwyxuVZm/bStwLjCQkh43XDtZmVDbTyxcnJPgtG/Bf4Kcn9Yy6YCUzXfpXLMxyl/o8\nXNCatf1r4ta3hqhjKBQCe06/oE+7SpLOvf9uCK2aVkFf4tDP1RseqKmpUsuutKS4DCQlpbBh2RlG\nTm6Xq3gxeBqmibqmOnXb10VJSQl9Y30cxjvgNNOJjx4fmdhyIltdt1Ki4o/vugGwqmjF9P3TOb35\nNNNmnv9hLXHDJ7Zl1/rLREXF5yrepnRhSpcuzLFTDyXFqamp0KNLHXZdl1a66dWmIocvvCZJpBTv\nqcf+vAuKoYNtUeq2s8FcO/1Jcc/rIOoVMcQsBxcmDRVl5tazpmCZ3EkNZ4XfxP7v4qcndYAHQVHM\nufuBpofc2X/nIxpqylhb6GVmJznhskcQBroaVC0rfsxfEAQ277zCICfp2fayjecZPrJ1rjtWFrr9\nQekKRahSU9qmrlg8Dfv+Y3HpGqUZvWY0sRGxOE5xREvnx0+xZqCAZQFmHprJ02tPcR5xkLS0/B9S\nsrKxwL55RWbO25vrYwx3bsPyDeck33gGOTVm255rmZ1GYlCskCFVy1hw/JG4VsrzzwJpXbUQVYsb\no6KsxLuIeEZc8mT9Uz/aW5tLut7f+DXxS5D69NvvsdTTpF+bMrid8cTtzGtJ8ZseBDDUQVxWn4Gb\n7j6kpipoWF/a4NArTz+ePfWmu2MDSXEZCA2NZtvqC7jM7Jyr+Owgl6cyfMg+3jzKXrb+8eXHWFWy\nwr6rfb5fQ07I0GUP8QvBqcdmkhLzL2PMgPOU9vyx+zY+n3K3IdiqTXUiI+O4dVeS/D8Vy1tSrKgp\nJ669lRQ3pFt1NtwRR+oNypqz5vxbrr/+zOBN9xjz0BsLHXWOdqhMY0tpeyP5IRuQgf/P2bpMJmsh\nk8neymSy9zKZbNJ31qz+8/9fyGSyKnk5309P6vX2PURDRYm+FQrRr6E1R1zsuf3mC8k57ORn4MPn\nGO4998extbQ+85VHXjF6WEvJ2faclScZMaoNGrn0xXSZuJW2XWtRolT+1rET4pPp020jkSGRFC9f\nPNu11ZpUY4TbiHw9vxRo6mgyfvN41DTU6NF+NdG5LJV8D+YFDek7vCmjxm7MVbyysjLjxndkjttx\nybGjh7Vi5aEXkmLaNbQhMCQWd++wHNf2qm/FiBalOfrABx0NVdYOqMno6pYU1P2roSC/6uVSkRWx\npwi/nhSCFMhkMmVgLdACKAv0kMlkZb5Z0wooKQiCNTAY2JCXc/70pG5f1IiRVf/SXLnqEYy+lppo\nHfUV9wIY1KUa2hIc3d94h/LA/T19ekjLtt+8C+D61RcMHtpSUlwGHt5/y60rHoyY3DZX8d9DVEQc\n3duuRM9Ij7Hrx6KhlX3HkEwmQ1Pnv+1cUFFTYdiyYRQvX5yuLZbxJUjaBmNO6DeiGe/fBHHh3ONc\nxffsbc+H90HcfSAt6+7Y1pbPX6K4+1S8yJeKijKjetVk+U0fUesHNLJmpZMtbn1rULawASZ1/97x\nJUVmN9wk501aKbCulP69jUhL5l7SF9ZEe7I/9r+TZv4XYAt8EATBRxAEOXAQaP/NmnbALgBBEB4C\nBjKZLNe1sp+e1KfVtqJeYUPUbS1QKAT23v7IkKbies2DIxM4fOE1o3tJa0lcdOAlo4a2REtLWjui\n66I/GDW2PXq5sK5LSZEzZMhaJs3tlmfru68RFBBOl+ZLsaluw+BFg1H5SbxDxUBJSQnHKY7YtbWj\nW4ul+Wq4oa6hyoxljjiP3JirFkc1NVUmT+3G1PmHJdXWVVSUmTimHfN3P5F0vkFdqnHzsS9vA8UN\nMKkoKxH8p+NS2jcCasmpCna8DGDXq8Bsj5GqUNDj1HOu57FvHcgcaFJRUsK4TBoH47x5lBRCTXVT\n3sqj2BEjTZ74F0Ih4Ovx2oA/f5bTmlybJ/z0pK6uooRhncIIgsCnkDic7EtSu5Q4jekld/zp074S\nZsbiXY7efQrj/OVnjBzcQtJ1Pnr8gYcPvBg2orWkuAxMn7sXi8JGtOwkrfafHT68DcKh+VLsu9rT\nY2KPHy418CMgk8loN6RduuFGm2W8fPIp345tZ1+WGnVKMW7S1lzFO/ZpyOfPkVy4LK2/3snRntdv\n/Xn4UnwnjI6WOqMca7Lwmrj3Hx6bxJDN94lLkqOspPQ3PRiFIFBIV4OV7j58zsYLVUVJCbdGZZj3\n/hUJqbkXYEtVKFjn/YGN3ukZ+XafjxQuoEZXnRLU1SzAIL3SxCrkpAr/rnpnfuDGo0/MWnc985UF\nxN7xv/1y5rpG9tOT+r3A9MdumUxGQSNNBjcRJ2PrHxbPvjMvmTRA2oj9rB1PGDuitaQ2RkEQGDt9\nD9NmdJec3QN4vPRh35ZrzHLrlW/E+8Ldm97t3Og6tiut+rfKl2P+l2jQuQED5g5gULc13LvumW/H\nnTTfgcunn3LntrTNd0jPuucu6I3LzL2SOlrU1VVxHd+J6Vuk9eSP6lWLy/e9eeWfcynKWFeDHcPr\noKPxVx1bEAQEQUBFSUaz4iZ0LV2AVY+z34BtUsyYWgUNWB8q/feTARUlJXpbWvI0MpIB7o8ITkyi\nXcFCNKqe7tt7Ot4PBQIqsr/T0X+h9Kh45S3pVV9LwYwGRTNfWSAQ+Lr+VYT0TDy7NYX//Fmu8NOT\n+snoOADeB8fgekC8mNLsKx8Z3LU6BUx0Rcc8eR3ErXtvGD1MGgn+cfw+sbGJ9OrbUFIcQHKynL59\nl+Myq0u+GUvfvvKKwd3XMWjBIOp2+LG6Mf8mqjaqyph1Yxg3eBvrd0nbbPweDAy1mbm8FwP6rSQ2\nVnoZpnVbW0xN9Vm7N8ss7bvo37shPr6hXL4nXh5BV1udiQPqMv2sl6j1xroanH0awMqzntz3CiXK\nxhABUFVWIipJTrw8DXVlpRw3Tpfa23DCK4R7YTlv1GYFhSBgqq7B5uo1qG9qSnVDI8w1NFCSyYi3\njMU/NY6uOiUy12aQefL/xibqY8BaJpMVk8lkaoADcOqbNaeAPgAymawWECUIQq5rXj89qT/7FEFE\nXDL77nxER0NcPfiFbwTnbnlJytIFQWDiujvMmNRZkm56fHwSLtP3smzlwFypMU6Yuo1CRU3o2NNO\ncmxWWLvzGROG7WDshrFUtv/xGuz/Nmyq2zB5x2T2LdrHktV38+WYDVtUwrauDcOd10mOlclkLHUb\nyPy5ByV5mKqqqrBwVk8mrrkpqR9/RA9bPN5/4cZrcfsLz30imLD3MSvPetJrzW16nnpB37MvmXfP\nm7BEOZ1tzHPcODXSVGVD87Ks9X+Tq+xZ6SsvhNDkZBIVaRipqaEQBDZ4f6BTyYKYKmtkrs14Wr2c\nGIhL2AO85dK9YX8WCIKQCowELgKewCFBEN7IZLIhMplsyJ9rzgEfZTLZB2ATMDwv55T9zGL2MplM\nWNq7OvffheAbFs/JiY0oYJB9V4YgCDR1u02X5uUY3t1W9LlOXH3D9I13eX53CSoq4sl53JwDBAWG\ns333WNExGbh5w4O+vZdz/NZMjCQ8UXwPi1fe4dSmU0zcNpEipaRb7/1KCPELYVH/RdTrWI85M5rn\nuWwVH5tEJ/s5LFrUjw6dakuOnzBuG3GxiexaPUR0jCAI1G8xk749GzCgofjBuMMXXrFwy20ezWwi\nagCv1cIrzOpamSrFjHjyKZznN30ooKOOtqoy9YsYEZOcikIQMNDIvpc8KTUNDRVlPr/NnZ4RwCbv\nD9wLD2OCTRm2fPRGSQbzyldEW0WFN8/+qbh6MzGYE/E+2Kqb0kM3+2G8NsEXEQQh1x8EmUwmpB7s\nk9twAFS6787TNeQHfvpMvU/9Etx+G0IxU50cCR3gyANfIqITGdylmuhzJCbJcVlxjZWL+koidI/X\nfuzZeZUFS5xEx2QgLCyG/n1XMH+NU54JXRAEps+6wIVdF5i+f/r/PKEDmBU1Y8aBGbhfcGfcuKN5\ntsjT1tVg6eZBjBq5AX8/6UYR02f14Mrl59y+J34gSSaTsXKRE9PnHSJKgmFI1+blMNDVYIunuDbP\ngY2smbL/CaoqStSyNmXowBq0LGFK/SJGvIuIZ8VjHxzP5Kz3rvHndyMvQ0lDrEpSzdCITR8/UFJH\nh0UVKqGtooJCEChdWSWzlz1NEFAIAg00LWijVZSzCf7sjnmf6/P+f8JPT+rGuhq49anOhHY5T3bG\nJKQw/uBz1k5rLYmclxx5S9VKxSUZYaSlKRgwejMz5zhSoICh6DhIt43r03cZrTrbUrdx7p2EMo41\nbtxR3C+6M33/dMyKSDfV/lVhYGqA6z5XPnl8YuiAPZI2K7NCxWrF6Tu8KT16LEYul9btoaenxbIV\nAxg0ZjNJSeKnYKtVKUH71tWZvkP8fpFMJmONaytmrbvOl6icbwadalpSv4w53p//0maPSpKz/qkf\nM26/JzE1jagkOdsldOPkBaOsS+FWqQrO1qVQV1YmVi7/W9mlVGUVlGUyEoVU3JNC2R/nTV/dUjTX\nSu/ye5acu9r+/xf89KSeplDQo24JqlvlrMY448onmtlZUbeqeINoL58w1m6+yIqFfSVd17KtF1FX\nV8FpgHRtmFkLDxAVEc/oaR0kx36N1NQ0hg7cwyePT7juc803E4tfCdp62kzaMYnYyFicemwmOUm8\naUpW6O/cHG1dTVwmS29zbNehFjalCzNzxQlJcQtm9OTIyQe4e4hveChvbU6/jlUYf0rc8NPUjhUp\nYa5DRFx6lm1evyhPv8QQJ09jUQMb9rSpyE6PwMzad07Iq4SA6p9lI7lCwfiXz/kUH5dZr1eWybCs\nIOOC5ieuJQbRWbs4TbUKYaKsgXtSKIsiX+AS9oB3Kf+Oa9avhp+e1F12P+aBCD3p+16hHLn0mqXj\nm4k+tiAIDF18jWkTOlGksHjP0/cfglm66AjrNo3I9PUUi3t3PNm57iLLtg1GNQ+DQMlJcvr13EJ0\neDQTt09EW0+aRd//EtQ11Rm7fiyqaqo4dlpLfGxSro+lpKTEog39OXvUnXNn3CXFymQy3FYPZvvW\nSzx59lF0nJGRDkvn9mbwgovIRcpfAMwcZs/DlwGcfZpzhq2qosSpx/7suP7X9OaKsfWISU4lOC6Z\nEgZaDKhUmA+R4i0id0R6EpgozVLyH9elpMT88hUppqWdman7JcQz/OkTwlOScalWEpe66Z0xQanx\nPEwOYbh+WbrplGBTzFvuJeZ9MOp/DT89qQ9takMt6+yHjRJTUhm4w51VU1phJKG/fNv1IOLjk3CW\nMNaflqagz4gNTJralZLW4je3IL1UMtJ5A/PW9qNQkdwbT8THJdGr8zqUVZRx2eCS49j//weoqKkw\nwm0E5kXN6d52JVGRudeLMTLRZfnWQbi4bCUlRVrmb2FhxOJl/ek9fJ2kMkyv7vUwNzVg+QnxdWMt\nTTU2z27HiF2PiYrP+Vw1rEzYetWLkOj0ko2elio2Rtqk/ZkhO5a1oLSEQT1ddRUGPn9AeHLesnYT\ndfVMQt/l84l5np4U1tRkReWqVNBPf/q0rCDDxySCu4lfqK9RgJoaZswxqkZBlX9PRfRXwU/f/ZJ6\nsA/CV36LWWHi+Q/4f47m0PJuoo/tHxxNte5buH52JuXLZjk0kCXmrj3NlYvPOHtptuQs3Tc2jPjY\nJLR1c0/CMdEJ9O60loJWBRkwdwBKEkw/fmY8ufoEbT1tStfInQZUxh7kAAAgAElEQVR9BgRB4MCS\nA3jc8WDvidGYmuvn+lgZfytLXfFPcRnX4OiwhGLFzVk5y1F0nK9fKNUbTObG9j6UtRK/NzJ87hkS\nk+Rs65HzntDMw895HxyDg10xVp57g7qKEieblkeepkBVWYmIRDlaqkqZm6I5Yc7dD5z/GMbOyrXR\nV82diN3XCE5MJDAxERtdXXRVVUkTBJT//O5HpKSw9N0b/BMSmF2uAinv/+rC+SCPZkzYg9/dL/wC\nmTqQLaHf9PzMgXMerJsmfjxfEAQGLbzK6GGtJBH685c+rHY7yaZtzrkidCBPhB4RFotDKzdKVCzB\ngHn/O4QuT5ET+D6QlSNWsmf+njx1sshkMnpM7IFtC1scWi4jKCD3glQZf6uMv52Ua1i1biiHDtzi\nhoRJVcuipsyd5kC/2RcklWGWuDTlzlM/TjzKWSRsZpdKNK5gwcbL72hVpRAXXJsSYa3PyQ8hjLzs\nSYP9Dxl5+Q1PRZpkT7ezon4RQwa+fEC0PO8yyRaamlQ3MkJXNb0LRlkmI1ae/rRkpKbGwgqVqKhv\nwOekRMpUUaVAOYFzWt5sjJYmrPa/jF8iU/8eIuOSqTb9Auunt6FVfXHyAQAbL/mzY+917l2ZJ7pL\nJiEhmWoNpzB+Umd6ONqLisl4wpBKClnhS1Akju1WUqNFDbqM7vKf6LikylMJ8QshxD+EyC+RxETE\n4B2aRNqfJQplVVWszDTQMdTByNwI08KmmFuao6omTkt79ejVFC5ZmE7OnXJ8OhODCzvT2zz3nByL\nZYm8dwVJzdgvXnjCqOEbeXlnCYaG4soagiDQvMN86tmVZloP8Vr+95/702n0QdzntqCgkfiSxJOP\n4ey/85HYRDllUaZnWQtOvA9h7+sgLov0IBAEgck3vQhLlDO/uPhWYjHwjY/nkL8f3YoUpZh2+r6R\nw/17tChQAEfLYhwJ8Od+WBiTy5Sh/vVrvzN14NeR7PsGgiAw/I9XtG1oI4nQ330KY8b8Q9y+OEdS\n2+PoGXupWKlEtoS+d/c1TEz1kaek0rZ9TWQyWZ77pwEC/cJwbLuChg4NaTs4f2V5s0P453A873ty\n+44XX954E+UXjLapEfqFzNAxNULTQA9VbU00DfQASJPL8Q5LIulDKHEhT4gJCkXHzIj2q6YCUNvs\n+wmE+yV3Qv1DGbE8Xcc9I9nIC7G3cGqBuqY6PVsvY9fxMZQsLW0PJCeEhESRlJhCAQtD1LK4cTVv\nUY227WsyYNwWjm4fI+q9yGQytq8fRrX6k2le3oAaFb4V9MsatSsXYahDdfrtfsr5UXVQUsr5XFc9\nghm/x51ONS0Z16YchYy0SH4UTDljHcy01IhMkmOYw0BSxjUvalCK2JQ09NST8zSc9C0stbUppKnJ\n0CePaVOwIElpaUTLU+he1JIXUZG8jIqiU+HCFNL8XVvPwC9L6tveRvHuUxi7FnQUHZMiT6XXjLPM\nntqN0qXEfVkAjp16yJXLz7nnvvy7a5Ys/IPTJx/RsnV17t7xZNeOKxw54Yp/fAQKhUJyuSYDPt5f\n6N1+Ba37t6ZZH/GdPbmBIAj4v/Pn2NFHfLrzhPiwSApVKYtFJRvKtLHHpGRRVNSl1U2/fhK8H/J3\noskg+aSEJC7vvUyTnk1QVlFGkab4W2nJ66kXqSmplK1VVvJ7aujQEFUNVXq3d2P7kVGUqSC+3PYt\nfGPDsNQ1wc83hLOn3Vm14iS2NUsRFBjOlZsLs4yZt7APDewmsW7fDUb2EqcNVLiQMWuW9qPXtEM8\nOdAPHZEica6D69N4wC6WPAhisl32n29BELjp+ZkxrcrS1z59UjM+Sc59/wg2PvenRQlTUYSeAZlM\nhp56Op0UKJ2/xO5oWYz6pmZs+eiNlY4OKytXRVNZmeshIZioq9PQ7LdN39f4JUn9hW8E01Zf49au\n/mioi//gzdj1AgtzA4YNFE+OPr4hDBm7haMnXNHXz7ptMCwshovnn7JpmzNly6WTRu8eS6lQbjhb\njozBonDuhLq83wXTp8MKOjl3omE36WJhYhEbEcuePbd5e+4W8sQkrBrWpMH4/piXLZnnun122WkG\nyXscvY0gCDTokm5KIvsqy4wOjyYsKIxLuy+ha6hLt3HdKGIjbWK2bvu6qGmo0a/TKrYcGkmFqtk7\nP2WHi/efcee0B5ERcezcM45adqVxdFjCrh1X6NvvnzMLGhpq7N7vQrOGrthXtRK9h9Otkx3nLj1j\nzJr7bJ1kLypGRUWZfYs7U8NhM3aGdahf5vtkJ5PJePwxnEJ/lmoevA/lXWA0z2PjqWymR1ebvBFl\nfhN7ES0t5pSvkPnv8ORkTgcFsb9WuqTDh7jY74X+v8Mvt9MWnZCCw7p7rJzcktIlxOmqA1y6+4F9\nh++wff1w0Y/0KSmpdBuwmnETOlHd9vslHhMTPeo1KMenj3+JLM3b3I+WHavj6ryT0M/ShyS8PAPo\n1W45Di4OP4zQfd/4Mm/0JsY0cSHc258GLk70PryCOiN6YlGh1A/diFWkpvH2/G2SomN5f+0BJR3a\ncT9EhiJN8be/j76xPnZt7Jh1eBbWVay5dvBars5n29yWgfMHMqjbWp49zJ3TTmJCMge23SA2NYmZ\ncx2pZVea1NQ0EhKSs7UvtCldmPmL+9K130ri48X30K9dNoDb995y6FGE6JjCBfTZMb8DvTfe53MO\n06YLe1bljwe+1Jh8hr23vHn8MZzKxYwYO6iG6O6X7GBmk8QTVf8fIqGrpqRERQN9lGUyIlNSOBUU\nlO/n+FXxS5G6QiHQf+9zmtpZ0VOC52hwaCz9Zp5hz+aRmJroiY5zmXsAE1M9Ro1pl+X/p6X91aFQ\n0rog06bs5uH9t/jGhqFQKBg2oQ3FS5oTHSVtQMPzpR99O6yk99TeP0Q61+uJF1N7LWHBgGUYFC1I\nr0NuNJk2lIKVyyDLZZlIKlJTUvC5+4RdnUeTGBFDUdv0v+eDMCXufTVPIghC5r5EnfZ1+PjqI1Gh\n6TdJr6dekgijaqOqDFkyhGGOG3h8T5x87ddY5HqY2OgERrt2IEFDTmRkHMeP3sPS0ow27Wwzrzcr\n9OrTiGrVrRk0frvoa9bR0eDgjtGMmriDD37iu3ha1LWmf8cqOG51R576/T2dSpZGbBlSm93OdRnX\nphzTO1ekr31JtDVU/2aqkVtEJsmZc9ebaT5P82SykRV0VVWpbmhE9wf3GPH0yX/mu/oz4pci9cX3\nA/kSHseKSeJdidLSFPSaeZ7BTo1pWF+8zsrx0484deIBm7eP+m5m36ndfC5dfAqkf2mdR7ejY9u5\nHNl9GyUlJZSUlPjoFYzHU/FuPa+f+9K/8yqcZjpRq7U0G76c4P3SmymOi1gxbgMl7GvQ+/AKqvVu\nh4Z+3hUipUJNS5MW88bQZsl4ZMpKPD90nviwSGR/aoDcD0l/yWQylJSU8H3jy4l1JzC2MMbA1IDQ\ngFAW9lmIaztXbh27Jfq8lepXYrjbcEb22cSjO+It1FJT00hLTWOUaweUlJTw8f7CniPXuH3zFVWq\nWaGlpZ5jx86KNYN5/tyb9ftviD5v1colmDGpCw5TTpOULH4QasYwezQ1VJh8MfunkuJmupQpZEAJ\nc13M9P8umJdXYjfWVON6D1uUZNDtyW28YvO3RNKveAmO2dVlVrnyjLfJ23zD/xJ+mZbGs08DGLbT\nnYcHBlPIXHy2PX3nCx48fs/F464oiywnfPD+TO1m0zh6wvW7ZRfXybsICAhj116Xv/387K3HzHbZ\ni3WZQoSHxqBIU7DlqDhZXo+nnxjksJYBcwdQrUn+tYaFBoSybv4hgl+8o7pTR8q0boDyT+ZVGukX\nRNDzt6jramNlb4tMJiMpJg7fBy8IefORKA9PbFvY0rh7Y3SNdFkzeg2FSxWmRrMa7Jm3B11DXUau\nHCn6fJ4PPFk7Zi0rtw+kVv0yOQcAq+Yf58HNt9RpVI6wkGiUlZVo2qAKDj3qA3+1sN6/+wbbWqWy\n1Nd/9zaAZg1duXTclSqVxNX2BUGgW98VGBvpsGFcPdHvMSI6gZrdtzBzuD09i4ufFP0WyY+Ccx0L\n6de/53UQ02+/Z2ptK9qpW/2Qllyrc2d+tzTyi5D628BoGs6/wvFV3bGrIr574dwtL4bMO8+TW4sw\nNxMndpWQkEzNptMZMKgZg4dlLR+wb891pk3ZzaeAHQAcOXwH7w/BKOkqU6dxOYxNdHnz0o80hYLy\nlYuhoZlzx0gGoQ+cP5CqjaqKfo/ZITkxmQ0rzvDq+GUqdmlO5e6tUNX8uSUFYj6HoqqhwZPdJ/C+\n6U6pJnYUqFAKw6IWGBS1oLZZeofOtE7TmHt0LkVLp38eQgNCMS2cvsfybffM9/Dm0RvWjFrDim0D\nqd1AHLEf3nmL8NAYqtYqiZGJLtZlCmGpa/K3DqdN689x7+4bdu1zyfIYRw7fYdb0vTy5vlB0/3pM\nTALVG0xhxqTOONqJ30t69f4Ljfrv5JSLPbYlpfXZA4TFJLH7ljfDTQ3zTMTvI+NZ88QXt0alCfPK\nWUZbKn6Tejp++vJLRFwyHVbdZtHYppII/VNAJP1nnuHQzjGiCV0QBAaN30a58kUZNPT7JZ5y5S3R\n1dVkxfLj7N55lXWrTxMvJPP2lT87111CoRCoUrMk1WuXEkXor5755DuhP7/xnLEtJhPlF4TDjgXU\n6Nfppyd0AL0Cpqioq4JMRlqyHGOrIhSvWxWDoumlgPshMnQMdGjRtwWbJm3i6sGrKBSKTEJ/6/6W\nvQv2snvubhList/LKGNbhlFrRjGm/xYe3BKng97NqT7DJrShYBFjrMuktw1+ig5BSUkps1Y+ZHgr\nUlPTmD/nYJbH6NKtLi1b16DnsHWi5xj09LQ4uteFsVN24eElXsSqvLU5W2a3p+uaOwRGSBffUlFW\n4tA9H+Z+FOe0lB2sDbVZ3aQsKkpKFCidnGelx9/IGj99pt7QtjhVyliwbEJz0XGJSXLqDthH3x4N\nGD1cvN/oql1X2bLpAtfvLMrS0i7AP4zgoAhq1CxFaGg0Pbsu5u0bf3acHo91mUKEBEfhMnAzLjM7\nU9nWStQ5PV/40r/LagbMG0C1xnkvucRExLBi2h6+eHrTYHy/zA3IXxFhH/x4vPMYUQFfaLtsIhoG\nuiir/FU2KhoTwLE1x+g/pz/a+tpc2HWBt4/eUql+Jfy9/An8EIjLJhfUsulMgfSMfbXzatbsGoJt\nXZscryslWc6OdZdo3r46xazMM8suRXWMM7NZn09faNbIFZcJnRiSxWdQLk+lZZPpNG5ambkunUT/\nTvYevMXsRUd4tNcJAz3x2e6irbc5cuk11yfaoy2h/xzSE6t3QTHULmWa51JMVsiv1sffmXo6fvpM\nXUdLjcXjmkqKGbnyDqWtCzLqO+WTrHD/oRfz5hzgwB+TsiR0hUJB25az6NBmDi+ef8LUVJ/LNxaw\ndv/IzIzNzMIAFRUlEhLEZSBvX/kzoOsa+s/pny+E7n7RnfGtp6JtYkj33Yt+aUIHMClZlBbzxtBg\nnBPaJoYkRcXyxfMvo+YHH2MJCIlDVUOVhNgETq4/Sfth7Wno0JAuY7tgVsSMpISkHLtNytiWwXmV\nM6OcNvPkQc4qiWrqqnTrW58CBdPNUTKI/KW/H4EBYQx0WsXWzRfR1tYgITHrz4Kqqgp7Dk5g25ZL\nnL8k3iCjV/f6tGxamd6zL0maVp40oC4VrM3pu/sZaRKnnI101KldKv1JKD+6Yr6FimUsM3ye4BOf\ne2XN3/gLPz2p71vSWfQGJ4CsaBkG9m3MljVDRNcAP3+JoovTCjZuGYlVyaw/tAOdVtGkWRUmTOnC\n7ZuvMn/+dUY+dcR2LAobY2ef8+Tj+zeB9Ou8ir4z+lK9aXVR1/k9JMQmMNd5IzsXH6Ll/DHUGemI\nqkb+DX7817ComJ49xwSHcmn2Om6v3E2ETyCvT1/HvEwJnkSrs2/hPirUq0Dx8umbjymJKfi980Oe\nIhf1OShbqyzDlw9nRK+NvHD3znG9obEuGppqREfFk5SYwrrFp1iz4ARduy5EWVmJZi2qcvSkK2Nd\nOiIIAtHR/yQsCwsjdu9zoe/w9XhLKG8sX9CHmNhE5uwTLxYmk8nYNKstkTFJTLqQ8/vLDvlN7Foq\nypQ01KLbwzvM839GQELeNNr/v+OnJ3WxI9Jfw66mTZbZdlZISUmls9MKnPo3oWXrrAWMVrqdIC4u\nkaVuAyhfoRibNpzj8SOvTKEuuTyVpw8+EBURz4J1/XI8p8+Hzzh1XIXjZEdsW4g3x84KH55/YHwb\nV1Q01HDYMZ8C5a3zdLyfGRYVStFzz2KUVJR5sOkQ+oXMqdilOQmR0bx45EWvqb0y1x5ZeQSzImYY\nFzAW3Rdevk55Bi8azJCe6/F84Ssq5sPbIPq1X86jO++o27g8rot7sGmbM/UblKeElQUfvYOpUWU0\n7VrOzjLerm5ZJk/tRvtey0UPJqmqqnB411h27LvOiaviPVHVVFU4tqo7F26/Z90L6T6sX0Pd1iLf\nhoq0VJWZWLMEz/rZYaypSscHt5n+O3PPNX76mrriVdZfhgx83RssKyqug+FrDJ20Az+/UA4fm5Kl\nPktKipyTxx/Quq0tWn/eYNyWHefGnZe4LupBgUJGX61NRU0t+1bBAN9QerRaTifnTplj8bmBQqFg\nw6pzPD94DvsJAyhRP2/Z/q8GeVJy5tNIyNtPvDpxhUaTBwFgGRfIbIfZLL24FD0j8e2vGXC/5M7O\nWTvZfWIs1mVz1gh6/sj7H3solrom3LzhwajhGxgyrBUfvT+TmJjMuk0j/hEvCAKD+68mRZ7K4c3O\nop8w3Z98oFWXhVzf1odyJcWrUPoERlK39zZW9apKR1vx1o9f475XKHOPvmBXXZtMzZf8QmSSnK0v\nA2hazJjKZnqia+6/a+rp+Okz9W9x4uobrj74yOkb6YMjGUqIuSH0zX/c5uqV52zdOea7gltqaqp0\ndaiHlpZ6phlxrVZl0dPX5v2b9NHkjBtjToQeEhxFr3YraTOoTZ4IPT46nhkDVvLx1mO6bpn7/47Q\ngb+Vl3TNjYn4FEDAk9d8fvWeDfMP0aJvC/SM9FCkSVfJrNGsBo5THHHqtArfjyE5rs8g9LQ0ReZn\n4X1YMLt3XMXUVJ/hzm1YtnIg794GsGfXP2UOZDIZq9cP5YNXEAs3nhN/ndVKsnx+HzqMPUJEtPiS\nRbFChpxa25NhO9y5/SZ3dnC2JY0pbqpDy7PPCY7LvX1gVjDUUGWCbXEqm6XfkDM6ZTK6ZeT5oHz6\nv4xfitR3nXyO66qrXL7vzcZD7rQamu4gr1ysnGSJW/cnH3CdtIuDR6ZgYPB9f09BEDK/qKqqKvjG\nhlGoiDEVqxfH1Xkn714HiMqsIsNjcWy/Evtu9jTtJW3j92v4vfVjQocZ6FmY0nHtdHQLSO89/l+D\npqEelbq24M6avbw8eolidlUo0C29oyS3+jV2be3o5NyJPh1WEhwgTntFWVmJ2Jh0vRU1dVWmrOiO\nppY6m9anE/XxMzPo3bdR1u9BU52DRyazesUprlx/Kfo6+/RsQPvWNeg+7RypqeKNNaqWLci+xV1w\nWHMHD79I0XEZUFZSYu2AmnSuWYzGx57yKvTfEdQSCsdQ6/olxno9Yp+vD16xsb8lAr7BL1N+iU9I\nob3zfib2r0uzOulSoX0mH+WBZwgXj7tSorh4VbmQ0Giq2U9hqdsA2nXIehQ/LCwGExO9zPKOIAj4\nxYX/rdyzYPJBbOuWokmb7HvL42OT6NbajXK1y9F9QnfR1/kt9hx6xM3lO6g7qjc2zerk+jj5CUGh\nQPEnmSipKP9r2jHfQ3JcAuo66cqDGX+r7HTcc8LZbWe5+cdNDl8Yj5FJ9nIKCfHJzJ2wj95Dm1C2\nYvpMxZkjD4nwjWb8pM5oauZcRrh18xV9HZfz4PI8ihcTV1JJTU2jTddFlCppwSrn2qJiMnDgnAeT\nll/ihmsTipnlbur0wJ2PjNv9mC1NytC02I9PMr7EJ3PVN5yb/pHcC4gkLFFOFxtztnsE5rn8Ei+x\n0+5baLtd/s/LL78MqQMs3HKLwub69G5XKf3/i5ZhwbJjnL34jH1bnSlmmfOXQC5PpVGHedSpV44Z\ns3tmuWas8yY+ffqCqak+tjVtaNq8CsWKm+MbG/anwJSAsnK6/kcxq+xvJinJcnp2XIu5pTn95/TP\n1VSeIAisXnycN2dv0mrBWExtci8dKxVpqalEfAwg7IMvXo8iSAwLJjkihJSYSFITYlHIU5AppY/D\nC4o0lFTUUNHSQU3PEHVDUzRMLChVwwjjkkUxsSqKskgXpPxGXoj9sNthPO54cPjsuBztCPdtucal\nU0/YdXoC3u+CWbf4FFVqlmTaeAcANq47h1yeip9fKFOnO2Q5Ubpu9Wn27LrGg0tzM/dxckJUVDy1\nGrvi4tyGgY3EewUArN3/kNV7H3BzamPMDXI36XnvXUj6DfQH9bJnh/DEFEITUqi26/5vUucXI/Wj\nlz2ZtvoqG6a3wd62OEJhG5SUlBgzaSfdO9tRKxt53AwMn7KLjx8/c+TE1Czr6Ns2X2TXjiucuzyH\nQ/tvERwcQWhINI06VcmsnYZ+jsK0QM5TqmlpCvr32opMJmPkipG5KgWkJKewcOwWYoJCaLlwHNrG\n4qZjcwtFmoKQN964n3pPlNdL4vzeo2FkhnZhK7QsLNE0LYi6kRlqeoaoaOuirKaRmZ0LgoAiJZnU\nhFhSYiJIjgglMTSIhM9+xAd4kxgahHahEhiUqkS1NiUpUMH6b8NEPxKJUbHULqiMpo500hIEge0z\nthPiF8K+YyNQy0HDf7HrIbzfBZMQn0xlWyuGurSmmIEp7VrORkdXE4ce9Xn8yIt37wI5e/GfjQCC\nIDCo32pS09I4tGmk6ETA630Q9VrM5NCSTjSoUUzSe5y9/jonrr7l6kR7DLTzbiD9bxM75J1Qf5P6\nvwCZTCZ0b1medg1L071VukD+7pPPGb3oPAtn98o0u+jax41aNaxxcc7e6m3zH7dZuugIN+8t/W4d\nff/eG/j6fGHKtPTMyuOlD0fP3iPkcxQDR7cgNiaRK2ee0mtw42wlAARBYJTzIYI/BjNh6wTRPp1f\nIy4qjtkDV6BlbECTaUMluw6JhSJNQeAzTx4dfkb4y/uo6hliVLY6+qUqo1eiDCqa399zkIK05CRi\nPr0h+v0LIj2fkBT+GaPyNanRpQpFapT/oQT/bP9ZvC7fZeb28RiaG0qOV6QpWDN6DUrKSmzb0z9H\nJ6u3r/zR0FClYBFjQkNi6N9hOY0bVsrsfklJkdOhzVzWbhhGCat/9n0nJibTpMFUuvWoz5Sh4qei\nr1x/Sa9Ba7i7y4kSRcSbswiCwNjFF3D3COTC2LqSp06zwr9N7L9JPR0/Pam3qmfNmQ29/vbzF1Ga\n9B+xgYrlipKYlMLHTyHcvTw32+6Tx0+9adllIeevzM10J8oKrzx86eu4jIlTuuLQoz6+sWF4vwtm\n4/IzNGhWkbqNyhEfl0ShotnXDmfMuYj7RXdc97mipSPdPzEsMIzZTkuwtKuC3bDuomvVUgybY7+E\nc33rA77cv4Sqrj5m1RtiUrkuGiYFJF9vbpAcFUbYszuEPrlBUtgXzGs1wX5gbfQL5b89mSAIPNlz\nCs9T15i2fQKFSkorUUD6U9OS/kuwLGuJm1tnUb/n6Kh4Zo7eTYVqxRkwqkWmefX1ay85fuQuy1YO\nzNLfFMDPNwT7upPZt3kkje0rZLkmK6zbfIH1Wy9xd0dv9HMoF30NhULBoJmn8AuO5uTw2mio5d0o\n4+m595Qzyb1CpBT8JvV0/PSkHvvIFW0tNc7ceMc7nzAMLIvTtGFFCpgbcOf+WxQKgbKlC1Oo4Pez\nktCwGKrZT2Hxsn6075jzRtLpkw85c+oRVpUL4uCU3np487IHF088Zu7qvjlOuK7c7M7xtceZcXAG\nhmbSs8KA9wHM67eEyt1bUambOKmDDGXCrzd2v0c6CRHRnJ51kKi3TzGt3hCLui3RLlRC8nXmJxK+\n+PP57gW+PLyCXvHSNBjaDIuKNvku0fr2/C3ubTjI5E3jsKokTp/na8THxDO3x1zqd6rPtEk5O1KF\nBEexct4xpi91RE1dFWVlJd7d82Pk0PW4zuiepQXe17h5wwOnXm48ujofy6Li1BkFQWCkyzY++YZy\ncmlbSQbraWkKek06SlxCCn8MroFaHhyQ4pLkVJ14muaVCzHXphCaqnm/SWSH36Sejp+e1Oc4N6KC\ntTlzNtygc5cGfPT5gpamOnOnOWTbipiB1NQ0Gneaj21NG2bP65XjekjfTL14/glHTt0jMSGZQWNa\nsnzWUaxsLJgwp2u2sfdueDJu0Dam7Z1GQSvp7vXeL7xZNMSNOiMdRXe4xASFcH3JNkysLVHX0aJa\nn/bpxK5Q/CPDf/nCGIU8hc/3LmBm2zjfSiv5hbSUJEIeXiXgyhHU9I2wd25HkerizU3EwOfuU64t\n3MLolcMpbyf92OHB4cx2mI3jFEeG9c5eX+f1c19cBm5m2/FxaOtocHzfXTavOMf6jSPo0Elcp8rq\nFSc5uP8W9y/OQVOE6iekf+5bdl5A+TJFcBteU1RMBuTyNLqOO4SqijL7nKqikgdbw6j4FIZvfcBr\n/yi2NixNRdMfZ8jym9TT8dOTekPb4ri/CuT88WnUrV2aLyFRdO+3CtfxHWnSMGfBqtEz9+H52o9j\np1yzNC34HnxiQgnyD+fAthuEfolGz0AL10U9so157xlI73ZujFozitI1pDuxvHV/y/IRq2k4eRDF\n64qT4E2IjOaMyxJsWtbDxKoonmdvkBgRTbsVU4C/yjEvXxhLvp7/EkJaGqFPbuJ3fh9qBqY0m9gZ\ns9L59zQR+OwNF6evZvjiQVRpWEVyvK+nL4v6LWLTgRE5KnJuW32RS6ceY12mEJ8DI5mysDuNqosv\npwiCgFNvN9RUVdi7bpjop5fIyLg/O2LaMqixtHJTckoqHbPkU+UAACAASURBVEcdwFBPk529KqGc\nh1ZVQRDYc+sjE/c+Zmybsow0N0LlB7S+/ib1dPz0pK54NZvrjz7RqMtfm0XtHBbj5GhPp3bZZyC7\nTz9k5rS93H6wFCMj8RlChqZLBuTyVFRzcAoKC4mhc5NFdBnThTrtpPeQv77/mhWj19Js5kiK1BCf\nPaYmp3Bz+U7qOjuirpuedR8fOQ9tU0OazRzxy5H5txDS0vh87wJ+5/dhULoqraZ2zLcOoC+e3pyd\ntIzBc/tRo1nWuj/Z4fmN52xx3cIflyZR2DL70khQQDiqqioYGuugoqKMQqGguL740f74+CTs60xi\n0JAWjHHKvmTzNTI6Yg4s7khDW2mtsIlJctqN3E9BU1229qiQJ2IH8A2Nw/XAU9YOqIXma/Geq2Lx\nm9TT8UtMlH5N6GMm7URZWSlHQn/9xh+X0VvYd2hinggdyJHQk5Pk9O++gbod6uaa0FeOXkeLuaNF\nE7rw5wStPDGZpKgYgl785bfZYfVUgt+FcmHNPcnX8rNBpqyMRb3WVJuxFTVdA/Y6TuXC6ruZ7z8v\nMC9rRdtlE9k8Yyful9wlx1e2r0z7Ie3p13UdcX9Okn4PBQsbY2qun0noSkpKWX7WvgdtbQ0OHpnM\n/LkHuf9QvGl2KeuCHNg+mp6Tj0syrwbQ1FDl5JoeBHyJYcjhVygUeUsALU112DuqPgbaaqjbWvwQ\nGd/f+AVIPUPTJSUllUePP+Dh6ceB7aOzjYmJSaBTHzfmL+5L5SriH9nfhQRJvj5BEBg5dB/GFsZ0\nchZvdpCBt+5vWTl6Hc3njqJQFXH6Nc8PnuPBlj8IfPYGTQNdKnRuxvVFW4gOTNfx8PAwpWiLniRH\nhGZe468OFQ0tinccSMUxSwh9fIO9TguJ9JP+9/oWpjbFabt0Apun7+DptaeS45v2bkpp29IM6ruN\nNJE6M1+3Q0r5zFmVtGDD5pF07beCkNBo0XGNGpRn9tRutB19hKgcbj7fQktTjVNre/IpIIrB+UDs\n3+I3sec//hNSl8lkXWUy2WuZTJYmk8myLR6Hh6drSigrK2FbvSSnDk5EIxsnG0EQ6DtqE/Xql6NX\nn6x1NrLCzRse9Gi+ULKGzNzF1wj0DmTwosGSOzU+vPjA8hGraTprhGhCv7lsB5/uPEXLUB+Po5e4\nu24/BSpYU6NfR44MX8Kj6+kj+4KQRnJkuhjVjzD5/a+gbWFJxbHLMKnWgMOD53Fh1d0837RMbYrT\nesl4NkzeisddD0mxMpmM3q69kSfLmTL1tORzD+66ipPH74te36pNDXr2sqfbwNWibyIAQ/o3pXnj\nSnSfdl6SRgyQ3n223pFPAZE/hNiVq5kz7qUvr8Pi8vW4/1/xX2XqHkBH4FZOCw0NtfnkE0Inx2XE\nxSXlODa9ZPMFfH1CWOI2QPTFBAdH0Lf3cibM7prjUMnXuHP1FRd2XmDs+rGoi9D1+Br+Xv4sHuxG\no6mDRXd3pKXISU1JofkcZyp1a0HVXu1Q09LgwebDKEo4YG7XHO/D6/Ha48anE9sxqiCt6+FXgUxJ\niUL27ak0bjlfHl3hwNBVJEblTVDKvIwVLeaPYdXY9bx/lrP70ddQUVXBeaUzD88/ZN3O55JiJ8zp\nwsjhG/D+IH5QZ/qsHshkMqYs/kPSuZYvSJeVddnwSFIc/EXsPoGRDDrkIdk9KTsIAtgU1Kf1iWeM\nePoRP4lPE7/xd/wnpC4IwltBEEQVBj3fBjBy/Dbq2ZVGR0cj26zz/kMvli89xt6DE7LN5r9GWloa\njo5L6NqnPnYNc3YsykCAbyjjh+5g5IqRGFtI24wMDQhlXr8l1B3Vm2J2OXdeyBPTpU2V1VRJS5Hj\nvuMYAGali1PMrgqRMQaEPLxC0RY9KNa2DyZV61PaaRJm1XPuo/6VoWVemEoubmhbWLKv93Q+v5JG\nxt+iYCUbmrgOZcnQFQS8D5AUq2uky5h1Y9g9dzfvXouPrVC1OMMmtMHBYRFJSSmiYpSVldmxZyz7\n997g3EXxJSMVFWUO7RzDpWsv2XI1UHRcBrS11Di9zhH/z9EM2P+S1FzIGmcFVRUlRrUqw5sVHbAw\n1KLOwUeMeeGDT/Rvcs8NfvqaetV6kyhbujDjR7XLdl14eCwOA1axbuNwiklQbJw8YxcAwya0ER2T\nlJjCoJ6baD+sveTWxdjIWGY7LaGqY1tKNbXLcf0XT29urdidWS+v1qcD8sRk3l26y8sXxgQnVUfD\nuACRb9O/3NqFSmBUrjq6xXI2UP5fgJKyCsU7DsTKYSSnxq/g4toHeTqeZe3K1HHuxbx+Swn/LG1j\n0bKMJb1cezHEcQMxEvTNew5sSBFLE5zHbhQdY2ZmwI494+g3ciP+AeI3XA0MtDl9aBLT5x3i1mMf\n0XEZ0NZKr7F/CY+n757nyFPzL2M30FZjrkMVPN06YKyrwWs95d8191zgh5G6TCa7LJPJPLJ4ZS/Q\n8g26dqjN0nm9s12jUCjoNWI9HTvb0bqteHu427decXjXLZZsGijJB3Xs6MNYFLegWe9momMgfcx8\nzsAVFK9blYpdmue4/sP1h9xcvoNidpUzR+d1zY0oYluBlxe9CH16G4ACds2Rx8WQHJX/bWK/Cowr\n1KSSixuBN05ybOqBTDng3MCmWR0qdG7KnH7LSIiT5pdZp10dKtWrxIhBu0XX+mUyGXNW9+XmpZec\nPvlQ/LnqlmXEqDZ0G7A608BFDKxLWrB3izPdJx3HJ1C6lrqWphon1/YgNj4Zx51PSMnD7zormOhp\nMK97FTrVTHdlyuiU+U3w4vDDSF0QhKaCIFTI4iVpN8mquBmzFhxm1oLD3LidtdHuks0XCA+LZc58\ncROjABERsTj1WcG81X1FKS5mYPW2J3g98WLAvAGSNiAFQWDhuK1omRhSe2jOmurRgSHcWr4TK/sa\nWNnbkpKQROznMORJKSSbt8OwbDX8L+zH78JBPNa4oqZrgLrBr92TnldomhWi8vgVJIV95uDw1aQk\n5P7xvUrPNhQob828YWtJk0haPaf0JPJLJAuX57hllAk9fS2Wbh7EiGHrCAoUf3MeN74jOrqaTF1y\nVNI1Nm1UkUlj2tPB5TjxCeLKPl9DQ12Vo6u6k6YQ6LrZnaSU/CX27yGtkim977xjz+sgTr0PYf49\n78zXb6TjPx0+kslk14HxgiA8+c7/C0LM4WyP8fipNy27LuTm3SWiyy6CINCpy3zMLQyYslC8aYXP\nh884NF/ClF1TKFr6+6JgWWHNkhN8uvuMjmuniVJbTElI4v2Ve3z28KJYnaq8PnkVLWNDPt1/TUkH\nZ4wr1SbW14tYn7co5CkUbtJF0vX8L0NIS+PDwTXEBXjTdd1YtAz1c3UcRWoaZyYswbBYYSZKSBgA\nQvxDmNV1Flv/cKZ8lWKi4zYuO8PDO++4cnm+6E37kJAo7Gq4sHvDcFFT1hkQBIH+wzcQF5/Eobkt\nctUlJZen0WfKMcKjEjg6tGa+qDtmh6SUNI4+9OWEux/XXgVTwkyX+mXNaaCuQafjz34PH/EfkbpM\nJusIrAZMgGjgmSAI/1CuyonU4+KSqNJgEjNmO9K5q/ihn9VbTrF9zSUOX3VFXeSHMCVZTvvGi2nY\nrSFNeoqf6AN4cvUJW2buosvmOWibiBf4SoqO5fWp67jvOEadkY4IVt0Jf3Efr/0rqTx+BZqm0rVl\n8gJBEJDHRpEUFkxyVBjyuGjSkhJQyOUAKKmooKyuhaquPmr6xmiYWKCmb/SftFQKgoDvmd2EPbtN\ntw0T0THL3VNMUkw8RwbPoFqfdvR3qi8p9uH5hxx2O8yZW9NyNNfIQGpqGk5tl9GkTRVmTs7axCUr\nXLv6giED1vD89mJMTcSbbSclpdCg5Sw6tKnB5K7SpS0gXQRs4IyTePtHcGqkHXpaP0Yi+lukpKbx\n6EMYt9+kt+5OP/Sb1OEXkAnIjtT7jtqEIAhs3Oos+pj+fqHUth3HtuPjKF2+iOi4CRNPEPwpmLHr\nx0oiqeBPwcxwmEurxS4UKFcyx/UZaosZSIiIJiYohJC0Wpk6Lu92L6Nw404/XFkxLSWJ6PevSL73\nlMgvXsRE+AAytPUKoKFthLqmPiqqWigpp0/cCopU5CmJpCTFkBQfQXzsZxSpcvSMLTE0s0bdrir6\nJSv8qyJiAVeOEHzrDF03TUavgDiVw28R4RPI8ZFzcd0+keLlpY3ab5m6Jf0zuiX7faGv4e8TSvem\nC7h0dX62MtHfwnXyLj68D+LUHhdJn9HAoAhsG05h68zWtKhrLTruaygUCpwXnMPdI5CzY+piLEHy\nN7+g0n33b1LnFyb146cfMX7GXu65u6GrK87NRhAEmjZ1xbaeDUPGtRZ9He533zF6wFYWnFqAnpH4\nLCg5MZkJHWdSoVMzyndo/N11cSHhBL14l9kN861s7otnhplqi18eXCbg6lHKDZ2NhnH+646nJsYT\n9vwu0TdvEP7ZE33j4hhblMPQ3AYD4xKoa0nTXUlOiiEm7BORIV6EB78mMvQ9hmalMKjXAJMq9VDV\nEf/7zC0Cr58g6PoJum2eiq557jL2D9cfcm/9AZaenIuOgXh98MS4RFw7uDJtfheatBYvHHZ45y3+\n2H2Le/eXi5bOTU6WY19nEkOGtWR4T3vR5wK4fe8NXXq7cW+3NHONryEIApPcLnPhznsuuDSgQC6t\n8XKL36Sejl+S1L+ERFGxzkQOHJ5ELTvxj4xL1xzj6N7b7LswWfQXJT4uiVZ2c+gzvY9kNb95Y7ag\nSEulybTvK+uFe/txZsJS5Ekp1BrcjfIdGmd2TXytrpiWlEjw3XOEPLxC6f5T0CograafE2I+vSX8\nxHE++7ljYlGeglZ1MCtSBVW1/M2qU+VJhAY8J+jjPUICnmFaqBIm7TuiX7LCDy3TBFw9yuc75+i+\n1RUto9zV2O+s3kN0UAhzt0l7WvN64sWqUas4e3dmjubVGRAEgcFdVlK9TikWzOwr+lyer/1o2WQ6\nD6/Ol2TGDrB6wzl27LvB3e290MxlbVwQBOZvvsXuk8+5OMEeS9N/xyADfpN6Bn45UhcEgfZ93Chl\nU4g588U/0gYGhFGz+lh2nhyPdVnxMqSjRh4iOSmZIYuGiI4B2LXvPu7bj9F12zzUtL7/KBrw5DUh\n7z5RpHp5rs7fRM1BXShetxqCQsHLF8Z/+X8q0v6vvfOOrrLY+vAzJ713AikkJBAgoYTem4B06SIi\niCjiFfsVG4iKKFdR7BdsgAKiSC8ivXcILYSSQgkJAdJ7PfP9EXJvPi7lzDmB5IT3WStrJeHsmTkJ\n2TPvnr1/m5QT+3ANCcfSvmL+UKSUpEUdIXHZQgpyUwkM7YNfSFdsbO/96RmgqDCHy9E7OH9qPZZW\ntvgMehyPpu0N7vKkysW1C0iJ3M/jP72FtYN6N6qSomKW/+MD6vfuyITn1dJZf/vkN1KvpDJ3oeGV\nzgnxKTz60HS2bJ9B/QZ+Btt99cUq/lpzkB1rpipVSEspGfX019jaWvHzW6YVrn2zaD+fzdvL+kld\naeBr3Caqirk5dSGEO/AHEABcAB6VUqbf4nUXgEygBCiSUt4xb7vKFx/dzPxV+4mJSWTyVMOzVgCe\nm/gdI5/upuTQD+87x5HNR3jibbXMh+SEZHZ99SsPvz/xjg4dwK9FGKH9u+EVEkjLJwex/4clXD0d\ny8mTXv8NuRzYTEHqdTzDO1aYQ8+Mi+LUh69y8bc51AntTbdHvyW4ySP3zaEDWFk7UCesL92Gf0VI\ns+EkrvyNE+9P/E8hVUVTu98TOAXU589XZlNSbHhedxkWVpb0fH8ih+avUK44HfbyMC5EXWDLX4bL\nCPj6e/D8GwN4ZvxXSppEL7zUn5ISPbPmblJaoxCCH76ewP5D0czfmaRkezMvjmrLBy90o8eMLUSc\nf3DrJ+7CW8AmKWUIsOXG17dCAl2llM3u5tDBzJz6tesZvPn6XL7/8UVs7tLRvTxrVx/gfHQSz75q\nWGs4KM12eevFRYx+dzQOLoaHIPR6PTNf+55mj/XDq75hl2q2zqXj132oDWEDu7N+2i/oi0szSlJO\n7sfKwbnC+oYWZqVz/osZnJ0znYAGPeg6dBa+dTuh093bVmN3QggdNQNb02nQp9QLH0rc/K+I/nQq\nBWnXK3geQd1HJyJ0Fqyc8odRQmCufjVpN2EEn70ym+JCwzcGa1trxn04jvdeX0xOVr7Bdo+N60pR\nYQlfzlllsI2FhQWzf3yBf320hPMXrhlsB+DoaMvSBf/kjXcXEhl9Vcn2ZsYOasZ3U/rTb+Z2dp42\nbaxqyiPALzc+/wUYdIfXGvwEYlZO/YXJvzJqdDdatDL8hj4nJ59XXv6BqZ+V9og0lI8+3YZ3gLdy\n84Sfvt9CcWER4SMNv4gtjwx+DO/W3Tk6YyIHp5QKMLk3MrxK9rbjSkn20vUc/WACNvYudBv+Nf4h\n3RCV6MxvRgiBT1A7ug77EhfPOhybPpH035ZXqHSwsLCgwbi3yYw9ZbSkQMP+XXHwdOO7WauV7ELb\nhBLWPox331tnsI2FhY73Zj3Bl9NXkJKSabBdvRBfXv7nIJ557Ufln19oAz9mTh/NiLdWkZunXphU\nnsE9GrLok6E8+vUu1h5Re7p5APCWUpbtdleB212CSGCzEOKwEGL83QY1G6e+YfMxDh04pxx2mfrh\nQpq1DqZNJ8MvVBMvp7B+3nrGvDtG6UIsOTGZg3OX0f2dCf8vLVEFIQS2Xr7kpyRRu89IPBq3NWqc\n8hTn5RD3xXSijy6jTa/JhLZ5Ekur+5uZoIKFpTUhzR+lff9pXDq7hXOfTKYw639CjUZjaWtP6LPv\ncWndAqNEwIQQdH3jaU4u36Qchhn5xkj2rt5LdJThglqhTWrTa1BLJr09V2mul155hOvXMpi/Sn3z\nevLxLrQID+LV70zT0gHo0S6YNd+NYsLcg/x2/sGS172DXMr/E7OSpTvv7XbfDlLKZkAfYKIQotMd\n5zSHi9L8/ELC2r3OZ18+Q6/eLQy2v3TxGm1bvcaKnVOp6Wt4mta4J36mVlAthr40VGm9U56aRY2G\nQbQaO1jJroyyptCnf/qIWp0H4B7W0qhxypOTeIGz37yPp08TwtqNxcJSTSK4stHrizlzaDEJsbup\n/48pONcxrkDmVqSc3E/sH//miYXTsHVRb4gcuWIz5zbu4ZM/pyhdSG5csJEjm4+wZO3LBh8a0tNy\nGND2Xdb9PY3GTQINnuvQgXM8NvxfnD4wy6BG7eXJysojvMMbfPpyV4b0NFzB9HZExV6j97MLeP2p\nDrwQblzNwJ2oiIvS2L6GC/sB7E9J5kDKf+8Mvo6JVrkoPUNprDxJCFEL2CalvON/cCHEe0C2lPLz\n273GLE7qM2b/RVijACWHDvD6mz8zavxDSg792MFYzkWco/94tV9uxNYI0i9dofnjanZllKUu6qys\naTh+SoU49NTIA0R+/gb1mg+nSacJZufQAXQ6S0LbjKZx+6c5/e1Uspf+XWFjezRui2ezDqx8x3Dx\nrfKEDXyIkuIS5v+yW8mu+8jupF1LY8fGEwbbuLo58Nzr/Xntnz8qzdWqTQh9+rZk8kw1bRgAJyc7\nFv30IhNn/M2V66bp1QOEBtdg14Kn+W7xQd7bdqFadORq6+HJyyH1//OhyGqgLF/1SWDlzS8QQtgL\nIZxufO4APExpP4rbUuWd+uWEFL79ajWffDZOye7okVgO7jnLUy8YnnompeT9d5Yz7JVhSk0vigqL\n+OnDRXR6ZQwW1qZrX+gsTR8j8/dVxMz/gtYPv41/va4mj1fZ1AxsTbt+H3Dm0EKSf1lUYeMGPvIU\n+clX2Py9esaN0Ono8s+x7P9hiZKao4WlBSPfGMlH765Q6l706NjOJMQns2WzWiOO9z4cxZLFOzl9\nVj2m3bZ1CM+O7cH4GVsqxAkH+Liy69dxrN8VzcTlpyu02YYZ8i+gpxDiHPDQja8RQvgIIcouXmoC\nu4QQx4ADwFop5cY7DVrlnfqbH/3B08/2IiDQ8M7rAG+9M4/n/tkPB0fDy5V3bjpJblYuHQd2VJrr\npzmbcQuoRe02hosplafslF5RpC78g3NHl9J+wIe4eYdU6NiVibN7bToM+Jj4c9tI+umnCnEyOitr\nQsZMIm7lz2RfT1W2r9EgiNqtGzPni7VKduFdw3FwceDbuYZvJlZWlrz0ziDeeWe+0nv38nLh9TeH\n8upU4zbDd98cSmJSGr/sqpgMlhoejmydO5aYSyk8Pi+CgqL7o/BY1ZBSpkope0gpQ6SUD5flqEsp\nE6WU/W58HielDL/x0UhKOeNu41Z5p7550zFem6TW0Hn/3jOcj0li6Og73if8P6SUfDp9LUNeGqJ0\nyZmTmUPEwjW0/8dIpTWWsf7LXeSnqqWd3Ym0RUs5H7mO9gM+xNHl/gp+3Q/sHD1o338aSRcOcn3e\n/AoZ09EviFqd+rH2/cVG2bcZP5xTq7aSdtVwbXIhBMNfHc6Kb1co9QztNbAFRUUlrFuj1pJuwvN9\nOHf2Mlt3RCrZQelmMn/287w5dVGFhGEAnB1tWTf7CYSA/t/srXBN9geZKu/U33hrGM7OatV/732w\nkGdf7Yu1taXBNjs3R1JcVEzLnmqx7B+/+5uA9uG41zG84q+MpFMxXN60pMIErnKWbSDm+Ara9X0f\ne8eKv4iqKtjYudCu73tcjt5J2qKlFTJm7V6PkZN4gYv71EIbAI41PGjYtzPfzzI8lxygYeuGuNdy\nZ91Swxtj6HQ6Jr4xgGkfLlY6rVtbWzH1g1G89eHvRj3hNG0cyPix3Xn5K7X7gzthY23J4pnDeXFU\nG2zDq88TZWVT5Z36uPFq5dhHI2KJPp3AwMfaKdl99dlGBjw7QCmLISczh8jlm2j5pHq2i5SSjTMW\nEzhwXIU49YzYU0Tu/Zk2vSdj71zxQl9VDRt7V9r2eZfoY8tIjTTcKd4OnZU1wcMmsOWzxUZVmzYf\nNYDoLfuVW+ANfG4g332xSali9KG+4eTnFbJ1y3GluYY92oGcnHz+2nBUya6MKZOGcvT4edbvMq0X\nbHksLHQM6t4QAF2j4Aob90Gmyjt1lcpRgBkzlzD6uR5KhUYnI86TnJhMmz5tlOaa+8MWAtqG4+Kr\nFu8HiNl6AKkvoUarh5Rtb6YgPZmzc6YT3vVFnN0DTB7PXLB39qZlj0lEz/uc3KumF7a4hbXGxq0G\nm2arhTYA7NycadCnE3O/U8vOadShEToLHXu2Rhlso9PpGPdiLz759E+luXQ6HW9PeZT3Zy4z6rRu\nZ2fNNzPH8fLMzRQoVNOqcDvHHhmfxlFNbsAgqrxTV+FKYiq7NkUybIzhsXSAb77ZSY9RPbAwULkR\nSjNeTi7bSLPH1StH9cUl7PpuOYGPPGWyeJXUlxDzzccENuyFt39zk8YyR9y961O/xQiiv52Ovsi0\n6kchBHUGPkX834spLlAfq+mjfTjz105yswzPhBFC0OvJXvz72+1Kc/Ub2pozpy5z5nS8kt2gIe3I\nzMxl285bt4a8G30ebkbD+r58vTbOKHtDKO/YzyRkMHzWdiYtOMwT3+xiyu/GPWU8SFQrp/7l7FX0\nGdIKZxfDY/DpqdlEbI2gy9AuSnMdWH8A90BfPILVJXBjtu7HyskV1wZqUr63IvnX3xBCR71wtcvk\n6kRAw144uNTkys9qOdy3wrF2PZwCG7BptnpIx8nbA//Wjfn1F8N7kwK069eO2BOxXL5ouNaNtY0V\nw0d3Yta3/5PafEd0Oh0vvzqQT/9tuFTBzcycPpqZX60mOS3H6DHuhq5RMEeFHXM2nSXAy5H17/Tk\n5GcDORybTNzVirmsra5UG6eu1+tZtnA3jyq2HJs9P4LwruE4ualVFK5asI3GQ9RlOqWU7P15Pf69\nHjNZPzw3KZ64k2sI7zKxSmm43G+EEDTuOIGEmF1knj9t8nj+vUaQsGW5UbH1xoN7cGrVVrVLTFtr\n2j/Snn//pBb2GTqmE2v/PEB+vtpTxYjHO3PkcDTRMVeU7MqoX8+HYYPa8umfpv+sb0dqRi5LNkTi\nXLsGHz1WevjZdeYqGblFeDqZXxHd/aTaOPWdOyJxdrWnYWO1k/PulbvpNFgtXJMYl0j65SQCOqif\ntBMiopAlJbiFmlYxKqXkwk9fUa/ZMOyd1GP61Q0bW2dC247l/LyvkXrT0uOcAupj416DuB2HlW1r\nNW2AlJLoo2qXiZ0Gd2LPqj1Km4GvvwcNm9Rm3ZpDSnPZ2dkw+snufLtgq5JdeaZMGsrPv27lavK9\n0XL5bd1JzsQl8+qYdtg1CyEhNZcTF9MY0qY2zvbW6PXmX416r6g2Tn3ur5sYMFxN/Opi3DVSk1IJ\naxemZLfk932E9GyPhaXhKZNl7J63k1pdHjH5lJ5yYh+F+ZkEhvY2aZzqhG9wRyytbMlaolYIdCt8\nujzCgYU7lO2EENTv3Ynlv+9VsgsMDcTK2ooTh9Vi1QOGt2H+ws1KNgBjx/Vg8aLtFBUZd+Hp6+PO\n48M78uWqc0bZ34miohL2HYvn1THt8HC1Jyk5i+3peiLj0wgPLJX80OkqtblQlaZaOPXi4hK2/HWM\n3oPVTr/zfz9By54tlYqNpJTEbN1Pve5qKZMAeelZpJ89So1WpnWVkXo9l/+cR8PWT1SqDnpVQwhB\naOsxnI1YYvKlqUfTduReuUhGgnoVZb3ubYndcRC9ggSAEIJWvVux8M87ynr8D937NePAzjNkZeUp\n2dWt50NQUE02bTVcf+ZmXn9pAD/O30JWToHRY9wKCwuBvZ0VyzZHcelKOl8u2M/ByMs81Ducnk3+\nW1CXkJrLnrPX+GxNJKfiK07F09ypFk59397T+Pi74+OnVm5/dOtRmndXyxhJjE2kOL+AGg2DlOwA\nYrcdwD20pcl56Skn9mJhaUONBzDb5W64eYfg7F6brKXGXwRCqf6OZ4su7Fqgnm3h4uuNvYcr5yLU\nTrEturcgYouaBo2ziz3hrYLYskm9aGr4iE4sXGm842o86AAAH09JREFUtG5gQA26dQ5j4e6KbYCh\n0+n4dnI/Lidl8twHa7CxsuCJ/k0Z2bfxfzJjdp+5yrSlx/hm/WmupOXR6yO1Lk/VmWrh1P9ctZsu\nPdV0V7Iyc7l09hINWzdUslu17jgB7cKNCp8cW3MUz+ZqF7m3Imn1UoKbDLqnjZrNmeAmg4iLXGuy\nNoxX884kHzWugjKwfTP+Wq9WHBQYFkhORg4J8Wr52J0fbsKy1XuUbAAeGdSGDesPK8kU3Mw/nn6Y\n7+eph3/uho21JSu+HsmSWY/y1jOdaNXovyf0U9aOfLIqktZ1vfhwRDM+H9OKSY+ohVCrM9XCqe/f\neYZ23dSc8+G90QQ3Ccba1lrJ7vKhSPxbqwt3FWTnkn3xLG4N1eSDbyYn8QJ52deoGWh6N6Tqiket\nMITQkRFtfGgBwDmoIQUZqWQlJSvb1m7dmPhDajorOp2O0LahHNh5RsmufddQ9ivaAPj6eeJfuwb7\nDhofF+/WOYyMzFyOnTEuk+ZuONrbcPBkAl8vOkBJiZ70zDy+Wrif7j0aMbClP/VqlfbU3Xg88Z7M\nb46YvVPPysojLjqJJs0N6wdaxvptF2jQSq3hQklxCVciz+Ebrt6oISEiCqc6DbGwMVw18lZkrPkb\nv7pdtFj6HRBC4B/yEOnrN5g2js4CtwbNiD+kFucG8A6tS9qlRPKy1WLd9VvWZ9OO80o2QSE1yc8r\n5HK8+ubzUI+mrN9tXCESlG5Ejw1tz5K9pjWqvhNdWgXSt1M9LCx0JKfnUlKip3ubIGq0Lz2dT1t6\nHAvt4vQ/mL1TjzgSQ4MwPyVZAIDYE7EEN1XTmkiIScDBw82oLjnHN13AtX5TZbvySCm5cn4fPsEd\nTBrnQcAnqD1JFw8iS0xLb3QJacqprerVkxbWVnjWrU3cSTXb4KbBxJ6IVbIRQtC0ZRAHD5xVsgPo\n1CWMPbsMlyi4FUMGtGHVOrW0SlVCAj0BOBOXTEZWPk0blDZi//bYdU5lFDF1mGl/W9UJs3fqx47G\nERqupncipeTi6YsEhKrZXYi6gFcDtSeCMrLOn8apjlqI6Gbyrl1Gry/G2T3QpHEeBOwcPbFz9DK5\nGMm5TkMyL6iHNgBq1A/iQtQFJRv/+v5cvXiVgvwiJbuw8AB2HFA/cbduU5+IIzFKzTpupmXzIK4n\nZxJ/JcPoMQwlwMeVqLjr/PDnYabN3s7c5RG89mR7RJC6Smp1xeyd+sGj56jXwFfJ5npSBpaWlrh4\nuKjNdewyHkH+SjYA+hI9OYnncfQzTYUu/exxPH0aaxekBuLp05iCnUdMGsO+pj+FGSkU5hiu51KG\ne5AfR0+oCY1ZWVvh5evFhVi1jJJ6DX2JOW14M+sy3Nwc8fB0JjbO+PCJTqejS8dQdh65aPQYhtI4\nxJv5Hw0mIuoKhUUl/DZzGO3C/dl2SC1kVZ0xe6d+Ke4adeqpSc1eOn8N70B1edr0y0m41q6lbJeV\ndB0rBxeTUxkLIiJx91bug/jA4u5dn7RrphXHCJ0F9t7+pF5Qv4hzq12L9Hh1Z1mzTk0uxak1Tgms\n681FRZsyQsNqE2VEq7vytGlZj4Pn750WTHnaNvVn9tT+TH+pO6HBNdiyP45VW4x7mqqOmL1TT4xP\nwae2p7KNp4+aDUDWlWSca6rbZSRcxdbL9C5EmakXcfYwLvzzIOLsEUhm6gWTx7H18iHTiCIkp5pe\nRmXOePp4cuWyWms939qeJManGJXGWSeoJrHnTcs1b9oogJNRaoqRplD2tCqlZOfhCwzvraU0lmHW\nTl2v15N8LRMvb7UwyvWrmbh6uSrPl5uSjoOXu7Jd9rVUbNzUN4PySCnJybhSLVvU3SvsHb0ozM+k\npNC0ikcbNy+j+pfae7qSl5ap1AADwNXLlcgLaqdeewcbrKwsychQDxP5+Xty/qppFZkhdWsRHXtv\n0hrvhBACdxc7/tpR8XIF5opZO/XMzDxsbK2U2tYBnEsswNHVUclGSkl+VjY2zuohlLy0TKyd1DeR\n8pTk54IAK5uKaX33ICB0FtjYuVGYYVpzBSsnVy6dU7u4BLCwtMTSzoY8xRJ+R1dHstPVhbJc3R1I\nSc5UtvPycuH6NdMuOX193Ll6LcOkC1djeXl0O4L81Q9b1RWzdurZWXk4OKnnfefn5GPnYKdkU5hf\niM7CwigRr4TzYGFiPL0oOwNrG2eTxngQsbZ1ojjHNP1tS1v70k3VmPntbcnLUXPqto625OfkK8/l\n4GirrAED4OJiT5ZCY49bYWVliYODDZnZFasDYyhz3htQKfNWRczaqRcWFimf0gGKi4qxsFYr3iku\nLMbCWi0Xvgx9cSE6S7XK1f8Zo6gQCxPHeBCxsLShpMg0R6OzsjZaIExnZUWxYus3SytLio1QT7S2\nsaTIiDZz1jZWFBSoP4ncjL2dDbmKqZgaFY9ZO3UpMSq9T0qJQM2u1EbDLDFRA8YUhED58lIgjNSt\nMc5OCFEhPyJj3qtGxaN+zK1CWFtbUmjECcOYk5CllSUlRmpPCwsrZIlpJxidpTV6E8d4ENGXFKKz\nMvEpqbgIYUTYDaCksBhLKzXboqIiZRsofXK1MuLJtbCgCBsb011BXl4hdrbGPc1WFU4fNf+/MbM+\nqTs42pJrhJazrYMt+blqMUtrO2tKioqUNLLL8K0DxUbGZMuwdHCisEDrzahKYUE2lvbqsg7lKcnP\nw9LW8L635SnKy8fWQe3epyC3QNkGIDenAEdHdbvMzFwcndTumG6mpERPVnY+Lo5aq7nKxqyduouL\nPbk5BcrSofVqWZOToZYyptPpsHZ0oCBTPSvBzsWJoizTUsYs7R3RlxRTXKR+EfagIqUkPycVGxc1\nnf2bKcpOx7+e+glUX1xCUW4+9k5qG0JORg4OLuoX6xlpObi5q29gydcz8fJUSwu+mStJaXh6OGFp\nqQnNVTZm7dQtLCxw83Qi+apaOpaXtwvp19SdrIOHKzkpRtjVcKcgXb0IpTxCCBycvcnJuP+5wOZK\nfk4KVtb2WNiadgotSEvGwVM9ZS4vPRMbF0csFB1d+vV0QmurbQQF+UXk5Rbi7q6WqguQkJBCQA3T\nnHp07BXqBtU0aQyNisGsnTqAj587ifFqhSE1fd1IuaKeu+zo7WFUhaCLTw3yr5vujJ3dAyqkQvJB\nITPlAk7uaqJttyI/+QrOPurNvbOSknHyVn9KSElMoZaf2iaSGJ9CTV83dDr1P+nzcUkEGSGbUZ6T\npy4R1lAT1aoKmL1Trx1UQ1n8qHZQDZIuqmtyuPrVJD1e3Tk7+9agMCOFkkL13OPy2ISHkXZVrUv9\ng0zatXO4edU1aQwpJblJl3CvoyYaB5AefwUXP/XTa9LFJAKC1DaRC7FXCQgyzjGfOR1Pw/rq7688\nBw7H0Cqwcgrj8vKLWPK3WkOS6ozZO/V6DX2JOaMmtlTTx438nHzluHrLJr6knlcXPrKwtMSupj85\nCaYpybnUa0JyonrDhgeV5MRIbDqZ1mkq//oVLO0csHVWD2ukxMbjXkft9FpSXMLVi1cJrKu2GcSc\nSaRuA3UJiaysPBIup1C/nvHyE1JKtu8+RacWpj8VqZKSnsvD439l1VZN0KsMs3fqnVuHEXVcTfJT\np9PhX9+fi6fV7AJCA7h+1jjH7BTQgCwTtb0dfOtQXJxPdrrWuutuFORlkJUWj0twI5PGyTwfhVOg\neqcrgOvnLtC5lZqjS4xNxL2WO/YOalkkp45dpGMrdb3+I4ejadwkECsjUijLiIyKx9rakrq172+p\n/oWENDqN/pm2Tf345Ynw+zp3VcbsnXrLVvU4deyicgZMUOMgYo7HKNkENAggI/GaUdraTXrUIf2c\naT0zhU5HrcA2JMbtNWmcB4HkxJPU8G9mco56RvQJGnYNUrbTF5dw7UyccnetmOMxBDVWm09KyYnD\ncbRuoy7LvGdXFO07hirblWfl2oM80qflfdX5P3bmCp3GzOW5Ea34pHdddFo7u/9g9k7d3d2JWn7u\nnD5xScmuV5dAzh1RU3aztLbEu0EQV06oK8L5tQgjI+Yk+mLTihtc+vbicvQ2pLz/wknmhG9wR+q8\n9KZJY0gpSTsdgX8r9dP+9XMXcKrpqSwcd+7IOXp0UZNXTriUTHGJnqBg9fj91s3H6dPReNlaKSWL\nl+5hePv7px66aW8svZ5dwBdv9uaFcK/7Nq+5YPZOHaBt54bs26EW2mjdsT5nD59Vriz1axFmVCNi\nOzdn7Gv6k3HuuLJteZwCG2BhacP1BNNO/Q8Cpp7Ss+Nj0FlZ4+qv3hgl/tBJ/FqobQZSSqL2R9Gm\no1q4Z9/207TpVF/5pJycnEnUqUt0am9ceAng4OEYCguLad9MvSOYMSxcc5zRby1jyYsdGeJjWhP3\n6kq1cOpDBrRj50Y1R+vu6YR3gDfRR9WySfr3a8qFvceM0rjwDO/E9aO7lO3KI4TAu+9QYk+sMmkc\njbuTHLETz2YdjQorXNx3jD591JohJ8QkIHSCwLpqWSw7Np1kUL92SjYA61Yf5KEeTbG1NX7zmzN3\nE+PHdr/noRcpJZ/8vIspX29h09vd6dTQtBTM6ky1cOpdujYm+nQCKdfVtKSbdWtGxJYIJZvA0EBK\nCouMyoLpPLoxKcf2mNy0oUarbuRkXCE16cG+8b/Txpoeblp6ndSXcO3QNjqOaqZsm5OcRtrFRBq2\nVru4jNgSQbOuzZQcZF5uAQd3naVXn+aqy+TPJbsZ+UgbZbsyrl5LZ+W6Q4x76N7mp5eU6Hl5xnp+\nW3uSXe/2JMzftN4E1Z1q4dRtba3p3LMxm9aqOegxI5pwaOMhpVO3EILgbq2J2bJfdZk41vDAKaA+\nySae1nWWVtRv8ShRBxc80Kp4ZVo4ZUJnFXnPkHY6AmtnNzyCayvbxmw9QGCH5lgqimsd3HCQkcMb\nK9ns3BRJkxZ18PBQ09pPuJzMsYhYBvRpqWRXnq/nrOexoe3xdLt3+en5BUWMnPQnkdFX2fZWN3zd\njdPgeZCoFk4dYOwTPVi75ICSTUioL9Y21sohmOEjO3B24x6kYpsygLZjOnNl51plu5txHNaXkuIC\nEmJN2yDMldSrZzmwfjon9/zI8Z2zyc26hhA6pNSbfEoHuLJzDa0e72qU7dm/dzFoRHslm8S4RNKu\npdGqg1oGy5o/9zN61ENKNgALftnK0OEdsLMzLvSSmprNnJ83MWn4vesNmp6ZR+8JC9AJwdoXO+Dq\noPUTMIRq49R7PBzOxbirStWlQgg6DurIrhVqjjGgYQDWDnZcPnJKdZkEtm9OUXYGGbHqtuUROgvq\nPPUyUft/oSBfvYWZOVOQl8GJ3XOo23QwQY0H4OBSk92r3yEz9SIZzZyM2mzLk5sUT9bFc4T0VHPM\nUJr1kpeRRVhbNWe3a8Uu2vdvj4WF4X+SKdczObT7LIOGqMXTi4tLmPvTRiaO6a5kV57PvlnN4AGt\nqePnZvQYdyLhaiadn5xL0/o1Wfhkc2wVm9o8yFQbp25tbcXAx9rz5y87lez+Ma4lB/8+qNQ+TAhB\no0HdiVyxWXWZ6Cx0+HYfyuWNfyjb3oxznQb4Bnfk5O4fHqgwjJW1Pa6edXH3ro+Dc01Cmj9K3aaD\n2f/XNLIvRSOM0D8pT/zGP/DpMgBLG/WTYeSKzYQO6IZOwTkXFxWze8VunntGLb694rc9dO/XDGdn\ntZDEqhX7CQz0JrxJoJJdGQmJqXw/dzNTR9+bgp/TsdfpOPpnRvVvwqz+IVoOuiLVxqkDvPL8QFYu\n3kt+nuGtx7x93GjQqgF71uxRmmv0qA4kHj9DZuI11WXS49kWZF+OI+viWWXbm6n1zLNkZyRw6az6\nBmNulMXM9fpi9PpiEmJ3/+ffghr1w++RUcRvXkpxXo7Rm1zetQRSIw/Sc2Inddv0LGK3HeDJcd2U\n7CK2RODl70W9hobrr5SU6Plj3g5ee3GQ0lxSSr78fCWvP99Xya48k6ct5tmneuBfyzRlx1ux71g8\nD42bzwcTuzGpjc99LWiqLlQrpx5ctxZNWwax+g+1S8yJL3Rh468b0Ss8tts62BLavxvH/livukws\nbayp3Xsk51fOM/mErbOyJmTiu5w59Bvp19UqZM2JwoJsIrZ+Udr0wsqOeuFDiTu5huhjy4HSbBev\n5l2wsLJG6CyMdgYX1vyC70ODsXFSj8ufXL6RoK6tcVHUJv/7l7+Z8HxXJZutfx3Ds4YLLVrVU7Pb\ncpzc3Hwe6WvcBemBQ9Fs3HqCt0eYVoV6K9ZuP8uglxbz0zNteCLYtMYmDzJV3qmXlKiV/785aTjz\nvttAiUKHojadGmBpZcnxHWqFQeOe78W5jXuM0ljv8VxLCjNSSI08qGx7M/Y1/Qke8wqHNn1CXrZp\nuu1VkZKSIo5u+xJ7pxpY2zgi9SU4ufnRrv8HXDy9kdMHF5B3LYHM2FNkXjhLcZ56IxOAzLgoMuOi\n6PVSZ2Xbwtw8Ipdv5qkX1E7AMcdjSE1KpecAw1MSpZT8/PXfvDFpmNJcUko+nvYHU14bbJREb3Fx\nCf947Sc+nTYKJ0Vtmrsxb8VRxr+3mlWvdaFPM9MUIx90qrxT/2Ox2iVmp85huLo5snH1EYNthBC8\n8FpPVn+/Wunk7OrlSv1eHTm6aI3SGqFUubH7pJHELZ1jdKf68ng2bU9QowHsX/8hhfnVq+3dgfUf\nYu/kTcPWowHISDlP+vUYHF186DzkM7Lsc7m8ZTmX1i+i7oiJ2Lh6Ks8h9SXELvk3dQY+hZWdeqXi\niSV/49eqET5BauXyq+espu+4vkodgw7sOkNmRi4DBrZWmmvjhgjS07N5bFgHJbsyvpmzHlcXex5v\np/7zvR1lRUXTZm9n6+TutK5bcWM/qFR5pz79g8UUKDSXFkIwderjzJ65Vimc0ntQK7LSsji1Ty0r\n5dlXB3Dm791GNc8IaNsUB78gLm34Xdn2VniMGUnNgJbsXz+NwgLjTqtVEXsnb7LTEyguLuD4rjlE\nH13G8V1ziNw3j+TAIuo9/jJ1R0wkbOJ0XEPUqjjLSNyxBgtbO7o/o26fn5HF8aUbmDBpiJLdxaiL\nxJ2M45XnDL8glVLy70/X8s47I7CwMHwj0Ov1vDd5IR9NHqGUYVPG+QvX+OizFcx5s+KqR/V6Pf+c\nuYFFa0+wc0oP6vtUfIz+QaTKO/XQsNrM/m6dkk2v3s2xd7Bh/YpDBttYWOh49a1+LPtqmfJpvfHg\nHuz/cYnSGsvo/95jJO1aR05CnFH2N1Nz/AQ8aoWxb937FOSptfmrapw/VXpfEd5lIk7utdnw61gK\n8zNp9fCbtOzxOnmuelJP7CuNoet0WDsZV2mYn5zEpfW/0e+DMUY5rMO/rCS4a2tqBqoJai39ain9\nx/fHViFXfO+2KJKvZTBipFqIaNGC7djb2zB4gNrpHkqd79MvzObNVwdSL8C0fq9lFBWV8OTbKzh0\nMoFtb2pFRRVJpTh1IcRMIcRpIcRxIcRyIcRtt+hZH4zii5kruKbQU1QIwYwZY/n6o1UUFhou2NV3\nSGvycvKUpQMmvNyPy4dPcTUqVskOwNHLncBBT3P2l89MVnCE0vfuM+F5vGu3YM/qd8y2p+nxnbOJ\n3PszsSdWA9Co3Tiadv4Hjdo/DUBR52BcQ8IpyEhBKt67lEfq9Zxb+Dn+PYfjVltdaTDtUiJnN+zm\n+TfUTulnD5/l0tlL/HOi4bnwer2eLz5czrRpo5XCNVlZeXwwdRFfTh9t1Kb13Q8byMsr5NVH1GSE\nb0dObiGDXlpMRnY+61/piJtjxcbnzQUhxHAhxCkhRIkQ4raXKkKI3kKIM0KIaCHEXaVHK+ukvhEI\nk1I2Bc4Bb9/uhQ1CfHlsVBfen7JIaYKu3ZoQEOzNH3O3G2xjYaFj8odD+H3m70rqjXaOdrR7bgQ7\nv5h/18KXhIio//lezwnNsfXw5sLq+QbPeSeEENR85hmCmgxkz5opJCfc+25JyYkV107sxO4fKCku\noNXDb5KXk0JRYWmHKr+6nbFz8CA93IGCtOskbFmGo28QQiEMcTOXNy9F6iW9X+16y3+/1e+rDCkl\nu79cQPMnHlHKeJFS8tsnvzH8leHY2FoZbLdu2UEsLHQMGaZWFPXpjD/p2q0JbVuHALB9l+EhxjPn\nEvjgX0uZ/15vo8I2N5OaUdqpyMvNnj/Ht8bexvjmHNWAk8Bg4LbFNUIIC+BboDcQCowUQtxRVKhS\nnLqUcpP8r1DHAeCOikDTXx/Khr+PcHC/Wl73rM+e4ftZf5GeZnjbus49GuHh48GWxVuU5npydAcs\nLC2JXHlnu4Sj/ysRLIRg4MdPcv3IjgrJhinDdeRgQsa/RcS2L4k+tvyearCnXDGtQraM+HPbKMhN\no/lDr+BWI4TkhONcvXgYKE1bTGtiR2FWOlE/TMOzWUe82z1s9FyZcVEkbF3OoE/G37ZY6Fa/rzJi\nth4g+3oqzz6vtoZ9a/ehL9HzwjjDxcLycgv4YtoKZn0+Xum0feZ0PL/M3czn7438z/cMdeoFBUU8\n/vTXTH93BCGBpl9gJl7LpOvYebQN9+Onx5pgZVnlo7/3FCnlGSnl3ZoztAZipJQXpJRFwO/AwDsZ\nVIWf6jjgrzu9wMXFns8/HM1LE+dQpHCCDmsUQK+BLfjm45UG2wgh+HjmcFZ+t5KMFMNj0jqdjpf/\nNY6Dc5eRfT3VYLsy7Fyd6PfR85xbOIv8ZPWm2LfDtX44TSd/w7VLEez/axq52dcrbOx7gYtnEC16\nvA6AjZ0L9VuO5NLZLSTWKv2ZCp0OS1t7goY9h1/P4UbPU5iZxumfP+bhKU/jVFPdYeVn5rDnm4W8\n8PE4LBVaweVl57H408V8+OmjSmmFP331N+GtgpS6FEkpeeWF73lr8qPUqqlezv/OB4up7efBsz1M\nV2GMuZRCpzFzGdm3MZ9qnYpU8AXiy319+cb3bss9c+pCiE1CiJO3+BhQ7jWTgUIp5W93G2/k8A74\n+nlwQPG0/unH44jYH0NOtuEyAHUb+NBpcCeObj2qNJdfPT8aD+nJuU3GtZvzaVof/16PcXnzUqPs\nb4eNmxehkz/D06cxe1ZPoaTYNOnfe4mzewA6nQVSX4KUEnfvBlgFB5B3tVTqWOpL0FlZ4xJsmpBU\n4s41eLftSWAHdclagLidhwjq0oqQFiFKdpF7I2ncsTHN29Y12KaoqJgdG0/w1awJanOdvIheL3n1\nqZ5KdgDXrmewYctxfnrb9GwXKSXPTF3Fm0935M12vg9UlaghfvAuKFcnisrSDBFCjAXGA92llLf0\nuEKIB0fQRENDw2SklEbvGBXlb1TXIITYBvxTSvk/GRpCiLbA+1LK3je+fhvQSyk/ud14lXJLIYTo\nDUwCutzOoYNpvyANDQ0NFSrZ39xu7sNAPSFEIJAIjABG3ua1QOXF1L8BHIFNQoijQoh/V9I6NDQ0\nNCoFIcRgIUQ80BZYJ4RYf+P7PkKIdQBSymLgBWADEAX8IaW8Y0PmSgu/aGhoaGhUPFUh++WOqBQq\nmQuGFh2YC6rFEVUdIcRcIcRVIcS9T/C/jwgh/IUQ227834sUQrxU2WsyFSGErRDigBDimBAiSggx\no7LXVNlUeaeOQqGSGXHXogNzwZjiCDNgHqXvp7pRBLwqpQyj9JF/orn/rm7cyXWTUoYDTYBuQoiO\nlbysSqXKO3XVQiVzwMCiA3NBuTiiqiOl3AWkVfY6KhopZZKU8tiNz7OB04C6NkIVQ0qZe+NTa8AC\nUC8UqUZUead+E3ctVNK47ygXR2hUPjeyKZpRelAya4QQOiHEMeAqsE1KeXtthweAKiG8IITYBNxK\n4u4dKeWaG68xuFCpKmDIe6omaDftZoYQwhFYCrx848Ru1tx4kg+/cd+2QQjRVUq5vZKXVWlUCacu\npbxjyduNQqW+gPHtz+8zd3tP1YgEwL/c1/6UntY1qiBCCCtgGbBQSmm4foYZIKXMuJEK2BLYXsnL\nqTSqfPilXKHSwDsVKpkx5l5g9Z/iCCGENaXFEasreU0at0CU1uf/DERJKb+s7PVUBEIITyGE643P\n7YCegJq+RzWjyjt1qmGh0u2KDswRY4ojqjpCiMXAXiBECBEvhHiqstdUQXQAnqA0Q+TojQ9zz/Kp\nBWy9EVM/AKyRUqpJrFYztOIjDQ0NjWqEOZzUNTQ0NDQMRHPqGhoaGtUIzalraGhoVCM0p66hoaFR\njdCcuoaGhkY1QnPqGhoaGtUIzalrVAluyBAfLffxxo3v/2juSoIaGvcTLU9do0oghMiSUjpV9jo0\nNMwd7aSuUaURQmwXQrS48fnDQoi9QogjQoglQgiHm15rKYQ4KITocuPrGUKI6ZWxbg2NykJz6hpV\nBbubwi/Db3xfAlII4QlMBrpLKVsAR4DXyg9wQ7JgLDBbCNED6AW8f7/egIZGVaBKqDRqaAB5Uspm\nt/k3QalOTiiwt1SXCmtK9Vn+H1LKKCHEQmAN0PaGo9fQeGDQnLqGObFJSvm4Aa9rTGnnIu97vB4N\njSqHFn7RMAcksB/oIIQIBhBCOAgh6t38QiHEEMAV6AJ8Ux0alWtoqKA5dY2qws0x9Y/L/ZuUUiZT\nGi9fLIQ4TmnopX75AW7E3WcAz0gpoyltiP3V/Vm+hkbVQEtp1KjSCCFOAAOklBcrey0aGuaAdlLX\nqLIIITYCJzSHrqFhONpJXUNDQ6MaoZ3UNTQ0NKoRmlPX0NDQqEZoTl1DQ0OjGqE5dQ0NDY1qhObU\nNTQ0NKoRmlPX0NDQqEb8HwxDbc3Ga5YHAAAAAElFTkSuQmCC\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x7f9ff2f38898>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"def g(x, y):\n",
" return np.cos(x) + np.sin(y) ** 2\n",
"\n",
"# Necesitamos muchos puntos en la malla, para que cuando se\n",
"# crucen las líneas no se vean irregularidades\n",
"x = np.linspace(-2, 3, 1000)\n",
"y = np.linspace(-2, 3, 1000)\n",
"\n",
"xx, yy = np.meshgrid(x, y)\n",
"\n",
"zz = g(xx, yy)\n",
"\n",
"# Podemos ajustar el tamaño de la figura con figsize\n",
"fig = plt.figure(figsize=(6, 6))\n",
"\n",
"# Ajustamos para que tenga 13 niveles y que use el colormap Spectral\n",
"# Tenemos que asignar la salida a la variable cs para luego crear el colorbar\n",
"cs = plt.contourf(xx, yy, zz, np.linspace(-1, 2, 13), cmap=plt.cm.Spectral)\n",
"\n",
"# Creamos la barra de colores\n",
"plt.colorbar()\n",
"\n",
"# Con `colors='k'` dibujamos todas las líneas negras\n",
"# Asignamos la salida a la variable cs2 para crear las etiquetas\n",
"cs = plt.contour(xx, yy, zz, np.linspace(-1, 2, 13), colors='k')\n",
"\n",
"# Creamos las etiquetas sobre las líneas\n",
"plt.clabel(cs)\n",
"\n",
"# Ponemos las etiquetas de los ejes\n",
"plt.xlabel(\"Eje x\")\n",
"plt.ylabel(\"Eje y\")\n",
"plt.title(r\"Función $g(x, y) = \\cos{x} + \\sin^2{y}$\")"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### El truco final: componentes interactivos"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"No tenemos mucho tiempo pero vamos a ver algo interesante que se ha introducido hace poco en el notebook: **componentes interactivos**."
]
},
{
"cell_type": "code",
"execution_count": 69,
"metadata": {},
"outputs": [
{
"data": {
"image/png": "iVBORw0KGgoAAAANSUhEUgAAAYcAAAElCAYAAAAPyi6bAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nUKBAgQIRFMKhQIECBQpE8P8DOytXBdByl9MAAAAASUVORK5CYII=\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x7f9ff2bd1c50>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"from IPython.html.widgets import interactive\n",
"\n",
"interactive(frecuencias, f1=(10.0,200.0), f2=(10.0,200.0))"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Referencias"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"* Guía de matplotlib para principiantes http://matplotlib.org/users/beginner.html\n",
"* Tutorial de matplotlib en español http://pybonacci.org/tag/tutorial-matplotlib-pyplot/\n",
"* Referencia rápida de matplotlib http://scipy-lectures.github.io/intro/matplotlib/matplotlib.html#quick-references"
]
},
{
"cell_type": "code",
"execution_count": 1,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"/* This template is inspired in the one used by Lorena Barba\n",
"in the numerical-mooc repository: https://github.com/numerical-mooc/numerical-mooc\n",
"We thank her work and hope you also enjoy the look of the notobooks with this style */\n",
"\n",
"<link href='http://fonts.googleapis.com/css?family=Source+Sans+Pro|Josefin+Sans:400,700,400italic|Ubuntu+Condensed' rel='stylesheet' type='text/css'>\n",
"\n",
"El estilo se ha aplicado =)\n",
"\n",
"<style>\n",
"\n",
"\n",
"\n",
"#notebook_panel { /* main background */\n",
" background: #f7f7f7;\n",
"}\n",
"\n",
"div.cell { /* set cell width */\n",
" width: 900px;\n",
"}\n",
"\n",
"div #notebook { /* centre the content */\n",
" background: #fff; /* white background for content */\n",
" width: 950px;\n",
" margin: auto;\n",
" padding-left: 0em;\n",
"}\n",
"\n",
"#notebook li { /* More space between bullet points */\n",
" margin-top:0.7em;\n",
"}\n",
"\n",
"/* draw border around running cells */\n",
"div.cell.border-box-sizing.code_cell.running { \n",
" border: 1px solid #111;\n",
"}\n",
"\n",
"/* Put a solid color box around each cell and its output, visually linking them*/\n",
"div.cell.code_cell {\n",
" font-family: 'Source Sans Pro', sans-serif;\n",
" background-color: rgb(256,256,256);\n",
" font-size: 110%;\n",
" border-radius: 0px; \n",
" padding: 0.5em;\n",
" margin-left:1em;\n",
" margin-top: 1em;\n",
"}\n",
"\n",
"div.text_cell_render{\n",
" font-family: 'Josefin Sans', serif;\n",
" line-height: 145%;\n",
" font-size: 125%;\n",
" font-weight: 500;\n",
" width:750px;\n",
" margin-left:auto;\n",
" margin-right:auto;\n",
"}\n",
"\n",
"\n",
"/* Formatting for header cells */\n",
".text_cell_render h1, .text_cell_render h2, .text_cell_render h3,\n",
".text_cell_render h4, .text_cell_render h5 {\n",
" font-family: 'Ubuntu Condensed', sans-serif;\n",
"}\n",
"/*\n",
".text_cell_render h1 {\n",
" font-family: Flux, 'Ubuntu Condensed', serif;\n",
" font-style:regular;\n",
" font-weight: 400; \n",
" font-size: 30pt;\n",
" text-align: center;\n",
" line-height: 100%;\n",
" color: #335082;\n",
" margin-bottom: 0.5em;\n",
" margin-top: 0.5em;\n",
" display: block;\n",
"}\n",
"*/\n",
".text_cell_render h1 {\n",
" font-weight: 600;\n",
" font-size: 35pt;\n",
" line-height: 100%;\n",
" color: #000000;\n",
" margin-bottom: 0.1em;\n",
" margin-top: 0.3em;\n",
" display: block;\n",
"}\n",
"\n",
".text_cell_render h2 {\n",
" margin-top:16px;\n",
" font-size: 27pt;\n",
" font-weight: 550;\n",
" margin-bottom: 0.1em;\n",
" margin-top: 0.3em;\n",
" font-style: regular;\n",
" color: #2c6391;\n",
"}\t\n",
"\n",
".text_cell_render h3 {\n",
" font-size: 20pt;\n",
" font-weight: 550\n",
" text-align: left;\n",
" margin-bottom: 0.1em;\n",
" margin-top: 0.3em;\n",
" font-style: regular;\n",
" color: #387eb8;\n",
"}\n",
"\n",
".text_cell_render h4 { /*Use this for captions*/\n",
" font-size: 18pt;\n",
" font-weight: 450\n",
" text-align: left;\n",
" margin-bottom: 0.1em;\n",
" margin-top: 0.3em;\n",
" font-style: regular;\n",
" color: #5797cc;\n",
"}\n",
"\n",
".text_cell_render h5 { /*Use this for small titles*/\n",
" font-size: 18pt;\n",
" font-weight: 550;\n",
" color: rgb(163,0,0);\n",
" font-style: italic;\n",
" margin-bottom: .1em;\n",
" margin-top: 0.8em;\n",
" display: block;\n",
" color: #b21c0d;\n",
"}\n",
"\n",
".text_cell_render h6 { /*use this for copyright note*/\n",
" font-family: 'Ubuntu Condensed', sans-serif;\n",
" font-weight: 300;\n",
" font-size: 14pt;\n",
" line-height: 100%;\n",
" color: #252525;\n",
" text-align: right;\n",
" margin-bottom: 1px;\n",
" margin-top: 1px;\n",
"}\n",
"\n",
".CodeMirror{\n",
" font-family: 'Duru Sans', sans-serif;\n",
" font-size: 100%;\n",
"}\n",
"\n",
"</style>\n",
"<script>\n",
" MathJax.Hub.Config({\n",
" TeX: {\n",
" extensions: [\"AMSmath.js\"],\n",
" equationNumbers: { autoNumber: \"AMS\", useLabelIds: true}\n",
" },\n",
" tex2jax: {\n",
" inlineMath: [ ['$','$'], [\"\\\\(\",\"\\\\)\"] ],\n",
" displayMath: [ ['$$','$$'], [\"\\\\[\",\"\\\\]\"] ]\n",
" },\n",
" displayAlign: 'center', // Change this to 'center' to center equations.\n",
" \"HTML-CSS\": {\n",
" styles: {'.MathJax_Display': {\"margin\": 4}}\n",
" }\n",
" });\n",
"</script>\n"
],
"text/plain": [
"<IPython.core.display.HTML object>"
]
},
"execution_count": 1,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# Esta celda da el estilo al notebook\n",
"from IPython.core.display import HTML\n",
"css_file = './css/aeropython.css'\n",
"HTML(open(css_file, \"r\").read())"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"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": "3.6.2"
}
},
"nbformat": 4,
"nbformat_minor": 1
}
| mit |
LargePanda/GEAR_Network | .ipynb_checkpoints/post_process-checkpoint.ipynb | 1 | 253943 | {
"cells": [
{
"cell_type": "code",
"execution_count": 1,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"import sys\n",
"reload(sys)\n",
"sys.setdefaultencoding('utf-8')"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"import json\n",
"import io"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"with open(\"./profile/original_profile.json\", \"r\") as f:\n",
" profile = json.load(f)"
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"{u'groups': [{u'slug': u'member', u'title': u'GEAR Members'},\n",
" {u'slug': u'graduate', u'title': u'Graduate Students'},\n",
" {u'slug': u'postdoc', u'title': u'Postdocs'}],\n",
" u'items': [{u'cluster_id': 0,\n",
" u'gear_collaborators': [],\n",
" u'member_id': 0,\n",
" u'member_type': u'member',\n",
" u'name': u'Ian',\n",
" u'organization': u'University of California, Berkeley',\n",
" u'other_collaborators': u'',\n",
" u'photo': u'',\n",
" u'pos_x': 0,\n",
" u'pos_y': 0,\n",
" u'research_interests': u'',\n",
" u'short_bio': u'',\n",
" u'surname': u'Agol',\n",
" u'title': u'GEAR Member',\n",
" u'website': u''},\n",
" {u'cluster_id': 0,\n",
" u'gear_collaborators': [],\n",
" u'member_id': 1,\n",
" u'member_type': u'member',\n",
" u'name': u'J\\xf8rgen',\n",
" u'organization': u'Aarhus University',\n",
" u'other_collaborators': u'',\n",
" u'photo': u'',\n",
" u'pos_x': 0,\n",
" u'pos_y': 0,\n",
" u'research_interests': u'',\n",
" u'short_bio': u'',\n",
" u'surname': u'Andersen',\n",
" u'title': u'GEAR Member',\n",
" u'website': u''},\n",
" {u'cluster_id': 0,\n",
" u'gear_collaborators': [],\n",
" u'member_id': 2,\n",
" u'member_type': u'member',\n",
" u'name': u'Jayadev',\n",
" u'organization': u'University of Illinois at Urbana-Champaign',\n",
" u'other_collaborators': u'Jon Chaika',\n",
" u'photo': u'',\n",
" u'pos_x': 0,\n",
" u'pos_y': 0,\n",
" u'research_interests': u'Dynamics on Moduli Spaces',\n",
" u'short_bio': u'Dynamics on moduli spaces of quadratic differentials, comparisons to dynamics on moduli spaces of lattices.',\n",
" u'surname': u'Athreya',\n",
" u'title': u'GEAR Member',\n",
" u'website': u'http://www.math.illinois.edu/~jathreya'},\n",
" {u'cluster_id': 0,\n",
" u'gear_collaborators': [],\n",
" u'member_id': 3,\n",
" u'member_type': u'member',\n",
" u'name': u'David',\n",
" u'organization': u'University of Adelaide',\n",
" u'other_collaborators': u'',\n",
" u'photo': u'',\n",
" u'pos_x': 0,\n",
" u'pos_y': 0,\n",
" u'research_interests': u'',\n",
" u'short_bio': u'',\n",
" u'surname': u'Baraglia',\n",
" u'title': u'GEAR Member',\n",
" u'website': u''},\n",
" {u'cluster_id': 0,\n",
" u'gear_collaborators': [],\n",
" u'member_id': 4,\n",
" u'member_type': u'member',\n",
" u'name': u'Ara',\n",
" u'organization': u'City University of New York',\n",
" u'other_collaborators': u'',\n",
" u'photo': u'',\n",
" u'pos_x': 0,\n",
" u'pos_y': 0,\n",
" u'research_interests': u'',\n",
" u'short_bio': u'',\n",
" u'surname': u'Basmajian',\n",
" u'title': u'GEAR Member',\n",
" u'website': u''},\n",
" {u'cluster_id': 0,\n",
" u'gear_collaborators': [],\n",
" u'member_id': 5,\n",
" u'member_type': u'member',\n",
" u'name': u'Mikhail',\n",
" u'organization': u'Instituto Nacional de Mathematica Pura e Aplicada',\n",
" u'other_collaborators': u'',\n",
" u'photo': u'',\n",
" u'pos_x': 0,\n",
" u'pos_y': 0,\n",
" u'research_interests': u'',\n",
" u'short_bio': u'',\n",
" u'surname': u'Belolipetsky',\n",
" u'title': u'GEAR Member',\n",
" u'website': u''},\n",
" {u'cluster_id': 0,\n",
" u'gear_collaborators': [],\n",
" u'member_id': 6,\n",
" u'member_type': u'member',\n",
" u'name': u'Yves',\n",
" u'organization': u'Universit\\xe9 Paris-Sud - Paris XI',\n",
" u'other_collaborators': u'',\n",
" u'photo': u'',\n",
" u'pos_x': 0,\n",
" u'pos_y': 0,\n",
" u'research_interests': u'',\n",
" u'short_bio': u'',\n",
" u'surname': u'Benoist',\n",
" u'title': u'GEAR Member',\n",
" u'website': u''},\n",
" {u'cluster_id': 0,\n",
" u'gear_collaborators': [],\n",
" u'member_id': 7,\n",
" u'member_type': u'member',\n",
" u'name': u'Olivier',\n",
" u'organization': u'\\xc9cole Normale Sup\\xe9rieure',\n",
" u'other_collaborators': u'',\n",
" u'photo': u'',\n",
" u'pos_x': 0,\n",
" u'pos_y': 0,\n",
" u'research_interests': u'',\n",
" u'short_bio': u'',\n",
" u'surname': u'Biquard',\n",
" u'title': u'GEAR Member',\n",
" u'website': u''},\n",
" {u'cluster_id': 0,\n",
" u'gear_collaborators': [],\n",
" u'member_id': 8,\n",
" u'member_type': u'member',\n",
" u'name': u'Hans',\n",
" u'organization': u'McMaster University',\n",
" u'other_collaborators': u'Cynthia Curtis, Stefan Friedl, Eric Harper, Christopher Herald, Benjamin Himpel, Paul Kirk, Andrew Nicas,',\n",
" u'photo': u'BodenHans.jpg',\n",
" u'pos_x': 0,\n",
" u'pos_y': 0,\n",
" u'research_interests': u'Gauge theory and low dimensional topology',\n",
" u'short_bio': u'Gauge theory and low dimensional topology, moduli spaces of flat connections and character varieties. Applications to invariants of knots and 3-manifolds.',\n",
" u'surname': u'Boden',\n",
" u'title': u'GEAR Member',\n",
" u'website': u'http://www.math.mcmaster.ca/boden'},\n",
" {u'cluster_id': 0,\n",
" u'gear_collaborators': [],\n",
" u'member_id': 9,\n",
" u'member_type': u'member',\n",
" u'name': u'Michel',\n",
" u'organization': u'Universit\\xe9 de Toulouse',\n",
" u'other_collaborators': u'',\n",
" u'photo': u'',\n",
" u'pos_x': 0,\n",
" u'pos_y': 0,\n",
" u'research_interests': u'',\n",
" u'short_bio': u'',\n",
" u'surname': u'Boileau',\n",
" u'title': u'GEAR Member',\n",
" u'website': u''},\n",
" {u'cluster_id': 0,\n",
" u'gear_collaborators': [],\n",
" u'member_id': 10,\n",
" u'member_type': u'member',\n",
" u'name': u'Francis',\n",
" u'organization': u'University of Southern California',\n",
" u'other_collaborators': u'Qingtao Chen, Helen Wong',\n",
" u'photo': u'BonahonFrancis.jpg',\n",
" u'pos_x': 0,\n",
" u'pos_y': 0,\n",
" u'research_interests': u'Geometric Structures and Teichm\\xfcller Spaces, Hyperbolic Manifolds, Geometric Group Theory',\n",
" u'short_bio': u'With respect to GEAR, the main thrust of my research is 2-fold. The first one is higher Teichm\\xfcller theory and representations of surface groups in SL_n(R) or SL_n(C). I am also interested in developing connections between 3-dimensional hyperbolic geometry, representations of surface groups in SL_2(C), and knot invariants based on the quantum group U_q(sl_2).',\n",
" u'surname': u'Bonahon',\n",
" u'title': u'GEAR Member',\n",
" u'website': u'http://www-bcf.usc.edu/~fbonahon/'},\n",
" {u'cluster_id': 0,\n",
" u'gear_collaborators': [],\n",
" u'member_id': 11,\n",
" u'member_type': u'member',\n",
" u'name': u'Steven',\n",
" u'organization': u'Universit\\xe9 du Qu\\xe9bec \\xe0 Montr\\xe9al',\n",
" u'other_collaborators': u'Adam Clay, Dale Rolfsen, Cameron Gordon, Liam Watson, Shicheng Wang, Radu Cebanu, Genevieve Walsh, Stephan Tillmann',\n",
" u'photo': u'',\n",
" u'pos_x': 0,\n",
" u'pos_y': 0,\n",
" u'research_interests': u'Low dimensional topology and geometry. Character varieties.',\n",
" u'short_bio': u'Low dimensional topology and geometry. Character varieties and their applications to low-dimensional manifolds.',\n",
" u'surname': u'Boyer',\n",
" u'title': u'GEAR Member',\n",
" u'website': u'http://www.cirget.uqam.ca/boyer/boyer.html'},\n",
" {u'cluster_id': 0,\n",
" u'gear_collaborators': [],\n",
" u'member_id': 12,\n",
" u'member_type': u'member',\n",
" u'name': u'Steven',\n",
" u'organization': u'University of Illinois at Urbana-Champaign',\n",
" u'other_collaborators': u'Indranil Biswas, Jim Glazebrook, Tomas Gomez, Adam Jacob, Franz Kamber, Vincent Mercat, Vicente Munoz, Peter Newstead, Mathias Stemmler',\n",
" u'photo': u'BradlowSteven.jpg',\n",
" u'pos_x': 0,\n",
" u'pos_y': 0,\n",
" u'research_interests': u'Higgs Bundles',\n",
" u'short_bio': u\"I'm interested in moduli spaces associated with holomorphic vector bundles. In particular, I'm a big fan of applications of Higgs bundle technology to the study of surface group representation varieties. Before I die, I'd like to be able to compute the surface group representation corresponding to any given Higgs bundle, and vice versa.\",\n",
" u'surname': u'Bradlow',\n",
" u'title': u'GEAR Member',\n",
" u'website': u''},\n",
" {u'cluster_id': 0,\n",
" u'gear_collaborators': [],\n",
" u'member_id': 13,\n",
" u'member_type': u'member',\n",
" u'name': u'Martin',\n",
" u'organization': u'Boston College',\n",
" u'other_collaborators': u'Edward Taylor, Petra Bonfert-Taylor',\n",
" u'photo': u'BridgemanMartin.jpg',\n",
" u'pos_x': 0,\n",
" u'pos_y': 0,\n",
" u'research_interests': u'Geometric Structures and Teichm\\xfcller Spaces',\n",
" u'short_bio': u'My current research interests are convex hulls of Kleinian groups, geometric identities on Moduli spaces and generalizations of Weil-Petersson geometry to higher Teichm\\xfcller spaces. ',\n",
" u'surname': u'Bridgeman',\n",
" u'title': u'GEAR Member',\n",
" u'website': u'https://www2.bc.edu/~bridgem/'},\n",
" {u'cluster_id': 0,\n",
" u'gear_collaborators': [],\n",
" u'member_id': 14,\n",
" u'member_type': u'member',\n",
" u'name': u'Lewis',\n",
" u'organization': u'University of Texas at Austin',\n",
" u'other_collaborators': u'',\n",
" u'photo': u'',\n",
" u'pos_x': 0,\n",
" u'pos_y': 0,\n",
" u'research_interests': u'',\n",
" u'short_bio': u'',\n",
" u'surname': u'Bowen',\n",
" u'title': u'GEAR Member',\n",
" u'website': u''},\n",
" {u'cluster_id': 0,\n",
" u'gear_collaborators': [],\n",
" u'member_id': 15,\n",
" u'member_type': u'member',\n",
" u'name': u'Jeffrey',\n",
" u'organization': u'Brown University',\n",
" u'other_collaborators': u'',\n",
" u'photo': u'',\n",
" u'pos_x': 0,\n",
" u'pos_y': 0,\n",
" u'research_interests': u'',\n",
" u'short_bio': u'',\n",
" u'surname': u'Brock',\n",
" u'title': u'GEAR Member',\n",
" u'website': u''},\n",
" {u'cluster_id': 0,\n",
" u'gear_collaborators': [],\n",
" u'member_id': 16,\n",
" u'member_type': u'member',\n",
" u'name': u'Kenneth',\n",
" u'organization': u'University of Utah',\n",
" u'other_collaborators': u'',\n",
" u'photo': u'',\n",
" u'pos_x': 0,\n",
" u'pos_y': 0,\n",
" u'research_interests': u'',\n",
" u'short_bio': u'',\n",
" u'surname': u'Bromberg',\n",
" u'title': u'GEAR Member',\n",
" u'website': u''},\n",
" {u'cluster_id': 0,\n",
" u'gear_collaborators': [],\n",
" u'member_id': 17,\n",
" u'member_type': u'member',\n",
" u'name': u'Michelle',\n",
" u'organization': u'Universit\\xe9 de Gen\\xe8ve',\n",
" u'other_collaborators': u'',\n",
" u'photo': u'',\n",
" u'pos_x': 0,\n",
" u'pos_y': 0,\n",
" u'research_interests': u'',\n",
" u'short_bio': u'',\n",
" u'surname': u'Bucher',\n",
" u'title': u'GEAR Member',\n",
" u'website': u''},\n",
" {u'cluster_id': 0,\n",
" u'gear_collaborators': [],\n",
" u'member_id': 18,\n",
" u'member_type': u'member',\n",
" u'name': u'Marc',\n",
" u'organization': u'Eidgen\\xf6ssische Technische Hochschule Z\\xfcrich',\n",
" u'other_collaborators': u'',\n",
" u'photo': u'',\n",
" u'pos_x': 0,\n",
" u'pos_y': 0,\n",
" u'research_interests': u'',\n",
" u'short_bio': u'',\n",
" u'surname': u'Burger',\n",
" u'title': u'GEAR Member',\n",
" u'website': u''},\n",
" {u'cluster_id': 0,\n",
" u'gear_collaborators': [],\n",
" u'member_id': 19,\n",
" u'member_type': u'member',\n",
" u'name': u'Danny',\n",
" u'organization': u'University of Chicago',\n",
" u'other_collaborators': u'',\n",
" u'photo': u'',\n",
" u'pos_x': 0,\n",
" u'pos_y': 0,\n",
" u'research_interests': u'',\n",
" u'short_bio': u'',\n",
" u'surname': u'Calegari',\n",
" u'title': u'GEAR Member',\n",
" u'website': u''},\n",
" {u'cluster_id': 0,\n",
" u'gear_collaborators': [],\n",
" u'member_id': 20,\n",
" u'member_type': u'member',\n",
" u'name': u'Richard',\n",
" u'organization': u'University of Michigan',\n",
" u'other_collaborators': u'',\n",
" u'photo': u'',\n",
" u'pos_x': 0,\n",
" u'pos_y': 0,\n",
" u'research_interests': u'',\n",
" u'short_bio': u'',\n",
" u'surname': u'Canary',\n",
" u'title': u'GEAR Member',\n",
" u'website': u''},\n",
" {u'cluster_id': 0,\n",
" u'gear_collaborators': [],\n",
" u'member_id': 21,\n",
" u'member_type': u'member',\n",
" u'name': u'Virginie',\n",
" u'organization': u'Universit\\xe9 de Sherbrooke',\n",
" u'other_collaborators': u'',\n",
" u'photo': u'',\n",
" u'pos_x': 0,\n",
" u'pos_y': 0,\n",
" u'research_interests': u'',\n",
" u'short_bio': u'',\n",
" u'surname': u'Charette',\n",
" u'title': u'GEAR Member',\n",
" u'website': u''},\n",
" {u'cluster_id': 0,\n",
" u'gear_collaborators': [],\n",
" u'member_id': 22,\n",
" u'member_type': u'member',\n",
" u'name': u'Daryl',\n",
" u'organization': u'University of California, Santa Barbara',\n",
" u'other_collaborators': u'',\n",
" u'photo': u'',\n",
" u'pos_x': 0,\n",
" u'pos_y': 0,\n",
" u'research_interests': u'',\n",
" u'short_bio': u'',\n",
" u'surname': u'Cooper',\n",
" u'title': u'GEAR Member',\n",
" u'website': u''},\n",
" {u'cluster_id': 0,\n",
" u'gear_collaborators': [],\n",
" u'member_id': 23,\n",
" u'member_type': u'member',\n",
" u'name': u'Marc',\n",
" u'organization': u'University of Illinois at Chicago',\n",
" u'other_collaborators': u'',\n",
" u'photo': u'',\n",
" u'pos_x': 0,\n",
" u'pos_y': 0,\n",
" u'research_interests': u'',\n",
" u'short_bio': u'',\n",
" u'surname': u'Culler',\n",
" u'title': u'GEAR Member',\n",
" u'website': u''},\n",
" {u'cluster_id': 0,\n",
" u'gear_collaborators': [],\n",
" u'member_id': 24,\n",
" u'member_type': u'member',\n",
" u'name': u'Georgios',\n",
" u'organization': u'Brown University',\n",
" u'other_collaborators': u'',\n",
" u'photo': u'',\n",
" u'pos_x': 0,\n",
" u'pos_y': 0,\n",
" u'research_interests': u'',\n",
" u'short_bio': u'',\n",
" u'surname': u'Daskalopoulos',\n",
" u'title': u'GEAR Member',\n",
" u'website': u''},\n",
" {u'cluster_id': 0,\n",
" u'gear_collaborators': [],\n",
" u'member_id': 25,\n",
" u'member_type': u'member',\n",
" u'name': u'Kelly',\n",
" u'organization': u'Buffalo State College',\n",
" u'other_collaborators': u'',\n",
" u'photo': u'',\n",
" u'pos_x': 0,\n",
" u'pos_y': 0,\n",
" u'research_interests': u'',\n",
" u'short_bio': u'',\n",
" u'surname': u'Delp',\n",
" u'title': u'GEAR Member',\n",
" u'website': u''},\n",
" {u'cluster_id': 0,\n",
" u'gear_collaborators': [],\n",
" u'member_id': 26,\n",
" u'member_type': u'member',\n",
" u'name': u'Martin',\n",
" u'organization': u'Universit\\xe9 de Grenoble',\n",
" u'other_collaborators': u'',\n",
" u'photo': u'',\n",
" u'pos_x': 0,\n",
" u'pos_y': 0,\n",
" u'research_interests': u'',\n",
" u'short_bio': u'',\n",
" u'surname': u'Deraux',\n",
" u'title': u'GEAR Member',\n",
" u'website': u''},\n",
" {u'cluster_id': 0,\n",
" u'gear_collaborators': [],\n",
" u'member_id': 27,\n",
" u'member_type': u'member',\n",
" u'name': u'Todd',\n",
" u'organization': u'Howard University',\n",
" u'other_collaborators': u'',\n",
" u'photo': u'',\n",
" u'pos_x': 0,\n",
" u'pos_y': 0,\n",
" u'research_interests': u'',\n",
" u'short_bio': u'',\n",
" u'surname': u'Drumm',\n",
" u'title': u'GEAR Member',\n",
" u'website': u''},\n",
" {u'cluster_id': 0,\n",
" u'gear_collaborators': [],\n",
" u'member_id': 28,\n",
" u'member_type': u'member',\n",
" u'name': u'Moon',\n",
" u'organization': u'Tufts University',\n",
" u'other_collaborators': u'',\n",
" u'photo': u'',\n",
" u'pos_x': 0,\n",
" u'pos_y': 0,\n",
" u'research_interests': u'',\n",
" u'short_bio': u'',\n",
" u'surname': u'Duchin',\n",
" u'title': u'GEAR Member',\n",
" u'website': u''},\n",
" {u'cluster_id': 0,\n",
" u'gear_collaborators': [],\n",
" u'member_id': 29,\n",
" u'member_type': u'member',\n",
" u'name': u'David',\n",
" u'organization': u'University of Illinois at Chicago',\n",
" u'other_collaborators': u'',\n",
" u'photo': u'',\n",
" u'pos_x': 0,\n",
" u'pos_y': 0,\n",
" u'research_interests': u'',\n",
" u'short_bio': u'',\n",
" u'surname': u'Dumas',\n",
" u'title': u'GEAR Member',\n",
" u'website': u''},\n",
" {u'cluster_id': 0,\n",
" u'gear_collaborators': [],\n",
" u'member_id': 30,\n",
" u'member_type': u'member',\n",
" u'name': u'Sorin',\n",
" u'organization': u'Universit\\xe9 de Nice Sophia-Antipolis',\n",
" u'other_collaborators': u'',\n",
" u'photo': u'',\n",
" u'pos_x': 0,\n",
" u'pos_y': 0,\n",
" u'research_interests': u'',\n",
" u'short_bio': u'',\n",
" u'surname': u'Dumitrescu',\n",
" u'title': u'GEAR Member',\n",
" u'website': u''},\n",
" {u'cluster_id': 0,\n",
" u'gear_collaborators': [],\n",
" u'member_id': 31,\n",
" u'member_type': u'member',\n",
" u'name': u'Nathan',\n",
" u'organization': u'University of Illinois at Urbana-Champaign',\n",
" u'other_collaborators': u'',\n",
" u'photo': u'',\n",
" u'pos_x': 0,\n",
" u'pos_y': 0,\n",
" u'research_interests': u'',\n",
" u'short_bio': u'',\n",
" u'surname': u'Dunfield',\n",
" u'title': u'GEAR Member',\n",
" u'website': u''},\n",
" {u'cluster_id': 0,\n",
" u'gear_collaborators': [],\n",
" u'member_id': 32,\n",
" u'member_type': u'member',\n",
" u'name': u'Alex',\n",
" u'organization': u'University of Chicago',\n",
" u'other_collaborators': u'Artur Avila, Jon Chaika, Maxim Kontsevich, Carlos Matheus Santos, Martin M\\xf6ller, Marcelo Viana, Anton Zorich',\n",
" u'photo': u'',\n",
" u'pos_x': 0,\n",
" u'pos_y': 0,\n",
" u'research_interests': u'Dynamics on Moduli Spaces, Geometric and Analytical Group Theory',\n",
" u'short_bio': u'',\n",
" u'surname': u'Eskin',\n",
" u'title': u'GEAR Member',\n",
" u'website': u'http://www.math.uchicago.edu/~eskin'},\n",
" {u'cluster_id': 0,\n",
" u'gear_collaborators': [],\n",
" u'member_id': 33,\n",
" u'member_type': u'member',\n",
" u'name': u'Elisha',\n",
" u'organization': u'Universit\\xe9 Pierre et Marie Curie - Paris VI',\n",
" u'other_collaborators': u'',\n",
" u'photo': u'',\n",
" u'pos_x': 0,\n",
" u'pos_y': 0,\n",
" u'research_interests': u'',\n",
" u'short_bio': u'',\n",
" u'surname': u'Falbel',\n",
" u'title': u'GEAR Member',\n",
" u'website': u''},\n",
" {u'cluster_id': 0,\n",
" u'gear_collaborators': [],\n",
" u'member_id': 34,\n",
" u'member_type': u'member',\n",
" u'name': u'Benson',\n",
" u'organization': u'University of Chicago',\n",
" u'other_collaborators': u'',\n",
" u'photo': u'',\n",
" u'pos_x': 0,\n",
" u'pos_y': 0,\n",
" u'research_interests': u'',\n",
" u'short_bio': u'',\n",
" u'surname': u'Farb',\n",
" u'title': u'GEAR Member',\n",
" u'website': u''},\n",
" {u'cluster_id': 0,\n",
" u'gear_collaborators': [],\n",
" u'member_id': 35,\n",
" u'member_type': u'member',\n",
" u'name': u'Roberto',\n",
" u'organization': u'Universit\\xe0 di Pisa',\n",
" u'other_collaborators': u'Riccardo Benedetti, Bruno Martelli, Cristina Pagliantini, Carlo Petronio, Alessandro Sisto',\n",
" u'photo': u'FrigerioRoberto.jpg',\n",
" u'pos_x': 0,\n",
" u'pos_y': 0,\n",
" u'research_interests': u'Geometric and Analytical Group Theory, Geometric Structures and Teichm\\xfcller Spaces, Hyperbolic Manifolds, Bounded cohomology and simplicial volume.',\n",
" u'short_bio': u'I am currently working on bounded cohomology of discrete groups, with a particular attention to results and techniques related to geometric group theory. I also used to study classical hyperbolic geometry, mainly in dimension 3, and I am still interested in the geometry and topology of low-dimensional manifolds.',\n",
" u'surname': u'Frigerio',\n",
" u'title': u'GEAR Member',\n",
" u'website': u'http://www.dm.unipi.it/~frigerio/'},\n",
" {u'cluster_id': 0,\n",
" u'gear_collaborators': [],\n",
" u'member_id': 36,\n",
" u'member_type': u'member',\n",
" u'name': u'Carlos',\n",
" u'organization': u'Universidade T\\xe9cnica de Lisboa',\n",
" u'other_collaborators': u'',\n",
" u'photo': u'',\n",
" u'pos_x': 0,\n",
" u'pos_y': 0,\n",
" u'research_interests': u'',\n",
" u'short_bio': u'',\n",
" u'surname': u'Florentino',\n",
" u'title': u'GEAR Member',\n",
" u'website': u''},\n",
" {u'cluster_id': 0,\n",
" u'gear_collaborators': [],\n",
" u'member_id': 37,\n",
" u'member_type': u'member',\n",
" u'name': u'Vladimir',\n",
" u'organization': u'Universit\\xe9 de Strasbourg',\n",
" u'other_collaborators': u'',\n",
" u'photo': u'',\n",
" u'pos_x': 0,\n",
" u'pos_y': 0,\n",
" u'research_interests': u'',\n",
" u'short_bio': u'',\n",
" u'surname': u'Fock',\n",
" u'title': u'GEAR Member',\n",
" u'website': u''},\n",
" {u'cluster_id': 0,\n",
" u'gear_collaborators': [],\n",
" u'member_id': 38,\n",
" u'member_type': u'member',\n",
" u'name': u'Giovanni',\n",
" u'organization': u'University of Maryland-College Park',\n",
" u'other_collaborators': u'',\n",
" u'photo': u'',\n",
" u'pos_x': 0,\n",
" u'pos_y': 0,\n",
" u'research_interests': u'',\n",
" u'short_bio': u'',\n",
" u'surname': u'Forni',\n",
" u'title': u'GEAR Member',\n",
" u'website': u''},\n",
" {u'cluster_id': 0,\n",
" u'gear_collaborators': [],\n",
" u'member_id': 39,\n",
" u'member_type': u'member',\n",
" u'name': u'Louis',\n",
" u'organization': u'Universit\\xe9 de Grenoble',\n",
" u'other_collaborators': u'',\n",
" u'photo': u'',\n",
" u'pos_x': 0,\n",
" u'pos_y': 0,\n",
" u'research_interests': u'Geometric and Analytical Group Theory',\n",
" u'short_bio': u'Mapping class groups',\n",
" u'surname': u'Funar',\n",
" u'title': u'GEAR Member',\n",
" u'website': u'http://www-fourier.ujf-grenoble.fr/~funar/'},\n",
" {u'cluster_id': 0,\n",
" u'gear_collaborators': [],\n",
" u'member_id': 40,\n",
" u'member_type': u'member',\n",
" u'name': u'David',\n",
" u'organization': u'Temple University',\n",
" u'other_collaborators': u'',\n",
" u'photo': u'',\n",
" u'pos_x': 0,\n",
" u'pos_y': 0,\n",
" u'research_interests': u'',\n",
" u'short_bio': u'',\n",
" u'surname': u'Futer',\n",
" u'title': u'GEAR Member',\n",
" u'website': u''},\n",
" {u'cluster_id': 0,\n",
" u'gear_collaborators': [],\n",
" u'member_id': 41,\n",
" u'member_type': u'member',\n",
" u'name': u'David',\n",
" u'organization': u'Princeton University',\n",
" u'other_collaborators': u'',\n",
" u'photo': u'',\n",
" u'pos_x': 0,\n",
" u'pos_y': 0,\n",
" u'research_interests': u'',\n",
" u'short_bio': u'',\n",
" u'surname': u'Gabai',\n",
" u'title': u'GEAR Member',\n",
" u'website': u''},\n",
" {u'cluster_id': 0,\n",
" u'gear_collaborators': [],\n",
" u'member_id': 42,\n",
" u'member_type': u'member',\n",
" u'name': u'Vaibhav',\n",
" u'organization': u'University of Warwick',\n",
" u'other_collaborators': u'',\n",
" u'photo': u'',\n",
" u'pos_x': 0,\n",
" u'pos_y': 0,\n",
" u'research_interests': u'',\n",
" u'short_bio': u'',\n",
" u'surname': u'Gadre',\n",
" u'title': u'GEAR Member',\n",
" u'website': u''},\n",
" {u'cluster_id': 0,\n",
" u'gear_collaborators': [],\n",
" u'member_id': 43,\n",
" u'member_type': u'member',\n",
" u'name': u'Oscar',\n",
" u'organization': u'Consejo Superior de Investigaciones Cient\\xedficas',\n",
" u'other_collaborators': u'',\n",
" u'photo': u'',\n",
" u'pos_x': 0,\n",
" u'pos_y': 0,\n",
" u'research_interests': u'',\n",
" u'short_bio': u'',\n",
" u'surname': u'Garc\\xeda-Prada',\n",
" u'title': u'GEAR Member',\n",
" u'website': u''},\n",
" {u'cluster_id': 0,\n",
" u'gear_collaborators': [],\n",
" u'member_id': 44,\n",
" u'member_type': u'member',\n",
" u'name': u'Jane',\n",
" u'organization': u'Rutgers University',\n",
" u'other_collaborators': u'',\n",
" u'photo': u'',\n",
" u'pos_x': 0,\n",
" u'pos_y': 0,\n",
" u'research_interests': u'',\n",
" u'short_bio': u'',\n",
" u'surname': u'Gilman',\n",
" u'title': u'GEAR Member',\n",
" u'website': u''},\n",
" {u'cluster_id': 0,\n",
" u'gear_collaborators': [],\n",
" u'member_id': 45,\n",
" u'member_type': u'member',\n",
" u'name': u'Yair',\n",
" u'organization': u'Ben-Gurion University of the Negev',\n",
" u'other_collaborators': u'',\n",
" u'photo': u'',\n",
" u'pos_x': 0,\n",
" u'pos_y': 0,\n",
" u'research_interests': u'',\n",
" u'short_bio': u'',\n",
" u'surname': u'Glasner',\n",
" u'title': u'GEAR Member',\n",
" u'website': u''},\n",
" {u'cluster_id': 0,\n",
" u'gear_collaborators': [],\n",
" u'member_id': 46,\n",
" u'member_type': u'member',\n",
" u'name': u'William',\n",
" u'organization': u'University of Maryland-College Park',\n",
" u'other_collaborators': u'',\n",
" u'photo': u'',\n",
" u'pos_x': 0,\n",
" u'pos_y': 0,\n",
" u'research_interests': u'',\n",
" u'short_bio': u'',\n",
" u'surname': u'Goldman',\n",
" u'title': u'GEAR Member',\n",
" u'website': u''},\n",
" {u'cluster_id': 0,\n",
" u'gear_collaborators': [],\n",
" u'member_id': 47,\n",
" u'member_type': u'member',\n",
" u'name': u'Alexander',\n",
" u'organization': u'Yale University',\n",
" u'other_collaborators': u'',\n",
" u'photo': u'',\n",
" u'pos_x': 0,\n",
" u'pos_y': 0,\n",
" u'research_interests': u'',\n",
" u'short_bio': u'',\n",
" u'surname': u'Goncharov',\n",
" u'title': u'GEAR Member',\n",
" u'website': u''},\n",
" {u'cluster_id': 0,\n",
" u'gear_collaborators': [],\n",
" u'member_id': 48,\n",
" u'member_type': u'member',\n",
" u'name': u'Matthias',\n",
" u'organization': u'Pixar Animation Studios',\n",
" u'other_collaborators': u'',\n",
" u'photo': u'',\n",
" u'pos_x': 0,\n",
" u'pos_y': 0,\n",
" u'research_interests': u'',\n",
" u'short_bio': u'',\n",
" u'surname': u'G\\xf6rner',\n",
" u'title': u'GEAR Member',\n",
" u'website': u''},\n",
" {u'cluster_id': 0,\n",
" u'gear_collaborators': [],\n",
" u'member_id': 49,\n",
" u'member_type': u'member',\n",
" u'name': u'Peter',\n",
" u'organization': u'Universidade do Porto',\n",
" u'other_collaborators': u'I. Biswas, S.B.S.D. Castro, S. Dakhlia, A.D. King, M. Logares, V. Mu\\xf1oz',\n",
" u'photo': u'GothenPeter.jpg',\n",
" u'pos_x': 0,\n",
" u'pos_y': 0,\n",
" u'research_interests': [u'Higgs bundles'],\n",
" u'short_bio': u'Geometry and topology of moduli spaces. Higgs bundles and their application to the study of character varieties of surface groups, especially for real Lie groups.',\n",
" u'surname': u'Gothen',\n",
" u'title': u'GEAR Member',\n",
" u'website': u'http://cmup.fc.up.pt/cmup/pbgothen/'},\n",
" {u'cluster_id': 0,\n",
" u'gear_collaborators': [],\n",
" u'member_id': 50,\n",
" u'member_type': u'member',\n",
" u'name': u'Marco',\n",
" u'organization': u'University of Toronto',\n",
" u'other_collaborators': u'',\n",
" u'photo': u'',\n",
" u'pos_x': 0,\n",
" u'pos_y': 0,\n",
" u'research_interests': u'',\n",
" u'short_bio': u'',\n",
" u'surname': u'Gualtieri',\n",
" u'title': u'GEAR Member',\n",
" u'website': u''},\n",
" {u'cluster_id': 0,\n",
" u'gear_collaborators': [],\n",
" u'member_id': 51,\n",
" u'member_type': u'member',\n",
" u'name': u'Olivier',\n",
" u'organization': u'Universit\\xe9 de Strasbourg',\n",
" u'other_collaborators': u'',\n",
" u'photo': u'GuichardOlivier.jpg',\n",
" u'pos_x': 0,\n",
" u'pos_y': 0,\n",
" u'research_interests': u'Geometric Structures and Teichm\\xfcller Spaces',\n",
" u'short_bio': u'Main interests are convex-cocompact subgroups and generalizations, the relations between geometric and dynamical properties for discrete subgroups of Lie groups with an emphasis on \"Higher Teichm\\xfcller spaces\".',\n",
" u'surname': u'Guichard',\n",
" u'title': u'GEAR Member',\n",
" u'website': u'http://www-irma.u-strasbg.fr/~guichard/'},\n",
" {u'cluster_id': 0,\n",
" u'gear_collaborators': [],\n",
" u'member_id': 52,\n",
" u'member_type': u'member',\n",
" u'name': u'Rosemary',\n",
" u'organization': u'University of Iowa',\n",
" u'other_collaborators': u'',\n",
" u'photo': u'',\n",
" u'pos_x': 0,\n",
" u'pos_y': 0,\n",
" u'research_interests': u'',\n",
" u'short_bio': u'',\n",
" u'surname': u'Guzman',\n",
" u'title': u'GEAR Member',\n",
" u'website': u''},\n",
" {u'cluster_id': 0,\n",
" u'gear_collaborators': [],\n",
" u'member_id': 53,\n",
" u'member_type': u'member',\n",
" u'name': u'Thomas',\n",
" u'organization': u'Universit\\xe9 Montpellier II',\n",
" u'other_collaborators': u'Dawid Kielak, Petra Schwer',\n",
" u'photo': u'HaettelThomas.jpg',\n",
" u'pos_x': 0,\n",
" u'pos_y': 0,\n",
" u'research_interests': u'Geometric and Analytical Group Theory, Geometric Structures and Teichm\\xfcller Spaces, Hyperbolic Manifolds',\n",
" u'short_bio': u'I am mainly interested in the asymptotic geometry of symmetric spaces, in degeneration of geometric structures and in the geometry of braid groups.',\n",
" u'surname': u'Haettel',\n",
" u'title': u'GEAR Member',\n",
" u'website': u'http://www.math.univ-montp2.fr/~haettel'},\n",
" {u'cluster_id': 0,\n",
" u'gear_collaborators': [],\n",
" u'member_id': 54,\n",
" u'member_type': u'member',\n",
" u'name': u'Tam\\xe1s',\n",
" u'organization': u'Ecole Polytechnique F\\xe9d\\xe9rale de Lausanne',\n",
" u'other_collaborators': u'',\n",
" u'photo': u'',\n",
" u'pos_x': 0,\n",
" u'pos_y': 0,\n",
" u'research_interests': u'',\n",
" u'short_bio': u'',\n",
" u'surname': u'Hausel',\n",
" u'title': u'GEAR Member',\n",
" u'website': u''},\n",
" {u'cluster_id': 0,\n",
" u'gear_collaborators': [],\n",
" u'member_id': 55,\n",
" u'member_type': u'member',\n",
" u'name': u'Zheng',\n",
" u'organization': u'City University of New York',\n",
" u'other_collaborators': u'Song Dai, Ren Guo, Marcello Lucia, Biao Wang',\n",
" u'photo': u'HuangZheng.jpg',\n",
" u'pos_x': 0,\n",
" u'pos_y': 0,\n",
" u'research_interests': u'Geometric Structures and Teichm\\xfcller Spaces',\n",
" u'short_bio': u'Minimal surface problems in hyperbolic manifolds and geometry of Teichm\\xfcller spaces.',\n",
" u'surname': u'Huang',\n",
" u'title': u'GEAR Member',\n",
" u'website': u'http://www.math.csi.cuny.edu/~huang/research.html'},\n",
" {u'cluster_id': 0,\n",
" u'gear_collaborators': [],\n",
" u'member_id': 56,\n",
" u'member_type': u'member',\n",
" u'name': u'Alessandra',\n",
" u'organization': u'Eidgen\\xf6ssische Technische Hochschule Z\\xfcrich',\n",
" u'other_collaborators': u'',\n",
" u'photo': u'',\n",
" u'pos_x': 0,\n",
" u'pos_y': 0,\n",
" u'research_interests': u'',\n",
" u'short_bio': u'',\n",
" u'surname': u'Iozzi',\n",
" u'title': u'GEAR Member',\n",
" u'website': u''},\n",
" {u'cluster_id': 0,\n",
" u'gear_collaborators': [],\n",
" u'member_id': 57,\n",
" u'member_type': u'member',\n",
" u'name': u'Lisa',\n",
" u'organization': u'University of Toronto',\n",
" u'other_collaborators': u'',\n",
" u'photo': u'',\n",
" u'pos_x': 0,\n",
" u'pos_y': 0,\n",
" u'research_interests': u'',\n",
" u'short_bio': u'',\n",
" u'surname': u'Jeffrey',\n",
" u'title': u'GEAR Member',\n",
" u'website': u''},\n",
" {u'cluster_id': 0,\n",
" u'gear_collaborators': [],\n",
" u'member_id': 58,\n",
" u'member_type': u'member',\n",
" u'name': u'Lizhen',\n",
" u'organization': u'University of Michigan',\n",
" u'other_collaborators': u'',\n",
" u'photo': u'',\n",
" u'pos_x': 0,\n",
" u'pos_y': 0,\n",
" u'research_interests': u'',\n",
" u'short_bio': u'',\n",
" u'surname': u'Ji',\n",
" u'title': u'GEAR Member',\n",
" u'website': u''},\n",
" {u'cluster_id': 0,\n",
" u'gear_collaborators': [],\n",
" u'member_id': 59,\n",
" u'member_type': u'member',\n",
" u'name': u'Christopher',\n",
" u'organization': u'Indiana University',\n",
" u'other_collaborators': u'Matthieu Gendulphe, Luc Hillairet',\n",
" u'photo': u'JudgeChristopher.jpg',\n",
" u'pos_x': 0,\n",
" u'pos_y': 0,\n",
" u'research_interests': u'Geometric Structures and Teichm\\xfcller Spaces',\n",
" u'short_bio': u'I am interested in the Teichm\\xfcller flow on moduli space, especially closed orbits (Teichm\\xfcller curves). I am also interested in Laplace spectrum of hyperbolic manifolds.',\n",
" u'surname': u'Judge',\n",
" u'title': u'GEAR Member',\n",
" u'website': u'http://pages.iu.edu/~cjudge/'},\n",
" {u'cluster_id': 0,\n",
" u'gear_collaborators': [],\n",
" u'member_id': 60,\n",
" u'member_type': u'member',\n",
" u'name': u'Ilya',\n",
" u'organization': u'University of Illinois at Urbana-Champaign',\n",
" u'other_collaborators': u\"Tatiana Nagnibeda, Richard Weidmann, Paul Schupp, Armando Martino, Stefano Francaviglia, Mathieu Cerette, Ken'ici Ohshika, Woojin Jeon, Vadim Kaimanovich, Alexei Myasnikov, Vladimir Shpilrain\",\n",
" u'photo': u'',\n",
" u'pos_x': 0,\n",
" u'pos_y': 0,\n",
" u'research_interests': u'Geometric and Analytical Group Theory',\n",
" u'short_bio': u'I am interested in the dynamics and geometry of the mapping class groups and of the outer automorphism group of a free group, in group actions on R-trees, in hyperbolic geometry and low-dimensional topology, and in the geometry of the Teichmuller space and its \"relatives\" and analogs.',\n",
" u'surname': u'Kapovich',\n",
" u'title': u'GEAR Member',\n",
" u'website': u'http://www.math.uiuc.edu/~kapovich'},\n",
" {u'cluster_id': 0,\n",
" u'gear_collaborators': [],\n",
" u'member_id': 61,\n",
" u'member_type': u'member',\n",
" u'name': u'Michael',\n",
" u'organization': u'University of California, Davis',\n",
" u'other_collaborators': u'Arkady Berenstein, Koji Fujiwara, Tom Haines, Bruce Kleiner, Janos Kollar, Shrawan Kumar, Al Marden, John Millson, Leonid Potyagailo, Ernest Vinberg',\n",
" u'photo': u'',\n",
" u'pos_x': 0,\n",
" u'pos_y': 0,\n",
" u'research_interests': u'Dynamics on Moduli Spaces, Geometric and Analytical Group Theory, Geometric Structures and Teichm\\xfcller Spaces, Hyperbolic Manifolds',\n",
" u'short_bio': u'Geometric topology, geometric geometry and geometric group theory.',\n",
" u'surname': u'Kapovich',\n",
" u'title': u'GEAR Member',\n",
" u'website': u'https://www.math.ucdavis.edu/~kapovich/'},\n",
" {u'cluster_id': 0,\n",
" u'gear_collaborators': [],\n",
" u'member_id': 62,\n",
" u'member_type': u'member',\n",
" u'name': u'Fanny',\n",
" u'organization': u'Universit\\xe9 Lille I',\n",
" u'other_collaborators': u'',\n",
" u'photo': u'',\n",
" u'pos_x': 0,\n",
" u'pos_y': 0,\n",
" u'research_interests': u'',\n",
" u'short_bio': u'',\n",
" u'surname': u'Kassel',\n",
" u'title': u'GEAR Member',\n",
" u'website': u''},\n",
" {u'cluster_id': 0,\n",
" u'gear_collaborators': [],\n",
" u'member_id': 63,\n",
" u'member_type': u'member',\n",
" u'name': u'Richard',\n",
" u'organization': u'University of Wisconsin-Madison',\n",
" u'other_collaborators': u'Eriko Hironaka, David Peifer, Saul Schleimer',\n",
" u'photo': u'KentRichard.jpg',\n",
" u'pos_x': 0,\n",
" u'pos_y': 0,\n",
" u'research_interests': u'Geometric and Analytical Group Theory, Geometric Structures and Teichm\\xfcller Spaces, Hyperbolic Manifolds',\n",
" u'short_bio': u'I am interested in the interplay of algebra, geometry, and analysis in the study of moduli of hyperbolic manifolds.',\n",
" u'surname': u'Kent',\n",
" u'title': u'GEAR Member',\n",
" u'website': u'https://www.math.wisc.edu/~rkent'},\n",
" {u'cluster_id': 0,\n",
" u'gear_collaborators': [],\n",
" u'member_id': 64,\n",
" u'member_type': u'member',\n",
" u'name': u'Steven',\n",
" u'organization': u'Stanford University',\n",
" u'other_collaborators': u'',\n",
" u'photo': u'',\n",
" u'pos_x': 0,\n",
" u'pos_y': 0,\n",
" u'research_interests': u'',\n",
" u'short_bio': u'',\n",
" u'surname': u'Kerckhoff',\n",
" u'title': u'GEAR Member',\n",
" u'website': u''},\n",
" {u'cluster_id': 0,\n",
" u'gear_collaborators': [],\n",
" u'member_id': 65,\n",
" u'member_type': u'member',\n",
" u'name': u'Bruno',\n",
" u'organization': u'Universit\\xe9 Paris Diderot - Paris VII',\n",
" u'other_collaborators': u'',\n",
" u'photo': u'',\n",
" u'pos_x': 0,\n",
" u'pos_y': 0,\n",
" u'research_interests': u'',\n",
" u'short_bio': u'',\n",
" u'surname': u'Klingler',\n",
" u'title': u'GEAR Member',\n",
" u'website': u''},\n",
" {u'cluster_id': 0,\n",
" u'gear_collaborators': [],\n",
" u'member_id': 66,\n",
" u'member_type': u'member',\n",
" u'name': u'Fran\\xe7ois',\n",
" u'organization': u'Universit\\xe9 Paris-Sud - Paris XI',\n",
" u'other_collaborators': u'',\n",
" u'photo': u'',\n",
" u'pos_x': 0,\n",
" u'pos_y': 0,\n",
" u'research_interests': u'',\n",
" u'short_bio': u'',\n",
" u'surname': u'Labourie',\n",
" u'title': u'GEAR Member',\n",
" u'website': u''},\n",
" {u'cluster_id': 0,\n",
" u'gear_collaborators': [],\n",
" u'member_id': 67,\n",
" u'member_type': u'member',\n",
" u'name': u'Sean',\n",
" u'organization': u'George Mason University',\n",
" u'other_collaborators': u'Indranil Biswas, Marina Logares, Lars Louder, Ben McReynolds, Dan Ramras',\n",
" u'photo': u'LawtonSean.jpg',\n",
" u'pos_x': 0,\n",
" u'pos_y': 0,\n",
" u'research_interests': u'Dynamics on Moduli Spaces, Geometric Structures and Teichm\\xfcller Spaces, Higgs Bundles, Character Varieties',\n",
" u'short_bio': u'The geometric, topological, algebraic, arithmetic, and dynamical structure of moduli spaces of Lie group valued representations of finitely generated groups and quivers; with applications to mathematical physics and geometric structure theory.',\n",
" u'surname': u'Lawton',\n",
" u'title': u'GEAR Member',\n",
" u'website': u'http://math.gmu.edu/~slawton3/'},\n",
" {u'cluster_id': 0,\n",
" u'gear_collaborators': [],\n",
" u'member_id': 68,\n",
" u'member_type': u'member',\n",
" u'name': u'Christopher',\n",
" u'organization': u'University of Illinois at Urbana-Champaign',\n",
" u'other_collaborators': u'',\n",
" u'photo': u'',\n",
" u'pos_x': 0,\n",
" u'pos_y': 0,\n",
" u'research_interests': u'',\n",
" u'short_bio': u'',\n",
" u'surname': u'Leininger',\n",
" u'title': u'GEAR Member',\n",
" u'website': u''},\n",
" {u'cluster_id': 0,\n",
" u'gear_collaborators': [],\n",
" u'member_id': 69,\n",
" u'member_type': u'member',\n",
" u'name': u'Enrico',\n",
" u'organization': u'Karlsruher Institut f\\xfcr Technologie',\n",
" u'other_collaborators': u'',\n",
" u'photo': u'',\n",
" u'pos_x': 0,\n",
" u'pos_y': 0,\n",
" u'research_interests': u'',\n",
" u'short_bio': u'',\n",
" u'surname': u'Leuzinger',\n",
" u'title': u'GEAR Member',\n",
" u'website': u''},\n",
" {u'cluster_id': 0,\n",
" u'gear_collaborators': [],\n",
" u'member_id': 70,\n",
" u'member_type': u'member',\n",
" u'name': u'John',\n",
" u'organization': u'Rutgers University',\n",
" u'other_collaborators': u'',\n",
" u'photo': u'',\n",
" u'pos_x': 0,\n",
" u'pos_y': 0,\n",
" u'research_interests': u'',\n",
" u'short_bio': u'',\n",
" u'surname': u'Loftin',\n",
" u'title': u'GEAR Member',\n",
" u'website': u''},\n",
" {u'cluster_id': 0,\n",
" u'gear_collaborators': [],\n",
" u'member_id': 71,\n",
" u'member_type': u'member',\n",
" u'name': u'Darren',\n",
" u'organization': u'University of California, Santa Barbara',\n",
" u'other_collaborators': u'',\n",
" u'photo': u'',\n",
" u'pos_x': 0,\n",
" u'pos_y': 0,\n",
" u'research_interests': u'',\n",
" u'short_bio': u'',\n",
" u'surname': u'Long',\n",
" u'title': u'GEAR Member',\n",
" u'website': u''},\n",
" {u'cluster_id': 0,\n",
" u'gear_collaborators': [],\n",
" u'member_id': 72,\n",
" u'member_type': u'member',\n",
" u'name': u'Feng',\n",
" u'organization': u'Rutgers University',\n",
" u'other_collaborators': u'',\n",
" u'photo': u'',\n",
" u'pos_x': 0,\n",
" u'pos_y': 0,\n",
" u'research_interests': u'Geometric Structures and Teichm\\xfcller Spaces',\n",
" u'short_bio': u'I am interested in finding geometric structures on low-dimensional spaces.',\n",
" u'surname': u'Luo',\n",
" u'title': u'GEAR Member',\n",
" u'website': u''},\n",
" {u'cluster_id': 0,\n",
" u'gear_collaborators': [],\n",
" u'member_id': 73,\n",
" u'member_type': u'member',\n",
" u'name': u'Martin',\n",
" u'organization': u'Universit\\xe9 Aix-Marseille III Paul Cezanne',\n",
" u'other_collaborators': u'',\n",
" u'photo': u'',\n",
" u'pos_x': 0,\n",
" u'pos_y': 0,\n",
" u'research_interests': u'',\n",
" u'short_bio': u'',\n",
" u'surname': u'Lustig',\n",
" u'title': u'GEAR Member',\n",
" u'website': u''},\n",
" {u'cluster_id': 0,\n",
" u'gear_collaborators': [],\n",
" u'member_id': 74,\n",
" u'member_type': u'member',\n",
" u'name': u'Aaron',\n",
" u'organization': u'University of Maryland-College Park',\n",
" u'other_collaborators': u'',\n",
" u'photo': u'',\n",
" u'pos_x': 0,\n",
" u'pos_y': 0,\n",
" u'research_interests': u'',\n",
" u'short_bio': u'',\n",
" u'surname': u'Magid',\n",
" u'title': u'GEAR Member',\n",
" u'website': u''},\n",
" {u'cluster_id': 0,\n",
" u'gear_collaborators': [],\n",
" u'member_id': 75,\n",
" u'member_type': u'member',\n",
" u'name': u'Jason',\n",
" u'organization': u'University at Buffalo',\n",
" u'other_collaborators': u'',\n",
" u'photo': u'',\n",
" u'pos_x': 0,\n",
" u'pos_y': 0,\n",
" u'research_interests': u'',\n",
" u'short_bio': u'',\n",
" u'surname': u'Manning',\n",
" u'title': u'GEAR Member',\n",
" u'website': u''},\n",
" {u'cluster_id': 0,\n",
" u'gear_collaborators': [],\n",
" u'member_id': 76,\n",
" u'member_type': u'member',\n",
" u'name': u'Christopher',\n",
" u'organization': u'George Mason University',\n",
" u'other_collaborators': u'',\n",
" u'photo': u'',\n",
" u'pos_x': 0,\n",
" u'pos_y': 0,\n",
" u'research_interests': u'',\n",
" u'short_bio': u'',\n",
" u'surname': u'Manon',\n",
" u'title': u'GEAR Member',\n",
" u'website': u''},\n",
" {u'cluster_id': 0,\n",
" u'gear_collaborators': [],\n",
" u'member_id': 77,\n",
" u'member_type': u'member',\n",
" u'name': u'Albert',\n",
" u'organization': u'University of Minnesota',\n",
" u'other_collaborators': u'',\n",
" u'photo': u'',\n",
" u'pos_x': 0,\n",
" u'pos_y': 0,\n",
" u'research_interests': u'',\n",
" u'short_bio': u'',\n",
" u'surname': u'Marden',\n",
" u'title': u'GEAR Member',\n",
" u'website': u''},\n",
" {u'cluster_id': 0,\n",
" u'gear_collaborators': [],\n",
" u'member_id': 78,\n",
" u'member_type': u'member',\n",
" u'name': u'Howard',\n",
" u'organization': u'University of Chicago',\n",
" u'other_collaborators': u'Keith Burns, Jon Chaika',\n",
" u'photo': u'',\n",
" u'pos_x': 0,\n",
" u'pos_y': 0,\n",
" u'research_interests': u'Dynamics on Moduli Spaces, Geometric Structures and Teichm\\xfcller Spaces',\n",
" u'short_bio': u'I study geometry of Teichmuller space and the mapping class group. I also study the dynamics of the SL(2,R) action moduli space of translation surfaces.',\n",
" u'surname': u'Masur',\n",
" u'title': u'GEAR Member',\n",
" u'website': u'http://math.uchicago.edu/~masur/'},\n",
" {u'cluster_id': 0,\n",
" u'gear_collaborators': [],\n",
" u'member_id': 79,\n",
" u'member_type': u'member',\n",
" u'name': u'Ben',\n",
" u'organization': u'Purdue University',\n",
" u'other_collaborators': u'',\n",
" u'photo': u'',\n",
" u'pos_x': 0,\n",
" u'pos_y': 0,\n",
" u'research_interests': u'',\n",
" u'short_bio': u'',\n",
" u'surname': u'McReynolds',\n",
" u'title': u'GEAR Member',\n",
" u'website': u''},\n",
" {u'cluster_id': 0,\n",
" u'gear_collaborators': [],\n",
" u'member_id': 80,\n",
" u'member_type': u'member',\n",
" u'name': u'Greg',\n",
" u'organization': u'Universit\\xe9 de Grenoble',\n",
" u'other_collaborators': u'',\n",
" u'photo': u'',\n",
" u'pos_x': 0,\n",
" u'pos_y': 0,\n",
" u'research_interests': u'',\n",
" u'short_bio': u'',\n",
" u'surname': u'McShane',\n",
" u'title': u'GEAR Member',\n",
" u'website': u''},\n",
" {u'cluster_id': 0,\n",
" u'gear_collaborators': [],\n",
" u'member_id': 81,\n",
" u'member_type': u'member',\n",
" u'name': u'Karin',\n",
" u'organization': u'University of Maryland-College Park',\n",
" u'other_collaborators': u'Uri Bader, Thierry Barbot, Andreas Cap, Mohammed Deffaf, Charles Frances, Katharina Neusser, Wouter van Limbeek',\n",
" u'photo': u'',\n",
" u'pos_x': 0,\n",
" u'pos_y': 0,\n",
" u'research_interests': u'Geometric and Analytical Group Theory, Geometric Structures and Teichm\\xfcller Spaces',\n",
" u'short_bio': u'Differential-geometric aspects of rigidity, Lorentzian Geometry, Conformal Geometry, other parabolic geometries',\n",
" u'surname': u'Melnick',\n",
" u'title': u'GEAR Member',\n",
" u'website': u'http://www.math.umd.edu/~karin'},\n",
" {u'cluster_id': 0,\n",
" u'gear_collaborators': [],\n",
" u'member_id': 82,\n",
" u'member_type': u'member',\n",
" u'name': u'John',\n",
" u'organization': u'University of Maryland-College Park',\n",
" u'other_collaborators': u'',\n",
" u'photo': u'',\n",
" u'pos_x': 0,\n",
" u'pos_y': 0,\n",
" u'research_interests': u'',\n",
" u'short_bio': u'',\n",
" u'surname': u'Millson',\n",
" u'title': u'GEAR Member',\n",
" u'website': u''},\n",
" {u'cluster_id': 0,\n",
" u'gear_collaborators': [],\n",
" u'member_id': 83,\n",
" u'member_type': u'member',\n",
" u'name': u'Yair',\n",
" u'organization': u'Yale University',\n",
" u'other_collaborators': u'Jason Behrstock, Tsachik Gelander, Grigori Margulis, Yoav Moriah',\n",
" u'photo': u'',\n",
" u'pos_x': 0,\n",
" u'pos_y': 0,\n",
" u'research_interests': u'Dynamics on Moduli Spaces, Geometric and Analytical Group Theory, Geometric Structures and Teichm\\xfcller Spaces, Hyperbolic Manifolds',\n",
" u'short_bio': u'I am interested in questions around hyperbolic manifolds, surfaces and mapping class groups, and connections to geometric group theory. For hyperbolic 3-manifolds I am interested in finding better explicit recipes for relating geometric to topological structures. Mapping class groups and other automorphism groups act on character varieties and I am interested in analysing this dynamical structure. Quasi-isometric rigidity is another topic I have worked on.',\n",
" u'surname': u'Minsky',\n",
" u'title': u'GEAR Member',\n",
" u'website': u'http://math.yale.edu/users/yair'},\n",
" {u'cluster_id': 0,\n",
" u'gear_collaborators': [],\n",
" u'member_id': 84,\n",
" u'member_type': u'member',\n",
" u'name': u'Maryam',\n",
" u'organization': u'Stanford University',\n",
" u'other_collaborators': u'',\n",
" u'photo': u'',\n",
" u'pos_x': 0,\n",
" u'pos_y': 0,\n",
" u'research_interests': u'',\n",
" u'short_bio': u'',\n",
" u'surname': u'Mirzakhani',\n",
" u'title': u'GEAR Member',\n",
" u'website': u''},\n",
" {u'cluster_id': 0,\n",
" u'gear_collaborators': [],\n",
" u'member_id': 85,\n",
" u'member_type': u'member',\n",
" u'name': u'Mahan',\n",
" u'organization': u'RKM Vivekananda University',\n",
" u'other_collaborators': u'Gautam Bharali, Indranil Biswas, Pranab Sardar, Saul Schleimer, Harish Seshadri',\n",
" u'photo': u'MahanMj.jpg',\n",
" u'pos_x': 0,\n",
" u'pos_y': 0,\n",
" u'research_interests': u'Geometric and Analytical Group Theory',\n",
" u'short_bio': u'My main interests lie at the interface of Geometric Group Theory and Kleinian groups. Another interest lies in K\\xe4hler groups, fundamental groups of (quasi) projective varieties.',\n",
" u'surname': u'Mj',\n",
" u'title': u'GEAR Member',\n",
" u'website': u'http://maths.rkmvu.ac.in/~mahan/'},\n",
" {u'cluster_id': 0,\n",
" u'gear_collaborators': [],\n",
" u'member_id': 86,\n",
" u'member_type': u'member',\n",
" u'name': u'Motohico',\n",
" u'organization': u'University of California, Davis',\n",
" u'other_collaborators': u'',\n",
" u'photo': u'',\n",
" u'pos_x': 0,\n",
" u'pos_y': 0,\n",
" u'research_interests': u'',\n",
" u'short_bio': u'',\n",
" u'surname': u'Mulase',\n",
" u'title': u'GEAR Member',\n",
" u'website': u''},\n",
" {u'cluster_id': 0,\n",
" u'gear_collaborators': [],\n",
" u'member_id': 87,\n",
" u'member_type': u'member',\n",
" u'name': u'Ignasi',\n",
" u'organization': u'Universitat de Barcelona',\n",
" u'other_collaborators': u'',\n",
" u'photo': u'',\n",
" u'pos_x': 0,\n",
" u'pos_y': 0,\n",
" u'research_interests': u'',\n",
" u'short_bio': u'',\n",
" u'surname': u'Mundet i Riera',\n",
" u'title': u'GEAR Member',\n",
" u'website': u''},\n",
" {u'cluster_id': 0,\n",
" u'gear_collaborators': [],\n",
" u'member_id': 88,\n",
" u'member_type': u'member',\n",
" u'name': u'Walter',\n",
" u'organization': u'Columbia University',\n",
" u'other_collaborators': u'',\n",
" u'photo': u'',\n",
" u'pos_x': 0,\n",
" u'pos_y': 0,\n",
" u'research_interests': u'Geometric and Analytical Group Theory, Geometric Structures and Teichm\\xfcller Spaces, Hyperbolic Manifolds',\n",
" u'short_bio': u'',\n",
" u'surname': u'Neumann',\n",
" u'title': u'GEAR Member',\n",
" u'website': u''},\n",
" {u'cluster_id': 0,\n",
" u'gear_collaborators': [],\n",
" u'member_id': 89,\n",
" u'member_type': u'member',\n",
" u'name': u'Andr\\xe9 Gama',\n",
" u'organization': u'Universidade de Tr\\xe1s-os-Montes e Alto Douro',\n",
" u'other_collaborators': u'',\n",
" u'photo': u'',\n",
" u'pos_x': 0,\n",
" u'pos_y': 0,\n",
" u'research_interests': u'',\n",
" u'short_bio': u'',\n",
" u'surname': u'Oliveira',\n",
" u'title': u'GEAR Member',\n",
" u'website': u''},\n",
" {u'cluster_id': 0,\n",
" u'gear_collaborators': [],\n",
" u'member_id': 90,\n",
" u'member_type': u'member',\n",
" u'name': u'Frederic',\n",
" u'organization': u'Aix-Marseille Universit\\xe9',\n",
" u'other_collaborators': u'Laurent Beaudou, Gregoire Dupont, Antoine Gerbaud, Laurent Grappe',\n",
" u'photo': u'',\n",
" u'pos_x': 0,\n",
" u'pos_y': 0,\n",
" u'research_interests': u'Dynamics on Moduli Spaces, Geometric Structures and Teichm\\xfcller Spaces, Hyperbolic Manifolds',\n",
" u'short_bio': u\"I'm interested in spaces of representations of surface groups and the natural action of the mapping class group on these spaces. Also interested in the relation between Teichmuller space and cluster algebras.\",\n",
" u'surname': u'Palesi',\n",
" u'title': u'GEAR Member',\n",
" u'website': u'http://www.latp.univ-mrs.fr/~fpalesi/'},\n",
" {u'cluster_id': 0,\n",
" u'gear_collaborators': [],\n",
" u'member_id': 91,\n",
" u'member_type': u'member',\n",
" u'name': u'Pierre',\n",
" u'organization': u'\\xc9cole Normale Sup\\xe9rieure',\n",
" u'other_collaborators': u'',\n",
" u'photo': u'',\n",
" u'pos_x': 0,\n",
" u'pos_y': 0,\n",
" u'research_interests': u'',\n",
" u'short_bio': u'',\n",
" u'surname': u'Pansu',\n",
" u'title': u'GEAR Member',\n",
" u'website': u''},\n",
" {u'cluster_id': 0,\n",
" u'gear_collaborators': [],\n",
" u'member_id': 92,\n",
" u'member_type': u'member',\n",
" u'name': u'Tony',\n",
" u'organization': u'University of Pensylvania',\n",
" u'other_collaborators': u'',\n",
" u'photo': u'',\n",
" u'pos_x': 0,\n",
" u'pos_y': 0,\n",
" u'research_interests': u'',\n",
" u'short_bio': u'',\n",
" u'surname': u'Pantev',\n",
" u'title': u'GEAR Member',\n",
" u'website': u''},\n",
" {u'cluster_id': 0,\n",
" u'gear_collaborators': [],\n",
" u'member_id': 93,\n",
" u'member_type': u'member',\n",
" u'name': u'Athanase',\n",
" u'organization': u'Universit\\xe9 de Strasbourg',\n",
" u'other_collaborators': u\"Norbert A'Campo, Charalampos Charitos, Mustafa Korkmaz, Lixin Liu, Yannis Papadoperakis, Roshdi Rashed, Weixu Su, Guillaume Th\\xe9ret, Marc Troyanov, Sumio Yamada\",\n",
" u'photo': u'PapadopoulosAthanase.jpg',\n",
" u'pos_x': 0,\n",
" u'pos_y': 0,\n",
" u'research_interests': u'Geometric Structures and Teichm\\xfcller Spaces, Hyperbolic Manifolds',\n",
" u'short_bio': u'Mapping class group actions, hyperbolic geometry, Finsler geometry.',\n",
" u'surname': u'Papadopoulos',\n",
" u'title': u'GEAR Member',\n",
" u'website': u'http://www-irma.u-strasbg.fr/~papadop/'},\n",
" {u'cluster_id': 0,\n",
" u'gear_collaborators': [],\n",
" u'member_id': 94,\n",
" u'member_type': u'member',\n",
" u'name': u'John',\n",
" u'organization': u'Durham University',\n",
" u'other_collaborators': u'Krishnendu Gongopadhyay, Jose Seade, Shigeyasu Kamiya',\n",
" u'photo': u'ParkerJohn.jpg',\n",
" u'pos_x': 0,\n",
" u'pos_y': 0,\n",
" u'research_interests': u'Hyperbolic Manifolds, Geometric and Analytical Group Theory, Geometric Structures and Teichm\\xfcller Spaces',\n",
" u'short_bio': u'My main research is on discrete groups of complex hyperbolic isometries, and their deformation spaces. More widely, I am interested in hyperbolic geometry and discrete groups.',\n",
" u'surname': u'Parker',\n",
" u'title': u'GEAR Member',\n",
" u'website': u'http://www.maths.dur.ac.uk/~dma0jrp/'},\n",
" {u'cluster_id': 0,\n",
" u'gear_collaborators': [],\n",
" u'member_id': 95,\n",
" u'member_type': u'member',\n",
" u'name': u'Hugo',\n",
" u'organization': u'University of Fribourg',\n",
" u'other_collaborators': u'',\n",
" u'photo': u'',\n",
" u'pos_x': 0,\n",
" u'pos_y': 0,\n",
" u'research_interests': u'',\n",
" u'short_bio': u'',\n",
" u'surname': u'Parlier',\n",
" u'title': u'GEAR Member',\n",
" u'website': u''},\n",
" {u'cluster_id': 0,\n",
" u'gear_collaborators': [],\n",
" u'member_id': 96,\n",
" u'member_type': u'member',\n",
" u'name': u'Anne',\n",
" u'organization': u'Universit\\xe9 de Grenoble',\n",
" u'other_collaborators': u'',\n",
" u'photo': u'',\n",
" u'pos_x': 0,\n",
" u'pos_y': 0,\n",
" u'research_interests': u'',\n",
" u'short_bio': u'',\n",
" u'surname': u'Parreau',\n",
" u'title': u'GEAR Member',\n",
" u'website': u''},\n",
" {u'cluster_id': 0,\n",
" u'gear_collaborators': [],\n",
" u'member_id': 97,\n",
" u'member_type': u'member',\n",
" u'name': u'Frederic',\n",
" u'organization': u'Universit\\xe9 Paris-Sud - Paris XI',\n",
" u'other_collaborators': u'Alexander Gorodnik, Jouni Parkkonen, Mark Pollicott, Barbara Schapira',\n",
" u'photo': u'Paulin.jpg',\n",
" u'pos_x': 0,\n",
" u'pos_y': 0,\n",
" u'research_interests': u'Geometric and Analytical Group Theory, Geometric Structures and Teichm\\xfcller Spaces, Hyperbolic Manifolds, Equidistribution problems in locally symmetric spaces.',\n",
" u'short_bio': u'Ergodic theory of geodesic flows in negative curvature (counting and equidistribution, Gibbs measures, ...) and arithmetic applications. Geometric group theory (group actions on trees and buildings, ...). Discrete subgroups of Lie groups. Homogeneous dynamics and arithmetic applications.',\n",
" u'surname': u'Paulin',\n",
" u'title': u'GEAR Member',\n",
" u'website': u'http://www.math.u-psud.fr/~paulin/'},\n",
" {u'cluster_id': 0,\n",
" u'gear_collaborators': [],\n",
" u'member_id': 98,\n",
" u'member_type': u'member',\n",
" u'name': u'Julien',\n",
" u'organization': u'Arizona State University',\n",
" u'other_collaborators': u'',\n",
" u'photo': u'PaupertJulien.jpg',\n",
" u'pos_x': 0,\n",
" u'pos_y': 0,\n",
" u'research_interests': u'Geometric Structures and Teichm\\xfcller Spaces, Hyperbolic Manifolds',\n",
" u'short_bio': u'My research interests lie in low-dimensional geometry and topology. More precisely, I study hyperbolic geometry (mostly complex), reflection groups, and lattices in rank 1 semisimple Lie groups.',\n",
" u'surname': u'Paupert',\n",
" u'title': u'GEAR Member',\n",
" u'website': u'https://math.la.asu.edu/~paupert/'},\n",
" {u'cluster_id': 0,\n",
" u'gear_collaborators': [],\n",
" u'member_id': 99,\n",
" u'member_type': u'member',\n",
" u'name': u'Robert',\n",
" u'organization': u'Aarhus University',\n",
" u'other_collaborators': u'',\n",
" u'photo': u'',\n",
" u'pos_x': 0,\n",
" u'pos_y': 0,\n",
" u'research_interests': u'',\n",
" u'short_bio': u'',\n",
" u'surname': u'Penner',\n",
" u'title': u'GEAR Member',\n",
" u'website': u''},\n",
" {u'cluster_id': 0,\n",
" u'gear_collaborators': [],\n",
" u'member_id': 100,\n",
" u'member_type': u'member',\n",
" u'name': u'Joan',\n",
" u'organization': u'Universitat Aut\\xf2noma de Barcelona',\n",
" u'other_collaborators': u'Bessi\\xe8res, Laurent; Besson, G\\xe9rard; Dicks, Warren; Francaviglia, Stefano; Heusener, Michael; Leeb, Bernhard; Maillot, Sylvain; Menal Ferrer, Pere; Paoluzzi, Luisa',\n",
" u'photo': u'PortiJoan.jpg',\n",
" u'pos_x': 0,\n",
" u'pos_y': 0,\n",
" u'research_interests': u'Geometric and Analytical Group Theory, Hyperbolic Manifolds',\n",
" u'short_bio': u'Varieties of representations of three manifold groups. Geometric structures on three manifolds. Reidemeister torsions. Discrete subgroups of isometries of symmetric spaces.',\n",
" u'surname': u'Porti',\n",
" u'title': u'GEAR Member',\n",
" u'website': u'http://mat.uab.es/~porti/'},\n",
" {u'cluster_id': 0,\n",
" u'gear_collaborators': [],\n",
" u'member_id': 101,\n",
" u'member_type': u'member',\n",
" u'name': u'Kasra',\n",
" u'organization': u'University of Toronto',\n",
" u'other_collaborators': u'',\n",
" u'photo': u'KasraRafi.jpg',\n",
" u'pos_x': 0,\n",
" u'pos_y': 0,\n",
" u'research_interests': u'Dynamics on Moduli Spaces, Geometric and Analytical Group Theory, Geometric Structures and Teichm\\xfcller Spaces, Hyperbolic Manifolds',\n",
" u'short_bio': u'',\n",
" u'surname': u'Rafi',\n",
" u'title': u'GEAR Member',\n",
" u'website': u'http://www.math.toronto.edu/~rafi/'},\n",
" {u'cluster_id': 0,\n",
" u'gear_collaborators': [],\n",
" u'member_id': 102,\n",
" u'member_type': u'member',\n",
" u'name': u'Alan',\n",
" u'organization': u'University of Texas at Austin',\n",
" u'other_collaborators': u'',\n",
" u'photo': u'',\n",
" u'pos_x': 0,\n",
" u'pos_y': 0,\n",
" u'research_interests': u'',\n",
" u'short_bio': u'',\n",
" u'surname': u'Reid',\n",
" u'title': u'GEAR Member',\n",
" u'website': u''},\n",
" {u'cluster_id': 0,\n",
" u'gear_collaborators': [],\n",
" u'member_id': 103,\n",
" u'member_type': u'member',\n",
" u'name': u'Andr\\xe9s',\n",
" u'organization': u'Universit\\xe9 Pierre et Marie Curie',\n",
" u'other_collaborators': u'',\n",
" u'photo': u'',\n",
" u'pos_x': 0,\n",
" u'pos_y': 0,\n",
" u'research_interests': u'Dynamics on Moduli Spaces, Geometric Structures and Teichm\\xfcller Spaces, Hyperbolic Manifolds',\n",
" u'short_bio': u'My main interest is to understand representations of hyperbolic groups on semi-simple Lie groups. The main objective is understand how the intrinsic geometry of the hyperbolic group interacts with that of the Lie group.',\n",
" u'surname': u'Sambarino',\n",
" u'title': u'GEAR Member',\n",
" u'website': u'http://www.math.jussieu.fr/~sambarino/'},\n",
" {u'cluster_id': 0,\n",
" u'gear_collaborators': [],\n",
" u'member_id': 104,\n",
" u'member_type': u'member',\n",
" u'name': u'Dragomir',\n",
" u'organization': u'City University of New York',\n",
" u'other_collaborators': u'',\n",
" u'photo': u'',\n",
" u'pos_x': 0,\n",
" u'pos_y': 0,\n",
" u'research_interests': u'',\n",
" u'short_bio': u'',\n",
" u'surname': u'Saric',\n",
" u'title': u'GEAR Member',\n",
" u'website': u''},\n",
" {u'cluster_id': 0,\n",
" u'gear_collaborators': [],\n",
" u'member_id': 105,\n",
" u'member_type': u'member',\n",
" u'name': u'Florent',\n",
" u'organization': u'Universidad de Los Andes',\n",
" u'other_collaborators': u'Chiu-Chu Melissa Liu',\n",
" u'photo': u'',\n",
" u'pos_x': 0,\n",
" u'pos_y': 0,\n",
" u'research_interests': u'Higgs Bundles, Real algebraic geometry',\n",
" u'short_bio': u'I work in the area at the intersection between the theory of Vector bundles on curves and Real algebraic geometry. Themes which are of special interest to me are:\\n-Topology of sets of real points of moduli schemes of vector bundles on a real algebraic curve (connected components, Betti numbers).\\n-Narasimhan-Seshadri, Hitchin-Kobayashi-Simpson and Donaldson-Corlette correspondences over real algebraic curves.\\n\\nResearch interests (AMS subject classification) :\\n-Vector bundles on curves and their moduli (14H60)\\n-Special connections and metrics on vector bundles (Hermite-Einstein-Yang-Mills) (53C07).\\n-Topology of real algebraic varieties (14P25).',\n",
" u'surname': u'Schaffhauser',\n",
" u'title': u'GEAR Member',\n",
" u'website': u'http://matematicas.uniandes.edu.co/~florent'},\n",
" {u'cluster_id': 0,\n",
" u'gear_collaborators': [],\n",
" u'member_id': 106,\n",
" u'member_type': u'member',\n",
" u'name': u'Jean-Marc',\n",
" u'organization': u'Universit\\xe9 du Luxembourg',\n",
" u'other_collaborators': u'Thierry Barbot(Avignon), Francesco Bonsante(Pavia), Jeff Danciger, Colin Guillarmou(ENS), Kirill Krasnov(Nottingham), Gabriele Mondello(Roma), Sergiu Moroianu(IMAR Bucarest), Carlos Scarinci(Erlangen)',\n",
" u'photo': u'SchlenkerJeanMarc.jpg',\n",
" u'pos_x': 0,\n",
" u'pos_y': 0,\n",
" u'research_interests': u'Geometric Structures and Teichm\\xfcller Spaces, Hyperbolic Manifolds',\n",
" u'short_bio': u'3d hyperbolic geometry, anti-de Sitter and other Lorentz geometry, Teichm\\xfcller theory, polyhedral and discrete geometry, etc.',\n",
" u'surname': u'Schlenker',\n",
" u'title': u'GEAR Member',\n",
" u'website': u'http://math.uni.lu/schlenker'},\n",
" {u'cluster_id': 0,\n",
" u'gear_collaborators': [],\n",
" u'member_id': 107,\n",
" u'member_type': u'member',\n",
" u'name': u'Martin',\n",
" u'organization': u'Universit\\xe9 du Luxembourg',\n",
" u'other_collaborators': u'',\n",
" u'photo': u'',\n",
" u'pos_x': 0,\n",
" u'pos_y': 0,\n",
" u'research_interests': u'',\n",
" u'short_bio': u'',\n",
" u'surname': u'Schlichenmaier',\n",
" u'title': u'GEAR Member',\n",
" u'website': u''},\n",
" {u'cluster_id': 0,\n",
" u'gear_collaborators': [],\n",
" u'member_id': 108,\n",
" u'member_type': u'member',\n",
" u'name': u'Caroline',\n",
" u'organization': u'University of Warwick',\n",
" u'other_collaborators': u'',\n",
" u'photo': u'',\n",
" u'pos_x': 0,\n",
" u'pos_y': 0,\n",
" u'research_interests': u'',\n",
" u'short_bio': u'',\n",
" u'surname': u'Series',\n",
" u'title': u'GEAR Member',\n",
" u'website': u''},\n",
" {u'cluster_id': 0,\n",
" u'gear_collaborators': [],\n",
" u'member_id': 109,\n",
" u'member_type': u'member',\n",
" u'name': u'Adam',\n",
" u'organization': u'State University of New York',\n",
" u'other_collaborators': u'',\n",
" u'photo': u'',\n",
" u'pos_x': 0,\n",
" u'pos_y': 0,\n",
" u'research_interests': u'',\n",
" u'short_bio': u'',\n",
" u'surname': u'Sikora',\n",
" u'title': u'GEAR Member',\n",
" u'website': u''},\n",
" {u'cluster_id': 0,\n",
" u'gear_collaborators': [],\n",
" u'member_id': 110,\n",
" u'member_type': u'member',\n",
" u'name': u'Carlos',\n",
" u'organization': u'Universit\\xe9 de Nice Sophia-Antipolis',\n",
" u'other_collaborators': u'',\n",
" u'photo': u'',\n",
" u'pos_x': 0,\n",
" u'pos_y': 0,\n",
" u'research_interests': u'',\n",
" u'short_bio': u'',\n",
" u'surname': u'Simpson',\n",
" u'title': u'GEAR Member',\n",
" u'website': u''},\n",
" {u'cluster_id': 0,\n",
" u'gear_collaborators': [],\n",
" u'member_id': 111,\n",
" u'member_type': u'member',\n",
" u'name': u'Juan',\n",
" u'organization': u'Universit\\xe9 de Rennes I',\n",
" u'other_collaborators': u'',\n",
" u'photo': u'',\n",
" u'pos_x': 0,\n",
" u'pos_y': 0,\n",
" u'research_interests': u'',\n",
" u'short_bio': u'',\n",
" u'surname': u'Souto',\n",
" u'title': u'GEAR Member',\n",
" u'website': u''},\n",
" {u'cluster_id': 0,\n",
" u'gear_collaborators': [],\n",
" u'member_id': 112,\n",
" u'member_type': u'member',\n",
" u'name': u'Matthew',\n",
" u'organization': u'Temple University',\n",
" u'other_collaborators': u'',\n",
" u'photo': u'',\n",
" u'pos_x': 0,\n",
" u'pos_y': 0,\n",
" u'research_interests': u'',\n",
" u'short_bio': u'',\n",
" u'surname': u'Stover',\n",
" u'title': u'GEAR Member',\n",
" u'website': u''},\n",
" {u'cluster_id': 0,\n",
" u'gear_collaborators': [],\n",
" u'member_id': 113,\n",
" u'member_type': u'member',\n",
" u'name': u'Ser Peow',\n",
" u'organization': u'National University of Singapore',\n",
" u'other_collaborators': u'Yoshinobu Kamishima, Inkang Kim, Joonhyung Kim, Sadayoshi Kojima, Shigeru Mizushima, Yasushi Yamashita, Yan Loi Wong, Ying Zhang',\n",
" u'photo': u'',\n",
" u'pos_x': 0,\n",
" u'pos_y': 0,\n",
" u'research_interests': u'Dynamics on Moduli Spaces, Geometric and Analytical Group Theory, Geometric Structures and Teichm\\xfcller Spaces, Hyperbolic Manifolds',\n",
" u'short_bio': u'I am interested in identities on hyperbolic surfaces and manifolds and their applications to the study of Teichm\\xfcller space and moduli spaces, and on dynamics of various group actions on moduli spaces.',\n",
" u'surname': u'Tan',\n",
" u'title': u'GEAR Member',\n",
" u'website': u'http://www.math.nus.edu.sg/~mattansp/'},\n",
" {u'cluster_id': 0,\n",
" u'gear_collaborators': [],\n",
" u'member_id': 114,\n",
" u'member_type': u'member',\n",
" u'name': u'Jing',\n",
" u'organization': u'University of Oklahoma',\n",
" u'other_collaborators': u'',\n",
" u'photo': u'',\n",
" u'pos_x': 0,\n",
" u'pos_y': 0,\n",
" u'research_interests': u'',\n",
" u'short_bio': u'',\n",
" u'surname': u'Tao',\n",
" u'title': u'GEAR Member',\n",
" u'website': u''},\n",
" {u'cluster_id': 0,\n",
" u'gear_collaborators': [],\n",
" u'member_id': 115,\n",
" u'member_type': u'member',\n",
" u'name': u'Dylan',\n",
" u'organization': u'Indiana University',\n",
" u'other_collaborators': u'Robert Lipshitz, Peter Ozsv\\xe1th, Kevin Pilgrim',\n",
" u'photo': u'',\n",
" u'pos_x': 0,\n",
" u'pos_y': 0,\n",
" u'research_interests': u'Hyperbolic Manifolds, Geometric Structures and Teichm\\xfcller Spaces, Dynamics on Moduli Spaces',\n",
" u'short_bio': u'I study a variety of geometric structures, especially 2- and 3-dimensional hyperbolic geometry and Teichm\\xfcller spaces. My recent work has been on characterizing post-critically finite rational maps.',\n",
" u'surname': u'Thurston',\n",
" u'title': u'GEAR Member',\n",
" u'website': u'http://math.columbia.edu/~dpt'},\n",
" {u'cluster_id': 0,\n",
" u'gear_collaborators': [],\n",
" u'member_id': 116,\n",
" u'member_type': u'member',\n",
" u'name': u'Domingo',\n",
" u'organization': u'University of Utah',\n",
" u'other_collaborators': u'Jim Carlson, Phillip Griffiths, Colleen Robles',\n",
" u'photo': u'',\n",
" u'pos_x': 0,\n",
" u'pos_y': 0,\n",
" u'research_interests': u'Topology and Geometry of Algebraic Varieties',\n",
" u'short_bio': u'I am interested in topology of algebraic varieties and of K\\xe4hler manifolds, particularly their fundamental groups. One way to study these groups is from their linear representations and from harmonic theory, a subject closely related to Higgs bundles. Many examples come from hyperbolic geometry (real, complex and quaternionic). Surface groups and Teichm\\xfcller theory.',\n",
" u'surname': u'Toledo',\n",
" u'title': u'GEAR Member',\n",
" u'website': u'http://www.math.utah.edu/~toledo/'},\n",
" {u'cluster_id': 0,\n",
" u'gear_collaborators': [],\n",
" u'member_id': 117,\n",
" u'member_type': u'member',\n",
" u'name': u'Anastasiia',\n",
" u'organization': u'University of California, Davis',\n",
" u'other_collaborators': u'',\n",
" u'photo': u'',\n",
" u'pos_x': 0,\n",
" u'pos_y': 0,\n",
" u'research_interests': u'Hyperbolic Manifolds',\n",
" u'short_bio': u'Intrinsic geometry of hyperbolic 3-manifolds (knot and link complements in particular), and connections between geometric invariants and invariants coming from other areas of mathematics (such as combinatorial, quantum, arithmetic invariants, etc).',\n",
" u'surname': u'Tsvietkova',\n",
" u'title': u'GEAR Member',\n",
" u'website': u'https://www.math.ucdavis.edu/~tsvietkova/'},\n",
" {u'cluster_id': 0,\n",
" u'gear_collaborators': [],\n",
" u'member_id': 118,\n",
" u'member_type': u'member',\n",
" u'name': u'Richard',\n",
" u'organization': u'University of Maryland-College Park',\n",
" u'other_collaborators': u'',\n",
" u'photo': u'',\n",
" u'pos_x': 0,\n",
" u'pos_y': 0,\n",
" u'research_interests': u'',\n",
" u'short_bio': u'',\n",
" u'surname': u'Wentworth',\n",
" u'title': u'GEAR Member',\n",
" u'website': u''},\n",
" {u'cluster_id': 0,\n",
" u'gear_collaborators': [],\n",
" u'member_id': 119,\n",
" u'member_type': u'member',\n",
" u'name': u'Anna',\n",
" u'organization': u'Universit\\xe4t Heidelberg',\n",
" u'other_collaborators': u'Jeffrey Dancinger',\n",
" u'photo': u'WienhardAnna.jpg',\n",
" u'pos_x': 0,\n",
" u'pos_y': 0,\n",
" u'research_interests': u'Geometric Structures and Teichm\\xfcller Spaces',\n",
" u'short_bio': u'I am interested in representation varieties, discrete subgroups of Lie groups, higher rank symmetric spaces and deformation spaces of geometric structures. I like the interplay of different methods involved in the study of these objects.',\n",
" u'surname': u'Wienhard',\n",
" u'title': u'GEAR Member',\n",
" u'website': u'http://www.mathi.uni-heidelberg.de/~wienhard/'},\n",
" {u'cluster_id': 0,\n",
" u'gear_collaborators': [],\n",
" u'member_id': 120,\n",
" u'member_type': u'member',\n",
" u'name': u'Graeme',\n",
" u'organization': u'National University of Singapore',\n",
" u'other_collaborators': u'',\n",
" u'photo': u'',\n",
" u'pos_x': 0,\n",
" u'pos_y': 0,\n",
" u'research_interests': u'',\n",
" u'short_bio': u'',\n",
" u'surname': u'Wilkin',\n",
" u'title': u'GEAR Member',\n",
" u'website': u''},\n",
" {u'cluster_id': 0,\n",
" u'gear_collaborators': [],\n",
" u'member_id': 121,\n",
" u'member_type': u'member',\n",
" u'name': u'Amie',\n",
" u'organization': u'Northwestern University',\n",
" u'other_collaborators': u'',\n",
" u'photo': u'',\n",
" u'pos_x': 0,\n",
" u'pos_y': 0,\n",
" u'research_interests': u'',\n",
" u'short_bio': u'',\n",
" u'surname': u'Wilkinson',\n",
" u'title': u'GEAR Member',\n",
" u'website': u''},\n",
" {u'cluster_id': 0,\n",
" u'gear_collaborators': [],\n",
" u'member_id': 122,\n",
" u'member_type': u'member',\n",
" u'name': u'Pierre',\n",
" u'organization': u'Universit\\xe9 de Grenoble',\n",
" u'other_collaborators': u'',\n",
" u'photo': u'',\n",
" u'pos_x': 0,\n",
" u'pos_y': 0,\n",
" u'research_interests': u'',\n",
" u'short_bio': u'',\n",
" u'surname': u'Will',\n",
" u'title': u'GEAR Member',\n",
" u'website': u''},\n",
" {u'cluster_id': 0,\n",
" u'gear_collaborators': [],\n",
" u'member_id': 123,\n",
" u'member_type': u'member',\n",
" u'name': u'Michael',\n",
" u'organization': u'Rice University',\n",
" u'other_collaborators': u'Francesco Bonsante, Jon Chaika, Matthias Weber',\n",
" u'photo': u'WolfMichael.jpg',\n",
" u'pos_x': 0,\n",
" u'pos_y': 0,\n",
" u'research_interests': u'Geometric Structures and Teichm\\xfcller Spaces',\n",
" u'short_bio': u'Teichm\\xfcller theory, including deformations of hyperbolic, conformal and projective structures and function theory and geometry of Teichm\\xfcller space. The Hitchin parametrization of the Hitchin component. Harmonic maps between surfaces, especially what invariants of those maps reveal about deformations of surfaces. Minimal surfaces, especially as elements of moduli spaces of candidates.',\n",
" u'surname': u'Wolf',\n",
" u'title': u'GEAR Member',\n",
" u'website': u'http://math.rice.edu/~mwolf/'},\n",
" {u'cluster_id': 0,\n",
" u'gear_collaborators': [],\n",
" u'member_id': 124,\n",
" u'member_type': u'member',\n",
" u'name': u'Maxime',\n",
" u'organization': u'Universit\\xe9 Pierre et Marie Curie - Paris VI',\n",
" u'other_collaborators': u'',\n",
" u'photo': u'',\n",
" u'pos_x': 0,\n",
" u'pos_y': 0,\n",
" u'research_interests': u'',\n",
" u'short_bio': u'',\n",
" u'surname': u'Wolff',\n",
" u'title': u'GEAR Member',\n",
" u'website': u''},\n",
" {u'cluster_id': 0,\n",
" u'gear_collaborators': [],\n",
" u'member_id': 125,\n",
" u'member_type': u'member',\n",
" u'name': u'Scott',\n",
" u'organization': u'University of Maryland-College Park',\n",
" u'other_collaborators': u'',\n",
" u'photo': u'WolpertScott.jpg',\n",
" u'pos_x': 0,\n",
" u'pos_y': 0,\n",
" u'research_interests': u'Dynamics on Moduli Spaces, Geometric Structures and Teichm\\xfcller Spaces, Hyperbolic Manifolds',\n",
" u'short_bio': u'Hyperbolic geometry and applications of deformations of Riemann surfaces. Most recently interested in convex projective structures.',\n",
" u'surname': u'Wolpert',\n",
" u'title': u'GEAR Member',\n",
" u'website': u'http://www2.math.umd.edu/~swolpert/'},\n",
" {u'cluster_id': 0,\n",
" u'gear_collaborators': [],\n",
" u'member_id': 126,\n",
" u'member_type': u'member',\n",
" u'name': u'Eugene',\n",
" u'organization': u'National Cheng-Kung University',\n",
" u'other_collaborators': u'',\n",
" u'photo': u'',\n",
" u'pos_x': 0,\n",
" u'pos_y': 0,\n",
" u'research_interests': u'',\n",
" u'short_bio': u'',\n",
" u'surname': u'Xia',\n",
" u'title': u'GEAR Member',\n",
" u'website': u''},\n",
" {u'cluster_id': 0,\n",
" u'gear_collaborators': [],\n",
" u'member_id': 127,\n",
" u'member_type': u'member',\n",
" u'name': u'Xingru',\n",
" u'organization': u'State University of New York',\n",
" u'other_collaborators': u'',\n",
" u'photo': u'',\n",
" u'pos_x': 0,\n",
" u'pos_y': 0,\n",
" u'research_interests': u'',\n",
" u'short_bio': u'',\n",
" u'surname': u'Zhang',\n",
" u'title': u'GEAR Member',\n",
" u'website': u''},\n",
" {u'cluster_id': 0,\n",
" u'gear_collaborators': [],\n",
" u'member_id': 128,\n",
" u'member_type': u'member',\n",
" u'name': u'Christian',\n",
" u'organization': u'University of Maryland-College Park',\n",
" u'other_collaborators': u'',\n",
" u'photo': u'',\n",
" u'pos_x': 0,\n",
" u'pos_y': 0,\n",
" u'research_interests': u'',\n",
" u'short_bio': u'',\n",
" u'surname': u'Zickert',\n",
" u'title': u'GEAR Member',\n",
" u'website': u''},\n",
" {u'cluster_id': 0,\n",
" u'gear_collaborators': [],\n",
" u'member_id': 129,\n",
" u'member_type': u'graduate',\n",
" u'name': u'Jorge',\n",
" u'organization': u'',\n",
" u'other_collaborators': u'',\n",
" u'photo': u'',\n",
" u'pos_x': 0,\n",
" u'pos_y': 0,\n",
" u'research_interests': u'',\n",
" u'short_bio': u'',\n",
" u'surname': u'Acosta',\n",
" u'title': u'Graduate Student',\n",
" u'website': u''},\n",
" {u'cluster_id': 0,\n",
" u'gear_collaborators': [],\n",
" u'member_id': 130,\n",
" u'member_type': u'graduate',\n",
" u'name': u'Dylan',\n",
" u'organization': u'Yale University (Advisor: Alexander Goncharov)',\n",
" u'other_collaborators': u'',\n",
" u'photo': u'AllegrettiDylan.jpg',\n",
" u'pos_x': 0,\n",
" u'pos_y': 0,\n",
" u'research_interests': u'Geometric Structures and Teichm\\xfcller Spaces, Mathematical Physics',\n",
" u'short_bio': u'Broadly speaking, I am interested in problems at the intersection of mathematics and theoretical physics. I am currently studying the relationships between Teichm\\xfcller theory and the theory of cluster algebras. Over the past few years, these subjects have been related to a number of topics in mathematical physics, including two-dimensional conformal field theory, wall crossing phenomena, and scattering amplitudes.',\n",
" u'surname': u'Allegretti',\n",
" u'title': u'Graduate Student',\n",
" u'website': u'http://users.math.yale.edu/~dga4/'},\n",
" {u'cluster_id': 0,\n",
" u'gear_collaborators': [],\n",
" u'member_id': 131,\n",
" u'member_type': u'graduate',\n",
" u'name': u'Artur',\n",
" u'organization': u'Universidade do Porto (Advisor: Peter Gothen)',\n",
" u'other_collaborators': u'',\n",
" u'photo': u'Arauj\\u03bfArtur.jpg',\n",
" u'pos_x': 0,\n",
" u'pos_y': 0,\n",
" u'research_interests': u'Geometric and Analytical Group Theory, Geometric Structures and Teichm\\xfcller Spaces, Higgs Bundles',\n",
" u'short_bio': u'The subject of my thesis is the study of the geometry and topology of character varieties for representations of surface groups in real Lie groups by methods of the theory of Higgs bundles. I am also interested in the study of geometric structures, especially using methods of Higgs bundles, as well as interactions with theoretical physics.',\n",
" u'surname': u'Ara\\xfajo',\n",
" u'title': u'Graduate Student',\n",
" u'website': u''},\n",
" {u'cluster_id': 0,\n",
" u'gear_collaborators': [],\n",
" u'member_id': 132,\n",
" u'member_type': u'graduate',\n",
" u'name': u'Brian',\n",
" u'organization': u'',\n",
" u'other_collaborators': u'',\n",
" u'photo': u'',\n",
" u'pos_x': 0,\n",
" u'pos_y': 0,\n",
" u'research_interests': u'',\n",
" u'short_bio': u'',\n",
" u'surname': u'Benson',\n",
" u'title': u'Graduate Student',\n",
" u'website': u''},\n",
" {u'cluster_id': 0,\n",
" u'gear_collaborators': [],\n",
" u'member_id': 133,\n",
" u'member_type': u'graduate',\n",
" u'name': u'Edgar',\n",
" u'organization': u'University of Illinois at Chicago (Advisor: Marc Culler)',\n",
" u'other_collaborators': u'',\n",
" u'photo': u'',\n",
" u'pos_x': 0,\n",
" u'pos_y': 0,\n",
" u'research_interests': u'Geometric and Analytical Group Theory, Hyperbolic Manifolds',\n",
" u'short_bio': u'Effective and randomized methods in the theory of hyperbolic 3 manifolds and geometric group theory.',\n",
" u'surname': u'Bering',\n",
" u'title': u'Graduate Student',\n",
" u'website': u''},\n",
" {u'cluster_id': 0,\n",
" u'gear_collaborators': [],\n",
" u'member_id': 134,\n",
" u'member_type': u'graduate',\n",
" u'name': u'Mette',\n",
" u'organization': u'',\n",
" u'other_collaborators': u'',\n",
" u'photo': u'',\n",
" u'pos_x': 0,\n",
" u'pos_y': 0,\n",
" u'research_interests': u'',\n",
" u'short_bio': u'',\n",
" u'surname': u'Bjerre',\n",
" u'title': u'Graduate Student',\n",
" u'website': u''},\n",
" {u'cluster_id': 0,\n",
" u'gear_collaborators': [],\n",
" u'member_id': 135,\n",
" u'member_type': u'graduate',\n",
" u'name': u'Jacob',\n",
" u'organization': u'University of Oxford',\n",
" u'other_collaborators': u'',\n",
" u'photo': u'BlaavandJakob.jpg',\n",
" u'pos_x': 0,\n",
" u'pos_y': 0,\n",
" u'research_interests': u'Higgs Bundles',\n",
" u'short_bio': u'My main interest is the geometry of moduli spaces of Higgs bundles. I study Higgs bundles via Nahm and Fourier--Mukai transforms.',\n",
" u'surname': u'Blaavand',\n",
" u'title': u'Graduate Student',\n",
" u'website': u'https://www.maths.ox.ac.uk/people/profiles/jakob.blaavand'},\n",
" {u'cluster_id': 0,\n",
" u'gear_collaborators': [],\n",
" u'member_id': 136,\n",
" u'member_type': u'graduate',\n",
" u'name': u'Daniele',\n",
" u'organization': u'',\n",
" u'other_collaborators': u'',\n",
" u'photo': u'',\n",
" u'pos_x': 0,\n",
" u'pos_y': 0,\n",
" u'research_interests': u'',\n",
" u'short_bio': u'',\n",
" u'surname': u'Boccalini',\n",
" u'title': u'Graduate Student',\n",
" u'website': u''},\n",
" {u'cluster_id': 0,\n",
" u'gear_collaborators': [],\n",
" u'member_id': 137,\n",
" u'member_type': u'graduate',\n",
" u'name': u'Jean-Fran\\xe7ois',\n",
" u'organization': u'',\n",
" u'other_collaborators': u'',\n",
" u'photo': u'',\n",
" u'pos_x': 0,\n",
" u'pos_y': 0,\n",
" u'research_interests': u'',\n",
" u'short_bio': u'',\n",
" u'surname': u'Bosc',\n",
" u'title': u'Graduate Student',\n",
" u'website': u''},\n",
" {u'cluster_id': 0,\n",
" u'gear_collaborators': [],\n",
" u'member_id': 138,\n",
" u'member_type': u'graduate',\n",
" u'name': u'Diallo',\n",
" u'organization': u'',\n",
" u'other_collaborators': u'',\n",
" u'photo': u'',\n",
" u'pos_x': 0,\n",
" u'pos_y': 0,\n",
" u'research_interests': u'',\n",
" u'short_bio': u'',\n",
" u'surname': u'Boubacar',\n",
" u'title': u'Graduate Student',\n",
" u'website': u''},\n",
" {u'cluster_id': 0,\n",
" u'gear_collaborators': [],\n",
" u'member_id': 139,\n",
" u'member_type': u'graduate',\n",
" u'name': u'Yohan',\n",
" u'organization': u'',\n",
" u'other_collaborators': u'',\n",
" u'photo': u'',\n",
" u'pos_x': 0,\n",
" u'pos_y': 0,\n",
" u'research_interests': u'',\n",
" u'short_bio': u'',\n",
" u'surname': u'Brunebarbe',\n",
" u'title': u'Graduate Student',\n",
" u'website': u''},\n",
" {u'cluster_id': 0,\n",
" u'gear_collaborators': [],\n",
" u'member_id': 140,\n",
" u'member_type': u'graduate',\n",
" u'name': u'Jean-Phillipe',\n",
" u'organization': u'University of Maryland (Advisor: William Goldman)',\n",
" u'other_collaborators': u'',\n",
" u'photo': u'BurelleJean-Philippe.jpg',\n",
" u'pos_x': 0,\n",
" u'pos_y': 0,\n",
" u'research_interests': u'Geometric Structures and Teichm\\xfcller Spaces, Hyperbolic Manifolds',\n",
" u'short_bio': u'I am interested in geometric structures such as projective, hyperbolic, lorentzian and symplectic structures on surfaces and 3-manifolds.',\n",
" u'surname': u'Burelle',\n",
" u'title': u'Graduate Student',\n",
" u'website': u''},\n",
" {u'cluster_id': 0,\n",
" u'gear_collaborators': [],\n",
" u'member_id': 141,\n",
" u'member_type': u'graduate',\n",
" u'name': u'Claire',\n",
" u'organization': u'',\n",
" u'other_collaborators': u'',\n",
" u'photo': u'',\n",
" u'pos_x': 0,\n",
" u'pos_y': 0,\n",
" u'research_interests': u'',\n",
" u'short_bio': u'',\n",
" u'surname': u'Burrin',\n",
" u'title': u'Graduate Student',\n",
" u'website': u''},\n",
" {u'cluster_id': 0,\n",
" u'gear_collaborators': [],\n",
" u'member_id': 142,\n",
" u'member_type': u'graduate',\n",
" u'name': u'Vittoria',\n",
" u'organization': u'',\n",
" u'other_collaborators': u'',\n",
" u'photo': u'',\n",
" u'pos_x': 0,\n",
" u'pos_y': 0,\n",
" u'research_interests': u'',\n",
" u'short_bio': u'',\n",
" u'surname': u'Bussi',\n",
" u'title': u'Graduate Student',\n",
" u'website': u''},\n",
" {u'cluster_id': 0,\n",
" u'gear_collaborators': [],\n",
" u'member_id': 143,\n",
" u'member_type': u'graduate',\n",
" u'name': u'Luis \\xc1ngel',\n",
" u'organization': u'Consejo Superior de Investigaciones Cient\\xedficas',\n",
" u'other_collaborators': u'',\n",
" u'photo': u'CalvoLuis.jpg',\n",
" u'pos_x': 0,\n",
" u'pos_y': 0,\n",
" u'research_interests': u'Dynamics on Moduli Spaces, Higgs Bundles',\n",
" u'short_bio': u'I)Higgs bundles, pairs, Hitchin-Kobayashi correspondence. II)Real bundles, K-theory.',\n",
" u'surname': u'Calvo Pascual',\n",
" u'title': u'Graduate Student',\n",
" u'website': u'http://www.icmat.es/luis.calvo'},\n",
" {u'cluster_id': 0,\n",
" u'gear_collaborators': [],\n",
" u'member_id': 144,\n",
" u'member_type': u'graduate',\n",
" u'name': u'Samuel',\n",
" u'organization': u'Northwestern University',\n",
" u'other_collaborators': u'',\n",
" u'photo': u'CavazosSamuel.jpg',\n",
" u'pos_x': 0,\n",
" u'pos_y': 0,\n",
" u'research_interests': u'Geometric Structures and Teichm\\xfcller Spaces, Hyperbolic Manifolds, Character Varieties',\n",
" u'short_bio': u'Study of moduli space of character varieties.',\n",
" u'surname': u'Cavazos',\n",
" u'title': u'Graduate Student',\n",
" u'website': u'http://samuelcavazos.com/'},\n",
" {u'cluster_id': 0,\n",
" u'gear_collaborators': [],\n",
" u'member_id': 145,\n",
" u'member_type': u'graduate',\n",
" u'name': u'Edward',\n",
" u'organization': u'',\n",
" u'other_collaborators': u'',\n",
" u'photo': u'',\n",
" u'pos_x': 0,\n",
" u'pos_y': 0,\n",
" u'research_interests': u'',\n",
" u'short_bio': u'',\n",
" u'surname': u'Chien',\n",
" u'title': u'Graduate Student',\n",
" u'website': u''},\n",
" {u'cluster_id': 0,\n",
" u'gear_collaborators': [],\n",
" u'member_id': 146,\n",
" u'member_type': u'graduate',\n",
" u'name': u'Alexandru',\n",
" u'organization': u'',\n",
" u'other_collaborators': u'',\n",
" u'photo': u'',\n",
" u'pos_x': 0,\n",
" u'pos_y': 0,\n",
" u'research_interests': u'',\n",
" u'short_bio': u'',\n",
" u'surname': u'Chirvasitu',\n",
" u'title': u'Graduate Student',\n",
" u'website': u''},\n",
" {u'cluster_id': 0,\n",
" u'gear_collaborators': [],\n",
" u'member_id': 147,\n",
" u'member_type': u'graduate',\n",
" u'name': u'Antoine',\n",
" u'organization': u'Universit\\xe9 Lille I (Advisor: Marc Bourdon)',\n",
" u'other_collaborators': u'',\n",
" u'photo': u'',\n",
" u'pos_x': 0,\n",
" u'pos_y': 0,\n",
" u'research_interests': u'Geometric and Analytical Group Theory',\n",
" u'short_bio': u'My field of research is geometric group theory. In particular I am interested in quasi-conformal properties of boundaries of hyperbolic groups and in groups acting on buildings.',\n",
" u'surname': u'Clais',\n",
" u'title': u'Graduate Student',\n",
" u'website': u'http://math.univ-lille1.fr/~clais/'},\n",
" {u'cluster_id': 0,\n",
" u'gear_collaborators': [],\n",
" u'member_id': 148,\n",
" u'member_type': u'graduate',\n",
" u'name': u'Neal',\n",
" u'organization': u'',\n",
" u'other_collaborators': u'',\n",
" u'photo': u'',\n",
" u'pos_x': 0,\n",
" u'pos_y': 0,\n",
" u'research_interests': u'',\n",
" u'short_bio': u'',\n",
" u'surname': u'Coleman',\n",
" u'title': u'Graduate Student',\n",
" u'website': u''},\n",
" {u'cluster_id': 0,\n",
" u'gear_collaborators': [],\n",
" u'member_id': 149,\n",
" u'member_type': u'graduate',\n",
" u'name': u'Brian',\n",
" u'organization': u'University of Illinois at Urbana-Champaign (Advisor: Steven Bradlow)',\n",
" u'other_collaborators': u'',\n",
" u'photo': u'',\n",
" u'pos_x': 0,\n",
" u'pos_y': 0,\n",
" u'research_interests': u'Geometric Structures and Teichm\\xfcller Spaces, Higgs Bundles',\n",
" u'short_bio': u'I am mainly interested in using Higgs bundles techniques to study the representation variety for real groups, particularly the higher Teichmuller spaces.',\n",
" u'surname': u'Collier',\n",
" u'title': u'Graduate Student',\n",
" u'website': u'http://www.math.illinois.edu/~collier3/'},\n",
" {u'cluster_id': 0,\n",
" u'gear_collaborators': [],\n",
" u'member_id': 150,\n",
" u'member_type': u'graduate',\n",
" u'name': u'Nelson',\n",
" u'organization': u'University of Iowa (Advisor: Charles Frohman)',\n",
" u'other_collaborators': u'Charles Frohman',\n",
" u'photo': u'ColonNelson.jpg',\n",
" u'pos_x': 0,\n",
" u'pos_y': 0,\n",
" u'research_interests': u'Geometric and Analytical Group Theory, Geometric Structures and Teichm\\xfcller Spaces',\n",
" u'short_bio': u\"My main interest is Quantum topology. In particular Skein algebras of manifolds of the form Fx[0,1] at roots of unity. I'm more interested in finite extensions of this algebras and the relations with the character variety.\",\n",
" u'surname': u'Col\\xf3n',\n",
" u'title': u'Graduate Student',\n",
" u'website': u'http://www.nelabdiel.com'},\n",
" {u'cluster_id': 0,\n",
" u'gear_collaborators': [],\n",
" u'member_id': 151,\n",
" u'member_type': u'graduate',\n",
" u'name': u'Jeremy',\n",
" u'organization': u'',\n",
" u'other_collaborators': u'',\n",
" u'photo': u'',\n",
" u'pos_x': 0,\n",
" u'pos_y': 0,\n",
" u'research_interests': u'',\n",
" u'short_bio': u'',\n",
" u'surname': u'Daniel',\n",
" u'title': u'Graduate Student',\n",
" u'website': u''},\n",
" {u'cluster_id': 0,\n",
" u'gear_collaborators': [],\n",
" u'member_id': 152,\n",
" u'member_type': u'graduate',\n",
" u'name': u'Kajal',\n",
" u'organization': u'\\xc9cole Normale Sup\\xe9rieure de Lyon (Advisor: Romain Tessera, Universit\\xe9 Paris-Sud 11)',\n",
" u'other_collaborators': u'',\n",
" u'photo': u'',\n",
" u'pos_x': 0,\n",
" u'pos_y': 0,\n",
" u'research_interests': u'Geometric and Analytical Group Theory, Hyperbolic Manifolds',\n",
" u'short_bio': u\"I am mainly interested in geometric group theory and measurable group theory. I recently wrote a paper with Romain Tessera, 'integrable measure equivalence and the central extension of surface groups', where we give an example of a pair of groups which are quasi-isometric and measure equivalent but not integrable measure equivalent. The topic of my PhD thesis is `the geometry of amenable groups'. I am also interested in TQFT.\",\n",
" u'surname': u'Das',\n",
" u'title': u'Graduate Student',\n",
" u'website': u''},\n",
" {u'cluster_id': 0,\n",
" u'gear_collaborators': [],\n",
" u'member_id': 153,\n",
" u'member_type': u'graduate',\n",
" u'name': u'Carlos Alberto',\n",
" u'organization': u'',\n",
" u'other_collaborators': u'',\n",
" u'photo': u'',\n",
" u'pos_x': 0,\n",
" u'pos_y': 0,\n",
" u'research_interests': u'',\n",
" u'short_bio': u'',\n",
" u'surname': u'de la Cruz Mengual',\n",
" u'title': u'Graduate Student',\n",
" u'website': u''},\n",
" {u'cluster_id': 0,\n",
" u'gear_collaborators': [],\n",
" u'member_id': 154,\n",
" u'member_type': u'graduate',\n",
" u'name': u'Jens Kristian',\n",
" u'organization': u'',\n",
" u'other_collaborators': u'',\n",
" u'photo': u'',\n",
" u'pos_x': 0,\n",
" u'pos_y': 0,\n",
" u'research_interests': u'',\n",
" u'short_bio': u'',\n",
" u'surname': u'Egsgaard',\n",
" u'title': u'Graduate Student',\n",
" u'website': u''},\n",
" {u'cluster_id': 0,\n",
" u'gear_collaborators': [],\n",
" u'member_id': 155,\n",
" u'member_type': u'graduate',\n",
" u'name': u'Aaron',\n",
" u'organization': u'University of Texas at Austin (Advisor: Andrew Neitzke)',\n",
" u'other_collaborators': u'',\n",
" u'photo': u'',\n",
" u'pos_x': 0,\n",
" u'pos_y': 0,\n",
" u'research_interests': u'Dynamics on Moduli Spaces, Geometric Structures and Teichm\\xfcller Spaces, Quantum Field Theories',\n",
" u'short_bio': u\"I'm interested in the geometric aspects of physics in general, and field theories in particular. My current project involves deformations of flat $SL_2 \\\\mathbb{C}$ connections which are similar in spirit to Dreyer's cataclysms for Anosov representations, and may also be distantly related to other wall-crossing-type constructions. I'd like to learn more about these constructions, in the hopes of distilling some useful knowledge about their common features.\",\n",
" u'surname': u'Fenyes',\n",
" u'title': u'Graduate Student',\n",
" u'website': u'http://www.ma.utexas.edu/users/afenyes/'},\n",
" {u'cluster_id': 0,\n",
" u'gear_collaborators': [],\n",
" u'member_id': 156,\n",
" u'member_type': u'graduate',\n",
" u'name': u'Laura',\n",
" u'organization': u'University of Texas at Austin (Advisor: Andrew Neitzke)',\n",
" u'other_collaborators': u'',\n",
" u'photo': u'FredricksonLaura.jpg',\n",
" u'pos_x': 0,\n",
" u'pos_y': 0,\n",
" u'research_interests': u'Higgs Bundles',\n",
" u'short_bio': u\"I am working on constructing approximate (non-singular) solutions of Hitchin's equations near the boundary of the moduli space via gluing methods.\",\n",
" u'surname': u'Fredrickson',\n",
" u'title': u'Graduate Student',\n",
" u'website': u'http://www.ma.utexas.edu/users/lfredrickson/'},\n",
" {u'cluster_id': 0,\n",
" u'gear_collaborators': [],\n",
" u'member_id': 157,\n",
" u'member_type': u'postdoc',\n",
" u'name': u'Ser-Wei',\n",
" u'organization': u'Temple University (Mentor: Matthew Stover)',\n",
" u'other_collaborators': u'',\n",
" u'photo': u'',\n",
" u'pos_x': 0,\n",
" u'pos_y': 0,\n",
" u'research_interests': u'Dynamics on Moduli Spaces, Geometric Structures and Teichm\\xfcller Spaces',\n",
" u'short_bio': u'',\n",
" u'surname': u'Fu',\n",
" u'title': u'Postdoc',\n",
" u'website': u''},\n",
" {u'cluster_id': 0,\n",
" u'gear_collaborators': [],\n",
" u'member_id': 158,\n",
" u'member_type': u'graduate',\n",
" u'name': u'Sourav',\n",
" u'organization': u'Universit\\xe9 Paris-Sud - Paris XI (Advisor: Fran\\xe7ois Labourie)',\n",
" u'other_collaborators': u'',\n",
" u'photo': u'GhoshSourav.jpg',\n",
" u'pos_x': 0,\n",
" u'pos_y': 0,\n",
" u'research_interests': u'Dynamics on Moduli Spaces, Geometric Structures and Teichm\\xfcller Spaces, Higgs Bundles, Hyperbolic Manifolds',\n",
" u'short_bio': u'I am currently working on the Moduli Space of Margulis Space Time.',\n",
" u'surname': u'Ghosh',\n",
" u'title': u'Graduate Student',\n",
" u'website': u''},\n",
" {u'cluster_id': 0,\n",
" u'gear_collaborators': [],\n",
" u'member_id': 159,\n",
" u'member_type': u'graduate',\n",
" u'name': u'Riccardo',\n",
" u'organization': u'',\n",
" u'other_collaborators': u'',\n",
" u'photo': u'',\n",
" u'pos_x': 0,\n",
" u'pos_y': 0,\n",
" u'research_interests': u'',\n",
" u'short_bio': u'',\n",
" u'surname': u'Grandi',\n",
" u'title': u'Graduate Student',\n",
" u'website': u''},\n",
" {u'cluster_id': 0,\n",
" u'gear_collaborators': [],\n",
" u'member_id': 160,\n",
" u'member_type': u'graduate',\n",
" u'name': u'Cl\\xe9ment',\n",
" u'organization': u'Universit\\xe9 de Strasbourg (Advisor: Olivier Guichard)',\n",
" u'other_collaborators': u'',\n",
" u'photo': u'GuerinClement.jpg',\n",
" u'pos_x': 0,\n",
" u'pos_y': 0,\n",
" u'research_interests': u'Geometric and Analytical Group Theory, Geometric Structures and Teichm\\xfcller Spaces',\n",
" u'short_bio': u'The variety of representations of finitely generated groups into (reductive/semi-simple/simple) Lie groups has been studied a lot, especially when the finitely generated group is a surface group. Nevertheless, when a Fuchsian group contains elliptic elements (e.g. triangular groups) this variety may not be so easy to describe because the stabilizers of this group acting on the hyperbolic 2-manifold are not all trivial. My aim is to describe the variety of representations of a triangular group despite these problems.',\n",
" u'surname': u'Gu\\xe9rin',\n",
" u'title': u'Graduate Student',\n",
" u'website': u''},\n",
" {u'cluster_id': 0,\n",
" u'gear_collaborators': [],\n",
" u'member_id': 161,\n",
" u'member_type': u'graduate',\n",
" u'name': u'Neha',\n",
" u'organization': u'',\n",
" u'other_collaborators': u'',\n",
" u'photo': u'',\n",
" u'pos_x': 0,\n",
" u'pos_y': 0,\n",
" u'research_interests': u'',\n",
" u'short_bio': u'',\n",
" u'surname': u'Gupta',\n",
" u'title': u'Graduate Student',\n",
" u'website': u''},\n",
" {u'cluster_id': 0,\n",
" u'gear_collaborators': [],\n",
" u'member_id': 162,\n",
" u'member_type': u'graduate',\n",
" u'name': u'Dennis',\n",
" u'organization': u'',\n",
" u'other_collaborators': u'',\n",
" u'photo': u'',\n",
" u'pos_x': 0,\n",
" u'pos_y': 0,\n",
" u'research_interests': u'',\n",
" u'short_bio': u'',\n",
" u'surname': u'Hasselstr\\xf8m Pedersen',\n",
" u'title': u'Graduate Student',\n",
" u'website': u''},\n",
" {u'cluster_id': 0,\n",
" u'gear_collaborators': [],\n",
" u'member_id': 163,\n",
" u'member_type': u'graduate',\n",
" u'name': u'Thomas',\n",
" u'organization': u'',\n",
" u'other_collaborators': u'',\n",
" u'photo': u'',\n",
" u'pos_x': 0,\n",
" u'pos_y': 0,\n",
" u'research_interests': u'',\n",
" u'short_bio': u'',\n",
" u'surname': u'Hawes',\n",
" u'title': u'Graduate Student',\n",
" u'website': u''},\n",
" {u'cluster_id': 0,\n",
" u'gear_collaborators': [],\n",
" u'member_id': 164,\n",
" u'member_type': u'graduate',\n",
" u'name': u'Maria',\n",
" u'organization': u'',\n",
" u'other_collaborators': u'',\n",
" u'photo': u'',\n",
" u'pos_x': 0,\n",
" u'pos_y': 0,\n",
" u'research_interests': u'',\n",
" u'short_bio': u'',\n",
" u'surname': u'Hempel',\n",
" u'title': u'Graduate Student',\n",
" u'website': u''},\n",
" {u'cluster_id': 0,\n",
" u'gear_collaborators': [],\n",
" u'member_id': 165,\n",
" u'member_type': u'graduate',\n",
" u'name': u'Yoe',\n",
" u'organization': u'',\n",
" u'other_collaborators': u'',\n",
" u'photo': u'',\n",
" u'pos_x': 0,\n",
" u'pos_y': 0,\n",
" u'research_interests': u'',\n",
" u'short_bio': u'',\n",
" u'surname': u'Herrera',\n",
" u'title': u'Graduate Student',\n",
" u'website': u''},\n",
" {u'cluster_id': 0,\n",
" u'gear_collaborators': [],\n",
" u'member_id': 167,\n",
" u'member_type': u'graduate',\n",
" u'name': u'Andy',\n",
" u'organization': u'Rice University (Advisor: Michael Wolf)',\n",
" u'other_collaborators': u'',\n",
" u'photo': u'HuangAndy.jpg',\n",
" u'pos_x': 0,\n",
" u'pos_y': 0,\n",
" u'research_interests': u'Geometric Structures and Teichm\\xfcller Spaces, Harmonic maps',\n",
" u'short_bio': u'I am interested in harmonic maps between higher genus to lower genus compact Riemann surfaces. This leads me to also consider minimal surfaces in H2 x R, limits of Hopf differentials on surfaces, and the interplay between geometric structures living on either surface.',\n",
" u'surname': u'Huang',\n",
" u'title': u'Graduate Student',\n",
" u'website': u'http://math.rice.edu/~ach3/'},\n",
" {u'cluster_id': 0,\n",
" u'gear_collaborators': [],\n",
" u'member_id': 168,\n",
" u'member_type': u'graduate',\n",
" u'name': u'Theo',\n",
" u'organization': u'',\n",
" u'other_collaborators': u'',\n",
" u'photo': u'',\n",
" u'pos_x': 0,\n",
" u'pos_y': 0,\n",
" u'research_interests': u'',\n",
" u'short_bio': u'',\n",
" u'surname': u'Johnson-Freyd',\n",
" u'title': u'Graduate Student',\n",
" u'website': u''},\n",
" {u'cluster_id': 0,\n",
" u'gear_collaborators': [],\n",
" u'member_id': 169,\n",
" u'member_type': u'graduate',\n",
" u'name': u'Tina',\n",
" u'organization': u'',\n",
" u'other_collaborators': u'',\n",
" u'photo': u'',\n",
" u'pos_x': 0,\n",
" u'pos_y': 0,\n",
" u'research_interests': u'',\n",
" u'short_bio': u'',\n",
" u'surname': u'Kanstrup',\n",
" u'title': u'Graduate Student',\n",
" u'website': u''},\n",
" {u'cluster_id': 0,\n",
" u'gear_collaborators': [],\n",
" u'member_id': 170,\n",
" u'member_type': u'graduate',\n",
" u'name': u'Ali',\n",
" u'organization': u'',\n",
" u'other_collaborators': u'',\n",
" u'photo': u'',\n",
" u'pos_x': 0,\n",
" u'pos_y': 0,\n",
" u'research_interests': u'',\n",
" u'short_bio': u'',\n",
" u'surname': u'Khardani',\n",
" u'title': u'Graduate Student',\n",
" u'website': u''},\n",
" {u'cluster_id': 0,\n",
" u'gear_collaborators': [],\n",
" u'member_id': 171,\n",
" u'member_type': u'graduate',\n",
" u'name': u'Tobias',\n",
" u'organization': u'',\n",
" u'other_collaborators': u'',\n",
" u'photo': u'',\n",
" u'pos_x': 0,\n",
" u'pos_y': 0,\n",
" u'research_interests': u'',\n",
" u'short_bio': u'',\n",
" u'surname': u'Kildetoft',\n",
" u'title': u'Graduate Student',\n",
" u'website': u''},\n",
" {u'cluster_id': 0,\n",
" u'gear_collaborators': [],\n",
" u'member_id': 172,\n",
" u'member_type': u'graduate',\n",
" u'name': u'Semin',\n",
" u'organization': u'Brown University (Advisor: Georgios Daskalopoulos)',\n",
" u'other_collaborators': u'',\n",
" u'photo': u'',\n",
" u'pos_x': 0,\n",
" u'pos_y': 0,\n",
" u'research_interests': u'Dynamics on Moduli Spaces, Geometric Structures and Teichm\\xfcller Spaces, Higgs Bundles',\n",
" u'short_bio': u'I am interested in applying analytic techniques to geometric problem, such as using harmonic maps to a compactification of Teichmuller space.',\n",
" u'surname': u'Kim',\n",
" u'title': u'Graduate Student',\n",
" u'website': u'http://math.brown.edu/~smkim'},\n",
" {u'cluster_id': 0,\n",
" u'gear_collaborators': [],\n",
" u'member_id': 173,\n",
" u'member_type': u'graduate',\n",
" u'name': u'Julien',\n",
" u'organization': u'Universit\\xe9 de Grenoble (Advisor: Louis Funar)',\n",
" u'other_collaborators': u'',\n",
" u'photo': u'KorinmanJulien.jpg',\n",
" u'pos_x': 0,\n",
" u'pos_y': 0,\n",
" u'research_interests': u'Dynamics on Moduli Spaces',\n",
" u'short_bio': u'I am interested in Topological Quantum Field Theories with compact gauge groups. More precisely, I study the projective representations of mapping class group that they define.',\n",
" u'surname': u'Korinman',\n",
" u'title': u'Graduate Student',\n",
" u'website': u'http://www-fourier.ujf-grenoble.fr/~koju/'},\n",
" {u'cluster_id': 0,\n",
" u'gear_collaborators': [],\n",
" u'member_id': 174,\n",
" u'member_type': u'graduate',\n",
" u'name': u'Jens-Jacob',\n",
" u'organization': u'',\n",
" u'other_collaborators': u'',\n",
" u'photo': u'',\n",
" u'pos_x': 0,\n",
" u'pos_y': 0,\n",
" u'research_interests': u'',\n",
" u'short_bio': u'',\n",
" u'surname': u'Kratmann Nissen',\n",
" u'title': u'Graduate Student',\n",
" u'website': u''},\n",
" {u'cluster_id': 0,\n",
" u'gear_collaborators': [],\n",
" u'member_id': 175,\n",
" u'member_type': u'graduate',\n",
" u'name': u'Georgios',\n",
" u'organization': u'University of Illinois at Urbana-Champaign (Advisor: Steven Bradlow)',\n",
" u'other_collaborators': u'',\n",
" u'photo': u'',\n",
" u'pos_x': 0,\n",
" u'pos_y': 0,\n",
" u'research_interests': u'Higgs bundles, Representation Varieties',\n",
" u'short_bio': u'',\n",
" u'surname': u'Kydonakis',\n",
" u'title': u'Graduate Student',\n",
" u'website': u''},\n",
" {u'cluster_id': 0,\n",
" u'gear_collaborators': [],\n",
" u'member_id': 176,\n",
" u'member_type': u'graduate',\n",
" u'name': u'Waltraud',\n",
" u'organization': u'',\n",
" u'other_collaborators': u'',\n",
" u'photo': u'',\n",
" u'pos_x': 0,\n",
" u'pos_y': 0,\n",
" u'research_interests': u'',\n",
" u'short_bio': u'',\n",
" u'surname': u'Lederle',\n",
" u'title': u'Graduate Student',\n",
" u'website': u''},\n",
" {u'cluster_id': 0,\n",
" u'gear_collaborators': [],\n",
" u'member_id': 177,\n",
" u'member_type': u'graduate',\n",
" u'name': u'Arielle',\n",
" u'organization': u'University of California, Santa Barbara (Advisor: Daryl Cooper)',\n",
" u'other_collaborators': u'',\n",
" u'photo': u'LeitnerArielle.jpg',\n",
" u'pos_x': 0,\n",
" u'pos_y': 0,\n",
" u'research_interests': u'Geometric Structures and Teichm\\xfcller Spaces, Hyperbolic Manifolds',\n",
" u'short_bio': u'I am interested in geometric transitions- paths of geometric structures that change type within a homogeneous space. Specifically, I am studying limits of the Cartan subgroup in SL(n,R). These techniques can also be used within the context of convex projective structures to study generalized cusps on manifolds.',\n",
" u'surname': u'Leitner',\n",
" u'title': u'Graduate Student',\n",
" u'website': u'http://www.math.ucsb.edu/~aleitner/'},\n",
" {u'cluster_id': 0,\n",
" u'gear_collaborators': [],\n",
" u'member_id': 178,\n",
" u'member_type': u'graduate',\n",
" u'name': u'Jonathan',\n",
" u'organization': u'',\n",
" u'other_collaborators': u'',\n",
" u'photo': u'',\n",
" u'pos_x': 0,\n",
" u'pos_y': 0,\n",
" u'research_interests': u'',\n",
" u'short_bio': u'',\n",
" u'surname': u'Lemaire-Beaucage',\n",
" u'title': u'Graduate Student',\n",
" u'website': u''},\n",
" {u'cluster_id': 0,\n",
" u'gear_collaborators': [],\n",
" u'member_id': 179,\n",
" u'member_type': u'graduate',\n",
" u'name': u'Qiongling',\n",
" u'organization': u'Rice University (Advisor: Michael Wolf)',\n",
" u'other_collaborators': u'',\n",
" u'photo': u'LiQiongling.jpg',\n",
" u'pos_x': 0,\n",
" u'pos_y': 0,\n",
" u'research_interests': u'Dynamics on Moduli Spaces, Geometric Structures and Teichm\\xfcller Spaces, Higgs Bundles',\n",
" u'short_bio': u'Riemannian metric structure on the Hitchin component, asymptotic behaviors of rays in Hitchin components, and algebra structure of the space of cross ratios of weak rank.',\n",
" u'surname': u'Li',\n",
" u'title': u'Graduate Student',\n",
" u'website': u'http://math.rice.edu/~ql4/'},\n",
" {u'cluster_id': 0,\n",
" u'gear_collaborators': [],\n",
" u'member_id': 180,\n",
" u'member_type': u'graduate',\n",
" u'name': u'Anton',\n",
" u'organization': u'',\n",
" u'other_collaborators': u'',\n",
" u'photo': u'',\n",
" u'pos_x': 0,\n",
" u'pos_y': 0,\n",
" u'research_interests': u'',\n",
" u'short_bio': u'',\n",
" u'surname': u'Lukyanenko',\n",
" u'title': u'Graduate Student',\n",
" u'website': u''},\n",
" {u'cluster_id': 0,\n",
" u'gear_collaborators': [],\n",
" u'member_id': 181,\n",
" u'member_type': u'graduate',\n",
" u'name': u'Damiano',\n",
" u'organization': u'',\n",
" u'other_collaborators': u'',\n",
" u'photo': u'',\n",
" u'pos_x': 0,\n",
" u'pos_y': 0,\n",
" u'research_interests': u'',\n",
" u'short_bio': u'',\n",
" u'surname': u'Lupi',\n",
" u'title': u'Graduate Student',\n",
" u'website': u''},\n",
" {u'cluster_id': 0,\n",
" u'gear_collaborators': [],\n",
" u'member_id': 182,\n",
" u'member_type': u'graduate',\n",
" u'name': u'Kathryn',\n",
" u'organization': u'University of Chicago (Advisor: Benson Farb)',\n",
" u'other_collaborators': u'',\n",
" u'photo': u'',\n",
" u'pos_x': 0,\n",
" u'pos_y': 0,\n",
" u'research_interests': u'Dynamics on Moduli Spaces, Geometric and Analytical Group Theory, Hyperbolic Manifolds',\n",
" u'short_bio': u\"I study diffeomorphism and homeomorphism groups of manifolds and moduli spaces of group actions. Recently, I've been thinking about Hom(G, Homeo+(S^1)) and Hom(G, Diff+(S^1)) -- spaces of actions of a group G on the circle.\",\n",
" u'surname': u'Mann',\n",
" u'title': u'Graduate Student',\n",
" u'website': u'http://math.uchicago.edu/~mann'},\n",
" {u'cluster_id': 0,\n",
" u'gear_collaborators': [],\n",
" u'member_id': 183,\n",
" u'member_type': u'graduate',\n",
" u'name': u'St\\xe9phane',\n",
" u'organization': u'',\n",
" u'other_collaborators': u'',\n",
" u'photo': u'',\n",
" u'pos_x': 0,\n",
" u'pos_y': 0,\n",
" u'research_interests': u'',\n",
" u'short_bio': u'',\n",
" u'surname': u'Marseglia',\n",
" u'title': u'Graduate Student',\n",
" u'website': u''},\n",
" {u'cluster_id': 0,\n",
" u'gear_collaborators': [],\n",
" u'member_id': 184,\n",
" u'member_type': u'graduate',\n",
" u'name': u'Simone',\n",
" u'organization': u'Aarhus University (Advisor: J\\xf8rgen Andersen)',\n",
" u'other_collaborators': u'',\n",
" u'photo': u'',\n",
" u'pos_x': 0,\n",
" u'pos_y': 0,\n",
" u'research_interests': u\"Geometric Structures and Teichm\\xfcller Spaces, Hyperbolic Manifolds, TQFT's\",\n",
" u'short_bio': u'',\n",
" u'surname': u'Marzioni',\n",
" u'title': u'Graduate Student',\n",
" u'website': u'http://pure.au.dk/portal/en/persons/id%2817bc9c1c-3d9b-44bc-9f4d-c8d0ee88ac82%29.html'},\n",
" {u'cluster_id': 0,\n",
" u'gear_collaborators': [],\n",
" u'member_id': 185,\n",
" u'member_type': u'graduate',\n",
" u'name': u'Paolo',\n",
" u'organization': u'',\n",
" u'other_collaborators': u'',\n",
" u'photo': u'',\n",
" u'pos_x': 0,\n",
" u'pos_y': 0,\n",
" u'research_interests': u'',\n",
" u'short_bio': u'',\n",
" u'surname': u'Masulli',\n",
" u'title': u'Graduate Student',\n",
" u'website': u''},\n",
" {u'cluster_id': 0,\n",
" u'gear_collaborators': [],\n",
" u'member_id': 186,\n",
" u'member_type': u'graduate',\n",
" u'name': u'Thomas',\n",
" u'organization': u'',\n",
" u'other_collaborators': u'',\n",
" u'photo': u'',\n",
" u'pos_x': 0,\n",
" u'pos_y': 0,\n",
" u'research_interests': u'',\n",
" u'short_bio': u'',\n",
" u'surname': u'Morzadec',\n",
" u'title': u'Graduate Student',\n",
" u'website': u''},\n",
" {u'cluster_id': 0,\n",
" u'gear_collaborators': [],\n",
" u'member_id': 187,\n",
" u'member_type': u'graduate',\n",
" u'name': u'Thang',\n",
" u'organization': u'Indiana University',\n",
" u'other_collaborators': u'',\n",
" u'photo': u'',\n",
" u'pos_x': 0,\n",
" u'pos_y': 0,\n",
" u'research_interests': u'Dynamics on Moduli Spaces, Geometric and Analytical Group Theory, Hyperbolic Manifolds',\n",
" u'short_bio': u'',\n",
" u'surname': u'Nguyen',\n",
" u'title': u'Graduate Student',\n",
" u'website': u''},\n",
" {u'cluster_id': 0,\n",
" u'gear_collaborators': [],\n",
" u'member_id': 188,\n",
" u'member_type': u'graduate',\n",
" u'name': u'Azizeh',\n",
" u'organization': u'',\n",
" u'other_collaborators': u'',\n",
" u'photo': u'',\n",
" u'pos_x': 0,\n",
" u'pos_y': 0,\n",
" u'research_interests': u'',\n",
" u'short_bio': u'',\n",
" u'surname': u'Nozad',\n",
" u'title': u'Graduate Student',\n",
" u'website': u''},\n",
" {u'cluster_id': 0,\n",
" u'gear_collaborators': [],\n",
" u'member_id': 189,\n",
" u'member_type': u'graduate',\n",
" u'name': u'Nickolas',\n",
" u'organization': u'',\n",
" u'other_collaborators': u'',\n",
" u'photo': u'',\n",
" u'pos_x': 0,\n",
" u'pos_y': 0,\n",
" u'research_interests': u'',\n",
" u'short_bio': u'',\n",
" u'surname': u'Pastant',\n",
" u'title': u'Graduate Student',\n",
" u'website': u''},\n",
" {u'cluster_id': 0,\n",
" u'gear_collaborators': [],\n",
" u'member_id': 191,\n",
" u'member_type': u'graduate',\n",
" u'name': u'Vincent',\n",
" u'organization': u'',\n",
" u'other_collaborators': u'',\n",
" u'photo': u'',\n",
" u'pos_x': 0,\n",
" u'pos_y': 0,\n",
" u'research_interests': u'',\n",
" u'short_bio': u'',\n",
" u'surname': u'Pecastaing',\n",
" u'title': u'Graduate Student',\n",
" u'website': u''},\n",
" {u'cluster_id': 0,\n",
" u'gear_collaborators': [],\n",
" u'member_id': 192,\n",
" u'member_type': u'graduate',\n",
" u'name': u'Bram',\n",
" u'organization': u'University of Fribourg (Advisor: Hugo Parlier)',\n",
" u'other_collaborators': u'',\n",
" u'photo': u'',\n",
" u'pos_x': 0,\n",
" u'pos_y': 0,\n",
" u'research_interests': u'Geometric Structures and Teichm\\xfcller Spaces, Hyperbolic Manifolds',\n",
" u'short_bio': u'My research focuses on low dimensional topology and geometry. In particular, I work on the study of random surfaces and (combinatorial) moduli spaces. The main goal is to understand the distribution of surfaces in various moduli spaces with respect to their geometric properties.',\n",
" u'surname': u'Petri',\n",
" u'title': u'Graduate Student',\n",
" u'website': u'http://perso.unifr.ch/bram.petri/'},\n",
" {u'cluster_id': 0,\n",
" u'gear_collaborators': [],\n",
" u'member_id': 193,\n",
" u'member_type': u'graduate',\n",
" u'name': u'Witsarut',\n",
" u'organization': u'',\n",
" u'other_collaborators': u'',\n",
" u'photo': u'',\n",
" u'pos_x': 0,\n",
" u'pos_y': 0,\n",
" u'research_interests': u'',\n",
" u'short_bio': u'',\n",
" u'surname': u'Pho-On',\n",
" u'title': u'Graduate Student',\n",
" u'website': u''},\n",
" {u'cluster_id': 0,\n",
" u'gear_collaborators': [],\n",
" u'member_id': 194,\n",
" u'member_type': u'graduate',\n",
" u'name': u'Romain',\n",
" u'organization': u'',\n",
" u'other_collaborators': u'',\n",
" u'photo': u'',\n",
" u'pos_x': 0,\n",
" u'pos_y': 0,\n",
" u'research_interests': u'',\n",
" u'short_bio': u'',\n",
" u'surname': u'Pochon',\n",
" u'title': u'Graduate Student',\n",
" u'website': u''},\n",
" {u'cluster_id': 0,\n",
" u'gear_collaborators': [],\n",
" u'member_id': 195,\n",
" u'member_type': u'graduate',\n",
" u'name': u'Maria Beatrice',\n",
" u'organization': u'Eidgen\\xf6ssische Technische Hochschule Z\\xfcrich (Advisor: Alessandra Iozzi)',\n",
" u'other_collaborators': u'',\n",
" u'photo': u'',\n",
" u'pos_x': 0,\n",
" u'pos_y': 0,\n",
" u'research_interests': u'Dynamics on Moduli Spaces, Geometric and Analytical Group Theory, Geometric Structures and Teichm\\xfcller Spaces, Hyperbolic Manifolds',\n",
" u'short_bio': u'',\n",
" u'surname': u'Pozzetti',\n",
" u'title': u'Graduate Student',\n",
" u'website': u''},\n",
" {u'cluster_id': 0,\n",
" u'gear_collaborators': [],\n",
" u'member_id': 196,\n",
" u'member_type': u'graduate',\n",
" u'name': u'Huygens Christian',\n",
" u'organization': u'Universit\\xe9 du Qu\\xe9bec \\xe0 Montr\\xe9al (Advisor: Steven Boyer)',\n",
" u'other_collaborators': u'',\n",
" u'photo': u'',\n",
" u'pos_x': 0,\n",
" u'pos_y': 0,\n",
" u'research_interests': u'Low dimensional topology, symplectic topology, Fukaya categories, 3-manifolds Character varieties',\n",
" u'short_bio': u'',\n",
" u'surname': u'Ravelomanana',\n",
" u'title': u'Graduate Student',\n",
" u'website': u''},\n",
" {u'cluster_id': 0,\n",
" u'gear_collaborators': [],\n",
" u'member_id': 197,\n",
" u'member_type': u'graduate',\n",
" u'name': u'Brian',\n",
" u'organization': u'',\n",
" u'other_collaborators': u'',\n",
" u'photo': u'',\n",
" u'pos_x': 0,\n",
" u'pos_y': 0,\n",
" u'research_interests': u'',\n",
" u'short_bio': u'',\n",
" u'surname': u'Ray',\n",
" u'title': u'Graduate Student',\n",
" u'website': u''},\n",
" {u'cluster_id': 0,\n",
" u'gear_collaborators': [],\n",
" u'member_id': 198,\n",
" u'member_type': u'graduate',\n",
" u'name': u'Pascal',\n",
" u'organization': u'',\n",
" u'other_collaborators': u'',\n",
" u'photo': u'',\n",
" u'pos_x': 0,\n",
" u'pos_y': 0,\n",
" u'research_interests': u'',\n",
" u'short_bio': u'',\n",
" u'surname': u'Rolli',\n",
" u'title': u'Graduate Student',\n",
" u'website': u''},\n",
" {u'cluster_id': 0,\n",
" u'gear_collaborators': [],\n",
" u'member_id': 199,\n",
" u'member_type': u'graduate',\n",
" u'name': u'Roberto',\n",
" u'organization': u'',\n",
" u'other_collaborators': u'',\n",
" u'photo': u'',\n",
" u'pos_x': 0,\n",
" u'pos_y': 0,\n",
" u'research_interests': u'',\n",
" u'short_bio': u'',\n",
" u'surname': u'Rubio',\n",
" u'title': u'Graduate Student',\n",
" u'website': u''},\n",
" {u'cluster_id': 0,\n",
" u'gear_collaborators': [],\n",
" u'member_id': 200,\n",
" u'member_type': u'graduate',\n",
" u'name': u'Shamil',\n",
" u'organization': u'',\n",
" u'other_collaborators': u'',\n",
" u'photo': u'',\n",
" u'pos_x': 0,\n",
" u'pos_y': 0,\n",
" u'research_interests': u'',\n",
" u'short_bio': u'',\n",
" u'surname': u'Shakirov',\n",
" u'title': u'Graduate Student',\n",
" u'website': u''},\n",
" {u'cluster_id': 0,\n",
" u'gear_collaborators': [],\n",
" u'member_id': 201,\n",
" u'member_type': u'graduate',\n",
" u'name': u'Shehryar',\n",
" u'organization': u'',\n",
" u'other_collaborators': u'',\n",
" u'photo': u'',\n",
" u'pos_x': 0,\n",
" u'pos_y': 0,\n",
" u'research_interests': u'',\n",
" u'short_bio': u'',\n",
" u'surname': u'Sikander',\n",
" u'title': u'Graduate Student',\n",
" u'website': u''},\n",
" {u'cluster_id': 0,\n",
" u'gear_collaborators': [],\n",
" u'member_id': 202,\n",
" u'member_type': u'graduate',\n",
" u'name': u'Sukhpreet',\n",
" u'organization': u'',\n",
" u'other_collaborators': u'',\n",
" u'photo': u'',\n",
" u'pos_x': 0,\n",
" u'pos_y': 0,\n",
" u'research_interests': u'',\n",
" u'short_bio': u'',\n",
" u'surname': u'Singh',\n",
" u'title': u'Graduate Student',\n",
" u'website': u''},\n",
" {u'cluster_id': 0,\n",
" u'gear_collaborators': [],\n",
" u'member_id': 203,\n",
" u'member_type': u'graduate',\n",
" u'name': u'Niccolo',\n",
" u'organization': u'',\n",
" u'other_collaborators': u'',\n",
" u'photo': u'',\n",
" u'pos_x': 0,\n",
" u'pos_y': 0,\n",
" u'research_interests': u'',\n",
" u'short_bio': u'',\n",
" u'surname': u'Skovg\\xe5rd Poulsen',\n",
" u'title': u'Graduate Student',\n",
" u'website': u''},\n",
" {u'cluster_id': 0,\n",
" u'gear_collaborators': [],\n",
" u'member_id': 204,\n",
" u'member_type': u'graduate',\n",
" u'name': u'Ilia',\n",
" u'organization': u'',\n",
" u'other_collaborators': u'',\n",
" u'photo': u'',\n",
" u'pos_x': 0,\n",
" u'pos_y': 0,\n",
" u'research_interests': u'',\n",
" u'short_bio': u'',\n",
" u'surname': u'Smilga',\n",
" u'title': u'Graduate Student',\n",
" u'website': u''},\n",
" {u'cluster_id': 0,\n",
" u'gear_collaborators': [],\n",
" u'member_id': 205,\n",
" u'member_type': u'graduate',\n",
" u'name': u'Yitzchak Elchanan',\n",
" u'organization': u'Brown University (Advisor: Jeffrey Brock)',\n",
" u'other_collaborators': u'',\n",
" u'photo': u'SolomonYitzchak.jpg',\n",
" u'pos_x': 0,\n",
" u'pos_y': 0,\n",
" u'research_interests': u'Dynamics on Moduli Spaces, Geometric and Analytical Group Theory, Geometric Structures and Teichm\\xfcller Spaces, Hyperbolic Manifolds',\n",
" u'short_bio': u'I am interested in understanding hyperbolic 3-manifolds and the geometry of moduli spaces.',\n",
" u'surname': u'Solomon',\n",
" u'title': u'Graduate Student',\n",
" u'website': u'http://www.math.brown.edu/~ysolomon'},\n",
" {u'cluster_id': 0,\n",
" u'gear_collaborators': [],\n",
" u'member_id': 206,\n",
" u'member_type': u'graduate',\n",
" u'name': u'Balazs',\n",
" u'organization': u'',\n",
" u'other_collaborators': u'',\n",
" u'photo': u'',\n",
" u'pos_x': 0,\n",
" u'pos_y': 0,\n",
" u'research_interests': u'',\n",
" u'short_bio': u'',\n",
" u'surname': u'Strenner',\n",
" u'title': u'Graduate Student',\n",
" u'website': u''},\n",
" {u'cluster_id': 0,\n",
" u'gear_collaborators': [],\n",
" u'member_id': 207,\n",
" u'member_type': u'graduate',\n",
" u'name': u'Hongbin',\n",
" u'organization': u'',\n",
" u'other_collaborators': u'',\n",
" u'photo': u'',\n",
" u'pos_x': 0,\n",
" u'pos_y': 0,\n",
" u'research_interests': u'',\n",
" u'short_bio': u'',\n",
" u'surname': u'Sun',\n",
" u'title': u'Graduate Student',\n",
" u'website': u''},\n",
" {u'cluster_id': 0,\n",
" u'gear_collaborators': [],\n",
" u'member_id': 208,\n",
" u'member_type': u'graduate',\n",
" u'name': u'Zhe',\n",
" u'organization': u'Universit\\xe9 Paris-Sud - Paris XI (Advisor: Fran\\xe7ois Labourie)',\n",
" u'other_collaborators': u'',\n",
" u'photo': u'ZheSun.jpg',\n",
" u'pos_x': 0,\n",
" u'pos_y': 0,\n",
" u'research_interests': u'Geometric Structures and Teichm\\xfcller Spaces, Higgs Bundles',\n",
" u'short_bio': u'Poisson algebra and quantization for Hitchin components and some configuration spaces, such as swapping algebra, cluster algebra, Goldman bi Lie algebra. Some enumeration problems related to moduli spaces. Discrete integrable systems and Poisson-Lie group structure.',\n",
" u'surname': u'Sun',\n",
" u'title': u'Graduate Student',\n",
" u'website': u''},\n",
" {u'cluster_id': 0,\n",
" u'gear_collaborators': [],\n",
" u'member_id': 209,\n",
" u'member_type': u'graduate',\n",
" u'name': u'Thomas',\n",
" u'organization': u'',\n",
" u'other_collaborators': u'',\n",
" u'photo': u'',\n",
" u'pos_x': 0,\n",
" u'pos_y': 0,\n",
" u'research_interests': u'',\n",
" u'short_bio': u'',\n",
" u'surname': u'Sutherland',\n",
" u'title': u'Graduate Student',\n",
" u'website': u''},\n",
" {u'cluster_id': 0,\n",
" u'gear_collaborators': [],\n",
" u'member_id': 210,\n",
" u'member_type': u'graduate',\n",
" u'name': u'Nurdin',\n",
" u'organization': u'',\n",
" u'other_collaborators': u'',\n",
" u'photo': u'',\n",
" u'pos_x': 0,\n",
" u'pos_y': 0,\n",
" u'research_interests': u'',\n",
" u'short_bio': u'',\n",
" u'surname': u'Takenov',\n",
" u'title': u'Graduate Student',\n",
" u'website': u''},\n",
" {u'cluster_id': 0,\n",
" u'gear_collaborators': [],\n",
" u'member_id': 211,\n",
" u'member_type': u'graduate',\n",
" u'name': u'Stefan',\n",
" u'organization': u'',\n",
" u'other_collaborators': u'',\n",
" u'photo': u'',\n",
" u'pos_x': 0,\n",
" u'pos_y': 0,\n",
" u'research_interests': u'',\n",
" u'short_bio': u'',\n",
" u'surname': u'Tornier',\n",
" u'title': u'Graduate Student',\n",
" u'website': u''},\n",
" {u'cluster_id': 0,\n",
" u'gear_collaborators': [],\n",
" u'member_id': 212,\n",
" u'member_type': u'graduate',\n",
" u'name': u'J\\xe9r\\xe9my',\n",
" u'organization': u'Universit\\xe9 de Luxembourg (Advisor: Jean-Marc Schlenker)',\n",
" u'other_collaborators': u'',\n",
" u'photo': u'ToulisseJeremy.jpg',\n",
" u'pos_x': 0,\n",
" u'pos_y': 0,\n",
" u'research_interests': u'Geometric Structures and Teichm\\xfcller Spaces, Hyperbolic Manifolds, Quantum Teichm\\xfcller theory',\n",
" u'short_bio': u'I am interested in the interaction between Globally Hyperbolic Anti-de Sitter 3-manifolds and particular types of diffeomorphisms (principally minimal Lagrangian) between hyperbolic surfaces with cone singularities. Moreover, I am interested in the topological invariants arising in the study of quantum Teichm\\xfcller space.',\n",
" u'surname': u'Toulisse',\n",
" u'title': u'Graduate Student',\n",
" u'website': u'http://math.uni.lu/~toulisse/'},\n",
" {u'cluster_id': 0,\n",
" u'gear_collaborators': [],\n",
" u'member_id': 213,\n",
" u'member_type': u'graduate',\n",
" u'name': u'Nicolaus',\n",
" u'organization': u'University of Heidelberg (Advisor: Anna Wienhard)',\n",
" u'other_collaborators': u'',\n",
" u'photo': u'TreibNicolaus.jpg',\n",
" u'pos_x': 0,\n",
" u'pos_y': 0,\n",
" u'research_interests': u'Geometric and Analytical Group Theory, Geometric Structures and Teichm\\xfcller Spaces, Hyperbolic Manifolds',\n",
" u'short_bio': u'I am interested in representations of surface groups or hyperbolic groups into semisimple Lie groups, in particular Anosov representations. One main motivation is understanding the behavior of proper actions under deformation towards the boundary of the space of Anosov representations, where interesting transitions can occur.',\n",
" u'surname': u'Treib',\n",
" u'title': u'Graduate Student',\n",
" u'website': u'http://www.mathi.uni-heidelberg.de/~ntreib/'},\n",
" {u'cluster_id': 0,\n",
" u'gear_collaborators': [],\n",
" u'member_id': 214,\n",
" u'member_type': u'graduate',\n",
" u'name': u'Weston',\n",
" u'organization': u'Stanford University',\n",
" u'other_collaborators': u'',\n",
" u'photo': u'',\n",
" u'pos_x': 0,\n",
" u'pos_y': 0,\n",
" u'research_interests': u'Dynamics on Moduli Spaces, Geometric Structures and Teichm\\xfcller Spaces, Hyperbolic Manifolds',\n",
" u'short_bio': u'',\n",
" u'surname': u'Ungemach',\n",
" u'title': u'Graduate Student',\n",
" u'website': u''},\n",
" {u'cluster_id': 0,\n",
" u'gear_collaborators': [],\n",
" u'member_id': 215,\n",
" u'member_type': u'graduate',\n",
" u'name': u'Caglar',\n",
" u'organization': u'University of Illinois at Urbana-Champaign (Advisors: Ilya Kapovich, Christopher Leininger)',\n",
" u'other_collaborators': u'',\n",
" u'photo': u'',\n",
" u'pos_x': 0,\n",
" u'pos_y': 0,\n",
" u'research_interests': u'',\n",
" u'short_bio': u'',\n",
" u'surname': u'Uyanik',\n",
" u'title': u'Graduate Student',\n",
" u'website': u'http://www.math.uiuc.edu/~cuyanik2/'},\n",
" {u'cluster_id': 0,\n",
" u'gear_collaborators': [],\n",
" u'member_id': 216,\n",
" u'member_type': u'graduate',\n",
" u'name': u'Harold',\n",
" u'organization': u'',\n",
" u'other_collaborators': u'',\n",
" u'photo': u'',\n",
" u'pos_x': 0,\n",
" u'pos_y': 0,\n",
" u'research_interests': u'',\n",
" u'short_bio': u'',\n",
" u'surname': u'Williams',\n",
" u'title': u'Graduate Student',\n",
" u'website': u''},\n",
" {u'cluster_id': 0,\n",
" u'gear_collaborators': [],\n",
" u'member_id': 217,\n",
" u'member_type': u'graduate',\n",
" u'name': u'Grace',\n",
" u'organization': u'University of Illinois at Urbana-Champaign (Advisor: Jayadev Athreya)',\n",
" u'other_collaborators': u'',\n",
" u'photo': u'WorkGrace.jpg',\n",
" u'pos_x': 0,\n",
" u'pos_y': 0,\n",
" u'research_interests': u'Dynamics on Moduli Spaces',\n",
" u'short_bio': u'I study translation surfaces. Specifically the distribution of the gaps between the slopes of saddle connections on n-gons and Ls.',\n",
" u'surname': u'Work',\n",
" u'title': u'Graduate Student',\n",
" u'website': u'http://www.math.illinois.edu/~work2/'},\n",
" {u'cluster_id': 0,\n",
" u'gear_collaborators': [],\n",
" u'member_id': 218,\n",
" u'member_type': u'graduate',\n",
" u'name': u'Conan',\n",
" u'organization': u'',\n",
" u'other_collaborators': u'',\n",
" u'photo': u'',\n",
" u'pos_x': 0,\n",
" u'pos_y': 0,\n",
" u'research_interests': u'',\n",
" u'short_bio': u'',\n",
" u'surname': u'Wu',\n",
" u'title': u'Graduate Student',\n",
" u'website': u''},\n",
" {u'cluster_id': 0,\n",
" u'gear_collaborators': [],\n",
" u'member_id': 219,\n",
" u'member_type': u'graduate',\n",
" u'name': u'Dimitri',\n",
" u'organization': u'',\n",
" u'other_collaborators': u'',\n",
" u'photo': u'',\n",
" u'pos_x': 0,\n",
" u'pos_y': 0,\n",
" u'research_interests': u'',\n",
" u'short_bio': u'',\n",
" u'surname': u'Wyss',\n",
" u'title': u'Graduate Student',\n",
" u'website': u''},\n",
" {u'cluster_id': 0,\n",
" u'gear_collaborators': [],\n",
" u'member_id': 221,\n",
" u'member_type': u'graduate',\n",
" u'name': u'Kaidi',\n",
" u'organization': u'Universit\\xe9 Aix-Marseille III Paul Cezanne',\n",
" u'other_collaborators': u'',\n",
" u'photo': u'',\n",
" u'pos_x': 0,\n",
" u'pos_y': 0,\n",
" u'research_interests': u'Dynamics on Moduli Spaces, Geometric and Analytical Group Theory, Geometric Structures and Teichm\\xfcller Spaces, Hyperbolic Manifolds',\n",
" u'short_bio': u'',\n",
" u'surname': u'Ye',\n",
" u'title': u'Graduate Student',\n",
" u'website': u''},\n",
" {u'cluster_id': 0,\n",
" u'gear_collaborators': [],\n",
" u'member_id': 222,\n",
" u'member_type': u'graduate',\n",
" u'name': u'Ronald',\n",
" u'organization': u'Universidade do Porto (Advisor: Peter Gothen)',\n",
" u'other_collaborators': u'',\n",
" u'photo': u'',\n",
" u'pos_x': 0,\n",
" u'pos_y': 0,\n",
" u'research_interests': u'Dynamics on Moduli Spaces, Geometric and Analytical Group Theory, Geometric Structures and Teichm\\xfcller Spaces, Higgs Bundles, Hyperbolic Manifolds',\n",
" u'short_bio': u'I am working with the Moduli Space of rank three Higgs Bundles and Hitchin Pairs. My main objective is generalize a result from Tam\\xe1s Hausel to compute the homotopy groups of the Moduli Spaces of k-Higgs Bundles, a particular kind of Hitchin Paris.',\n",
" u'surname': u'Z\\xfa\\xf1iga',\n",
" u'title': u'Graduate Student',\n",
" u'website': u''},\n",
" {u'cluster_id': 0,\n",
" u'gear_collaborators': [],\n",
" u'member_id': 223,\n",
" u'member_type': u'postdoc',\n",
" u'name': u'Daniele',\n",
" u'organization': u'',\n",
" u'other_collaborators': u'',\n",
" u'photo': u'',\n",
" u'pos_x': 0,\n",
" u'pos_y': 0,\n",
" u'research_interests': u'',\n",
" u'short_bio': u'',\n",
" u'surname': u'Alessandrini',\n",
" u'title': u'Postdoc',\n",
" u'website': u''},\n",
" {u'cluster_id': 0,\n",
" u'gear_collaborators': [],\n",
" u'member_id': 224,\n",
" u'member_type': u'postdoc',\n",
" u'name': u'Shinpei',\n",
" u'organization': u'California Institute of Technology (Mentor: Vlad Markovic)',\n",
" u'other_collaborators': u'',\n",
" u'photo': u'BabaShinpei.jpg',\n",
" u'pos_x': 0,\n",
" u'pos_y': 0,\n",
" u'research_interests': u'Dynamics on Moduli Spaces, Geometric Structures and Teichm\\xfcller Spaces, Hyperbolic Manifolds',\n",
" u'short_bio': u'I) Understand relations between complex projective structures on a surface and the corresponding PSL(2, C)-character variety. II)Teichm\\xfcller rays and grafting rays. ',\n",
" u'surname': u'Baba',\n",
" u'title': u'Postdoc',\n",
" u'website': u'http://www.its.caltech.edu/~shinpei/'},\n",
" {u'cluster_id': 0,\n",
" u'gear_collaborators': [],\n",
" u'member_id': 225,\n",
" u'member_type': u'postdoc',\n",
" u'name': u'Thomas',\n",
" u'organization': u'',\n",
" u'other_collaborators': u'',\n",
" u'photo': u'',\n",
" u'pos_x': 0,\n",
" u'pos_y': 0,\n",
" u'research_interests': u'',\n",
" u'short_bio': u'',\n",
" u'surname': u'Baier',\n",
" u'title': u'Postdoc',\n",
" u'website': u''},\n",
" {u'cluster_id': 0,\n",
" u'gear_collaborators': [],\n",
" u'member_id': 226,\n",
" u'member_type': u'postdoc',\n",
" u'name': u'Sam',\n",
" u'organization': u'University of California, Santa Barbara (Mentor: Daryl Cooper)',\n",
" u'other_collaborators': u'',\n",
" u'photo': u'',\n",
" u'pos_x': 0,\n",
" u'pos_y': 0,\n",
" u'research_interests': u'Geometric and Analytical Group Theory, Geometric Structures and Teichm\\xfcller Spaces, Hyperbolic Manifolds',\n",
" u'short_bio': u'I am interested in projective structures on low dimensional manifolds and their relationship to character varieties. In particular, I am interested in convex projective structures and foliated structures arising from Hitchin representations.',\n",
" u'surname': u'Ballas',\n",
" u'title': u'Postdoc',\n",
" u'website': u'http://math.ucsb.edu/~sballas/'},\n",
" {u'cluster_id': 0,\n",
" u'gear_collaborators': [],\n",
" u'member_id': 227,\n",
" u'member_type': u'postdoc',\n",
" u'name': u'Jessica',\n",
" u'organization': u'',\n",
" u'other_collaborators': u'',\n",
" u'photo': u'',\n",
" u'pos_x': 0,\n",
" u'pos_y': 0,\n",
" u'research_interests': u'',\n",
" u'short_bio': u'',\n",
" u'surname': u'Banks',\n",
" u'title': u'Postdoc',\n",
" u'website': u''},\n",
" {u'cluster_id': 0,\n",
" u'gear_collaborators': [],\n",
" u'member_id': 228,\n",
" u'member_type': u'postdoc',\n",
" u'name': u'Michael',\n",
" u'organization': u'Universit\\xe9 du Qu\\xe9bec \\xe0 Montr\\xe9al (Mentors: Steve Boyer, Olivier Collin, Octav Cornea, Fran\\xe7ois Lalonde)',\n",
" u'other_collaborators': u'Swiatoslaw Gal, Jarek Kedra, Michal Marcinkowski, Egor Shelukhin',\n",
" u'photo': u'',\n",
" u'pos_x': 0,\n",
" u'pos_y': 0,\n",
" u'research_interests': u'Geometric and Analytical Group Theory, Geometric Structures and Teichm\\xfcller Spaces, Hyperbolic Manifolds',\n",
" u'short_bio': u'Knot Theory: Vassiliev invariants, Heegaard Floer and Khovanov homologies, concordance group;\\nSymplectic geometry and low-dimensional topology: Groups of diffeomorphisms;\\nBraid groups, mapping class groups: Quasi-isometries, quasi-morphisms, quasi-norms;\\nGeometric group theory: Quasi-isometric embeddings of finitely generated groups, bi-invariant word metrics.',\n",
" u'surname': u'Brandenbursky',\n",
" u'title': u'Postdoc',\n",
" u'website': u'http://www.thales.math.uqam.ca/~brandenm/'},\n",
" {u'cluster_id': 0,\n",
" u'gear_collaborators': [],\n",
" u'member_id': 229,\n",
" u'member_type': u'postdoc',\n",
" u'name': u'Spencer',\n",
" u'organization': u'',\n",
" u'other_collaborators': u'',\n",
" u'photo': u'',\n",
" u'pos_x': 0,\n",
" u'pos_y': 0,\n",
" u'research_interests': u'',\n",
" u'short_bio': u'',\n",
" u'surname': u'Dowdall',\n",
" u'title': u'Postdoc',\n",
" u'website': u''},\n",
" {u'cluster_id': 0,\n",
" u'gear_collaborators': [],\n",
" u'member_id': 230,\n",
" u'member_type': u'postdoc',\n",
" u'name': u'Guillaume',\n",
" u'organization': u'University of Notre Dame (Mentor: Fran\\xe7ois Ledrappier)',\n",
" u'other_collaborators': u'Michael Gekhtman, Fran\\xe7ois Ledrappier, Andrew Sanders',\n",
" u'photo': u'DreyerGuillaume.jpg',\n",
" u'pos_x': 0,\n",
" u'pos_y': 0,\n",
" u'research_interests': u'Dynamics on Moduli Spaces, Geometric Structures and Teichm\\xfcller Spaces',\n",
" u'short_bio': u'I am mostly interested in analyzing how concepts and techniques from classic hyperbolic geometry extend to higher Teichm\\xfcller theory. This includes the investigation of geometric invariants and deformations for Hitchin and Anosov representations, parametrizations of Hitchin components, symplectic and Poisson geometry of character varieties, compactification methods for Hitchin components.',\n",
" u'surname': u'Dreyer',\n",
" u'title': u'Postdoc',\n",
" u'website': u'http://math.nd.edu/people/visiting-faculty/guillaume-dreyer/'},\n",
" {u'cluster_id': 0,\n",
" u'gear_collaborators': [],\n",
" u'member_id': 231,\n",
" u'member_type': u'postdoc',\n",
" u'name': u'Ariadna',\n",
" u'organization': u'',\n",
" u'other_collaborators': u'',\n",
" u'photo': u'',\n",
" u'pos_x': 0,\n",
" u'pos_y': 0,\n",
" u'research_interests': u'',\n",
" u'short_bio': u'',\n",
" u'surname': u'Fossas',\n",
" u'title': u'Postdoc',\n",
" u'website': u''},\n",
" {u'cluster_id': 0,\n",
" u'gear_collaborators': [],\n",
" u'member_id': 232,\n",
" u'member_type': u'postdoc',\n",
" u'name': u'S\\xf8ren',\n",
" u'organization': u'',\n",
" u'other_collaborators': u'',\n",
" u'photo': u'',\n",
" u'pos_x': 0,\n",
" u'pos_y': 0,\n",
" u'research_interests': u'',\n",
" u'short_bio': u'',\n",
" u'surname': u'Fuglede J\\xf8rgensen',\n",
" u'title': u'Postdoc',\n",
" u'website': u''},\n",
" {u'cluster_id': 0,\n",
" u'gear_collaborators': [],\n",
" u'member_id': 233,\n",
" u'member_type': u'postdoc',\n",
" u'name': u'Mario',\n",
" u'organization': u'Ecole Polytechnique F\\xe9d\\xe9rale de Lausanne (Mentor: Tamas Hausel)',\n",
" u'other_collaborators': u'Luis Alvarez-Consul, Bjorn Andreas, Carl Tipler, Sara A. Filippini, Jacopo Stoppa, Julius Ross',\n",
" u'photo': u'',\n",
" u'pos_x': 0,\n",
" u'pos_y': 0,\n",
" u'research_interests': u'Geometric Structures and Teichm\\xfcller Spaces, Higgs Bundles',\n",
" u'short_bio': u'I am interested in geometric structures, partial differential equations and moduli spaces which arise in the interface between differential geometry, algebraic geometry and mathematical physics. In particular, I study special metrics and connections in complex and Kahler geometry, stability conditions for bundles and varieties and generalized geometry. Current projects where I participate include: gravitating vortices, geometry of heterotic string compactifications, Higgs bundles, hyperKahler metrics, irregular connections, geometric structures on spaces of stability conditions. ',\n",
" u'surname': u'Garcia Fernandez',\n",
" u'title': u'Postdoc',\n",
" u'website': u'http://people.epfl.ch/mario.garcia'},\n",
" {u'cluster_id': 0,\n",
" u'gear_collaborators': [],\n",
" u'member_id': 234,\n",
" u'member_type': u'postdoc',\n",
" u'name': u'Jonah',\n",
" u'organization': u'Boston College (Mentor: Martin Bridgeman)',\n",
" u'other_collaborators': u'',\n",
" u'photo': u'',\n",
" u'pos_x': 0,\n",
" u'pos_y': 0,\n",
" u'research_interests': u'Geometric Structures and Teichm\\xfcller Spaces, Hyperbolic Manifolds',\n",
" u'short_bio': u\"I am interested in curves on surfaces, Teichmuller theory, and hyperbolic structures on manifolds. My recent work has been abut Thurston's skinning map, and certain notions of 'maximality' for arrangements of curves on surfaces.\",\n",
" u'surname': u'Gaster',\n",
" u'title': u'Postdoc',\n",
" u'website': u''},\n",
" {u'cluster_id': 0,\n",
" u'gear_collaborators': [],\n",
" u'member_id': 235,\n",
" u'member_type': u'postdoc',\n",
" u'name': u'Ilya',\n",
" u'organization': u'',\n",
" u'other_collaborators': u'',\n",
" u'photo': u'',\n",
" u'pos_x': 0,\n",
" u'pos_y': 0,\n",
" u'research_interests': u'',\n",
" u'short_bio': u'',\n",
" u'surname': u'Gekhtman',\n",
" u'title': u'Postdoc',\n",
" u'website': u''},\n",
" {u'cluster_id': 0,\n",
" u'gear_collaborators': [],\n",
" u'member_id': 236,\n",
" u'member_type': u'postdoc',\n",
" u'name': u'Funda',\n",
" u'organization': u'University of Illinois at Urbana-Champain (Mentors: Ilya Kapovitch, Christopher Leininger)',\n",
" u'other_collaborators': u'Matt Clay, Johanna Mangahas',\n",
" u'photo': u'',\n",
" u'pos_x': 0,\n",
" u'pos_y': 0,\n",
" u'research_interests': u'Geometric and Analytical Group Theory',\n",
" u'short_bio': u'Mapping class group, Out(F_n) and low dimensional topology (3-manifolds topology specifically).',\n",
" u'surname': u'Gultepe',\n",
" u'title': u'Postdoc',\n",
" u'website': u'http://math.illinois.edu/~fgultepe'},\n",
" {u'cluster_id': 0,\n",
" u'gear_collaborators': [],\n",
" u'member_id': 237,\n",
" u'member_type': u'postdoc',\n",
" u'name': u'Subhojoy',\n",
" u'organization': u'',\n",
" u'other_collaborators': u'',\n",
" u'photo': u'GuptaSubhojoy.jpg',\n",
" u'pos_x': 0,\n",
" u'pos_y': 0,\n",
" u'research_interests': u'Dynamics on Moduli Spaces, Geometric Structures and Teichm\\xfcller Spaces, Hyperbolic Manifolds',\n",
" u'short_bio': u'I am interested in understanding the dynamics, in particular the asymptotic behavior, of certain deformations in moduli spaces of geometric structures on surfaces. Examples include grafting deformations of complex projective structures, and Teichm\\xfcller geodesic rays in Teichm\\xfcller space. My approach involves studying certain structures (eg. meromorphic quadratic differentials with higher order poles) that arise as limits of such deformations.',\n",
" u'surname': u'Gupta',\n",
" u'title': u'Postdoc',\n",
" u'website': u'http://www.its.caltech.edu/~subhojoy'},\n",
" {u'cluster_id': 0,\n",
" u'gear_collaborators': [],\n",
" u'member_id': 238,\n",
" u'member_type': u'postdoc',\n",
" u'name': u'Sebastian',\n",
" u'organization': u'University of Chicago (Mentor: Benson Farb)',\n",
" u'other_collaborators': u'Jon Chaika, Ursula Hamenstaedt, Dawid Kielak, Damian Osajda, Piotr Przytycki, Richard Webb',\n",
" u'photo': u'',\n",
" u'pos_x': 0,\n",
" u'pos_y': 0,\n",
" u'research_interests': u'Geometric and Analytical Group Theory, Geometric Structures and Teichm\\xfcller Spaces, Hyperbolic Manifolds',\n",
" u'short_bio': u'My main interest lies in the intersection between geometric group theory and low-dimensional topology. I am interested in mapping class groups (of surfaces or other things) and how they act on associated spaces (Teichm\\xfcller space, complex of curves, etc).',\n",
" u'surname': u'Hensel',\n",
" u'title': u'Postdoc',\n",
" u'website': u'http://math.uchicago.edu/~hensel'},\n",
" {u'cluster_id': 0,\n",
" u'gear_collaborators': [],\n",
" u'member_id': 239,\n",
" u'member_type': u'postdoc',\n",
" u'name': u'Hengnan',\n",
" u'organization': u'',\n",
" u'other_collaborators': u'',\n",
" u'photo': u'',\n",
" u'pos_x': 0,\n",
" u'pos_y': 0,\n",
" u'research_interests': u'',\n",
" u'short_bio': u'',\n",
" u'surname': u'Hu',\n",
" u'title': u'Postdoc',\n",
" u'website': u''},\n",
" {u'cluster_id': 0,\n",
" u'gear_collaborators': [],\n",
" u'member_id': 240,\n",
" u'member_type': u'postdoc',\n",
" u'name': u'Gye-Seon',\n",
" u'organization': u'University of Heidelberg (Mentor: Anna Wienhard)',\n",
" u'other_collaborators': u'',\n",
" u'photo': u'LeeGyeseon.jpg',\n",
" u'pos_x': 0,\n",
" u'pos_y': 0,\n",
" u'research_interests': u'Geometric Structures and Teichm\\xfcller Spaces, Hyperbolic Manifolds',\n",
" u'short_bio': u\"I am interested in the study of deformation spaces of geometric structures on manifolds (and orbifolds) and associated representation varieties. Over the years I've tried to understand the deformation spaces of real projective structures on Coxeter orbifolds.\",\n",
" u'surname': u'Lee',\n",
" u'title': u'Postdoc',\n",
" u'website': u'http://www.mathi.uni-heidelberg.de/~lee/'},\n",
" {u'cluster_id': 0,\n",
" u'gear_collaborators': [],\n",
" u'member_id': 241,\n",
" u'member_type': u'postdoc',\n",
" u'name': u'Terence',\n",
" u'organization': u'',\n",
" u'other_collaborators': u'',\n",
" u'photo': u'',\n",
" u'pos_x': 0,\n",
" u'pos_y': 0,\n",
" u'research_interests': u'',\n",
" u'short_bio': u'',\n",
" u'surname': u'Long',\n",
" u'title': u'Postdoc',\n",
" u'website': u''},\n",
" {u'cluster_id': 0,\n",
" u'gear_collaborators': [],\n",
" u'member_id': 242,\n",
" u'member_type': u'postdoc',\n",
" u'name': u'Brice',\n",
" u'organization': u'',\n",
" u'other_collaborators': u'',\n",
" u'photo': u'',\n",
" u'pos_x': 0,\n",
" u'pos_y': 0,\n",
" u'research_interests': u'',\n",
" u'short_bio': u'',\n",
" u'surname': u'Loustau',\n",
" u'title': u'Postdoc',\n",
" u'website': u''},\n",
" {u'cluster_id': 0,\n",
" u'gear_collaborators': [],\n",
" u'member_id': 243,\n",
" u'member_type': u'postdoc',\n",
" u'name': u'Sara',\n",
" u'organization': u'Brown University (Mentor: Jeffrey Brock)',\n",
" u'other_collaborators': u'Jeffrey Danciger, Yuichi Kabaya',\n",
" u'photo': u'MaloniSara.jpg',\n",
" u'pos_x': 0,\n",
" u'pos_y': 0,\n",
" u'research_interests': u'Dynamics on Moduli Spaces, Geometric and Analytical Group Theory, Geometric Structures and Teichm\\xfcller Spaces, Hyperbolic Manifolds',\n",
" u'short_bio': u'My research interests lie at the intersection of hyperbolic geometry and low-dimensional topology. In particular, as a graduate student, I studied Kleinian groups and their limits. Now, as a postdoc, I am interested in different generalisations of quasifuchsian groups to other kind of geometries such as anti-de Sitter geometry and complex hyperbolic geometry, or, more generally, to Anosov representations into complex Lie groups. Another central theme of my research is the geometric and dynamical decompositions of character varieties.',\n",
" u'surname': u'Maloni',\n",
" u'title': u'Postdoc',\n",
" u'website': u'http://www.math.brown.edu/~maloni'},\n",
" {u'cluster_id': 0,\n",
" u'gear_collaborators': [],\n",
" u'member_id': 244,\n",
" u'member_type': u'postdoc',\n",
" u'name': u'Paul',\n",
" u'organization': u'',\n",
" u'other_collaborators': u'',\n",
" u'photo': u'',\n",
" u'pos_x': 0,\n",
" u'pos_y': 0,\n",
" u'research_interests': u'',\n",
" u'short_bio': u'',\n",
" u'surname': u'Mercat',\n",
" u'title': u'Postdoc',\n",
" u'website': u''},\n",
" {u'cluster_id': 0,\n",
" u'gear_collaborators': [],\n",
" u'member_id': 245,\n",
" u'member_type': u'postdoc',\n",
" u'name': u'Martin',\n",
" u'organization': u'',\n",
" u'other_collaborators': u'',\n",
" u'photo': u'',\n",
" u'pos_x': 0,\n",
" u'pos_y': 0,\n",
" u'research_interests': u'',\n",
" u'short_bio': u'',\n",
" u'surname': u'Mereb',\n",
" u'title': u'Postdoc',\n",
" u'website': u''},\n",
" {u'cluster_id': 0,\n",
" u'gear_collaborators': [],\n",
" u'member_id': 246,\n",
" u'member_type': u'postdoc',\n",
" u'name': u'Xin',\n",
" u'organization': u'',\n",
" u'other_collaborators': u'',\n",
" u'photo': u'',\n",
" u'pos_x': 0,\n",
" u'pos_y': 0,\n",
" u'research_interests': u'',\n",
" u'short_bio': u'',\n",
" u'surname': u'Nie',\n",
" u'title': u'Postdoc',\n",
" u'website': u''},\n",
" {u'cluster_id': 0,\n",
" u'gear_collaborators': [],\n",
" u'member_id': 247,\n",
" u'member_type': u'postdoc',\n",
" u'name': u'Susovan',\n",
" u'organization': u'',\n",
" u'other_collaborators': u'',\n",
" u'photo': u'',\n",
" u'pos_x': 0,\n",
" u'pos_y': 0,\n",
" u'research_interests': u'',\n",
" u'short_bio': u'',\n",
" u'surname': u'Pal',\n",
" u'title': u'Postdoc',\n",
" u'website': u''},\n",
" {u'cluster_id': 0,\n",
" u'gear_collaborators': [],\n",
" u'member_id': 248,\n",
" u'member_type': u'postdoc',\n",
" u'name': u'Priyam',\n",
" u'organization': u'Purdue University (Mentor: David Ben McReynolds)',\n",
" u'other_collaborators': u'Mark Hagen, Khalid Bou-Rabee',\n",
" u'photo': u'',\n",
" u'pos_x': 0,\n",
" u'pos_y': 0,\n",
" u'research_interests': u'Geometric and Analytical Group Theory, Geometric Structures and Teichm\\xfcller Spaces, Hyperbolic Manifolds',\n",
" u'short_bio': u'',\n",
" u'surname': u'Patel',\n",
" u'title': u'Postdoc',\n",
" u'website': u'http://priyampatel.weebly.com'},\n",
" {u'cluster_id': 0,\n",
" u'gear_collaborators': [],\n",
" u'member_id': 249,\n",
" u'member_type': u'postdoc',\n",
" u'name': u'Anna',\n",
" u'organization': u'Universit\\xe4t Heidelberg (Mentor: Anna Wienhard)',\n",
" u'other_collaborators': u'',\n",
" u'photo': u'PeonNietoAna.jpg',\n",
" u'pos_x': 0,\n",
" u'pos_y': 0,\n",
" u'research_interests': u'Geometric Structures and Teichm\\xfcller Spaces, Higgs Bundles, Relations to Physics',\n",
" u'short_bio': u'My work focuses on the Hitchin system for Higgs bundles associated to real forms. This relates to physics via mirror symmetry, for example, and to higher Teichm\\xfcller theory via the Hitchin section, even if a higher dimensional geometric picture remains unclear.',\n",
" u'surname': u'Pe\\xf3n Nieto ',\n",
" u'title': u'Postdoc',\n",
" u'website': u'http://www.mathi.uni-heidelberg.de/~apeonnieto/'},\n",
" {u'cluster_id': 0,\n",
" u'gear_collaborators': [],\n",
" u'member_id': 250,\n",
" u'member_type': u'postdoc',\n",
" u'name': u'Julien',\n",
" u'organization': u'',\n",
" u'other_collaborators': u'',\n",
" u'photo': u'',\n",
" u'pos_x': 0,\n",
" u'pos_y': 0,\n",
" u'research_interests': u'',\n",
" u'short_bio': u'',\n",
" u'surname': u'Roger',\n",
" u'title': u'Postdoc',\n",
" u'website': u''},\n",
" {u'cluster_id': 0,\n",
" u'gear_collaborators': [],\n",
" u'member_id': 251,\n",
" u'member_type': u'postdoc',\n",
" u'name': u'Clara',\n",
" u'organization': u'Universit\\xe9 Paris Sud-Paris XI',\n",
" u'other_collaborators': u'',\n",
" u'photo': u'RossiSalveminiClara.jpg',\n",
" u'pos_x': 0,\n",
" u'pos_y': 0,\n",
" u'research_interests': u'Geometric Structures and Teichm\\xfcller Spaces, Hyperbolic Manifolds, Lorentzian manifolds',\n",
" u'short_bio': u\"I work on conformally flats Lorentzian manifolds on dimension greater than 3, which are special case of (G,X)-structures. I specially work on the globally hyperbolic ones, which is a very restrictive hypothesis on their 'causal structure'.\",\n",
" u'surname': u'Rossi Salvemini',\n",
" u'title': u'Postdoc',\n",
" u'website': u''},\n",
" {u'cluster_id': 0,\n",
" u'gear_collaborators': [],\n",
" u'member_id': 252,\n",
" u'member_type': u'postdoc',\n",
" u'name': u'Andrew',\n",
" u'organization': u'University of Illinois at Chicago (Mentor: David Dumas)',\n",
" u'other_collaborators': u'',\n",
" u'photo': u'',\n",
" u'pos_x': 0,\n",
" u'pos_y': 0,\n",
" u'research_interests': u'Dynamics on Moduli Spaces, Geometric and Analytical Group Theory, Geometric Structures and Teichm\\xfcller Spaces, Higgs Bundles, Hyperbolic Manifolds',\n",
" u'short_bio': u'My primary interest is studying the interactions between harmonics maps and, particularly minimal surfaces, in symmetric spaces and the theory of representations of surface groups. Important elements which arise come from dynamical systems, Higgs bundles and geometry of symmetric spaces.',\n",
" u'surname': u'Sanders',\n",
" u'title': u'Postdoc',\n",
" u'website': u'http://www.math.uic.edu/~andysan'},\n",
" {u'cluster_id': 0,\n",
" u'gear_collaborators': [],\n",
" u'member_id': 253,\n",
" u'member_type': u'postdoc',\n",
" u'name': u'Florian',\n",
" u'organization': u'Aarhus University (Mentor: J\\xf8rgen Andersen)',\n",
" u'other_collaborators': u'',\n",
" u'photo': u'',\n",
" u'pos_x': 0,\n",
" u'pos_y': 0,\n",
" u'research_interests': u'Iterated Integrals, Configuration Spaces, Deformation Theory',\n",
" u'short_bio': u'I am interested in the theory of higher holonomies and its applications, e.g. to the study of the geometry / topology of configuration spaces. I am also interested in deformation problems in geometry and physics.',\n",
" u'surname': u'Sch\\xe4tz',\n",
" u'title': u'Postdoc',\n",
" u'website': u'http://home.imf.au.dk/fschaetz/'},\n",
" {u'cluster_id': 0,\n",
" u'gear_collaborators': [],\n",
" u'member_id': 254,\n",
" u'member_type': u'postdoc',\n",
" u'name': u'Laura',\n",
" u'organization': u'',\n",
" u'other_collaborators': u'',\n",
" u'photo': u'',\n",
" u'pos_x': 0,\n",
" u'pos_y': 0,\n",
" u'research_interests': u'',\n",
" u'short_bio': u'',\n",
" u'surname': u'Schaposnik',\n",
" u'title': u'Postdoc',\n",
" u'website': u''},\n",
" {u'cluster_id': 0,\n",
" u'gear_collaborators': [],\n",
" u'member_id': 255,\n",
" u'member_type': u'postdoc',\n",
" u'name': u'Dmitry',\n",
" u'organization': u'Universit\\xe9 de Strasbourg (Mentor: Olivier Guichard)',\n",
" u'other_collaborators': u'',\n",
" u'photo': u'',\n",
" u'pos_x': 0,\n",
" u'pos_y': 0,\n",
" u'research_interests': u'Geometric and Analytical Group Theory, Geometric Structures and Teichm\\xfcller Spaces, Hyperbolic Manifolds',\n",
" u'short_bio': u'My PhD thesis was about quasi-Fuchsian hyperbolic manifolds and the flexibility of polyhedra in hyperbolic space. Currently, I study geometric group theory and projective structures on manifolds.',\n",
" u'surname': u'Slutsky',\n",
" u'title': u'Postdoc',\n",
" u'website': u''},\n",
" {u'cluster_id': 0,\n",
" u'gear_collaborators': [],\n",
" u'member_id': 256,\n",
" u'member_type': u'postdoc',\n",
" u'name': u'Marco',\n",
" u'organization': u'Universit\\xe9 de Grenoble (Mentor: Philippe Eyssidieux)',\n",
" u'other_collaborators': u'',\n",
" u'photo': u'',\n",
" u'pos_x': 0,\n",
" u'pos_y': 0,\n",
" u'research_interests': u'Higgs Bundles',\n",
" u'short_bio': u'In my Ph.D. thesis I have studied the character variety and the moduli space of Higgs bundles on K\\xe4hler manifolds. Currently, I am focusing on the applications of this theory to the representation theory of complex hyperbolic lattices.',\n",
" u'surname': u'Spinaci',\n",
" u'title': u'Postdoc',\n",
" u'website': u'http://www-fourier.ujf-grenoble.fr/~spinaci/'},\n",
" {u'cluster_id': 0,\n",
" u'gear_collaborators': [],\n",
" u'member_id': 257,\n",
" u'member_type': u'postdoc',\n",
" u'name': u'Michael',\n",
" u'organization': u'',\n",
" u'other_collaborators': u'',\n",
" u'photo': u'',\n",
" u'pos_x': 0,\n",
" u'pos_y': 0,\n",
" u'research_interests': u'',\n",
" u'short_bio': u'',\n",
" u'surname': u'Wong',\n",
" u'title': u'Postdoc',\n",
" u'website': u''},\n",
" {u'cluster_id': 0,\n",
" u'gear_collaborators': [],\n",
" u'member_id': 258,\n",
" u'member_type': u'postdoc',\n",
" u'name': u'Yunhui',\n",
" u'organization': u'',\n",
" u'other_collaborators': u'Grigori Avramidi, Tam Nguyen Phan, Meng Wu',\n",
" u'photo': u'WuYunhui.jpg',\n",
" u'pos_x': 0,\n",
" u'pos_y': 0,\n",
" u'research_interests': u'Dynamics on Moduli Spaces, Geometric and Analytical Group Theory, Geometric Structures and Teichm\\xfcller Spaces',\n",
" u'short_bio': u'I am very interested in Teichmuller theory, especially in the Weil-Petersson geometry of the Teichmuller space. I am also interested in the geometry and topology of open nonpositively curved manifolds, especially its relation to Teichmuller theory.',\n",
" u'surname': u'Wu',\n",
" u'title': u'Postdoc',\n",
" u'website': u'http://math.rice.edu/~yw22/'},\n",
" {u'cluster_id': 0,\n",
" u'gear_collaborators': [],\n",
" u'member_id': 259,\n",
" u'member_type': u'postdoc',\n",
" u'name': u'Binbin',\n",
" u'organization': u'',\n",
" u'other_collaborators': u'',\n",
" u'photo': u'',\n",
" u'pos_x': 0,\n",
" u'pos_y': 0,\n",
" u'research_interests': u'',\n",
" u'short_bio': u'',\n",
" u'surname': u'Xu',\n",
" u'title': u'Postdoc',\n",
" u'website': u''},\n",
" {u'cluster_id': 0,\n",
" u'gear_collaborators': [],\n",
" u'member_id': 260,\n",
" u'member_type': u'postdoc',\n",
" u'name': u'Tian',\n",
" u'organization': u'Stanford University (Mentor: Steven Kerckhoff)',\n",
" u'other_collaborators': u'',\n",
" u'photo': u'YangTian.jpg',\n",
" u'pos_x': 0,\n",
" u'pos_y': 0,\n",
" u'research_interests': u'Dynamics on Moduli Spaces, Geometric Structures and Teichm\\xfcller Spaces, Hyperbolic Manifolds',\n",
" u'short_bio': u'',\n",
" u'surname': u'Yang',\n",
" u'title': u'Postdoc',\n",
" u'website': u'http://www.math.rutgers.edu/~tianyang/'},\n",
" {u'cluster_id': 0,\n",
" u'gear_collaborators': [],\n",
" u'member_id': 261,\n",
" u'member_type': u'member',\n",
" u'name': u'Jason',\n",
" u'organization': u'University of Pittsburgh',\n",
" u'other_collaborators': u'',\n",
" u'photo': u'',\n",
" u'pos_x': 0,\n",
" u'pos_y': 0,\n",
" u'research_interests': u'',\n",
" u'short_bio': u'',\n",
" u'surname': u'DeBlois',\n",
" u'title': u'GEAR Member',\n",
" u'website': u''},\n",
" {u'cluster_id': 0,\n",
" u'gear_collaborators': [],\n",
" u'member_id': 262,\n",
" u'member_type': u'postdoc',\n",
" u'name': u'Joel',\n",
" u'organization': u'Smith College',\n",
" u'other_collaborators': u'Matt Clay, Max Forester',\n",
" u'photo': u'LouwsmaJoel.jpg',\n",
" u'pos_x': 0,\n",
" u'pos_y': 0,\n",
" u'research_interests': u'Geometric and Analytical Group Theory, Hyperbolic Manifolds',\n",
" u'short_bio': u'My research is in the areas of geometric group theory and low-dimensional topology. Much of my work has involved stable commutator length and quasimorphisms.',\n",
" u'surname': u'Louwsma',\n",
" u'title': u'Postdoc',\n",
" u'website': u'http://www.math.smith.edu/~jlouwsma/'},\n",
" {u'cluster_id': 0,\n",
" u'gear_collaborators': [],\n",
" u'member_id': 263,\n",
" u'member_type': u'member',\n",
" u'name': u'Youngju',\n",
" u'organization': u'Korea Institute for Advanced Study',\n",
" u'other_collaborators': u'',\n",
" u'photo': u'',\n",
" u'pos_x': 0,\n",
" u'pos_y': 0,\n",
" u'research_interests': u'Hyperbolic Manifolds',\n",
" u'short_bio': u'',\n",
" u'surname': u'Kim',\n",
" u'title': u'GEAR Member',\n",
" u'website': u''},\n",
" {u'cluster_id': 0,\n",
" u'gear_collaborators': [],\n",
" u'member_id': 264,\n",
" u'member_type': u'member',\n",
" u'name': u'Jeremy',\n",
" u'organization': u'City University of New York',\n",
" u'other_collaborators': u'Tanya Firsova, Mikhail Lyubich, Vladimir Markovic, Nikita Selinger',\n",
" u'photo': u'KahnJeremy.jpg',\n",
" u'pos_x': 0,\n",
" u'pos_y': 0,\n",
" u'research_interests': u'Dynamics on Moduli Spaces, Geometric and Analytical Group Theory, Geometric Structures and Teichm\\xfcller Spaces, Hyperbolic Manifolds',\n",
" u'short_bio': u'Dynamics of one complex variables. Teichmuller theory especially quadratic differentials and the Teichmuller metric and its relation to Fenchel-Nielsen coordinates. Hyberbolic manifolds.',\n",
" u'surname': u'Kahn',\n",
" u'title': u'GEAR Member',\n",
" u'website': u'http://jkahn.gc.cuny.edu/'},\n",
" {u'cluster_id': 0,\n",
" u'gear_collaborators': [],\n",
" u'member_id': 265,\n",
" u'member_type': u'member',\n",
" u'name': u'Ian',\n",
" u'organization': u'The University of York',\n",
" u'other_collaborators': u'',\n",
" u'photo': u'',\n",
" u'pos_x': 0,\n",
" u'pos_y': 0,\n",
" u'research_interests': u'Higgs Bundles, Hyperbolic Manifolds, Minimal Surfaces',\n",
" u'short_bio': u'My main interest is in the role minimal surfaces play in characterising hyperbolic 3-manifolds and complex hyperbolic 4-manifolds, and how to detect minimal surfaces amongst the harmonic maps which solve the Higgs bundle equations.',\n",
" u'surname': u'McIntosh',\n",
" u'title': u'GEAR Member',\n",
" u'website': u'http://maths.york.ac.uk/www/im7'},\n",
" {u'cluster_id': 0,\n",
" u'gear_collaborators': [],\n",
" u'member_id': 266,\n",
" u'member_type': u'member',\n",
" u'name': u'Jessica',\n",
" u'organization': u'Brigham Young University',\n",
" u'other_collaborators': u'',\n",
" u'photo': u'',\n",
" u'pos_x': 0,\n",
" u'pos_y': 0,\n",
" u'research_interests': u'',\n",
" u'short_bio': u'',\n",
" u'surname': u'Purcell',\n",
" u'title': u'GEAR Member',\n",
" u'website': u''},\n",
" {u'cluster_id': 0,\n",
" u'gear_collaborators': [],\n",
" u'member_id': 267,\n",
" u'member_type': u'member',\n",
" u'name': u'Hartmut',\n",
" u'organization': u'Christian-Albrechts-Universit\\xe4t zu Kiel',\n",
" u'other_collaborators': u'Bernd Ammann, Rafe Mazzeo, Gregoire Montcouquiol, Jan Swoboda, Frederik Witt',\n",
" u'photo': u'WeissHartmut.jpg',\n",
" u'pos_x': 0,\n",
" u'pos_y': 0,\n",
" u'research_interests': u'Geometric Structures and Teichm\\xfcller Spaces, Higgs Bundles, Hyperbolic Manifolds',\n",
" u'short_bio': u'Deformations of cone-manifold structures in dimension 2 and 3, Rigidity questions for cone-manifolds and polyhedra, Hyperk\\xe4hler geometry of the Higgs bundle moduli space.',\n",
" u'surname': u'Weiss',\n",
" u'title': u'GEAR Member',\n",
" u'website': u'http://www.math.uni-kiel.de/geometrie/de/hartmut-weiss'},\n",
" {u'cluster_id': 0,\n",
" u'gear_collaborators': [],\n",
" u'member_id': 268,\n",
" u'member_type': u'graduate',\n",
" u'name': u'Federica',\n",
" u'organization': u'University of Fribourg (Advisor: Hugo Parlier)',\n",
" u'other_collaborators': u'',\n",
" u'photo': u'',\n",
" u'pos_x': 0,\n",
" u'pos_y': 0,\n",
" u'research_interests': u'Geometric Structures and Teichm\\xfcller Spaces',\n",
" u'short_bio': u'Hyperbolic surfaces and orbifolds, Teichm\\xfcller and moduli spaces',\n",
" u'surname': u'Fanoni',\n",
" u'title': u'Graduate Student',\n",
" u'website': u'http://perso.unifr.ch/federica.fanoni/'},\n",
" {u'cluster_id': 0,\n",
" u'gear_collaborators': [],\n",
" u'member_id': 269,\n",
" u'member_type': u'graduate',\n",
" u'name': u'Vincent',\n",
" u'organization': u\"Universit\\xe9 de Strasbourg (Advisors: Ken'ichi Ohshika and Athanase Papadopoulos)\",\n",
" u'other_collaborators': u'',\n",
" u'photo': u'AlbergeVincent.jpg',\n",
" u'pos_x': 0,\n",
" u'pos_y': 0,\n",
" u'research_interests': u'Geometric Structures and Teichm\\xfcller Spaces',\n",
" u'short_bio': u'My field of research is the classical Teichm\\xfcller theory. In particular, I am interested in the Gardiner-Masur compactification of the Teichm\\xfcller space.',\n",
" u'surname': u'Alberge',\n",
" u'title': u'Graduate Student',\n",
" u'website': u''},\n",
" {u'cluster_id': 0,\n",
" u'gear_collaborators': [],\n",
" u'member_id': 270,\n",
" u'member_type': u'member',\n",
" u'name': u'Dominique',\n",
" u'organization': u'Universit\\xe9 Paris-Sud 11',\n",
" u'other_collaborators': u'',\n",
" u'photo': u'',\n",
" u'pos_x': 0,\n",
" u'pos_y': 0,\n",
" u'research_interests': u'Dynamics on Moduli Spaces, Geometric Structures and Teichm\\xfcller Spaces',\n",
" u'short_bio': u\"Elliptic non linear PDE's, Cubic differentials, Conformal geometry\",\n",
" u'surname': u'Hulin',\n",
" u'title': u'GEAR Member',\n",
" u'website': u'http://www.math.u-psud.fr/~hulin/'},\n",
" {u'cluster_id': 0,\n",
" u'gear_collaborators': [],\n",
" u'member_id': 271,\n",
" u'member_type': u'member',\n",
" u'name': u'Mladen',\n",
" u'organization': u'University of Utah',\n",
" u'other_collaborators': u'',\n",
" u'photo': u'',\n",
" u'pos_x': 0,\n",
" u'pos_y': 0,\n",
" u'research_interests': u'',\n",
" u'short_bio': u'',\n",
" u'surname': u'Bestvina',\n",
" u'title': u'GEAR Member',\n",
" u'website': u''},\n",
" {u'cluster_id': 0,\n",
" u'gear_collaborators': [],\n",
" u'member_id': 272,\n",
" u'member_type': u'member',\n",
" u'name': u'Vincent',\n",
" u'organization': u'Laboratoire Bordelais de Recherche en Informatique',\n",
" u'other_collaborators': u'',\n",
" u'photo': u'',\n",
" u'pos_x': 0,\n",
" u'pos_y': 0,\n",
" u'research_interests': u'',\n",
" u'short_bio': u'',\n",
" u'surname': u'Delecroix',\n",
" u'title': u'GEAR Member',\n",
" u'website': u''},\n",
" {u'cluster_id': 0,\n",
" u'gear_collaborators': [],\n",
" u'member_id': 273,\n",
" u'member_type': u'member',\n",
" u'name': u'Efstratia',\n",
" u'organization': u'Michigan State University',\n",
" u'other_collaborators': u'Oliver Dasbach, Stefan Friedl, Christine Lee, Ahn Tran',\n",
" u'photo': u'',\n",
" u'pos_x': 0,\n",
" u'pos_y': 0,\n",
" u'research_interests': u'Hyperbolic Manifolds, Quantum Topology',\n",
" u'short_bio': u\"Classical and Quantum 3-dimensional Topology, Knot Theory, Hyperbolic Geometry, Braid Groups, Combinatorics. I'm particularly interested in exploring connections between quantum invariants and geometric structures of 3-manifolds.\",\n",
" u'surname': u'Kalfagianni',\n",
" u'title': u'GEAR Member',\n",
" u'website': u'http://www.math.msu.edu/~kalfagia'},\n",
" {u'cluster_id': 0,\n",
" u'gear_collaborators': [],\n",
" u'member_id': 274,\n",
" u'member_type': u'member',\n",
" u'name': u'Jens',\n",
" u'organization': u'University of Durham',\n",
" u'other_collaborators': u'J. Bruinier, O. Imamoglu, S. Kudla',\n",
" u'photo': u'',\n",
" u'pos_x': 0,\n",
" u'pos_y': 0,\n",
" u'research_interests': u'Geometric and Analytical Group Theory, Arithmetic groups and automorphic forms',\n",
" u'short_bio': u'The principal goal of my research is to use the theory of the theta correspondence, representation theory, and automorphic forms to construct and study interesting geometrically defined cohomology classes for arithmetic quotients of orthogonal and unitary groups.',\n",
" u'surname': u'Funke',\n",
" u'title': u'GEAR Member',\n",
" u'website': u'http://www.maths.dur.ac.uk/~dma0jf/'},\n",
" {u'cluster_id': 0,\n",
" u'gear_collaborators': [],\n",
" u'member_id': 275,\n",
" u'member_type': u'member',\n",
" u'name': u'Fran\\xe7ois',\n",
" u'organization': u'Laboratoire Paul-Painlev\\xe9',\n",
" u'other_collaborators': u'',\n",
" u'photo': u'',\n",
" u'pos_x': 0,\n",
" u'pos_y': 0,\n",
" u'research_interests': u'',\n",
" u'short_bio': u'',\n",
" u'surname': u'Gu\\xe9ritaud',\n",
" u'title': u'GEAR Member',\n",
" u'website': u''},\n",
" {u'cluster_id': 0,\n",
" u'gear_collaborators': [],\n",
" u'member_id': 276,\n",
" u'member_type': u'member',\n",
" u'name': u'Cyril',\n",
" u'organization': u'Universit\\xe9 Paul Sabatier',\n",
" u'other_collaborators': u'',\n",
" u'photo': u'',\n",
" u'pos_x': 0,\n",
" u'pos_y': 0,\n",
" u'research_interests': u'',\n",
" u'short_bio': u'',\n",
" u'surname': u'Lecuire',\n",
" u'title': u'GEAR Member',\n",
" u'website': u''},\n",
" {u'cluster_id': 0,\n",
" u'gear_collaborators': [],\n",
" u'member_id': 277,\n",
" u'member_type': u'postdoc',\n",
" u'name': u'Robert',\n",
" u'organization': u'University of Oklahoma (Mentor: Jing Tao)',\n",
" u'other_collaborators': u'Richard Webb, Max Forester',\n",
" u'photo': u'',\n",
" u'pos_x': 0,\n",
" u'pos_y': 0,\n",
" u'research_interests': u'Geometric and Analytical Group Theory, Geometric Structures and Teichm\\xfcller Spaces',\n",
" u'short_bio': u'Curves on surfaces, curve complexes, mapping class groups, flat surfaces, Teichm\\xfcller theory.',\n",
" u'surname': u'Tang',\n",
" u'title': u'Postdoc',\n",
" u'website': u''},\n",
" {u'cluster_id': 0,\n",
" u'gear_collaborators': [],\n",
" u'member_id': 278,\n",
" u'member_type': u'postdoc',\n",
" u'name': u'Valentina',\n",
" u'organization': u'',\n",
" u'other_collaborators': u'',\n",
" u'photo': u'',\n",
" u'pos_x': 0,\n",
" u'pos_y': 0,\n",
" u'research_interests': u'',\n",
" u'short_bio': u'',\n",
" u'surname': u'Di Sarlo',\n",
" u'title': u'Postdoc',\n",
" u'website': u''},\n",
" {u'cluster_id': 0,\n",
" u'gear_collaborators': [],\n",
" u'member_id': 279,\n",
" u'member_type': u'postdoc',\n",
" u'name': u'Curtis',\n",
" u'organization': u'',\n",
" u'other_collaborators': u'',\n",
" u'photo': u'',\n",
" u'pos_x': 0,\n",
" u'pos_y': 0,\n",
" u'research_interests': u'',\n",
" u'short_bio': u'',\n",
" u'surname': u'Kent',\n",
" u'title': u'Postdoc',\n",
" u'website': u''},\n",
" {u'cluster_id': 0,\n",
" u'gear_collaborators': [],\n",
" u'member_id': 280,\n",
" u'member_type': u'postdoc',\n",
" u'name': u'Thomas',\n",
" u'organization': u'',\n",
" u'other_collaborators': u'',\n",
" u'photo': u'',\n",
" u'pos_x': 0,\n",
" u'pos_y': 0,\n",
" u'research_interests': u'',\n",
" u'short_bio': u'',\n",
" u'surname': u'Koberda',\n",
" u'title': u'Postdoc',\n",
" u'website': u''},\n",
" {u'cluster_id': 0,\n",
" u'gear_collaborators': [],\n",
" u'member_id': 281,\n",
" u'member_type': u'postdoc',\n",
" u'name': u'Kenji',\n",
" u'organization': u'',\n",
" u'other_collaborators': u'',\n",
" u'photo': u'',\n",
" u'pos_x': 0,\n",
" u'pos_y': 0,\n",
" u'research_interests': u'',\n",
" u'short_bio': u'',\n",
" u'surname': u'Kozai',\n",
" u'title': u'Postdoc',\n",
" u'website': u''},\n",
" {u'cluster_id': 0,\n",
" u'gear_collaborators': [],\n",
" u'member_id': 282,\n",
" u'member_type': u'postdoc',\n",
" u'name': u'Michelle',\n",
" u'organization': u'',\n",
" u'other_collaborators': u'',\n",
" u'photo': u'',\n",
" u'pos_x': 0,\n",
" u'pos_y': 0,\n",
" u'research_interests': u'',\n",
" u'short_bio': u'',\n",
" u'surname': u'Lee',\n",
" u'title': u'Postdoc',\n",
" u'website': u''},\n",
" {u'cluster_id': 0,\n",
" u'gear_collaborators': [],\n",
" u'member_id': 283,\n",
" u'member_type': u'postdoc',\n",
" u'name': u'Benjamin',\n",
" u'organization': u'',\n",
" u'other_collaborators': u'',\n",
" u'photo': u'',\n",
" u'pos_x': 0,\n",
" u'pos_y': 0,\n",
" u'research_interests': u'',\n",
" u'short_bio': u'',\n",
" u'surname': u'Linowitz',\n",
" u'title': u'Postdoc',\n",
" u'website': u''},\n",
" {u'cluster_id': 0,\n",
" u'gear_collaborators': [],\n",
" u'member_id': 284,\n",
" u'member_type': u'postdoc',\n",
" u'name': u'Pere',\n",
" u'organization': u'',\n",
" u'other_collaborators': u'',\n",
" u'photo': u'',\n",
" u'pos_x': 0,\n",
" u'pos_y': 0,\n",
" u'research_interests': u'',\n",
" u'short_bio': u'',\n",
" u'surname': u'Menal-Ferrer',\n",
" u'title': u'Postdoc',\n",
" u'website': u''},\n",
" {u'cluster_id': 0,\n",
" u'gear_collaborators': [],\n",
" u'member_id': 285,\n",
" u'member_type': u'postdoc',\n",
" u'name': u'Babak',\n",
" u'organization': u'',\n",
" u'other_collaborators': u'',\n",
" u'photo': u'',\n",
" u'pos_x': 0,\n",
" u'pos_y': 0,\n",
" u'research_interests': u'',\n",
" u'short_bio': u'',\n",
" u'surname': u'Modami',\n",
" u'title': u'Postdoc',\n",
" u'website': u''},\n",
" {u'cluster_id': 0,\n",
" u'gear_collaborators': [],\n",
" u'member_id': 286,\n",
" u'member_type': u'postdoc',\n",
" u'name': u'Jordan',\n",
" u'organization': u'',\n",
" u'other_collaborators': u'',\n",
" u'photo': u'',\n",
" u'pos_x': 0,\n",
" u'pos_y': 0,\n",
" u'research_interests': u'',\n",
" u'short_bio': u'',\n",
" u'surname': u'Sahattchieve',\n",
" u'title': u'Postdoc',\n",
" u'website': u''},\n",
" {u'cluster_id': 0,\n",
" u'gear_collaborators': [],\n",
" u'member_id': 287,\n",
" u'member_type': u'postdoc',\n",
" u'name': u'Sarah',\n",
" u'organization': u'',\n",
" u'other_collaborators': u'',\n",
" u'photo': u'',\n",
" u'pos_x': 0,\n",
" u'pos_y': 0,\n",
" u'research_interests': u'',\n",
" u'short_bio': u'',\n",
" u'surname': u'Koch',\n",
" u'title': u'Postdoc',\n",
" u'website': u''},\n",
" {u'cluster_id': 0,\n",
" u'gear_collaborators': [],\n",
" u'member_id': 288,\n",
" u'member_type': u'postdoc',\n",
" u'name': u'Johanna',\n",
" u'organization': u'',\n",
" u'other_collaborators': u'',\n",
" u'photo': u'',\n",
" u'pos_x': 0,\n",
" u'pos_y': 0,\n",
" u'research_interests': u'',\n",
" u'short_bio': u'',\n",
" u'surname': u'Mangahas',\n",
" u'title': u'Postdoc',\n",
" u'website': u''},\n",
" {u'cluster_id': 0,\n",
" u'gear_collaborators': [],\n",
" u'member_id': 289,\n",
" u'member_type': u'postdoc',\n",
" u'name': u'Sam',\n",
" u'organization': u'',\n",
" u'other_collaborators': u'',\n",
" u'photo': u'',\n",
" u'pos_x': 0,\n",
" u'pos_y': 0,\n",
" u'research_interests': u'',\n",
" u'short_bio': u'',\n",
" u'surname': u'Taylor',\n",
" u'title': u'Postdoc',\n",
" u'website': u''},\n",
" {u'cluster_id': 0,\n",
" u'gear_collaborators': [],\n",
" u'member_id': 290,\n",
" u'member_type': u'postdoc',\n",
" u'name': u'Richard',\n",
" u'organization': u'',\n",
" u'other_collaborators': u'',\n",
" u'photo': u'',\n",
" u'pos_x': 0,\n",
" u'pos_y': 0,\n",
" u'research_interests': u'',\n",
" u'short_bio': u'',\n",
" u'surname': u'Webb',\n",
" u'title': u'Postdoc',\n",
" u'website': u''},\n",
" {u'cluster_id': 0,\n",
" u'gear_collaborators': [],\n",
" u'member_id': 291,\n",
" u'member_type': u'postdoc',\n",
" u'name': u'Andrew',\n",
" u'organization': u'',\n",
" u'other_collaborators': u'',\n",
" u'photo': u'',\n",
" u'pos_x': 0,\n",
" u'pos_y': 0,\n",
" u'research_interests': u'',\n",
" u'short_bio': u'',\n",
" u'surname': u'Zimmer',\n",
" u'title': u'Postdoc',\n",
" u'website': u''},\n",
" {u'cluster_id': 0,\n",
" u'gear_collaborators': [],\n",
" u'member_id': 292,\n",
" u'member_type': u'postdoc',\n",
" u'name': u'Caleb',\n",
" u'organization': u'',\n",
" u'other_collaborators': u'',\n",
" u'photo': u'',\n",
" u'pos_x': 0,\n",
" u'pos_y': 0,\n",
" u'research_interests': u'',\n",
" u'short_bio': u'',\n",
" u'surname': u'Ashley',\n",
" u'title': u'Postdoc',\n",
" u'website': u''},\n",
" {u'cluster_id': 0,\n",
" u'gear_collaborators': [],\n",
" u'member_id': 293,\n",
" u'member_type': u'postdoc',\n",
" u'name': u'Jeffrey',\n",
" u'organization': u'',\n",
" u'other_collaborators': u'',\n",
" u'photo': u'',\n",
" u'pos_x': 0,\n",
" u'pos_y': 0,\n",
" u'research_interests': u'',\n",
" u'short_bio': u'',\n",
" u'surname': u'Danciger',\n",
" u'title': u'Postdoc',\n",
" u'website': u''},\n",
" {u'cluster_id': 0,\n",
" u'gear_collaborators': [],\n",
" u'member_id': 294,\n",
" u'member_type': u'postdoc',\n",
" u'name': u'Ben',\n",
" u'organization': u'',\n",
" u'other_collaborators': u'',\n",
" u'photo': u'',\n",
" u'pos_x': 0,\n",
" u'pos_y': 0,\n",
" u'research_interests': u'',\n",
" u'short_bio': u'',\n",
" u'surname': u'Davison',\n",
" u'title': u'Postdoc',\n",
" u'website': u''},\n",
" {u'cluster_id': 0,\n",
" u'gear_collaborators': [],\n",
" u'member_id': 295,\n",
" u'member_type': u'postdoc',\n",
" u'name': u'Viveka',\n",
" u'organization': u'',\n",
" u'other_collaborators': u'',\n",
" u'photo': u'',\n",
" u'pos_x': 0,\n",
" u'pos_y': 0,\n",
" u'research_interests': u'',\n",
" u'short_bio': u'',\n",
" u'surname': u'Erlandsson',\n",
" u'title': u'Postdoc',\n",
" u'website': u''},\n",
" {u'cluster_id': 0,\n",
" u'gear_collaborators': [],\n",
" u'member_id': 296,\n",
" u'member_type': u'postdoc',\n",
" u'name': u'Steven',\n",
" u'organization': u'',\n",
" u'other_collaborators': u'',\n",
" u'photo': u'',\n",
" u'pos_x': 0,\n",
" u'pos_y': 0,\n",
" u'research_interests': u'',\n",
" u'short_bio': u'',\n",
" u'surname': u'Frankel',\n",
" u'title': u'Postdoc',\n",
" u'website': u''},\n",
" {u'cluster_id': 0,\n",
" u'gear_collaborators': [],\n",
" u'member_id': 297,\n",
" u'member_type': u'postdoc',\n",
" u'name': u'Mark',\n",
" u'organization': u'',\n",
" u'other_collaborators': u'',\n",
" u'photo': u'',\n",
" u'pos_x': 0,\n",
" u'pos_y': 0,\n",
" u'research_interests': u'',\n",
" u'short_bio': u'',\n",
" u'surname': u'Hagen',\n",
" u'title': u'Postdoc',\n",
" u'website': u''},\n",
" {u'cluster_id': 0,\n",
" u'gear_collaborators': [],\n",
" u'member_id': 298,\n",
" u'member_type': u'postdoc',\n",
" u'name': u'Tyler',\n",
" u'organization': u'',\n",
" u'other_collaborators': u'',\n",
" u'photo': u'',\n",
" u'pos_x': 0,\n",
" u'pos_y': 0,\n",
" u'research_interests': u'',\n",
" u'short_bio': u'',\n",
" u'surname': u'Kelly',\n",
" u'title': u'Postdoc',\n",
" u'website': u''},\n",
" {u'cluster_id': 0,\n",
" u'gear_collaborators': [],\n",
" u'member_id': 299,\n",
" u'member_type': u'postdoc',\n",
" u'name': u'Ian',\n",
" u'organization': u'',\n",
" u'other_collaborators': u'',\n",
" u'photo': u'',\n",
" u'pos_x': 0,\n",
" u'pos_y': 0,\n",
" u'research_interests': u'',\n",
" u'short_bio': u'',\n",
" u'surname': u'Le',\n",
" u'title': u'Postdoc',\n",
" u'website': u''},\n",
" {u'cluster_id': 0,\n",
" u'gear_collaborators': [],\n",
" u'member_id': 300,\n",
" u'member_type': u'postdoc',\n",
" u'name': u'Ronen',\n",
" u'organization': u'',\n",
" u'other_collaborators': u'',\n",
" u'photo': u'',\n",
" u'pos_x': 0,\n",
" u'pos_y': 0,\n",
" u'research_interests': u'',\n",
" u'short_bio': u'',\n",
" u'surname': u'Mukamel',\n",
" u'title': u'Postdoc',\n",
" u'website': u''},\n",
" {u'cluster_id': 0,\n",
" u'gear_collaborators': [],\n",
" u'member_id': 301,\n",
" u'member_type': u'postdoc',\n",
" u'name': u'Swarnava',\n",
" u'organization': u'',\n",
" u'other_collaborators': u'',\n",
" u'photo': u'',\n",
" u'pos_x': 0,\n",
" u'pos_y': 0,\n",
" u'research_interests': u'',\n",
" u'short_bio': u'',\n",
" u'surname': u'Mukhopadhyay',\n",
" u'title': u'Postdoc',\n",
" u'website': u''},\n",
" {u'cluster_id': 0,\n",
" u'gear_collaborators': [],\n",
" u'member_id': 302,\n",
" u'member_type': u'postdoc',\n",
" u'name': u'Steven',\n",
" u'organization': u'',\n",
" u'other_collaborators': u'',\n",
" u'photo': u'',\n",
" u'pos_x': 0,\n",
" u'pos_y': 0,\n",
" u'research_interests': u'',\n",
" u'short_bio': u'',\n",
" u'surname': u'Rayan',\n",
" u'title': u'Postdoc',\n",
" u'website': u''},\n",
" {u'cluster_id': 0,\n",
" u'gear_collaborators': [],\n",
" u'member_id': 303,\n",
" u'member_type': u'postdoc',\n",
" u'name': u'Jenya',\n",
" u'organization': u'',\n",
" u'other_collaborators': u'',\n",
" u'photo': u'',\n",
" u'pos_x': 0,\n",
" u'pos_y': 0,\n",
" u'research_interests': u'',\n",
" u'short_bio': u'',\n",
" u'surname': u'Sapir',\n",
" u'title': u'Postdoc',\n",
" u'website': u''},\n",
" {u'cluster_id': 0,\n",
" u'gear_collaborators': [],\n",
" u'member_id': 304,\n",
" u'member_type': u'postdoc',\n",
" u'name': u'Mehmet',\n",
" u'organization': u'',\n",
" u'other_collaborators': u'',\n",
" u'photo': u'',\n",
" u'pos_x': 0,\n",
" u'pos_y': 0,\n",
" u'research_interests': u'',\n",
" u'short_bio': u'',\n",
" u'surname': u'Sengun',\n",
" u'title': u'Postdoc',\n",
" u'website': u''},\n",
" {u'cluster_id': 0,\n",
" u'gear_collaborators': [],\n",
" u'member_id': 305,\n",
" u'member_type': u'postdoc',\n",
" u'name': u'Benjamin',\n",
" u'organization': u'',\n",
" u'other_collaborators': u'',\n",
" u'photo': u'',\n",
" u'pos_x': 0,\n",
" u'pos_y': 0,\n",
" u'research_interests': u'',\n",
" u'short_bio': u'',\n",
" u'surname': u'Sibley',\n",
" u'title': u'Postdoc',\n",
" u'website': u''},\n",
" {u'cluster_id': 0,\n",
" u'gear_collaborators': [],\n",
" u'member_id': 306,\n",
" u'member_type': u'postdoc',\n",
" u'name': u'Bo Gwang',\n",
" u'organization': u'',\n",
" u'other_collaborators': u'',\n",
" u'photo': u'',\n",
" u'pos_x': 0,\n",
" u'pos_y': 0,\n",
" u'research_interests': u'',\n",
" u'short_bio': u'',\n",
" u'surname': u'Jeon',\n",
" u'title': u'Postdoc',\n",
" u'website': u''},\n",
" {u'cluster_id': 0,\n",
" u'gear_collaborators': [],\n",
" u'member_id': 307,\n",
" u'member_type': u'postdoc',\n",
" u'name': u'Scott',\n",
" u'organization': u'',\n",
" u'other_collaborators': u'',\n",
" u'photo': u'',\n",
" u'pos_x': 0,\n",
" u'pos_y': 0,\n",
" u'research_interests': u'',\n",
" u'short_bio': u'',\n",
" u'surname': u'Thomson',\n",
" u'title': u'Postdoc',\n",
" u'website': u''},\n",
" {u'cluster_id': 0,\n",
" u'gear_collaborators': [],\n",
" u'member_id': 308,\n",
" u'member_type': u'graduate',\n",
" u'name': u'Christopher',\n",
" u'organization': u'',\n",
" u'other_collaborators': u'',\n",
" u'photo': u'',\n",
" u'pos_x': 0,\n",
" u'pos_y': 0,\n",
" u'research_interests': u'',\n",
" u'short_bio': u'',\n",
" u'surname': u'Abram',\n",
" u'title': u'Graduate Student',\n",
" u'website': u''},\n",
" {u'cluster_id': 0,\n",
" u'gear_collaborators': [],\n",
" u'member_id': 309,\n",
" u'member_type': u'graduate',\n",
" u'name': u'Fabi\\xe1n',\n",
" u'organization': u'',\n",
" u'other_collaborators': u'',\n",
" u'photo': u'',\n",
" u'pos_x': 0,\n",
" u'pos_y': 0,\n",
" u'research_interests': u'',\n",
" u'short_bio': u'',\n",
" u'surname': u'Arias',\n",
" u'title': u'Graduate Student',\n",
" u'website': u''},\n",
" {u'cluster_id': 0,\n",
" u'gear_collaborators': [],\n",
" u'member_id': 310,\n",
" u'member_type': u'graduate',\n",
" u'name': u'Duv\\xe1n',\n",
" u'organization': u'',\n",
" u'other_collaborators': u'',\n",
" u'photo': u'',\n",
" u'pos_x': 0,\n",
" u'pos_y': 0,\n",
" u'research_interests': u'',\n",
" u'short_bio': u'',\n",
" u'surname': u'Cardona',\n",
" u'title': u'Graduate Student',\n",
" u'website': u''},\n",
" {u'cluster_id': 0,\n",
" u'gear_collaborators': [],\n",
" u'member_id': 311,\n",
" u'member_type': u'graduate',\n",
" u'name': u'Ellie',\n",
" u'organization': u'',\n",
" u'other_collaborators': u'',\n",
" u'photo': u'',\n",
" u'pos_x': 0,\n",
" u'pos_y': 0,\n",
" u'research_interests': u'',\n",
" u'short_bio': u'',\n",
" u'surname': u'Dannenberg',\n",
" u'title': u'Graduate Student',\n",
" u'website': u''},\n",
" {u'cluster_id': 0,\n",
" u'gear_collaborators': [],\n",
" u'member_id': 312,\n",
" u'member_type': u'graduate',\n",
" u'name': u'Saikat',\n",
" u'organization': u'',\n",
" u'other_collaborators': u'',\n",
" u'photo': u'',\n",
" u'pos_x': 0,\n",
" u'pos_y': 0,\n",
" u'research_interests': u'',\n",
" u'short_bio': u'',\n",
" u'surname': u'Das',\n",
" u'title': u'Graduate Student',\n",
" u'website': u''},\n",
" {u'cluster_id': 0,\n",
" u'gear_collaborators': [],\n",
" u'member_id': 313,\n",
" u'member_type': u'graduate',\n",
" u'name': u'Benjamin',\n",
" u'organization': u'',\n",
" u'other_collaborators': u'',\n",
" u'photo': u'',\n",
" u'pos_x': 0,\n",
" u'pos_y': 0,\n",
" u'research_interests': u'',\n",
" u'short_bio': u'',\n",
" u'surname': u'Dazier',\n",
" u'title': u'Graduate Student',\n",
" u'website': u''},\n",
" {u'cluster_id': 0,\n",
" u'gear_collaborators': [],\n",
" u'member_id': 314,\n",
" u'member_type': u'graduate',\n",
" u'name': u'Yen',\n",
" u'organization': u'',\n",
" u'other_collaborators': u'',\n",
" u'photo': u'',\n",
" u'pos_x': 0,\n",
" u'pos_y': 0,\n",
" u'research_interests': u'',\n",
" u'short_bio': u'',\n",
" u'surname': u'Duong',\n",
" u'title': u'Graduate Student',\n",
" u'website': u''},\n",
" {u'cluster_id': 0,\n",
" u'gear_collaborators': [],\n",
" u'member_id': 315,\n",
" u'member_type': u'graduate',\n",
" u'name': u'Matthew',\n",
" u'organization': u'',\n",
" u'other_collaborators': u'',\n",
" u'photo': u'',\n",
" u'pos_x': 0,\n",
" u'pos_y': 0,\n",
" u'research_interests': u'',\n",
" u'short_bio': u'',\n",
" u'surname': u'Durham',\n",
" u'title': u'Graduate Student',\n",
" u'website': u''},\n",
" {u'cluster_id': 0,\n",
" u'gear_collaborators': [],\n",
" u'member_id': 316,\n",
" u'member_type': u'graduate',\n",
" u'name': u'Lindsey',\n",
" u'organization': u'',\n",
" u'other_collaborators': u'',\n",
" u'photo': u'',\n",
" u'pos_x': 0,\n",
" u'pos_y': 0,\n",
" u'research_interests': u'',\n",
" u'short_bio': u'',\n",
" u'surname': u'Gamard',\n",
" u'title': u'Graduate Student',\n",
" u'website': u''},\n",
" {u'cluster_id': 0,\n",
" u'gear_collaborators': [],\n",
" u'member_id': 317,\n",
" u'member_type': u'graduate',\n",
" u'name': u'Jordane',\n",
" u'organization': u'',\n",
" u'other_collaborators': u'',\n",
" u'photo': u'',\n",
" u'pos_x': 0,\n",
" u'pos_y': 0,\n",
" u'research_interests': u'',\n",
" u'short_bio': u'',\n",
" u'surname': u'Granier',\n",
" u'title': u'Graduate Student',\n",
" u'website': u''},\n",
" {u'cluster_id': 0,\n",
" u'gear_collaborators': [],\n",
" u'member_id': 318,\n",
" u'member_type': u'graduate',\n",
" u'name': u'Mustafa',\n",
" u'organization': u'',\n",
" u'other_collaborators': u'',\n",
" u'photo': u'',\n",
" u'pos_x': 0,\n",
" u'pos_y': 0,\n",
" u'research_interests': u'',\n",
" u'short_bio': u'',\n",
" u'surname': u'Hajij',\n",
" u'title': u'Graduate Student',\n",
" u'website': u''},\n",
" {u'cluster_id': 0,\n",
" u'gear_collaborators': [],\n",
" u'member_id': 319,\n",
" u'member_type': u'graduate',\n",
" u'name': u'Son',\n",
" u'organization': u'',\n",
" u'other_collaborators': u'',\n",
" u'photo': u'',\n",
" u'pos_x': 0,\n",
" u'pos_y': 0,\n",
" u'research_interests': u'',\n",
" u'short_bio': u'',\n",
" u'surname': u'Ho',\n",
" u'title': u'Graduate Student',\n",
" u'website': u''},\n",
" {u'cluster_id': 0,\n",
" u'gear_collaborators': [],\n",
" u'member_id': 320,\n",
" u'member_type': u'graduate',\n",
" u'name': u'Gahye',\n",
" u'organization': u'',\n",
" u'other_collaborators': u'',\n",
" u'photo': u'',\n",
" u'pos_x': 0,\n",
" u'pos_y': 0,\n",
" u'research_interests': u'',\n",
" u'short_bio': u'',\n",
" u'surname': u'Jeong',\n",
" u'title': u'Graduate Student',\n",
" u'website': u''},\n",
" {u'cluster_id': 0,\n",
" u'gear_collaborators': [],\n",
" u'member_id': 321,\n",
" u'member_type': u'graduate',\n",
" u'name': u'Muhammad Ali',\n",
" u'organization': u'',\n",
" u'other_collaborators': u'',\n",
" u'photo': u'',\n",
" u'pos_x': 0,\n",
" u'pos_y': 0,\n",
" u'research_interests': u'',\n",
" u'short_bio': u'',\n",
" u'surname': u'Khan',\n",
" u'title': u'Graduate Student',\n",
" u'website': u''},\n",
" {u'cluster_id': 0,\n",
" u'gear_collaborators': [],\n",
" u'member_id': 322,\n",
" u'member_type': u'graduate',\n",
" u'name': u'Michael',\n",
" u'organization': u'',\n",
" u'other_collaborators': u'',\n",
" u'photo': u'',\n",
" u'pos_x': 0,\n",
" u'pos_y': 0,\n",
" u'research_interests': u'',\n",
" u'short_bio': u'',\n",
" u'surname': u'Landry',\n",
" u'title': u'Graduate Student',\n",
" u'website': u''},\n",
" {u'cluster_id': 0,\n",
" u'gear_collaborators': [],\n",
" u'member_id': 323,\n",
" u'member_type': u'graduate',\n",
" u'name': u'John',\n",
" u'organization': u'',\n",
" u'other_collaborators': u'',\n",
" u'photo': u'',\n",
" u'pos_x': 0,\n",
" u'pos_y': 0,\n",
" u'research_interests': u'',\n",
" u'short_bio': u'',\n",
" u'surname': u'Lawson',\n",
" u'title': u'Graduate Student',\n",
" u'website': u''},\n",
" {u'cluster_id': 0,\n",
" u'gear_collaborators': [],\n",
" u'member_id': 324,\n",
" u'member_type': u'graduate',\n",
" u'name': u'Ivan',\n",
" u'organization': u'',\n",
" u'other_collaborators': u'',\n",
" u'photo': u'',\n",
" u'pos_x': 0,\n",
" u'pos_y': 0,\n",
" u'research_interests': u'',\n",
" u'short_bio': u'',\n",
" u'surname': u'Levcovitz',\n",
" u'title': u'Graduate Student',\n",
" u'website': u''},\n",
" {u'cluster_id': 0,\n",
" u'gear_collaborators': [],\n",
" u'member_id': 325,\n",
" u'member_type': u'graduate',\n",
" u'name': u'Tianyu',\n",
" u'organization': u'',\n",
" u'other_collaborators': u'',\n",
" u'photo': u'',\n",
" u'pos_x': 0,\n",
" u'pos_y': 0,\n",
" u'research_interests': u'',\n",
" u'short_bio': u'',\n",
" u'surname': u'Ma',\n",
" u'title': u'Graduate Student',\n",
" u'website': u''},\n",
" {u'cluster_id': 0,\n",
" u'gear_collaborators': [],\n",
" u'member_id': 326,\n",
" u'member_type': u'graduate',\n",
" u'name': u'Fran\\xe7ois',\n",
" u'organization': u'',\n",
" u'other_collaborators': u'',\n",
" u'photo': u'',\n",
" u'pos_x': 0,\n",
" u'pos_y': 0,\n",
" u'research_interests': u'',\n",
" u'short_bio': u'',\n",
" u'surname': u'Malabre',\n",
" u'title': u'Graduate Student',\n",
" u'website': u''},\n",
" {u'cluster_id': 0,\n",
" u'gear_collaborators': [],\n",
" u'member_id': 327,\n",
" u'member_type': u'graduate',\n",
" u'name': u'Justin',\n",
" u'organization': u'',\n",
" u'other_collaborators': u'',\n",
" u'photo': u'',\n",
" u'pos_x': 0,\n",
" u'pos_y': 0,\n",
" u'research_interests': u'',\n",
" u'short_bio': u'',\n",
" u'surname': u'Martel',\n",
" u'title': u'Graduate Student',\n",
" u'website': u''},\n",
" {u'cluster_id': 0,\n",
" u'gear_collaborators': [],\n",
" u'member_id': 328,\n",
" u'member_type': u'graduate',\n",
" u'name': u'David',\n",
" u'organization': u'',\n",
" u'other_collaborators': u'',\n",
" u'photo': u'',\n",
" u'pos_x': 0,\n",
" u'pos_y': 0,\n",
" u'research_interests': u'',\n",
" u'short_bio': u'',\n",
" u'surname': u'Renardy',\n",
" u'title': u'Graduate Student',\n",
" u'website': u''},\n",
" {u'cluster_id': 0,\n",
" u'gear_collaborators': [],\n",
" u'member_id': 329,\n",
" u'member_type': u'graduate',\n",
" u'name': u'Doreen',\n",
" u'organization': u'',\n",
" u'other_collaborators': u'',\n",
" u'photo': u'',\n",
" u'pos_x': 0,\n",
" u'pos_y': 0,\n",
" u'research_interests': u'',\n",
" u'short_bio': u'',\n",
" u'surname': u'Reuchsel',\n",
" u'title': u'Graduate Student',\n",
" u'website': u''},\n",
" {u'cluster_id': 0,\n",
" u'gear_collaborators': [],\n",
" u'member_id': 330,\n",
" u'member_type': u'graduate',\n",
" u'name': u'David',\n",
" u'organization': u'',\n",
" u'other_collaborators': u'',\n",
" u'photo': u'',\n",
" u'pos_x': 0,\n",
" u'pos_y': 0,\n",
" u'research_interests': u'',\n",
" u'short_bio': u'',\n",
" u'surname': u'Reynoso Valle',\n",
" u'title': u'Graduate Student',\n",
" u'website': u''},\n",
" {u'cluster_id': 0,\n",
" u'gear_collaborators': [],\n",
" u'member_id': 331,\n",
" u'member_type': u'graduate',\n",
" u'name': u'Anna-Sofie',\n",
" u'organization': u'',\n",
" u'other_collaborators': u'',\n",
" u'photo': u'',\n",
" u'pos_x': 0,\n",
" u'pos_y': 0,\n",
" u'research_interests': u'',\n",
" u'short_bio': u'',\n",
" u'surname': u'Schilling',\n",
" u'title': u'Graduate Student',\n",
" u'website': u''},\n",
" {u'cluster_id': 0,\n",
" u'gear_collaborators': [],\n",
" u'member_id': 332,\n",
" u'member_type': u'graduate',\n",
" u'name': u'Nicolas',\n",
" u'organization': u'',\n",
" u'other_collaborators': u'',\n",
" u'photo': u'',\n",
" u'pos_x': 0,\n",
" u'pos_y': 0,\n",
" u'research_interests': u'',\n",
" u'short_bio': u'',\n",
" u'surname': u'Tholozan',\n",
" u'title': u'Graduate Student',\n",
" u'website': u''},\n",
" {u'cluster_id': 0,\n",
" u'gear_collaborators': [],\n",
" u'member_id': 333,\n",
" u'member_type': u'graduate',\n",
" u'name': u'Nicholas',\n",
" u'organization': u'',\n",
" u'other_collaborators': u'',\n",
" u'photo': u'',\n",
" u'pos_x': 0,\n",
" u'pos_y': 0,\n",
" u'research_interests': u'',\n",
" u'short_bio': u'',\n",
" u'surname': u'Vlamis',\n",
" u'title': u'Graduate Student',\n",
" u'website': u''},\n",
" {u'cluster_id': 0,\n",
" u'gear_collaborators': [],\n",
" u'member_id': 334,\n",
" u'member_type': u'graduate',\n",
" u'name': u'Yohsuke',\n",
" u'organization': u'',\n",
" u'other_collaborators': u'',\n",
" u'photo': u'',\n",
" u'pos_x': 0,\n",
" u'pos_y': 0,\n",
" u'research_interests': u'',\n",
" u'short_bio': u'',\n",
" u'surname': u'Watanabe',\n",
" u'title': u'Graduate Student',\n",
" u'website': u''},\n",
" {u'cluster_id': 0,\n",
" u'gear_collaborators': [],\n",
" u'member_id': 335,\n",
" u'member_type': u'graduate',\n",
" u'name': u'Joseph',\n",
" u'organization': u'',\n",
" u'other_collaborators': u'',\n",
" u'photo': u'',\n",
" u'pos_x': 0,\n",
" u'pos_y': 0,\n",
" u'research_interests': u'',\n",
" u'short_bio': u'',\n",
" u'surname': u'Wells',\n",
" u'title': u'Graduate Student',\n",
" u'website': u''},\n",
" {u'cluster_id': 0,\n",
" u'gear_collaborators': [],\n",
" u'member_id': 336,\n",
" u'member_type': u'graduate',\n",
" u'name': u'Daping',\n",
" u'organization': u'',\n",
" u'other_collaborators': u'',\n",
" u'photo': u'',\n",
" u'pos_x': 0,\n",
" u'pos_y': 0,\n",
" u'research_interests': u'',\n",
" u'short_bio': u'',\n",
" u'surname': u'Weng',\n",
" u'title': u'Graduate Student',\n",
" u'website': u''},\n",
" {u'cluster_id': 0,\n",
" u'gear_collaborators': [],\n",
" u'member_id': 337,\n",
" u'member_type': u'graduate',\n",
" u'name': u'Jonathan',\n",
" u'organization': u'',\n",
" u'other_collaborators': u'',\n",
" u'photo': u'',\n",
" u'pos_x': 0,\n",
" u'pos_y': 0,\n",
" u'research_interests': u'',\n",
" u'short_bio': u'',\n",
" u'surname': u'Wilson',\n",
" u'title': u'Graduate Student',\n",
" u'website': u''},\n",
" {u'cluster_id': 0,\n",
" u'gear_collaborators': [],\n",
" u'member_id': 338,\n",
" u'member_type': u'graduate',\n",
" u'name': u'Andrew',\n",
" u'organization': u'',\n",
" u'other_collaborators': u'',\n",
" u'photo': u'',\n",
" u'pos_x': 0,\n",
" u'pos_y': 0,\n",
" u'research_interests': u'',\n",
" u'short_bio': u'',\n",
" u'surname': u'Yarmola',\n",
" u'title': u'Graduate Student',\n",
" u'website': u''},\n",
" {u'cluster_id': 0,\n",
" u'gear_collaborators': [],\n",
" u'member_id': 339,\n",
" u'member_type': u'graduate',\n",
" u'name': u'Ren',\n",
" u'organization': u'',\n",
" u'other_collaborators': u'',\n",
" u'photo': u'',\n",
" u'pos_x': 0,\n",
" u'pos_y': 0,\n",
" u'research_interests': u'',\n",
" u'short_bio': u'',\n",
" u'surname': u'Yi',\n",
" u'title': u'Graduate Student',\n",
" u'website': u''},\n",
" {u'cluster_id': 0,\n",
" u'gear_collaborators': [],\n",
" u'member_id': 340,\n",
" u'member_type': u'graduate',\n",
" u'name': u'Tengren',\n",
" u'organization': u'',\n",
" u'other_collaborators': u'',\n",
" u'photo': u'',\n",
" u'pos_x': 0,\n",
" u'pos_y': 0,\n",
" u'research_interests': u'',\n",
" u'short_bio': u'',\n",
" u'surname': u'Zhang',\n",
" u'title': u'Graduate Student',\n",
" u'website': u''},\n",
" {u'cluster_id': 0,\n",
" u'gear_collaborators': [],\n",
" u'member_id': 342,\n",
" u'member_type': u'graduate',\n",
" u'name': u'Du',\n",
" u'organization': u'',\n",
" u'other_collaborators': u'',\n",
" u'photo': u'',\n",
" u'pos_x': 0,\n",
" u'pos_y': 0,\n",
" u'research_interests': u'',\n",
" u'short_bio': u'',\n",
" u'surname': u'Pei',\n",
" u'title': u'Graduate Student',\n",
" u'website': u''},\n",
" {u'cluster_id': 0,\n",
" u'gear_collaborators': [],\n",
" u'member_id': 343,\n",
" u'member_type': u'graduate',\n",
" u'name': u'Tarik',\n",
" u'organization': u'',\n",
" u'other_collaborators': u'',\n",
" u'photo': u'',\n",
" u'pos_x': 0,\n",
" u'pos_y': 0,\n",
" u'research_interests': u'',\n",
" u'short_bio': u'',\n",
" u'surname': u'Aougab',\n",
" u'title': u'Graduate Student',\n",
" u'website': u''},\n",
" {u'cluster_id': 0,\n",
" u'gear_collaborators': [],\n",
" u'member_id': 344,\n",
" u'member_type': u'graduate',\n",
" u'name': u'Mark',\n",
" u'organization': u'',\n",
" u'other_collaborators': u'',\n",
" u'photo': u'',\n",
" u'pos_x': 0,\n",
" u'pos_y': 0,\n",
" u'research_interests': u'',\n",
" u'short_bio': u'',\n",
" u'surname': u'Bell',\n",
" u'title': u'Graduate Student',\n",
" u'website': u''},\n",
" {u'cluster_id': 0,\n",
" u'gear_collaborators': [],\n",
" u'member_id': 345,\n",
" u'member_type': u'graduate',\n",
" u'name': u'Benoit',\n",
" u'organization': u'',\n",
" u'other_collaborators': u'',\n",
" u'photo': u'',\n",
" u'pos_x': 0,\n",
" u'pos_y': 0,\n",
" u'research_interests': u'',\n",
" u'short_bio': u'',\n",
" u'surname': u'Cadorel',\n",
" u'title': u'Graduate Student',\n",
" u'website': u''},\n",
" {u'cluster_id': 0,\n",
" u'gear_collaborators': [],\n",
" u'member_id': 346,\n",
" u'member_type': u'graduate',\n",
" u'name': u'Michael',\n",
" u'organization': u'',\n",
" u'other_collaborators': u'',\n",
" u'photo': u'',\n",
" u'pos_x': 0,\n",
" u'pos_y': 0,\n",
" u'research_interests': u'',\n",
" u'short_bio': u'',\n",
" u'surname': u'Cantrell',\n",
" u'title': u'Graduate Student',\n",
" u'website': u''},\n",
" {u'cluster_id': 0,\n",
" u'gear_collaborators': [],\n",
" u'member_id': 347,\n",
" u'member_type': u'graduate',\n",
" u'name': u'William',\n",
" u'organization': u'',\n",
" u'other_collaborators': u'',\n",
" u'photo': u'',\n",
" u'pos_x': 0,\n",
" u'pos_y': 0,\n",
" u'research_interests': u'',\n",
" u'short_bio': u'',\n",
" u'surname': u'Cavendish',\n",
" u'title': u'Graduate Student',\n",
" u'website': u''},\n",
" {u'cluster_id': 0,\n",
" u'gear_collaborators': [],\n",
" u'member_id': 348,\n",
" u'member_type': u'graduate',\n",
" u'name': u'Otis',\n",
" u'organization': u'',\n",
" u'other_collaborators': u'',\n",
" u'photo': u'',\n",
" u'pos_x': 0,\n",
" u'pos_y': 0,\n",
" u'research_interests': u'',\n",
" u'short_bio': u'',\n",
" u'surname': u'Chodosh',\n",
" u'title': u'Graduate Student',\n",
" u'website': u''},\n",
" {u'cluster_id': 0,\n",
" u'gear_collaborators': [],\n",
" u'member_id': 349,\n",
" u'member_type': u'graduate',\n",
" u'name': u'Yongfei',\n",
" u'organization': u'',\n",
" u'other_collaborators': u'',\n",
" u'photo': u'',\n",
" u'pos_x': 0,\n",
" u'pos_y': 0,\n",
" u'research_interests': u'',\n",
" u'short_bio': u'',\n",
" u'surname': u'Ci',\n",
" u'title': u'Graduate Student',\n",
" u'website': u''},\n",
" {u'cluster_id': 0,\n",
" u'gear_collaborators': [],\n",
" u'member_id': 350,\n",
" u'member_type': u'graduate',\n",
" u'name': u'Vladimir',\n",
" u'organization': u'',\n",
" u'other_collaborators': u'',\n",
" u'photo': u'',\n",
" u'pos_x': 0,\n",
" u'pos_y': 0,\n",
" u'research_interests': u'',\n",
" u'short_bio': u'',\n",
" u'surname': u'Finkelshtein',\n",
" u'title': u'Graduate Student',\n",
" u'website': u''},\n",
" {u'cluster_id': 0,\n",
" u'gear_collaborators': [],\n",
" u'member_id': 351,\n",
" u'member_type': u'graduate',\n",
" u'name': u'Maxime',\n",
" u'organization': u'',\n",
" u'other_collaborators': u'',\n",
" u'photo': u'',\n",
" u'pos_x': 0,\n",
" u'pos_y': 0,\n",
" u'research_interests': u'',\n",
" u'short_bio': u'',\n",
" u'surname': u'Fortier Bourques',\n",
" u'title': u'Graduate Student',\n",
" u'website': u''},\n",
" {u'cluster_id': 0,\n",
" u'gear_collaborators': [],\n",
" u'member_id': 352,\n",
" u'member_type': u'graduate',\n",
" u'name': u'Dominik',\n",
" u'organization': u'',\n",
" u'other_collaborators': u'',\n",
" u'photo': u'',\n",
" u'pos_x': 0,\n",
" u'pos_y': 0,\n",
" u'research_interests': u'',\n",
" u'short_bio': u'',\n",
" u'surname': u'Francoeur',\n",
" u'title': u'Graduate Student',\n",
" u'website': u''},\n",
" {u'cluster_id': 0,\n",
" u'gear_collaborators': [],\n",
" u'member_id': 353,\n",
" u'member_type': u'graduate',\n",
" u'name': u'Richard',\n",
" u'organization': u'',\n",
" u'other_collaborators': u'',\n",
" u'photo': u'',\n",
" u'pos_x': 0,\n",
" u'pos_y': 0,\n",
" u'research_interests': u'',\n",
" u'short_bio': u'',\n",
" u'surname': u'Hughes',\n",
" u'title': u'Graduate Student',\n",
" u'website': u''},\n",
" {u'cluster_id': 0,\n",
" u'gear_collaborators': [],\n",
" u'member_id': 354,\n",
" u'member_type': u'graduate',\n",
" u'name': u'Gregory',\n",
" u'organization': u'',\n",
" u'other_collaborators': u'',\n",
" u'photo': u'',\n",
" u'pos_x': 0,\n",
" u'pos_y': 0,\n",
" u'research_interests': u'',\n",
" u'short_bio': u'',\n",
" u'surname': u'Laun',\n",
" u'title': u'Graduate Student',\n",
" u'website': u''},\n",
" {u'cluster_id': 0,\n",
" u'gear_collaborators': [],\n",
" u'member_id': 355,\n",
" u'member_type': u'graduate',\n",
" u'name': u'Tong',\n",
" u'organization': u'',\n",
" u'other_collaborators': u'',\n",
" u'photo': u'',\n",
" u'pos_x': 0,\n",
" u'pos_y': 0,\n",
" u'research_interests': u'',\n",
" u'short_bio': u'',\n",
" u'surname': u'Li',\n",
" u'title': u'Graduate Student',\n",
" u'website': u''},\n",
" {u'cluster_id': 0,\n",
" u'gear_collaborators': [],\n",
" u'member_id': 356,\n",
" u'member_type': u'graduate',\n",
" u'name': u'Brian',\n",
" u'organization': u'',\n",
" u'other_collaborators': u'',\n",
" u'photo': u'',\n",
" u'pos_x': 0,\n",
" u'pos_y': 0,\n",
" u'research_interests': u'',\n",
" u'short_bio': u'',\n",
" u'surname': u'Mann',\n",
" u'title': u'Graduate Student',\n",
" u'website': u''},\n",
" {u'cluster_id': 0,\n",
" u'gear_collaborators': [],\n",
" u'member_id': 357,\n",
" u'member_type': u'graduate',\n",
" u'name': u'Robert',\n",
" u'organization': u'',\n",
" u'other_collaborators': u'',\n",
" u'photo': u'',\n",
" u'pos_x': 0,\n",
" u'pos_y': 0,\n",
" u'research_interests': u'',\n",
" u'short_bio': u'',\n",
" u'surname': u'Maschal',\n",
" u'title': u'Graduate Student',\n",
" u'website': u''},\n",
" {u'cluster_id': 0,\n",
" u'gear_collaborators': [],\n",
" u'member_id': 358,\n",
" u'member_type': u'graduate',\n",
" u'name': u'Nikita',\n",
" u'organization': u'',\n",
" u'other_collaborators': u'',\n",
" u'photo': u'',\n",
" u'pos_x': 0,\n",
" u'pos_y': 0,\n",
" u'research_interests': u'',\n",
" u'short_bio': u'',\n",
" u'surname': u'Nikolaev',\n",
" u'title': u'Graduate Student',\n",
" u'website': u''},\n",
" {u'cluster_id': 0,\n",
" u'gear_collaborators': [],\n",
" u'member_id': 359,\n",
" u'member_type': u'graduate',\n",
" u'name': u'Alexander',\n",
" u'organization': u'',\n",
" u'other_collaborators': u'',\n",
" u'photo': u'',\n",
" u'pos_x': 0,\n",
" u'pos_y': 0,\n",
" u'research_interests': u'',\n",
" u'short_bio': u'',\n",
" u'surname': u'Rasmussen',\n",
" u'title': u'Graduate Student',\n",
" u'website': u''},\n",
" {u'cluster_id': 0,\n",
" u'gear_collaborators': [],\n",
" u'member_id': 360,\n",
" u'member_type': u'graduate',\n",
" u'name': u'Domingo',\n",
" u'organization': u'',\n",
" u'other_collaborators': u'',\n",
" u'photo': u'',\n",
" u'pos_x': 0,\n",
" u'pos_y': 0,\n",
" u'research_interests': u'',\n",
" u'short_bio': u'',\n",
" u'surname': u'Ruiz',\n",
" u'title': u'Graduate Student',\n",
" u'website': u''},\n",
" {u'cluster_id': 0,\n",
" u'gear_collaborators': [],\n",
" u'member_id': 361,\n",
" u'member_type': u'graduate',\n",
" u'name': u'Partha',\n",
" u'organization': u'',\n",
" u'other_collaborators': u'',\n",
" u'photo': u'',\n",
" u'pos_x': 0,\n",
" u'pos_y': 0,\n",
" u'research_interests': u'',\n",
" u'short_bio': u'',\n",
" u'surname': u'Solapurkar',\n",
" u'title': u'Graduate Student',\n",
" u'website': u''},\n",
" {u'cluster_id': 0,\n",
" u'gear_collaborators': [],\n",
" u'member_id': 362,\n",
" u'member_type': u'graduate',\n",
" u'name': u'Daniel',\n",
" u'organization': u'',\n",
" u'other_collaborators': u'',\n",
" u'photo': u'',\n",
" u'pos_x': 0,\n",
" u'pos_y': 0,\n",
" u'research_interests': u'',\n",
" u'short_bio': u'',\n",
" u'surname': u'Studenmund',\n",
" u'title': u'Graduate Student',\n",
" u'website': u''},\n",
" {u'cluster_id': 0,\n",
" u'gear_collaborators': [],\n",
" u'member_id': 363,\n",
" u'member_type': u'graduate',\n",
" u'name': u'Bo',\n",
" u'organization': u'',\n",
" u'other_collaborators': u'',\n",
" u'photo': u'',\n",
" u'pos_x': 0,\n",
" u'pos_y': 0,\n",
" u'research_interests': u'',\n",
" u'short_bio': u'',\n",
" u'surname': u'Tian',\n",
" u'title': u'Graduate Student',\n",
" u'website': u''},\n",
" {u'cluster_id': 0,\n",
" u'gear_collaborators': [],\n",
" u'member_id': 364,\n",
" u'member_type': u'graduate',\n",
" u'name': u'Bena',\n",
" u'organization': u'',\n",
" u'other_collaborators': u'',\n",
" u'photo': u'',\n",
" u'pos_x': 0,\n",
" u'pos_y': 0,\n",
" u'research_interests': u'',\n",
" u'short_bio': u'',\n",
" u'surname': u'Tshishiku',\n",
" u'title': u'Graduate Student',\n",
" u'website': u''},\n",
" {u'cluster_id': 0,\n",
" u'gear_collaborators': [],\n",
" u'member_id': 365,\n",
" u'member_type': u'graduate',\n",
" u'name': u'Wouter',\n",
" u'organization': u'',\n",
" u'other_collaborators': u'',\n",
" u'photo': u'',\n",
" u'pos_x': 0,\n",
" u'pos_y': 0,\n",
" u'research_interests': u'',\n",
" u'short_bio': u'',\n",
" u'surname': u'van Limbeek',\n",
" u'title': u'Graduate Student',\n",
" u'website': u''},\n",
" {u'cluster_id': 0,\n",
" u'gear_collaborators': [],\n",
" u'member_id': 366,\n",
" u'member_type': u'graduate',\n",
" u'name': u'Alden',\n",
" u'organization': u'',\n",
" u'other_collaborators': u'',\n",
" u'photo': u'',\n",
" u'pos_x': 0,\n",
" u'pos_y': 0,\n",
" u'research_interests': u'',\n",
" u'short_bio': u'',\n",
" u'surname': u'Walker',\n",
" u'title': u'Graduate Student',\n",
" u'website': u''},\n",
" {u'cluster_id': 0,\n",
" u'gear_collaborators': [],\n",
" u'member_id': 367,\n",
" u'member_type': u'graduate',\n",
" u'name': u'Yuecheng',\n",
" u'organization': u'',\n",
" u'other_collaborators': u'',\n",
" u'photo': u'',\n",
" u'pos_x': 0,\n",
" u'pos_y': 0,\n",
" u'research_interests': u'',\n",
" u'short_bio': u'',\n",
" u'surname': u'Zhu',\n",
" u'title': u'Graduate Student',\n",
" u'website': u''},\n",
" {u'cluster_id': 0,\n",
" u'gear_collaborators': [],\n",
" u'member_id': 368,\n",
" u'member_type': u'member',\n",
" u'name': u'Ted',\n",
" u'organization': u'University of Pennsylvania',\n",
" u'other_collaborators': u'David Harbater, Robert Guralnick, Frauke Bleher, George Pappas, Martin Taylor, Ralph Greenberg, Romyar Sharifi, Mahesh Kakde, Christophe Soule, Quentin Guignard, Peter Symonds, Bjorn Poonen, Rachel Pries, Nadia Heninger, Brett Hemenway, Zach Scherr, Eduardo Friedman, James Sundstrom, Aristides Kontogeorgis, Amy Ksir',\n",
" u'photo': u'ChinburgTed.jpg',\n",
" u'pos_x': 0,\n",
" u'pos_y': 0,\n",
" u'research_interests': u'Geometric and Analytical Group Theory, Hyperbolic Manifolds, applications of number theory to geometry',\n",
" u'short_bio': u\"I'm interested in applications of number theory and arithmetic geometry to hyperbolic geometry and the theory of discrete groups. Some particular applications have been to volumes, closed geodesics, length spectra, commensurability classes and invariant quaternion algebras.\",\n",
" u'surname': u'Chinburg',\n",
" u'title': u'GEAR Member',\n",
" u'website': u'http://www.math.upenn.edu/~ted'},\n",
" {u'cluster_id': 0,\n",
" u'gear_collaborators': [],\n",
" u'member_id': 369,\n",
" u'member_type': u'member',\n",
" u'name': u'Max',\n",
" u'organization': u'University of Oklahoma',\n",
" u'other_collaborators': u'',\n",
" u'photo': u'',\n",
" u'pos_x': 0,\n",
" u'pos_y': 0,\n",
" u'research_interests': u'',\n",
" u'short_bio': u'',\n",
" u'surname': u'Forester',\n",
" u'title': u'GEAR Member',\n",
" u'website': u''},\n",
" {u'cluster_id': 0,\n",
" u'gear_collaborators': [],\n",
" u'member_id': 370,\n",
" u'member_type': u'member',\n",
" u'name': u'Yael',\n",
" u'organization': u'University of Haifa',\n",
" u'other_collaborators': u'',\n",
" u'photo': u'',\n",
" u'pos_x': 0,\n",
" u'pos_y': 0,\n",
" u'research_interests': u'',\n",
" u'short_bio': u'',\n",
" u'surname': u'Algom-Kfir',\n",
" u'title': u'GEAR Member',\n",
" u'website': u''},\n",
" {u'cluster_id': 0,\n",
" u'gear_collaborators': [],\n",
" u'member_id': 371,\n",
" u'member_type': u'member',\n",
" u'name': u'Andrew',\n",
" u'organization': u'The University of Texas at Austin',\n",
" u'other_collaborators': u'Davide Gaiotto, Greg Moore, Lotte Hollands, Cumrun Vafa, Sergio Cecotti, Anindya Dey, Boris Pioline, Sergei Alexandrov, Tom Mainiero, Dmitry Galakhov, Pietro Longhi',\n",
" u'photo': u'NeitzkeAndrew.jpg',\n",
" u'pos_x': 0,\n",
" u'pos_y': 0,\n",
" u'research_interests': u'Geometric Structures and Teichm\\xfcller Spaces, Higgs Bundles',\n",
" u'short_bio': u\"I work on supersymmetric quantum field theory. Recently the study of 'quantum field theories of class S' has turned out to be closely connected to the moduli space of Higgs bundles.\",\n",
" u'surname': u'Neitzke',\n",
" u'title': u'GEAR Member',\n",
" u'website': u'http://www.ma.utexas.edu/users/neitzke/'},\n",
" {u'cluster_id': 0,\n",
" u'gear_collaborators': [],\n",
" u'member_id': 372,\n",
" u'member_type': u'member',\n",
" u'name': u'Saul',\n",
" u'organization': u'University of Warwick',\n",
" u'other_collaborators': u'',\n",
" u'photo': u'',\n",
" u'pos_x': 0,\n",
" u'pos_y': 0,\n",
" u'research_interests': u'',\n",
" u'short_bio': u'',\n",
" u'surname': u'Schleimer',\n",
" u'title': u'GEAR Member',\n",
" u'website': u''},\n",
" {u'cluster_id': 0,\n",
" u'gear_collaborators': [],\n",
" u'member_id': 373,\n",
" u'member_type': u'member',\n",
" u'name': u'Helen',\n",
" u'organization': u'Carleton College',\n",
" u'other_collaborators': u'',\n",
" u'photo': u'',\n",
" u'pos_x': 0,\n",
" u'pos_y': 0,\n",
" u'research_interests': u'',\n",
" u'short_bio': u'',\n",
" u'surname': u'Wong',\n",
" u'title': u'GEAR Member',\n",
" u'website': u''},\n",
" {u'cluster_id': 0,\n",
" u'gear_collaborators': [],\n",
" u'member_id': 374,\n",
" u'member_type': u'member',\n",
" u'name': u'Goulnara',\n",
" u'organization': u'University of Vienna',\n",
" u'other_collaborators': u'',\n",
" u'photo': u'',\n",
" u'pos_x': 0,\n",
" u'pos_y': 0,\n",
" u'research_interests': u'',\n",
" u'short_bio': u'',\n",
" u'surname': u'Arzhantseva',\n",
" u'title': u'GEAR Member',\n",
" u'website': u''}]}"
]
},
"execution_count": 4,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"profile"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"with open(\"./profile/data.json\", \"r\") as f:\n",
" profile = json.load(f)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"def output_website_input(file_name):\n",
" with open(\"./source_files/original_profile.json\", \"r\") as f:\n",
" profile = json.load(f)\n",
" \n",
" with open(\"./Re__new_cocitation_graph/\"+file_name, \"r\") as f:\n",
" year_file = json.load(f)\n",
" \n",
" translator = {}\n",
" for node in year_file['nodes']:\n",
" id = node['id'] \n",
" translator[id] = node['label']\n",
" # trans is from graph id to member_id\n",
" \n",
" mg = {}\n",
" for key in trans:\n",
" mg[trans[key]] = key\n",
" \n",
" edge_weight = {}\n",
" for edge in year_file['edges']:\n",
" src = trans[edge['source']]\n",
" tgt = trans[edge['target']]\n",
" wt = edge['size']\n",
" \n",
" if src in edge_weight:\n",
" edge_weight[src][tgt] = wt\n",
" else:\n",
" edge_weight[src] = {}\n",
" edge_weight[src][tgt] = wt\n",
" \n",
" if tgt in edge_weight:\n",
" edge_weight[tgt][src] = wt\n",
" else:\n",
" edge_weight[tgt] = {}\n",
" edge_weight[tgt][src] = wt\n",
" \n",
" def get_person(idd):\n",
" for person in oct_file['items']:\n",
" person_id = str(person['member_id'])\n",
" if idd == person_id:\n",
" return person\n",
" \n",
" for node in year_file['nodes']:\n",
" id = trans[node['id']]\n",
" person = get_person(id)\n",
" \n",
" person[\"size\"] = node['size']\n",
" person[\"pos_x\"] = (node['x']+5000)/10000*800\n",
" person[\"pos_y\"] = (node['y']+5000)/10000*600\n",
" person[\"cluster_id\"] = int(node['attributes']['Modularity Class'])\n",
" try:\n",
" person[\"edges\"] = edge_weight[id]\n",
" except:\n",
" person[id][\"edges\"] = {}\n",
" \n",
" \n",
" \n",
" with open(\"./website_input/\"+file_name, \"wb\") as f:\n",
" json.dump(oct_file, f, indent=4, separators=(',', ': '), ensure_ascii=False)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": []
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"output_year_profile(\"2011.json\")"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"output_year_profile(\"2012.json\")"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"output_year_profile(\"2013.json\")"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"output_year_profile(\"2014.json\")"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"output_year_profile(\"2015.json\")"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"output_year_profile(\"2016.json\")"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"with open(\"/Users/erichsu/Downloads/2011.json\", \"r\") as f:\n",
" oct_file = json.load(f)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"oct_file"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"oct_file['items'][id]"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": []
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 2",
"language": "python",
"name": "python2"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 2
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython2",
"version": "2.7.11"
}
},
"nbformat": 4,
"nbformat_minor": 0
}
| gpl-3.0 |
jpn--/larch | book/example/legacy/301_itin_mnl.ipynb | 1 | 4124 | {
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# 301: Itinerary Choice using MNL"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"import pandas as pd\n",
"import larch\n",
"larch.__version__"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"This example is an itinerary choice model built using the example\n",
"itinerary choice dataset included with Larch. We'll begin by loading\n",
"that example data."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"from larch.data_warehouse import example_file\n",
"itin = pd.read_csv(example_file(\"arc\"), index_col=['id_case','id_alt'])\n",
"d = larch.DataFrames(itin, ch='choice', crack=True, autoscale_weights=True)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Now let's make our model. We'll use a few variables to define our\n",
"linear-in-parameters utility function."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"m = larch.Model(dataservice=d)\n",
"\n",
"v = [\n",
" \"timeperiod==2\",\n",
" \"timeperiod==3\",\n",
" \"timeperiod==4\",\n",
" \"timeperiod==5\",\n",
" \"timeperiod==6\",\n",
" \"timeperiod==7\",\n",
" \"timeperiod==8\",\n",
" \"timeperiod==9\",\n",
" \"carrier==2\",\n",
" \"carrier==3\",\n",
" \"carrier==4\",\n",
" \"carrier==5\",\n",
" \"equipment==2\",\n",
" \"fare_hy\", \n",
" \"fare_ly\", \n",
" \"elapsed_time\", \n",
" \"nb_cnxs\", \n",
"]\n"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"The `larch.roles` module defines a few convenient classes for declaring data and parameter.\n",
"One we will use here is `PX` which creates a linear-in-parameter term that represents one data\n",
"element (a column from our data, or an expression that can be evaluated on the data alone) multiplied\n",
"by a parameter with the same name.\n"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"from larch.roles import PX\n",
"m.utility_ca = sum(PX(i) for i in v)\n",
"m.choice_ca_var = 'choice'"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Since we are estimating just an MNL model in this example, this is all we need to do to build\n",
"our model, and we're ready to go. To estimate the likelihood maximizing parameters, we give:\n"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"m.load_data()\n",
"m.maximize_loglike()"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"tags": [
"remove_cell"
]
},
"outputs": [],
"source": [
"# TEST\n",
"result = _\n",
"from pytest import approx\n",
"assert result.loglike == approx(-777770.0688722526)\n",
"assert result.x['carrier==2'] == approx(0.11720047917232307)\n",
"assert result.logloss == approx(3.306873650593341)"
]
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3 (ipykernel)",
"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": "3.9.9"
},
"toc": {
"base_numbering": 1,
"nav_menu": {},
"number_sections": false,
"sideBar": true,
"skip_h1_title": false,
"title_cell": "Table of Contents",
"title_sidebar": "Contents",
"toc_cell": false,
"toc_position": {},
"toc_section_display": true,
"toc_window_display": false
}
},
"nbformat": 4,
"nbformat_minor": 4
}
| gpl-3.0 |
Parisson/TimeSide | docs/ipynb/01_Timeside_API.ipynb | 1 | 6823 | {
"cells": [
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"%pylab inline\n",
"import matplotlib.pylab as pylab\n",
"pylab.rcParams['figure.figsize'] = 16, 8 # that's default image size for this interactive session"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# TimeSide API"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"*Timeside* API is based on different core processing unit called *processors* :\n",
"\n",
"- Decoders (*timeside.api.IDecoder*) that enables to decode a giving audio source and split it up into frames for further processing\n",
"- Analyzers (*timeside.api.IAnalyzer*) that provides some signal processing module to analyze incoming audio frames\n",
"- Encoders (*timeside.api.IEncoder*) that can encode incoming frames back into an audio object\n",
"- Graphers (*timeside.api.IGrapher*) that can display some representations of the signal or corresponding extracted features"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Decoders"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"\n",
"import timeside.core\n",
"\n",
"from timeside.core import list_processors\n",
"\n",
"list_processors(timeside.core.api.IDecoder)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Analyzers"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"list_processors(timeside.core.api.IAnalyzer)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Encoders"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"list_processors(timeside.core.api.IEncoder)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Graphers"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"list_processors(timeside.core.api.IGrapher)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Processors pipeline"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"All these processors can be chained to form a process pipeline.\n",
"\n",
"Let first define a decoder that reads and decodes audio from a file"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"from timeside.core import get_processor\n",
"\n",
"from timeside.core.tools.test_samples import samples\n",
"file_decoder = get_processor('file_decoder')(samples['C4_scale.wav'])"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"And then some other processors"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"# analyzers\n",
"pitch = get_processor('aubio_pitch')()\n",
"level = get_processor('level')()\n",
"\n",
"# Encoder\n",
"mp3 = get_processor('mp3_encoder')('/tmp/guitar.mp3', overwrite=True)\n",
"\n",
"# Graphers\n",
"specgram = get_processor('spectrogram_lin')()\n",
"waveform = get_processor('waveform_simple')()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Let's now define a process pipeline with all these processors and run it"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"pipe = (file_decoder | pitch | level | mp3 | specgram | waveform)\n",
"pipe.run()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Analyzers results are available through the pipe:"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"pipe.results.keys()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"or from the analyzer:"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"pitch.results.keys()"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"pitch.results['aubio_pitch.pitch'].keys()"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"pitch.results['aubio_pitch.pitch']"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Grapher result can also be display or save into a file"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"imshow(specgram.render(), origin='lower')"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"imshow(waveform.render(), origin='lower')"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"waveform.render('/tmp/waveform.png')"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## And TimeSide can be embedded into a web page dynamically. For example, in Telemeta:"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"from IPython.display import HTML\n",
"HTML('<iframe width=1300 height=260 frameborder=0 scrolling=no marginheight=0 marginwidth=0 src=http://demo.telemeta.org/archives/items/6/player/1200x170></iframe>')"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": []
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 2",
"language": "python",
"name": "python2"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 2
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython2",
"version": "2.7.12"
}
},
"nbformat": 4,
"nbformat_minor": 0
}
| agpl-3.0 |
google/eng-edu | ml/cc/prework/zh-CN/tensorflow_programming_concepts.ipynb | 1 | 15931 | {
"nbformat_minor": 0,
"nbformat": 4,
"cells": [
{
"source": [
"#### Copyright 2017 Google LLC."
],
"cell_type": "markdown",
"metadata": {
"colab_type": "text",
"id": "copyright-notice"
}
},
{
"source": [
"# Licensed under the Apache License, Version 2.0 (the \"License\");\n",
"# you may not use this file except in compliance with the License.\n",
"# You may obtain a copy of the License at\n",
"#\n",
"# https://www.apache.org/licenses/LICENSE-2.0\n",
"#\n",
"# Unless required by applicable law or agreed to in writing, software\n",
"# distributed under the License is distributed on an \"AS IS\" BASIS,\n",
"# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n",
"# See the License for the specific language governing permissions and\n",
"# limitations under the License."
],
"cell_type": "code",
"metadata": {
"colab_type": "code",
"id": "copyright-notice2",
"colab": {
"autoexec": {
"wait_interval": 0,
"startup": false
}
},
"cellView": "both"
},
"execution_count": 0,
"outputs": []
},
{
"source": [
" # TensorFlow \u7f16\u7a0b\u6982\u5ff5"
],
"cell_type": "markdown",
"metadata": {
"colab_type": "text",
"id": "rFpcvnCJ4Xkf"
}
},
{
"source": [
" **\u5b66\u4e60\u76ee\u6807\uff1a**\n",
" * \u5b66\u4e60 TensorFlow \u7f16\u7a0b\u6a21\u578b\u7684\u57fa\u7840\u77e5\u8bc6\uff0c\u91cd\u70b9\u4e86\u89e3\u4ee5\u4e0b\u6982\u5ff5\uff1a\n",
" * \u5f20\u91cf\n",
" * \u6307\u4ee4\n",
" * \u56fe\n",
" * \u4f1a\u8bdd\n",
" * \u6784\u5efa\u4e00\u4e2a\u7b80\u5355\u7684 TensorFlow \u7a0b\u5e8f\uff0c\u4f7f\u7528\u8be5\u7a0b\u5e8f\u7ed8\u5236\u4e00\u4e2a\u9ed8\u8ba4\u56fe\u5e76\u521b\u5efa\u4e00\u4e2a\u8fd0\u884c\u8be5\u56fe\u7684\u4f1a\u8bdd"
],
"cell_type": "markdown",
"metadata": {
"colab_type": "text",
"id": "9IkBOsrNzahz"
}
},
{
"source": [
" **\u6ce8\u610f\uff1a**\u8bf7\u4ed4\u7ec6\u9605\u8bfb\u672c\u6559\u7a0b\u3002TensorFlow \u7f16\u7a0b\u6a21\u578b\u5f88\u53ef\u80fd\u4e0e\u60a8\u9047\u5230\u7684\u5176\u4ed6\u6a21\u578b\u4e0d\u540c\uff0c\u56e0\u6b64\u53ef\u80fd\u4e0d\u5982\u60a8\u671f\u671b\u7684\u90a3\u6837\u76f4\u89c2\u3002"
],
"cell_type": "markdown",
"metadata": {
"colab_type": "text",
"id": "YMG9mHPdzahz"
}
},
{
"source": [
" ## \u6982\u5ff5\u6982\u89c8\n\n",
"TensorFlow \u7684\u540d\u79f0\u6e90\u81ea**\u5f20\u91cf**\uff0c\u5f20\u91cf\u662f\u4efb\u610f\u7ef4\u5ea6\u7684\u6570\u7ec4\u3002\u501f\u52a9 TensorFlow\uff0c\u60a8\u53ef\u4ee5\u64cd\u63a7\u5177\u6709\u5927\u91cf\u7ef4\u5ea6\u7684\u5f20\u91cf\u3002\u5373\u4fbf\u5982\u6b64\uff0c\u5728\u5927\u591a\u6570\u60c5\u51b5\u4e0b\uff0c\u60a8\u4f1a\u4f7f\u7528\u4ee5\u4e0b\u4e00\u4e2a\u6216\u591a\u4e2a\u4f4e\u7ef4\u5f20\u91cf\uff1a\n\n",
" * **\u6807\u91cf**\u662f\u96f6\u7ef4\u6570\u7ec4\uff08\u96f6\u9636\u5f20\u91cf\uff09\u3002\u4f8b\u5982\uff0c`\\'Howdy\\'` \u6216 `5`\n",
" * **\u77e2\u91cf**\u662f\u4e00\u7ef4\u6570\u7ec4\uff08\u4e00\u9636\u5f20\u91cf\uff09\u3002\u4f8b\u5982\uff0c`[2, 3, 5, 7, 11]` \u6216 `[5]`\n",
" * **\u77e9\u9635**\u662f\u4e8c\u7ef4\u6570\u7ec4\uff08\u4e8c\u9636\u5f20\u91cf\uff09\u3002\u4f8b\u5982\uff0c`[[3.1, 8.2, 5.9][4.3, -2.7, 6.5]]`\n\n",
"TensorFlow **\u6307\u4ee4**\u4f1a\u521b\u5efa\u3001\u9500\u6bc1\u548c\u64cd\u63a7\u5f20\u91cf\u3002\u5178\u578b TensorFlow \u7a0b\u5e8f\u4e2d\u7684\u5927\u591a\u6570\u4ee3\u7801\u884c\u90fd\u662f\u6307\u4ee4\u3002\n\n",
"TensorFlow **\u56fe**\uff08\u4e5f\u79f0\u4e3a**\u8ba1\u7b97\u56fe**\u6216**\u6570\u636e\u6d41\u56fe**\uff09\u662f\u4e00\u79cd\u56fe\u6570\u636e\u7ed3\u6784\u3002\u5f88\u591a TensorFlow \u7a0b\u5e8f\u7531\u5355\u4e2a\u56fe\u6784\u6210\uff0c\u4f46\u662f TensorFlow \u7a0b\u5e8f\u53ef\u4ee5\u9009\u62e9\u521b\u5efa\u591a\u4e2a\u56fe\u3002\u56fe\u7684\u8282\u70b9\u662f\u6307\u4ee4\uff1b\u56fe\u7684\u8fb9\u662f\u5f20\u91cf\u3002\u5f20\u91cf\u6d41\u7ecf\u56fe\uff0c\u5728\u6bcf\u4e2a\u8282\u70b9\u7531\u4e00\u4e2a\u6307\u4ee4\u64cd\u63a7\u3002\u4e00\u4e2a\u6307\u4ee4\u7684\u8f93\u51fa\u5f20\u91cf\u901a\u5e38\u4f1a\u53d8\u6210\u540e\u7eed\u6307\u4ee4\u7684\u8f93\u5165\u5f20\u91cf\u3002TensorFlow \u4f1a\u5b9e\u73b0**\u5ef6\u8fdf\u6267\u884c\u6a21\u578b**\uff0c\u610f\u5473\u7740\u7cfb\u7edf\u4ec5\u4f1a\u6839\u636e\u76f8\u5173\u8282\u70b9\u7684\u9700\u6c42\u5728\u9700\u8981\u65f6\u8ba1\u7b97\u8282\u70b9\u3002\n\n",
"\u5f20\u91cf\u53ef\u4ee5\u4f5c\u4e3a**\u5e38\u91cf**\u6216**\u53d8\u91cf**\u5b58\u50a8\u5728\u56fe\u4e2d\u3002\u60a8\u53ef\u80fd\u5df2\u7ecf\u731c\u5230\uff0c\u5e38\u91cf\u5b58\u50a8\u7684\u662f\u503c\u4e0d\u4f1a\u53d1\u751f\u66f4\u6539\u7684\u5f20\u91cf\uff0c\u800c\u53d8\u91cf\u5b58\u50a8\u7684\u662f\u503c\u4f1a\u53d1\u751f\u66f4\u6539\u7684\u5f20\u91cf\u3002\u4e0d\u8fc7\uff0c\u60a8\u53ef\u80fd\u6ca1\u6709\u731c\u5230\u7684\u662f\uff0c\u5e38\u91cf\u548c\u53d8\u91cf\u90fd\u53ea\u662f\u56fe\u4e2d\u7684\u4e00\u79cd\u6307\u4ee4\u3002\u5e38\u91cf\u662f\u59cb\u7ec8\u4f1a\u8fd4\u56de\u540c\u4e00\u5f20\u91cf\u503c\u7684\u6307\u4ee4\u3002\u53d8\u91cf\u662f\u4f1a\u8fd4\u56de\u5206\u914d\u7ed9\u5b83\u7684\u4efb\u4f55\u5f20\u91cf\u7684\u6307\u4ee4\u3002\n\n",
"\u8981\u5b9a\u4e49\u5e38\u91cf\uff0c\u8bf7\u4f7f\u7528 `tf.constant` \u6307\u4ee4\uff0c\u5e76\u4f20\u5165\u5b83\u7684\u503c\u3002\u4f8b\u5982\uff1a\n\n",
"```\n",
" x = tf.constant([5.2])\n",
"```\n\n",
"\u540c\u6837\uff0c\u60a8\u53ef\u4ee5\u521b\u5efa\u5982\u4e0b\u53d8\u91cf\uff1a\n\n",
"```\n",
" y = tf.Variable([5])\n",
"```\n\n",
"\u6216\u8005\uff0c\u60a8\u4e5f\u53ef\u4ee5\u5148\u521b\u5efa\u53d8\u91cf\uff0c\u7136\u540e\u518d\u5982\u4e0b\u6240\u793a\u5730\u5206\u914d\u4e00\u4e2a\u503c\uff08\u6ce8\u610f\uff1a\u60a8\u59cb\u7ec8\u9700\u8981\u6307\u5b9a\u4e00\u4e2a\u9ed8\u8ba4\u503c\uff09\uff1a\n\n",
"```\n",
" y = tf.Variable([0])\n",
" y = y.assign([5])\n",
"```\n\n",
"\u5b9a\u4e49\u4e00\u4e9b\u5e38\u91cf\u6216\u53d8\u91cf\u540e\uff0c\u60a8\u53ef\u4ee5\u5c06\u5b83\u4eec\u4e0e\u5176\u4ed6\u6307\u4ee4\uff08\u5982 `tf.add`\uff09\u7ed3\u5408\u4f7f\u7528\u3002\u5728\u8bc4\u4f30 `tf.add` \u6307\u4ee4\u65f6\uff0c\u5b83\u4f1a\u8c03\u7528\u60a8\u7684 `tf.constant` \u6216 `tf.Variable` \u6307\u4ee4\uff0c\u4ee5\u83b7\u53d6\u5b83\u4eec\u7684\u503c\uff0c\u7136\u540e\u8fd4\u56de\u4e00\u4e2a\u5305\u542b\u8fd9\u4e9b\u503c\u4e4b\u548c\u7684\u65b0\u5f20\u91cf\u3002\n\n",
"\u56fe\u5fc5\u987b\u5728 TensorFlow **\u4f1a\u8bdd**\u4e2d\u8fd0\u884c\uff0c\u4f1a\u8bdd\u5b58\u50a8\u4e86\u5b83\u6240\u8fd0\u884c\u7684\u56fe\u7684\u72b6\u6001\uff1a\n\n",
"```\n",
"with tf.Session() as sess:\n",
" initialization = tf.global_variables_initializer()\n",
" print(y.eval())\n",
"```\n\n",
"\u5728\u4f7f\u7528 `tf.Variable` \u65f6\uff0c\u60a8\u5fc5\u987b\u5728\u4f1a\u8bdd\u5f00\u59cb\u65f6\u8c03\u7528 `tf.global_variables_initializer`\uff0c\u4ee5\u660e\u786e\u521d\u59cb\u5316\u8fd9\u4e9b\u53d8\u91cf\uff0c\u5982\u4e0a\u6240\u793a\u3002\n\n",
"**\u6ce8\u610f\uff1a**\u4f1a\u8bdd\u53ef\u4ee5\u5c06\u56fe\u5206\u53d1\u5230\u591a\u4e2a\u673a\u5668\u4e0a\u6267\u884c\uff08\u5047\u8bbe\u7a0b\u5e8f\u5728\u67d0\u4e2a\u5206\u5e03\u5f0f\u8ba1\u7b97\u6846\u67b6\u4e0a\u8fd0\u884c\uff09\u3002\u6709\u5173\u8be6\u60c5\uff0c\u8bf7\u53c2\u9605[\u5206\u5e03\u5f0f TensorFlow](https://www.tensorflow.org/deploy/distributed)\u3002\n\n",
"### \u603b\u7ed3\n\n",
"TensorFlow \u7f16\u7a0b\u672c\u8d28\u4e0a\u662f\u4e00\u4e2a\u4e24\u6b65\u6d41\u7a0b\uff1a\n\n",
"1. \u5c06\u5e38\u91cf\u3001\u53d8\u91cf\u548c\u6307\u4ee4\u6574\u5408\u5230\u4e00\u4e2a\u56fe\u4e2d\u3002\n",
"2. \u5728\u4e00\u4e2a\u4f1a\u8bdd\u4e2d\u8bc4\u4f30\u8fd9\u4e9b\u5e38\u91cf\u3001\u53d8\u91cf\u548c\u6307\u4ee4\u3002\n",
""
],
"cell_type": "markdown",
"metadata": {
"colab_type": "text",
"id": "NzKsjX-ufyVY"
}
},
{
"source": [
" ## \u521b\u5efa\u4e00\u4e2a\u7b80\u5355\u7684 TensorFlow \u7a0b\u5e8f\n",
"\n",
"\u6211\u4eec\u6765\u770b\u770b\u5982\u4f55\u7f16\u5199\u4e00\u4e2a\u5c06\u4e24\u4e2a\u5e38\u91cf\u76f8\u52a0\u7684\u7b80\u5355 TensorFlow \u7a0b\u5e8f\u3002"
],
"cell_type": "markdown",
"metadata": {
"colab_type": "text",
"id": "VL0yWNNdgBpG"
}
},
{
"source": [
" ### \u6dfb\u52a0 import \u8bed\u53e5\n",
"\n",
"\u4e0e\u51e0\u4e4e\u6240\u6709 Python \u7a0b\u5e8f\u4e00\u6837\uff0c\u60a8\u9996\u5148\u8981\u6dfb\u52a0\u4e00\u4e9b `import` \u8bed\u53e5\u3002\n",
"\u5f53\u7136\uff0c\u8fd0\u884c TensorFlow \u7a0b\u5e8f\u6240\u9700\u7684 `import` \u8bed\u53e5\u7ec4\u5408\u53d6\u51b3\u4e8e\u60a8\u7684\u7a0b\u5e8f\u5c06\u8981\u8bbf\u95ee\u7684\u529f\u80fd\u3002\u81f3\u5c11\uff0c\u60a8\u5fc5\u987b\u5728\u6240\u6709 TensorFlow \u7a0b\u5e8f\u4e2d\u6dfb\u52a0 `import tensorflow` \u8bed\u53e5\uff1a"
],
"cell_type": "markdown",
"metadata": {
"colab_type": "text",
"id": "mN4R0gmMzah3"
}
},
{
"source": [
"import tensorflow as tf"
],
"cell_type": "code",
"metadata": {
"colab_type": "code",
"id": "SDbi6heigEGA",
"colab": {
"autoexec": {
"wait_interval": 0,
"startup": false
}
}
},
"outputs": [],
"execution_count": 0
},
{
"source": [
" **\u8bf7\u52ff\u5fd8\u8bb0\u6267\u884c\u524d\u9762\u7684\u4ee3\u7801\u5757\uff08`import` \u8bed\u53e5\uff09\u3002**\n",
"\n",
"\u5176\u4ed6\u5e38\u89c1\u7684 import \u8bed\u53e5\u5305\u62ec\uff1a\n\n",
"```\n",
"import matplotlib.pyplot as plt # \u6570\u636e\u96c6\u53ef\u89c6\u5316\u3002\n",
"import numpy as np # \u4f4e\u7ea7\u6570\u5b57 Python \u5e93\u3002\n",
"import pandas as pd # \u8f83\u9ad8\u7ea7\u522b\u7684\u6570\u5b57 Python \u5e93\u3002\n",
"```"
],
"cell_type": "markdown",
"metadata": {
"colab_type": "text",
"id": "6RRT4YIA4fQd"
}
},
{
"source": [
" TensorFlow \u63d0\u4f9b\u4e86\u4e00\u4e2a**\u9ed8\u8ba4\u56fe**\u3002\u4e0d\u8fc7\uff0c\u6211\u4eec\u5efa\u8bae\u60a8\u660e\u786e\u521b\u5efa\u81ea\u5df1\u7684 `Graph`\uff0c\u4ee5\u4fbf\u8ddf\u8e2a\u72b6\u6001\uff08\u4f8b\u5982\uff0c\u60a8\u53ef\u80fd\u5e0c\u671b\u5728\u6bcf\u4e2a\u5355\u5143\u683c\u4e2d\u4f7f\u7528\u4e00\u4e2a\u4e0d\u540c\u7684 `Graph`\uff09\u3002"
],
"cell_type": "markdown",
"metadata": {
"colab_type": "text",
"id": "1aNpvufRgbTD"
}
},
{
"source": [
"from __future__ import print_function\n\n",
"import tensorflow as tf\n",
"\n",
"# Create a graph.\n",
"g = tf.Graph()\n",
"\n",
"# Establish the graph as the \"default\" graph.\n",
"with g.as_default():\n",
" # Assemble a graph consisting of the following three operations:\n",
" # * Two tf.constant operations to create the operands.\n",
" # * One tf.add operation to add the two operands.\n",
" x = tf.constant(8, name=\"x_const\")\n",
" y = tf.constant(5, name=\"y_const\")\n",
" sum = tf.add(x, y, name=\"x_y_sum\")\n",
"\n",
"\n",
" # Now create a session.\n",
" # The session will run the default graph.\n",
" with tf.Session() as sess:\n",
" print(sum.eval())"
],
"cell_type": "code",
"metadata": {
"colab_type": "code",
"id": "Md8ze8e9geMi",
"colab": {
"autoexec": {
"wait_interval": 0,
"startup": false
}
}
},
"outputs": [],
"execution_count": 0
},
{
"source": [
" ## \u7ec3\u4e60\uff1a\u5f15\u5165\u7b2c\u4e09\u4e2a\u8fd0\u7b97\u6570\n",
"\n",
"\u4fee\u6539\u4e0a\u9762\u7684\u4ee3\u7801\u5217\u8868\uff0c\u4ee5\u5c06\u4e09\u4e2a\u6574\u6570\uff08\u800c\u4e0d\u662f\u4e24\u4e2a\uff09\u76f8\u52a0\uff1a\n",
"\n",
" 1. \u5b9a\u4e49\u7b2c\u4e09\u4e2a\u6807\u91cf\u6574\u6570\u5e38\u91cf `z`\uff0c\u5e76\u4e3a\u5176\u5206\u914d\u4e00\u4e2a\u503c `4`\u3002\n",
" 2. \u5c06 `sum` \u4e0e `z` \u76f8\u52a0\uff0c\u4ee5\u5f97\u51fa\u4e00\u4e2a\u65b0\u7684\u548c\u3002\n",
" \n",
" **\u63d0\u793a\uff1a**\u8bf7\u53c2\u9605\u6709\u5173 [tf.add()](https://www.tensorflow.org/api_docs/python/tf/add) \u7684 API \u6587\u6863\uff0c\u4e86\u89e3\u6709\u5173\u5176\u51fd\u6570\u7b7e\u540d\u7684\u66f4\u591a\u8be6\u7ec6\u4fe1\u606f\u3002\n",
" \n",
" 3. \u91cd\u65b0\u8fd0\u884c\u4fee\u6539\u540e\u7684\u4ee3\u7801\u5757\u3002\u8be5\u7a0b\u5e8f\u662f\u5426\u751f\u6210\u4e86\u6b63\u786e\u7684\u603b\u548c\uff1f"
],
"cell_type": "markdown",
"metadata": {
"colab_type": "text",
"id": "7mSz5GVqggZy"
}
},
{
"source": [
" ### \u89e3\u51b3\u65b9\u6848\n",
"\n",
"\u70b9\u51fb\u4e0b\u65b9\uff0c\u67e5\u770b\u89e3\u51b3\u65b9\u6848\u3002"
],
"cell_type": "markdown",
"metadata": {
"colab_type": "text",
"id": "Zep4W80H9awM"
}
},
{
"source": [
"# Create a graph.\n",
"g = tf.Graph()\n",
"\n",
"# Establish our graph as the \"default\" graph.\n",
"with g.as_default():\n",
" # Assemble a graph consisting of three operations. \n",
" # (Creating a tensor is an operation.)\n",
" x = tf.constant(8, name=\"x_const\")\n",
" y = tf.constant(5, name=\"y_const\")\n",
" sum = tf.add(x, y, name=\"x_y_sum\")\n",
" \n",
" # Task 1: Define a third scalar integer constant z.\n",
" z = tf.constant(4, name=\"z_const\")\n",
" # Task 2: Add z to `sum` to yield a new sum.\n",
" new_sum = tf.add(sum, z, name=\"x_y_z_sum\")\n",
"\n",
" # Now create a session.\n",
" # The session will run the default graph.\n",
" with tf.Session() as sess:\n",
" # Task 3: Ensure the program yields the correct grand total.\n",
" print(new_sum.eval())"
],
"cell_type": "code",
"metadata": {
"colab_type": "code",
"id": "4OTZPqhS9kzu",
"colab": {
"autoexec": {
"wait_interval": 0,
"startup": false
}
}
},
"outputs": [],
"execution_count": 0
},
{
"source": [
" ## \u66f4\u591a\u4fe1\u606f\n",
"\n",
"\u8981\u8fdb\u4e00\u6b65\u63a2\u7d22\u57fa\u672c TensorFlow \u56fe\uff0c\u8bf7\u4f7f\u7528\u4ee5\u4e0b\u6559\u7a0b\u8fdb\u884c\u5b9e\u9a8c\uff1a\n",
"\n",
" * [Mandelbrot \u96c6](https://www.tensorflow.org/tutorials/non-ml/mandelbrot)"
],
"cell_type": "markdown",
"metadata": {
"colab_type": "text",
"id": "BrlnLTesgywL"
}
}
],
"metadata": {
"colab": {
"default_view": {},
"version": "0.3.2",
"collapsed_sections": [
"Zep4W80H9awM",
"copyright-notice"
],
"name": "tensorflow_programming_concepts.ipynb",
"views": {}
},
"kernelspec": {
"name": "python3",
"display_name": "Python 3"
}
}
}
| apache-2.0 |
jasdumas/jasdumas.github.io | post_data/final_project_jasmine_dumas.ipynb | 1 | 180720 | {
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Final Project\n",
"## Jasmine Dumas (1523905)\n",
"## CSC 478: Programming Machine Learning Applications - Autumn 2016\n",
"## Due: Tuesday, November 22, 2016\n",
"\n",
"____\n",
"\n",
"#### Final Project Objective: \n",
"* Analyze Lending Club's issued loans: https://www.kaggle.com/wendykan/lending-club-loan-data\n",
"\n",
"#### Data Analysis Tasks:\n",
"\n",
"1. Supervised Learning: Classifier using k Nearest Neighbor of payment status (Current, Late, Fully Paid, etc.)\n",
" 1. Exploratory Data Analysis\n",
" 2. Pre-processing & Data Cleaning\n",
" 3. Building the Classifier\n",
" 4. Evaluating the model\n",
"\n"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## 1. Load Libraries"
]
},
{
"cell_type": "code",
"execution_count": 48,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"## load libraries\n",
"import sys\n",
"from numpy import *\n",
"import numpy as np\n",
"import pandas as pd\n",
"import matplotlib.pyplot as plt\n",
"import operator\n",
"%matplotlib inline\n",
"from sklearn.feature_extraction import DictVectorizer\n",
"from sklearn import preprocessing\n",
"from sklearn import neighbors, tree, naive_bayes\n",
"from sklearn.metrics import confusion_matrix\n",
"from sklearn.metrics import classification_report\n",
"from sklearn.cross_validation import train_test_split\n",
"from sklearn.neighbors import KNeighborsClassifier"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## 2. Load the data"
]
},
{
"cell_type": "code",
"execution_count": 49,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"data = pd.read_csv(\"loan.csv\", low_memory=False)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### a. Data reduction for computation\n",
"* From previous attempts to create a model matrix below and having the kernal crash, I'm going to reduce the data set size to compute better by selecting a random sample of 20% from the original dataset"
]
},
{
"cell_type": "code",
"execution_count": 50,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"# 5% of the data without replacement\n",
"data = data.sample(frac=0.05, replace=False, random_state=123) "
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## 3. Explore the data\n",
"* visaully and descriptive methods"
]
},
{
"cell_type": "code",
"execution_count": 51,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"(44369, 74)"
]
},
"execution_count": 51,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"data.shape"
]
},
{
"cell_type": "code",
"execution_count": 52,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>id</th>\n",
" <th>member_id</th>\n",
" <th>loan_amnt</th>\n",
" <th>funded_amnt</th>\n",
" <th>funded_amnt_inv</th>\n",
" <th>term</th>\n",
" <th>int_rate</th>\n",
" <th>installment</th>\n",
" <th>grade</th>\n",
" <th>sub_grade</th>\n",
" <th>...</th>\n",
" <th>total_bal_il</th>\n",
" <th>il_util</th>\n",
" <th>open_rv_12m</th>\n",
" <th>open_rv_24m</th>\n",
" <th>max_bal_bc</th>\n",
" <th>all_util</th>\n",
" <th>total_rev_hi_lim</th>\n",
" <th>inq_fi</th>\n",
" <th>total_cu_tl</th>\n",
" <th>inq_last_12m</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>70467</th>\n",
" <td>8556040</td>\n",
" <td>10308118</td>\n",
" <td>13000.0</td>\n",
" <td>13000.0</td>\n",
" <td>12950.0</td>\n",
" <td>36 months</td>\n",
" <td>9.67</td>\n",
" <td>417.47</td>\n",
" <td>B</td>\n",
" <td>B1</td>\n",
" <td>...</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>19200.0</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>165635</th>\n",
" <td>3355435</td>\n",
" <td>4168922</td>\n",
" <td>8000.0</td>\n",
" <td>8000.0</td>\n",
" <td>8000.0</td>\n",
" <td>36 months</td>\n",
" <td>13.11</td>\n",
" <td>269.98</td>\n",
" <td>B</td>\n",
" <td>B4</td>\n",
" <td>...</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>12400.0</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>496523</th>\n",
" <td>66611115</td>\n",
" <td>71336859</td>\n",
" <td>10000.0</td>\n",
" <td>10000.0</td>\n",
" <td>10000.0</td>\n",
" <td>36 months</td>\n",
" <td>10.64</td>\n",
" <td>325.69</td>\n",
" <td>B</td>\n",
" <td>B4</td>\n",
" <td>...</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>18200.0</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>182496</th>\n",
" <td>1685370</td>\n",
" <td>1967570</td>\n",
" <td>16000.0</td>\n",
" <td>16000.0</td>\n",
" <td>16000.0</td>\n",
" <td>36 months</td>\n",
" <td>15.80</td>\n",
" <td>560.94</td>\n",
" <td>C</td>\n",
" <td>C3</td>\n",
" <td>...</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>24000.0</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>554977</th>\n",
" <td>63286796</td>\n",
" <td>67528574</td>\n",
" <td>10000.0</td>\n",
" <td>10000.0</td>\n",
" <td>10000.0</td>\n",
" <td>36 months</td>\n",
" <td>7.89</td>\n",
" <td>312.86</td>\n",
" <td>A</td>\n",
" <td>A5</td>\n",
" <td>...</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>13000.0</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"<p>5 rows × 74 columns</p>\n",
"</div>"
],
"text/plain": [
" id member_id loan_amnt funded_amnt funded_amnt_inv \\\n",
"70467 8556040 10308118 13000.0 13000.0 12950.0 \n",
"165635 3355435 4168922 8000.0 8000.0 8000.0 \n",
"496523 66611115 71336859 10000.0 10000.0 10000.0 \n",
"182496 1685370 1967570 16000.0 16000.0 16000.0 \n",
"554977 63286796 67528574 10000.0 10000.0 10000.0 \n",
"\n",
" term int_rate installment grade sub_grade ... \\\n",
"70467 36 months 9.67 417.47 B B1 ... \n",
"165635 36 months 13.11 269.98 B B4 ... \n",
"496523 36 months 10.64 325.69 B B4 ... \n",
"182496 36 months 15.80 560.94 C C3 ... \n",
"554977 36 months 7.89 312.86 A A5 ... \n",
"\n",
" total_bal_il il_util open_rv_12m open_rv_24m max_bal_bc all_util \\\n",
"70467 NaN NaN NaN NaN NaN NaN \n",
"165635 NaN NaN NaN NaN NaN NaN \n",
"496523 NaN NaN NaN NaN NaN NaN \n",
"182496 NaN NaN NaN NaN NaN NaN \n",
"554977 NaN NaN NaN NaN NaN NaN \n",
"\n",
" total_rev_hi_lim inq_fi total_cu_tl inq_last_12m \n",
"70467 19200.0 NaN NaN NaN \n",
"165635 12400.0 NaN NaN NaN \n",
"496523 18200.0 NaN NaN NaN \n",
"182496 24000.0 NaN NaN NaN \n",
"554977 13000.0 NaN NaN NaN \n",
"\n",
"[5 rows x 74 columns]"
]
},
"execution_count": 52,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"data.head(n=5)"
]
},
{
"cell_type": "code",
"execution_count": 53,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"Index(['id', 'member_id', 'loan_amnt', 'funded_amnt', 'funded_amnt_inv',\n",
" 'term', 'int_rate', 'installment', 'grade', 'sub_grade', 'emp_title',\n",
" 'emp_length', 'home_ownership', 'annual_inc', 'verification_status',\n",
" 'issue_d', 'loan_status', 'pymnt_plan', 'url', 'desc', 'purpose',\n",
" 'title', 'zip_code', 'addr_state', 'dti', 'delinq_2yrs',\n",
" 'earliest_cr_line', 'inq_last_6mths', 'mths_since_last_delinq',\n",
" 'mths_since_last_record', 'open_acc', 'pub_rec', 'revol_bal',\n",
" 'revol_util', 'total_acc', 'initial_list_status', 'out_prncp',\n",
" 'out_prncp_inv', 'total_pymnt', 'total_pymnt_inv', 'total_rec_prncp',\n",
" 'total_rec_int', 'total_rec_late_fee', 'recoveries',\n",
" 'collection_recovery_fee', 'last_pymnt_d', 'last_pymnt_amnt',\n",
" 'next_pymnt_d', 'last_credit_pull_d', 'collections_12_mths_ex_med',\n",
" 'mths_since_last_major_derog', 'policy_code', 'application_type',\n",
" 'annual_inc_joint', 'dti_joint', 'verification_status_joint',\n",
" 'acc_now_delinq', 'tot_coll_amt', 'tot_cur_bal', 'open_acc_6m',\n",
" 'open_il_6m', 'open_il_12m', 'open_il_24m', 'mths_since_rcnt_il',\n",
" 'total_bal_il', 'il_util', 'open_rv_12m', 'open_rv_24m', 'max_bal_bc',\n",
" 'all_util', 'total_rev_hi_lim', 'inq_fi', 'total_cu_tl',\n",
" 'inq_last_12m'],\n",
" dtype='object')"
]
},
"execution_count": 53,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"data.columns"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"The **loan_status** column is the target!"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### a. How many classes are there?"
]
},
{
"cell_type": "code",
"execution_count": 54,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"array(['Current', 'Fully Paid', 'Charged Off', 'In Grace Period',\n",
" 'Late (31-120 days)', 'Issued', 'Default', 'Late (16-30 days)',\n",
" 'Does not meet the credit policy. Status:Fully Paid',\n",
" 'Does not meet the credit policy. Status:Charged Off'], dtype=object)"
]
},
"execution_count": 54,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"pd.unique(data['loan_status'].values.ravel())"
]
},
{
"cell_type": "code",
"execution_count": 55,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Amount of Classes: 10\n"
]
}
],
"source": [
"print(\"Amount of Classes: \", len(pd.unique(data['loan_status'].values.ravel())))"
]
},
{
"cell_type": "code",
"execution_count": 56,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"860"
]
},
"execution_count": 56,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"len(pd.unique(data['zip_code'].values.ravel())) # want to make sure this was not too unique"
]
},
{
"cell_type": "code",
"execution_count": 57,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"44369"
]
},
"execution_count": 57,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"len(pd.unique(data['url'].values.ravel())) # drop url"
]
},
{
"cell_type": "code",
"execution_count": 58,
"metadata": {
"collapsed": false,
"scrolled": true
},
"outputs": [
{
"data": {
"text/plain": [
"94"
]
},
"execution_count": 58,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"len(pd.unique(data['last_pymnt_d'].values.ravel()))"
]
},
{
"cell_type": "code",
"execution_count": 59,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"64"
]
},
"execution_count": 59,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"len(pd.unique(data['next_pymnt_d'].values.ravel()))"
]
},
{
"cell_type": "code",
"execution_count": 60,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Column term has 2 unique instances\n",
"Column grade has 7 unique instances\n",
"Column sub_grade has 35 unique instances\n",
"Column emp_title has 23766 unique instances\n",
"Column emp_length has 12 unique instances\n",
"Column home_ownership has 5 unique instances\n",
"Column verification_status has 3 unique instances\n",
"Column issue_d has 102 unique instances\n",
"Column loan_status has 10 unique instances\n",
"Column pymnt_plan has 1 unique instances\n",
"Column url has 44369 unique instances\n",
"Column desc has 6294 unique instances\n",
"Column purpose has 14 unique instances\n",
"Column title has 4681 unique instances\n",
"Column zip_code has 860 unique instances\n",
"Column addr_state has 50 unique instances\n",
"Column earliest_cr_line has 591 unique instances\n",
"Column initial_list_status has 2 unique instances\n",
"Column last_pymnt_d has 94 unique instances\n",
"Column next_pymnt_d has 64 unique instances\n",
"Column last_credit_pull_d has 87 unique instances\n",
"Column application_type has 2 unique instances\n",
"Column verification_status_joint has 4 unique instances\n"
]
}
],
"source": [
"for col in data.select_dtypes(include=['object']).columns:\n",
" print (\"Column {} has {} unique instances\".format( col, len(data[col].unique())) )"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### b. Are there unique customers in the data or repeats? "
]
},
{
"cell_type": "code",
"execution_count": 62,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"True"
]
},
"execution_count": 62,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"len(pd.unique(data['member_id'].values.ravel())) == data.shape[0]"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### c. Drop some of the junk variables (id, member_id, ...)\n",
"* Reasons: High Cardinality\n",
"* pre-pre-processing 😃"
]
},
{
"cell_type": "code",
"execution_count": 63,
"metadata": {
"collapsed": false,
"scrolled": true
},
"outputs": [],
"source": [
"data = data.drop('id', 1) #\n",
"data = data.drop('member_id', 1)#\n",
"data = data.drop('url', 1)#\n",
"data = data.drop('purpose', 1)\n",
"data = data.drop('title', 1)#\n",
"data = data.drop('zip_code', 1)#\n",
"data = data.drop('emp_title', 1)#\n",
"data = data.drop('earliest_cr_line', 1)#\n",
"data = data.drop('term', 1)\n",
"data = data.drop('sub_grade', 1) #\n",
"data = data.drop('last_pymnt_d', 1)#\n",
"data = data.drop('next_pymnt_d', 1)#\n",
"data = data.drop('last_credit_pull_d', 1)\n",
"data = data.drop('issue_d', 1) ##\n",
"data = data.drop('desc', 1)##\n",
"data = data.drop('addr_state', 1)##"
]
},
{
"cell_type": "code",
"execution_count": 65,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"(44369, 58)"
]
},
"execution_count": 65,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"data.shape"
]
},
{
"cell_type": "code",
"execution_count": 66,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Column grade has 7 unique instances\n",
"Column emp_length has 12 unique instances\n",
"Column home_ownership has 5 unique instances\n",
"Column verification_status has 3 unique instances\n",
"Column loan_status has 10 unique instances\n",
"Column pymnt_plan has 1 unique instances\n",
"Column initial_list_status has 2 unique instances\n",
"Column application_type has 2 unique instances\n",
"Column verification_status_joint has 4 unique instances\n"
]
}
],
"source": [
"# yay this is better\n",
"for col in data.select_dtypes(include=['object']).columns:\n",
" print (\"Column {} has {} unique instances\".format( col, len(data[col].unique())) )"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### d. Exploratory Data Analysis: What is the distribution of the loan amount?\n",
"* In general the loans amount was usually under $15,000"
]
},
{
"cell_type": "code",
"execution_count": 70,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"<matplotlib.axes._subplots.AxesSubplot at 0x11aea1898>"
]
},
"execution_count": 70,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"image/png": "iVBORw0KGgoAAAANSUhEUgAAAjkAAAFkCAYAAADCEyrZAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\n1EmGHEmS1EmGHEmS1EmGHEmS1En/PzH70zAfHCjYAAAAAElFTkSuQmCC\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x11ae98a58>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"data['loan_amnt'].plot(kind=\"hist\", bins=10)"
]
},
{
"cell_type": "code",
"execution_count": 71,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"<matplotlib.axes._subplots.AxesSubplot at 0x149d0d198>"
]
},
"execution_count": 71,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"image/png": "iVBORw0KGgoAAAANSUhEUgAAAhYAAAFhCAYAAAAsiOM3AAAABHNCSVQICAgIfAhkiAAAAAlwSFlz\nAAAPYQAAD2EBqD+naQAAIABJREFUeJzt3X+QXeV93/H3xxCJQCsJolgyjZXYQytvk9hGawNuamZs\npaGOHdepOw1LqBPT1AkGSrZ1ayd1JhRmGpdOEMUQlxq3/gFs6+B6TAxFNo5LXUxRgvCP2kK1J6Zr\nm0juFiGIGIGRvv3jnDVXd6TVD567V0Lv18wOus/zPec857DS/dzn/LipKiRJklp4wbgHIEmSnj8M\nFpIkqRmDhSRJasZgIUmSmjFYSJKkZgwWkiSpGYOFJElqxmAhSZKaMVhIkqRmDBaSJKmZww4WSV6b\n5PYk302yN8mbF6j9d33NPx5qX5rkhiRzSZ5IcluSFw7VnJrkliQ7k+xIclOSU4ZqXpzkjiS7kmxL\ncnUSw5IkSWNyJG/CpwBfAt4JHPCLRpL8InA28N39dF8LvBF4K3AucDrwiaGaW4EJYH1fey5w48D6\nXwDcCZwInAP8CvCrwJWHv0uSJKmFPJcvIUuyF3hLVd0+1P5XgPuA8+je/DdU1XV93zLg/wLnV9Un\n+7a1wBbgnKralGQC+BowWVUP9jXnAXcAP1ZV25K8AbgdeFFVzfU1vw68D/jRqnrmiHdMkiQdkean\nDZIE+ChwdVVt2U/JJN0sw+fmG6pqKzALvKZvOgfYMR8qenfTzZCcPVDz1flQ0dsILAd+ssGuSJKk\nwzSK6xHeAzxdVdcfoH913//4UPv2vm++5nuDnVW1B3h0qGb7ftbBQI0kSVpEJ7ZcWZJJ4B8DZ7Zc\nbytJfoTu9MzDwO7xjkaSpGPKScBPABur6v8dqKhpsAD+JvCjwLe7MyIAnABck+Q3q+qlwDZgSZJl\nQ7MWq/o++v8O3yVyAnDaUM2rh7a/aqBvf84DbjmsPZIkSYN+me4Gi/1qHSw+Cnx2qO0zfft/7F8/\nADxDd7fH4MWba+gu+KT/74okZw5cZ7EeCHD/QM1vJ1k5cJ3FzwE7ga8fYHwPA9x8881MTEwcyf41\nMT09zYYNG8a2/aOJx6Ljceh4HJ7lseh4HJ417mOxZcsWLrzwQujfSw/ksINF/yyJM+je5AFemuQV\nwKNV9W1gx1D994FtVfUNgKp6PMmH6GYxdgBPANcB91bVpr7moSQbgQ8muRhYArwfmKmq+dmIz9AF\niI8leTfwIuAq4Pqq+v4Bhr8bYGJignXr1h3urjezfPnysW7/aOKx6HgcOh6HZ3ksOh6HZx1Fx2LB\nSwmOZMbiVcDn6e7QKOD3+/aPABftp35/97NOA3uA24ClwF3AJUM1FwDX090NsrevvfwHK63am+RN\nwAeALwK7gA8Dv3sE+yRJkho47GBRVfdwGHeT9NdVDLc9BVzW/xxouceACw+y7m8DbzrUsUiSpNHy\n8deSJKkZg8UYTE1NjXsIRw2PRcfj0PE4PMtj0fE4POtYORbP6ZHex5ok64AHHnjggaPlAhhJko4J\nmzdvZnJyErqv29h8oDpnLCRJUjMGC0mS1IzBQpIkNWOwkCRJzRgsJElSMwYLSZLUjMFCkiQ1Y7CQ\nJEnNGCwkSVIzBgtJktSMwUKSJDVjsJAkSc0YLCRJUjMGC0mS1IzBQpIkNWOwkCRJzRgsJElSMwYL\nSZLUjMFCkiQ1c+K4B3Asmp2dZW5ubqxjWLlyJWvWrBnrGCRJGmawOEyzs7OsXTvB7t1PjnUcJ510\nMlu3bjFcSJKOKgaLwzQ3N9eHipuBiTGNYgu7d1/I3NycwUKSdFQxWByxCWDduAchSdJRxYs3JUlS\nMwYLSZLUjMFCkiQ1Y7CQJEnNGCwkSVIzBgtJktSMwUKSJDVz2MEiyWuT3J7ku0n2JnnzQN+JSf51\nkq8k+Yu+5iNJXjS0jqVJbkgyl+SJJLcleeFQzalJbkmyM8mOJDclOWWo5sVJ7kiyK8m2JFcnMSxJ\nkjQmR/KArFOALwEfAv7LUN/JwCuBfwl8BTgVuA74FHDWQN21wBuAtwKPAzcAnwBeO1BzK7AKWA8s\nAT4M3AhcCNAHiDuBR4BzgNOBjwFPA+89gv3SYfI7UyRJww47WFTVXcBdAEky1Pc4cN5gW5JLgfuT\n/FhVfSfJMuAi4PyquqeveTuwJclZVbUpyUS/nsmqerCvuQy4I8m7qmpb3/8y4HVVNQd8NcnvAO9L\nckVVPXO4+6ZD53emSJL2ZzEe6b0CKOCx/vVkv93PzRdU1dYks8BrgE10MxA75kNF7+5+PWfTzYCc\nA3y1DxXzNgIfAH4S+PJI9kaA35kiSdq/kQaLJEuB9wG3VtVf9M2rgaf72Y1B2/u++ZrvDXZW1Z4k\njw7VbN/POub7DBaLwu9MkSQ9a2QXOiY5EfhDulmGd45qO5Ik6egxkhmLgVDxYuD1A7MVANuAJUmW\nDc1arOr75muG7xI5AThtqObVQ5teNdB3QNPT0yxfvnyftqmpKaamphZaTJKk48LMzAwzMzP7tO3c\nufOQlm0eLAZCxUvpLqzcMVTyAPAM3d0en+yXWQusAe7ra+4DViQ5c+A6i/VAgPsHan47ycqB6yx+\nDtgJfH2hMW7YsIF165y+lyRpf/b3YXvz5s1MTk4edNnDDhb9syTOoHuTB3hpklcAjwJ/Tnfb6CuB\nNwE/lGR+FuHRqvp+VT2e5EPANUl2AE/Q3ZJ6b1VtAqiqh5JsBD6Y5GK6203fD8z0d4QAfIYuQHws\nybuBFwFXAddX1fcPd78kSdJzdyQzFq8CPk937UQBv9+3f4Tu+RW/0Ld/qW9P//p1wH/v26aBPcBt\nwFK621cvGdrOBcD1dHeD7O1rL5/vrKq9Sd5EdxfIF4FddM+6+N0j2CdJktTAkTzH4h4WvujzoBeE\nVtVTwGX9z4FqHqN/GNYCNd+mmxmRJElHAR9/LUmSmjFYSJKkZgwWkiSpGYOFJElqxmAhSZKaMVhI\nkqRmDBaSJKkZg4UkSWrGYCFJkpoxWEiSpGYMFpIkqRmDhSRJasZgIUmSmjFYSJKkZgwWkiSpGYOF\nJElqxmAhSZKaMVhIkqRmDBaSJKkZg4UkSWrGYCFJkpoxWEiSpGYMFpIkqRmDhSRJasZgIUmSmjFY\nSJKkZgwWkiSpGYOFJElqxmAhSZKaMVhIkqRmDBaSJKkZg4UkSWrGYCFJkpoxWEiSpGYOO1gkeW2S\n25N8N8neJG/eT82VSR5J8mSSzyY5Y6h/aZIbkswleSLJbUleOFRzapJbkuxMsiPJTUlOGap5cZI7\nkuxKsi3J1UkMS5IkjcmRvAmfAnwJeCdQw51J3g1cCrwDOAvYBWxMsmSg7FrgjcBbgXOB04FPDK3q\nVmACWN/XngvcOLCdFwB3AicC5wC/AvwqcOUR7JMkSWrgxMNdoKruAu4CSJL9lFwOXFVVn+5r3gZs\nB94CfDzJMuAi4PyquqeveTuwJclZVbUpyQRwHjBZVQ/2NZcBdyR5V1Vt6/tfBryuquaAryb5HeB9\nSa6oqmcOd98kSdJz0/S0QZKXAKuBz823VdXjwP3Aa/qmV9EFmsGarcDsQM05wI75UNG7m26G5OyB\nmq/2oWLeRmA58JONdkmSJB2G1tcjrKZ7898+1L697wNYBTzdB44D1awGvjfYWVV7gEeHava3HQZq\nJEnSIjrsUyHPB9PT0yxfvnyftqmpKaampsY0IkmSjh4zMzPMzMzs07Zz585DWrZ1sNgGhG5WYnA2\nYRXw4EDNkiTLhmYtVvV98zXDd4mcAJw2VPPqoe2vGug7oA0bNrBu3bqD7owkScej/X3Y3rx5M5OT\nkwddtumpkKr6Ft2b+vr5tv5izbOBL/ZNDwDPDNWsBdYA9/VN9wErkpw5sPr1dKHl/oGan06ycqDm\n54CdwNcb7ZIkSToMhz1j0T9L4gy6N3mAlyZ5BfBoVX2b7lbS9yb5JvAwcBXwHeBT0F3MmeRDwDVJ\ndgBPANcB91bVpr7moSQbgQ8muRhYArwfmOnvCAH4DF2A+Fh/i+uL+m1dX1XfP9z9kiRJz92RnAp5\nFfB5uos0C/j9vv0jwEVVdXWSk+meObEC+ALwhqp6emAd08Ae4DZgKd3tq5cMbecC4Hq6u0H29rWX\nz3dW1d4kbwI+QDcbsgv4MPC7R7BPkiSpgSN5jsU9HOQUSlVdAVyxQP9TwGX9z4FqHgMuPMh2vg28\naaEaSZK0eHz8tSRJasZgIUmSmjFYSJKkZgwWkiSpGYOFJElqxmAhSZKaMVhIkqRmDBaSJKkZg4Uk\nSWrGYCFJkpoxWEiSpGYMFpIkqRmDhSRJasZgIUmSmjFYSJKkZgwWkiSpGYOFJElqxmAhSZKaMVhI\nkqRmDBaSJKkZg4UkSWrGYCFJkpoxWEiSpGYMFpIkqRmDhSRJasZgIUmSmjFYSJKkZgwWkiSpGYOF\nJElqxmAhSZKaMVhIkqRmDBaSJKkZg4UkSWqmebBI8oIkVyX5syRPJvlmkvfup+7KJI/0NZ9NcsZQ\n/9IkNySZS/JEktuSvHCo5tQktyTZmWRHkpuSnNJ6nyRJ0qEZxYzFe4BfB94JvAz458A/T3LpfEGS\ndwOXAu8AzgJ2ARuTLBlYz7XAG4G3AucCpwOfGNrWrcAEsL6vPRe4sf0uSZKkQ3HiCNb5GuBTVXVX\n/3o2yQV0AWLe5cBVVfVpgCRvA7YDbwE+nmQZcBFwflXd09e8HdiS5Kyq2pRkAjgPmKyqB/uay4A7\nkryrqraNYN8kSdICRjFj8UVgfZK/CpDkFcDPAHf2r18CrAY+N79AVT0O3E8XSgBeRRd6Bmu2ArMD\nNecAO+ZDRe9uoICzm++VJEk6qFHMWLwPWAY8lGQPXXj5F1X1n/r+1XRv/tuHltve9wGsAp7uA8eB\nalYD3xvsrKo9SR4dqJEkSYtoFMHil4ALgPOBrwOvBP5tkkeq6mMj2J4kSTpKjCJYXA38XlX9Yf/6\na0l+Avgt4GPANiB0sxKDsxargPnTGtuAJUmWDc1arOr75muG7xI5AThtoGa/pqenWb58+T5tU1NT\nTE1NHcLuSZL0/DYzM8PMzMw+bTt37jykZUcRLE4G9gy17aW/nqOqvpVkG92dHF8B6C/WPBu4oa9/\nAHimr/lkX7MWWAPc19fcB6xIcubAdRbr6ULL/QsNcMOGDaxbt+5I90+SpOe1/X3Y3rx5M5OTkwdd\ndhTB4o+A9yb5DvA1YB0wDdw0UHNtX/NN4GHgKuA7wKegu5gzyYeAa5LsAJ4ArgPurapNfc1DSTYC\nH0xyMbAEeD8w4x0hkiSNxyiCxaV0QeEGulMVjwAf6NsAqKqrk5xM98yJFcAXgDdU1dMD65mmm/m4\nDVgK3AVcMrStC4Dr6e4G2dvXXt5+lyRJ0qFoHiyqahfwT/qfhequAK5YoP8p4LL+50A1jwEXHsk4\nJUlSe35XiCRJasZgIUmSmjFYSJKkZgwWkiSpGYOFJElqxmAhSZKaGcVzLKTjyuzsLHNzc+MeBitX\nrmTNmjXjHoak45zBQnoOZmdnWbt2gt27nxz3UDjppJPZunWL4ULSWBkspOdgbm6uDxU3AxNjHMkW\ndu++kLm5OYOFpLEyWEhNTNB9LY4kHd+8eFOSJDVjsJAkSc0YLCRJUjMGC0mS1IwXb0pq5mh4pofP\n85DGy2AhqYmj5ZkePs9DGi+DhaQmjo5nevg8D2ncDBaSGvOZHtLxzIs3JUlSMwYLSZLUjMFCkiQ1\nY7CQJEnNGCwkSVIzBgtJktSMwUKSJDVjsJAkSc0YLCRJUjMGC0mS1IzBQpIkNWOwkCRJzRgsJElS\nMwYLSZLUjMFCkiQ1M5JgkeT0JB9LMpfkySRfTrJuqObKJI/0/Z9NcsZQ/9IkN/TreCLJbUleOFRz\napJbkuxMsiPJTUlOGcU+SZKkg2seLJKsAO4FngLOAyaAfwrsGKh5N3Ap8A7gLGAXsDHJkoFVXQu8\nEXgrcC5wOvCJoc3d2q9/fV97LnBj632SJEmH5sQRrPM9wGxV/dpA2/8ZqrkcuKqqPg2Q5G3AduAt\nwMeTLAMuAs6vqnv6mrcDW5KcVVWbkkzQBZfJqnqwr7kMuCPJu6pq2wj2TZIkLWAUp0J+AfjTJB9P\nsj3J5iQ/CBlJXgKsBj4331ZVjwP3A6/pm15FF3oGa7YCswM15wA75kNF726ggLOb75UkSTqoUQSL\nlwIXA1uBnwM+AFyX5B/0/avp3vy3Dy23ve8DWAU83QeOA9WsBr432FlVe4BHB2okSdIiGsWpkBcA\nm6rqd/rXX07yU8BvAB8bwfYkSdJRYhTB4s+BLUNtW4C/2/95GxC6WYnBWYtVwIMDNUuSLBuatVjV\n983XDN8lcgJw2kDNfk1PT7N8+fJ92qamppiamlpoMUmSjgszMzPMzMzs07Zz585DWnYUweJeYO1Q\n21r6Czir6ltJttHdyfEVgP5izbOBG/r6B4Bn+ppP9jVrgTXAfX3NfcCKJGcOXGexni603L/QADds\n2MC6desWKpEk6bi1vw/bmzdvZnJy8qDLjiJYbADuTfJbwMfpAsOvAf9ooOZa4L1Jvgk8DFwFfAf4\nFHQXcyb5EHBNkh3AE8B1wL1VtamveSjJRuCDSS4GlgDvB2a8I0SSpPFoHiyq6k+T/CLwPuB3gG8B\nl1fVfxqouTrJyXTPnFgBfAF4Q1U9PbCqaWAPcBuwFLgLuGRocxcA19PdDbK3r7289T5JkqRDM4oZ\nC6rqTuDOg9RcAVyxQP9TwGX9z4FqHgMuPKJBSpKk5vyuEEmS1IzBQpIkNWOwkCRJzRgsJElSMwYL\nSZLUjMFCkiQ1Y7CQJEnNGCwkSVIzBgtJktSMwUKSJDVjsJAkSc0YLCRJUjMGC0mS1IzBQpIkNWOw\nkCRJzRgsJElSMwYLSZLUjMFCkiQ1Y7CQJEnNGCwkSVIzBgtJktSMwUKSJDVjsJAkSc0YLCRJUjMG\nC0mS1IzBQpIkNWOwkCRJzRgsJElSMwYLSZLUjMFCkiQ1Y7CQJEnNGCwkSVIzBgtJktTMyINFkvck\n2ZvkmqH2K5M8kuTJJJ9NcsZQ/9IkNySZS/JEktuSvHCo5tQktyTZmWRHkpuSnDLqfZIkSfs30mCR\n5NXAO4AvD7W/G7i07zsL2AVsTLJkoOxa4I3AW4FzgdOBTwxt4lZgAljf154L3Nh8RyRJ0iEZWbBI\n8peAm4FfAx4b6r4cuKqqPl1V/wt4G11weEu/7DLgImC6qu6pqgeBtwM/k+SsvmYCOA/4h1X1p1X1\nReAy4Pwkq0e1X5Ik6cBGOWNxA/BHVfXHg41JXgKsBj4331ZVjwP3A6/pm14FnDhUsxWYHag5B9jR\nh455dwMFnN10TyRJ0iE5cRQrTXI+8Eq6gDBsNd2b//ah9u19H8Aq4Ok+cByoZjXwvcHOqtqT5NGB\nGkmStIiaB4skP0Z3fcTPVtX3W69fkiQdvUYxYzEJ/CiwOUn6thOAc5NcCrwMCN2sxOCsxSpg/rTG\nNmBJkmVDsxar+r75muG7RE4AThuo2a/p6WmWL1++T9vU1BRTU1OHtIOSJD2fzczMMDMzs0/bzp07\nD2nZUQSLu4GfHmr7MLAFeF9V/VmSbXR3cnwFfnCx5tl012UAPAA809d8sq9ZC6wB7utr7gNWJDlz\n4DqL9XSh5f6FBrhhwwbWrVt3pPsnSdLz2v4+bG/evJnJycmDLts8WFTVLuDrg21JdgH/r6q29E3X\nAu9N8k3gYeAq4DvAp/p1PJ7kQ8A1SXYATwDXAfdW1aa+5qEkG4EPJrkYWAK8H5ipqgVnLCRJ0miM\n5OLN/ah9XlRdneRkumdOrAC+ALyhqp4eKJsG9gC3AUuBu4BLhtZ7AXA93SzJ3r728lHsgCRJOrhF\nCRZV9fr9tF0BXLHAMk/RPZfisgVqHgMufO4jlCRJLfhdIZIkqRmDhSRJasZgIUmSmjFYSJKkZgwW\nkiSpmcW63VSSjhuzs7PMzc2NdQwrV65kzZo1Yx2Djk8GC0lqaHZ2lrVrJ9i9+8mxjuOkk05m69Yt\nhgstOoOFJDU0NzfXh4qbgYkxjWILu3dfyNzcnMFCi85gIUkjMQH4nUQ6/njxpiRJasZgIUmSmjFY\nSJKkZgwWkiSpGYOFJElqxmAhSZKaMVhIkqRmDBaSJKkZg4UkSWrGYCFJkpoxWEiSpGYMFpIkqRmD\nhSRJasZgIUmSmjFYSJKkZgwWkiSpGYOFJElqxmAhSZKaMVhIkqRmDBaSJKkZg4UkSWrGYCFJkpox\nWEiSpGYMFpIkqRmDhSRJaqZ5sEjyW0k2JXk8yfYkn0zy1/ZTd2WSR5I8meSzSc4Y6l+a5IYkc0me\nSHJbkhcO1Zya5JYkO5PsSHJTklNa75MkSTo0o5ixeC3wfuBs4GeBHwI+k+SH5wuSvBu4FHgHcBaw\nC9iYZMnAeq4F3gi8FTgXOB34xNC2bgUmgPV97bnAje13SZIkHYoTW6+wqn5+8HWSXwW+B0wC/6Nv\nvhy4qqo+3de8DdgOvAX4eJJlwEXA+VV1T1/zdmBLkrOqalOSCeA8YLKqHuxrLgPuSPKuqtrWet8k\nSdLCFuMaixVAAY8CJHkJsBr43HxBVT0O3A+8pm96FV3oGazZCswO1JwD7JgPFb27+22dPYodkSRJ\nCxtpsEgSulMa/6Oqvt43r6Z7898+VL697wNYBTzdB44D1aymmwn5garaQxdgViNJkhZd81MhQ/4A\n+OvAz4x4O4dlenqa5cuX79M2NTXF1NTUmEYkSdLRY2ZmhpmZmX3adu7ceUjLjixYJLke+HngtVX1\n5wNd24DQzUoMzlqsAh4cqFmSZNnQrMWqvm++ZvgukROA0wZq9mvDhg2sW7fu8HZIkqTjxP4+bG/e\nvJnJycmDLjuSUyF9qPg7wOuqanawr6q+RffGv36gfhnddRFf7JseAJ4ZqlkLrAHu65vuA1YkOXNg\n9evpQsv9LfdHkiQdmuYzFkn+AJgC3gzsSrKq79pZVbv7P18LvDfJN4GHgauA7wCfgu5iziQfAq5J\nsgN4ArgOuLeqNvU1DyXZCHwwycXAErrbXGe8I0SSpPEYxamQ36C7OPO/DbW/HfgoQFVdneRkumdO\nrAC+ALyhqp4eqJ8G9gC3AUuBu4BLhtZ5AXA93d0ge/vayxvuiyRJOgyjeI7FIZ1eqaorgCsW6H8K\nuKz/OVDNY8CFhzdCSZI0Kn5XiCRJasZgIUmSmjFYSJKkZgwWkiSpGYOFJElqxmAhSZKaMVhIkqRm\nDBaSJKkZg4UkSWrGYCFJkpoxWEiSpGZG8SVkkiQBMDs7y9zc3FjHsHLlStasWTPWMRxPDBaSpJGY\nnZ1l7doJdu9+cqzjOOmkk9m6dYvhYpEYLCRJIzE3N9eHipuBiTGNYgu7d1/I3NycwWKRGCwkSSM2\nAawb9yC0SLx4U5IkNWOwkCRJzRgsJElSMwYLSZLUjMFCkiQ1Y7CQJEnNGCwkSVIzBgtJktSMwUKS\nJDVjsJAkSc0YLCRJUjMGC0mS1IzBQpIkNWOwkCRJzRgsJElSMwYLSZLUjMFCkiQ1c+K4ByBJ0vPd\n7Owsc3NzYx3DypUrWbNmzci3c8wHiySXAO8CVgNfBi6rqj8Z76gOZgaYGvcgjhIei47HoeNxeJbH\nonPsH4fZ2VnWrp1g9+4nxzqOk046ma1bt4w8XBzTwSLJLwG/D7wD2ARMAxuT/LWqGm80XNCx/xel\nHY9Fx+PQ8Tg8y2PROfaPw9zcXB8qbgYmnsOapoENR7jsFnbvvpC5uTmDxUFMAzdW1UcBkvwG8Ebg\nIuDqcQ5MkqR9TQDrnsPyy5/j8ovjmL14M8kPAZPA5+bbqqqAu4HXjGtckiQdz47ZYAGsBE4Atg+1\nb6e73kKSJC2yY/1UyOE6CWDLli1HvIJnl70TONL1fAe45YjHAN8aGsvia3Mc4LkdC4/Ds54vx8Lj\n8Kxj++/Gvtv3d6JzbP9ODCx70kJ16c4eHHv6UyFPAm+tqtsH2j8MLK+qX9zPMhfw3H5DJUk63v1y\nVd16oM5jdsaiqr6f5AFgPXA7QJL0r687wGIbgV8GHgZ2L8IwJUl6vjgJ+Am699IDOmZnLACS/H3g\nw8Bv8Oztpn8PeFlV/d8xDk2SpOPSMTtjAVBVH0+yErgSWAV8CTjPUCFJ0ngc0zMWkiTp6HIs324q\nSZKOMgYL6SiS5KfGPQZJei4MFhq5JK9P8vUky/bTtzzJ15KcN46xHQ2S/OUk70iyie6L9CQdp5L8\ncJI3Dbz+vSTXDPz8myQLPkdi3AwWiyTJjwz8+cVJrux/QV47znEtkt8EPlhVjw93VNVO4EbgskUf\n1ZglOTfJR4A/p/uG3j8GzhnvqBZXkhckuSjJp5P8ryRfTXJ7krf1t48/7yW5M8nygdfvSbJi4PWP\nJPn6eEa3uJK89Hj5/76AXwF+feD1pcDfAM7sfy4ELh7DuA6ZwWLEkvx0koeB7yV5KMkrgT+huzX2\n14HPJ3nLOMe4CF4B3LVA/2eAly/SWMYqyer+jeMbwB8CjwNLgbdU1Xuq6k/GO8LF07+B3A7cBPwV\n4KvA14Afp7uN/JNjG9ziOo/ud2DebwOnDbw+EVi7qCMan28APzr/Isl/TrJqjOMZh18G/v1Q2wVV\n9bqqeh3wz4C/v/jDOnQGi9G7mu4fzHOB/wZ8GriD7mvqVtB9Wn/PuAa3SFYB31+g/xkG/jF5vkry\nR8BWuhD1m8DpVXXczdQM+FW6vxfrq+rMqpqqqvOr6hXAzwKvT/K2sY5wcQx/Qj+eP7EP7/vPA6eM\nYyBjdAbde8a83cDegdebgL++qCM6TMf0cyyOEa8GXl9VX0nyZeAdwB9U1V6AJO8H/uc4B7gIvgv8\nFPDNA/S/nO50wPPdG+ieCvuBqvrGuAdzFJgC/lVVfX64o6r+OMn76D69fXTRRyaNzwoGZrCqavhD\n1wvYd4brqOOMxeidBmwDqKq/AHYBOwb6dwB/eQzjWkx3Alft74KjJD8M/Eu6mZznu79J9//6gST3\nJ7m0f8Db8erlLHyK7L/SnUZ7vqv+Z7jteOSx6L5pbKG7w17e1xy1fEDWiCXZC6yafxpokieAl1fV\nt/rXq4BBdYjLAAABfUlEQVRHquqEMQ5zpPp93AzsAa6nOx0A8DLgEuAEYF1VbR/PCBdXklOAXwIu\nAs6i2/9/AvyHqnpinGNbTEmeBn68qvY7W5XkdOBbVXVUfzp7rvp/I/4r8FTf9At0F/Lu6l8vBf72\n8/nfiHmHcCwAqKq/u8hDWzRJ/i3dqcDJqto91PfDwJ8Cd1fV5eMY36EwWIyY/2h0kvw48AG6C9Xm\nz6MW3ZfZXDIftI43SdYC/xD4B3RToJ+tqjePd1SLI8keYPWBHsF/PIRugCT/8VDqqurtox7LuHks\nfvB7/yXgaboPYv+771pLd4fIicCZR/MHMYPFiPkXZV9JTqW7OCnAN6pqx0EWOS4kOYEudF50HAWL\n4dA97LgI3dKwJC+h+yD2t9j3g9hngXdW1Z+Na2yHwmAhaSwM3dLCkpxG90EM4JtV9eg4x3OoDBaS\nJKkZ7wqRJEnNGCwkSVIzBgtJktSMwUKSJDVjsJAkSc0YLCRJUjMGC0mS1IzBQpIkNfP/AbOAQRIs\ndlw7AAAAAElFTkSuQmCC\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x122363630>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"data['grade'].value_counts().plot(kind='bar')"
]
},
{
"cell_type": "code",
"execution_count": 72,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"<matplotlib.axes._subplots.AxesSubplot at 0x118ddb828>"
]
},
"execution_count": 72,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"image/png": "iVBORw0KGgoAAAANSUhEUgAAAhYAAAGeCAYAAADITEj7AAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nzhgsJElSZwwWkiSpM/8/404GuI8jvCgAAAAASUVORK5CYII=\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x11787ba58>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"data['emp_length'].value_counts().plot(kind='bar')"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### e. What is the distribution of target class?\n",
"* Most of this dataset the loans are in a current state (in-payment?), or Fully paid off\n",
"* Looks like a [Poisson Distribution](https://en.wikipedia.org/wiki/Poisson_distribution)?!"
]
},
{
"cell_type": "code",
"execution_count": 16,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"<matplotlib.axes._subplots.AxesSubplot at 0x14a033e80>"
]
},
"execution_count": 16,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"image/png": "iVBORw0KGgoAAAANSUhEUgAAAhYAAAK/CAYAAAA1VIT1AAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nERERUZv/D/xIvJvsblX+AAAAAElFTkSuQmCC\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x14a00df60>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"data['loan_status'].value_counts().plot(kind='bar')"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### f. What are the numeric columns?\n",
"* For pre-processing and scaling"
]
},
{
"cell_type": "code",
"execution_count": 76,
"metadata": {
"collapsed": false,
"scrolled": true
},
"outputs": [
{
"data": {
"text/plain": [
"Index(['loan_amnt', 'funded_amnt', 'funded_amnt_inv', 'int_rate',\n",
" 'installment', 'annual_inc', 'dti', 'delinq_2yrs', 'inq_last_6mths',\n",
" 'mths_since_last_delinq', 'mths_since_last_record', 'open_acc',\n",
" 'pub_rec', 'revol_bal', 'revol_util', 'total_acc', 'out_prncp',\n",
" 'out_prncp_inv', 'total_pymnt', 'total_pymnt_inv', 'total_rec_prncp',\n",
" 'total_rec_int', 'total_rec_late_fee', 'recoveries',\n",
" 'collection_recovery_fee', 'last_pymnt_amnt',\n",
" 'collections_12_mths_ex_med', 'mths_since_last_major_derog',\n",
" 'policy_code', 'annual_inc_joint', 'dti_joint', 'acc_now_delinq',\n",
" 'tot_coll_amt', 'tot_cur_bal', 'open_acc_6m', 'open_il_6m',\n",
" 'open_il_12m', 'open_il_24m', 'mths_since_rcnt_il', 'total_bal_il',\n",
" 'il_util', 'open_rv_12m', 'open_rv_24m', 'max_bal_bc', 'all_util',\n",
" 'total_rev_hi_lim', 'inq_fi', 'total_cu_tl', 'inq_last_12m'],\n",
" dtype='object')"
]
},
"execution_count": 76,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"data._get_numeric_data().columns"
]
},
{
"cell_type": "code",
"execution_count": 77,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"'There are 49 numeric columns in the data set'"
]
},
"execution_count": 77,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"\"There are {} numeric columns in the data set\".format(len(data._get_numeric_data().columns) ) "
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### g. What are the character columns?\n",
"* For one-hot encoding into a model matrix"
]
},
{
"cell_type": "code",
"execution_count": 78,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"Index(['grade', 'emp_length', 'home_ownership', 'verification_status',\n",
" 'loan_status', 'pymnt_plan', 'initial_list_status', 'application_type',\n",
" 'verification_status_joint'],\n",
" dtype='object')"
]
},
"execution_count": 78,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"data.select_dtypes(include=['object']).columns"
]
},
{
"cell_type": "code",
"execution_count": 79,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"'There are 8 Character columns in the data set (minus the target)'"
]
},
"execution_count": 79,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"\"There are {} Character columns in the data set (minus the target)\".format(len(data.select_dtypes(include=['object']).columns) -1) "
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## 4. Pre-processing the data"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### a. Remove the target from the entire dataset"
]
},
{
"cell_type": "code",
"execution_count": 80,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"X = data.drop(\"loan_status\", axis=1, inplace = False)\n",
"y = data.loan_status"
]
},
{
"cell_type": "code",
"execution_count": 81,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"70467 Current\n",
"165635 Fully Paid\n",
"496523 Current\n",
"182496 Fully Paid\n",
"554977 Current\n",
"Name: loan_status, dtype: object"
]
},
"execution_count": 81,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"y.head()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### b. Transform the data into a model matrix with one-hot encoding\n",
"* isolate the variables of char class"
]
},
{
"cell_type": "code",
"execution_count": 83,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"def model_matrix(df , columns):\n",
" dummified_cols = pd.get_dummies(df[columns])\n",
" df = df.drop(columns, axis = 1, inplace=False)\n",
" df_new = df.join(dummified_cols)\n",
" return df_new\n",
"\n",
"X = model_matrix(X, ['grade', 'emp_length', 'home_ownership', 'verification_status',\n",
" 'pymnt_plan', 'initial_list_status', 'application_type', 'verification_status_joint'])\n",
"\n",
"# 'issue_d' 'desc' 'addr_state'"
]
},
{
"cell_type": "code",
"execution_count": 84,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>loan_amnt</th>\n",
" <th>funded_amnt</th>\n",
" <th>funded_amnt_inv</th>\n",
" <th>int_rate</th>\n",
" <th>installment</th>\n",
" <th>annual_inc</th>\n",
" <th>dti</th>\n",
" <th>delinq_2yrs</th>\n",
" <th>inq_last_6mths</th>\n",
" <th>mths_since_last_delinq</th>\n",
" <th>...</th>\n",
" <th>verification_status_Source Verified</th>\n",
" <th>verification_status_Verified</th>\n",
" <th>pymnt_plan_n</th>\n",
" <th>initial_list_status_f</th>\n",
" <th>initial_list_status_w</th>\n",
" <th>application_type_INDIVIDUAL</th>\n",
" <th>application_type_JOINT</th>\n",
" <th>verification_status_joint_Not Verified</th>\n",
" <th>verification_status_joint_Source Verified</th>\n",
" <th>verification_status_joint_Verified</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>70467</th>\n",
" <td>13000.0</td>\n",
" <td>13000.0</td>\n",
" <td>12950.0</td>\n",
" <td>9.67</td>\n",
" <td>417.47</td>\n",
" <td>96000.0</td>\n",
" <td>22.75</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>NaN</td>\n",
" <td>...</td>\n",
" <td>0.0</td>\n",
" <td>1.0</td>\n",
" <td>1.0</td>\n",
" <td>1.0</td>\n",
" <td>0.0</td>\n",
" <td>1.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>165635</th>\n",
" <td>8000.0</td>\n",
" <td>8000.0</td>\n",
" <td>8000.0</td>\n",
" <td>13.11</td>\n",
" <td>269.98</td>\n",
" <td>58000.0</td>\n",
" <td>17.46</td>\n",
" <td>1.0</td>\n",
" <td>0.0</td>\n",
" <td>18.0</td>\n",
" <td>...</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>1.0</td>\n",
" <td>1.0</td>\n",
" <td>0.0</td>\n",
" <td>1.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>496523</th>\n",
" <td>10000.0</td>\n",
" <td>10000.0</td>\n",
" <td>10000.0</td>\n",
" <td>10.64</td>\n",
" <td>325.69</td>\n",
" <td>52000.0</td>\n",
" <td>25.04</td>\n",
" <td>1.0</td>\n",
" <td>0.0</td>\n",
" <td>14.0</td>\n",
" <td>...</td>\n",
" <td>0.0</td>\n",
" <td>1.0</td>\n",
" <td>1.0</td>\n",
" <td>1.0</td>\n",
" <td>0.0</td>\n",
" <td>1.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>182496</th>\n",
" <td>16000.0</td>\n",
" <td>16000.0</td>\n",
" <td>16000.0</td>\n",
" <td>15.80</td>\n",
" <td>560.94</td>\n",
" <td>90000.0</td>\n",
" <td>18.16</td>\n",
" <td>0.0</td>\n",
" <td>3.0</td>\n",
" <td>34.0</td>\n",
" <td>...</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>1.0</td>\n",
" <td>0.0</td>\n",
" <td>1.0</td>\n",
" <td>1.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>554977</th>\n",
" <td>10000.0</td>\n",
" <td>10000.0</td>\n",
" <td>10000.0</td>\n",
" <td>7.89</td>\n",
" <td>312.86</td>\n",
" <td>75000.0</td>\n",
" <td>8.47</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>NaN</td>\n",
" <td>...</td>\n",
" <td>0.0</td>\n",
" <td>1.0</td>\n",
" <td>1.0</td>\n",
" <td>0.0</td>\n",
" <td>1.0</td>\n",
" <td>1.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"<p>5 rows × 84 columns</p>\n",
"</div>"
],
"text/plain": [
" loan_amnt funded_amnt funded_amnt_inv int_rate installment \\\n",
"70467 13000.0 13000.0 12950.0 9.67 417.47 \n",
"165635 8000.0 8000.0 8000.0 13.11 269.98 \n",
"496523 10000.0 10000.0 10000.0 10.64 325.69 \n",
"182496 16000.0 16000.0 16000.0 15.80 560.94 \n",
"554977 10000.0 10000.0 10000.0 7.89 312.86 \n",
"\n",
" annual_inc dti delinq_2yrs inq_last_6mths \\\n",
"70467 96000.0 22.75 0.0 0.0 \n",
"165635 58000.0 17.46 1.0 0.0 \n",
"496523 52000.0 25.04 1.0 0.0 \n",
"182496 90000.0 18.16 0.0 3.0 \n",
"554977 75000.0 8.47 0.0 0.0 \n",
"\n",
" mths_since_last_delinq ... \\\n",
"70467 NaN ... \n",
"165635 18.0 ... \n",
"496523 14.0 ... \n",
"182496 34.0 ... \n",
"554977 NaN ... \n",
"\n",
" verification_status_Source Verified verification_status_Verified \\\n",
"70467 0.0 1.0 \n",
"165635 0.0 0.0 \n",
"496523 0.0 1.0 \n",
"182496 0.0 0.0 \n",
"554977 0.0 1.0 \n",
"\n",
" pymnt_plan_n initial_list_status_f initial_list_status_w \\\n",
"70467 1.0 1.0 0.0 \n",
"165635 1.0 1.0 0.0 \n",
"496523 1.0 1.0 0.0 \n",
"182496 1.0 0.0 1.0 \n",
"554977 1.0 0.0 1.0 \n",
"\n",
" application_type_INDIVIDUAL application_type_JOINT \\\n",
"70467 1.0 0.0 \n",
"165635 1.0 0.0 \n",
"496523 1.0 0.0 \n",
"182496 1.0 0.0 \n",
"554977 1.0 0.0 \n",
"\n",
" verification_status_joint_Not Verified \\\n",
"70467 0.0 \n",
"165635 0.0 \n",
"496523 0.0 \n",
"182496 0.0 \n",
"554977 0.0 \n",
"\n",
" verification_status_joint_Source Verified \\\n",
"70467 0.0 \n",
"165635 0.0 \n",
"496523 0.0 \n",
"182496 0.0 \n",
"554977 0.0 \n",
"\n",
" verification_status_joint_Verified \n",
"70467 0.0 \n",
"165635 0.0 \n",
"496523 0.0 \n",
"182496 0.0 \n",
"554977 0.0 \n",
"\n",
"[5 rows x 84 columns]"
]
},
"execution_count": 84,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"X.head()"
]
},
{
"cell_type": "code",
"execution_count": 85,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"(44369, 84)"
]
},
"execution_count": 85,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"X.shape"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### c. Scale the continuous variables use min max calculation"
]
},
{
"cell_type": "code",
"execution_count": 86,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>loan_amnt</th>\n",
" <th>funded_amnt</th>\n",
" <th>funded_amnt_inv</th>\n",
" <th>int_rate</th>\n",
" <th>installment</th>\n",
" <th>annual_inc</th>\n",
" <th>dti</th>\n",
" <th>delinq_2yrs</th>\n",
" <th>inq_last_6mths</th>\n",
" <th>mths_since_last_delinq</th>\n",
" <th>...</th>\n",
" <th>verification_status_Source Verified</th>\n",
" <th>verification_status_Verified</th>\n",
" <th>pymnt_plan_n</th>\n",
" <th>initial_list_status_f</th>\n",
" <th>initial_list_status_w</th>\n",
" <th>application_type_INDIVIDUAL</th>\n",
" <th>application_type_JOINT</th>\n",
" <th>verification_status_joint_Not Verified</th>\n",
" <th>verification_status_joint_Source Verified</th>\n",
" <th>verification_status_joint_Verified</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>70467</th>\n",
" <td>13000.0</td>\n",
" <td>13000.0</td>\n",
" <td>12950.0</td>\n",
" <td>9.67</td>\n",
" <td>417.47</td>\n",
" <td>96000.0</td>\n",
" <td>22.75</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>...</td>\n",
" <td>0.0</td>\n",
" <td>1.0</td>\n",
" <td>1.0</td>\n",
" <td>1.0</td>\n",
" <td>0.0</td>\n",
" <td>1.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>165635</th>\n",
" <td>8000.0</td>\n",
" <td>8000.0</td>\n",
" <td>8000.0</td>\n",
" <td>13.11</td>\n",
" <td>269.98</td>\n",
" <td>58000.0</td>\n",
" <td>17.46</td>\n",
" <td>1.0</td>\n",
" <td>0.0</td>\n",
" <td>18.0</td>\n",
" <td>...</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>1.0</td>\n",
" <td>1.0</td>\n",
" <td>0.0</td>\n",
" <td>1.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>496523</th>\n",
" <td>10000.0</td>\n",
" <td>10000.0</td>\n",
" <td>10000.0</td>\n",
" <td>10.64</td>\n",
" <td>325.69</td>\n",
" <td>52000.0</td>\n",
" <td>25.04</td>\n",
" <td>1.0</td>\n",
" <td>0.0</td>\n",
" <td>14.0</td>\n",
" <td>...</td>\n",
" <td>0.0</td>\n",
" <td>1.0</td>\n",
" <td>1.0</td>\n",
" <td>1.0</td>\n",
" <td>0.0</td>\n",
" <td>1.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>182496</th>\n",
" <td>16000.0</td>\n",
" <td>16000.0</td>\n",
" <td>16000.0</td>\n",
" <td>15.80</td>\n",
" <td>560.94</td>\n",
" <td>90000.0</td>\n",
" <td>18.16</td>\n",
" <td>0.0</td>\n",
" <td>3.0</td>\n",
" <td>34.0</td>\n",
" <td>...</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>1.0</td>\n",
" <td>0.0</td>\n",
" <td>1.0</td>\n",
" <td>1.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>554977</th>\n",
" <td>10000.0</td>\n",
" <td>10000.0</td>\n",
" <td>10000.0</td>\n",
" <td>7.89</td>\n",
" <td>312.86</td>\n",
" <td>75000.0</td>\n",
" <td>8.47</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>...</td>\n",
" <td>0.0</td>\n",
" <td>1.0</td>\n",
" <td>1.0</td>\n",
" <td>0.0</td>\n",
" <td>1.0</td>\n",
" <td>1.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"<p>5 rows × 84 columns</p>\n",
"</div>"
],
"text/plain": [
" loan_amnt funded_amnt funded_amnt_inv int_rate installment \\\n",
"70467 13000.0 13000.0 12950.0 9.67 417.47 \n",
"165635 8000.0 8000.0 8000.0 13.11 269.98 \n",
"496523 10000.0 10000.0 10000.0 10.64 325.69 \n",
"182496 16000.0 16000.0 16000.0 15.80 560.94 \n",
"554977 10000.0 10000.0 10000.0 7.89 312.86 \n",
"\n",
" annual_inc dti delinq_2yrs inq_last_6mths \\\n",
"70467 96000.0 22.75 0.0 0.0 \n",
"165635 58000.0 17.46 1.0 0.0 \n",
"496523 52000.0 25.04 1.0 0.0 \n",
"182496 90000.0 18.16 0.0 3.0 \n",
"554977 75000.0 8.47 0.0 0.0 \n",
"\n",
" mths_since_last_delinq ... \\\n",
"70467 0.0 ... \n",
"165635 18.0 ... \n",
"496523 14.0 ... \n",
"182496 34.0 ... \n",
"554977 0.0 ... \n",
"\n",
" verification_status_Source Verified verification_status_Verified \\\n",
"70467 0.0 1.0 \n",
"165635 0.0 0.0 \n",
"496523 0.0 1.0 \n",
"182496 0.0 0.0 \n",
"554977 0.0 1.0 \n",
"\n",
" pymnt_plan_n initial_list_status_f initial_list_status_w \\\n",
"70467 1.0 1.0 0.0 \n",
"165635 1.0 1.0 0.0 \n",
"496523 1.0 1.0 0.0 \n",
"182496 1.0 0.0 1.0 \n",
"554977 1.0 0.0 1.0 \n",
"\n",
" application_type_INDIVIDUAL application_type_JOINT \\\n",
"70467 1.0 0.0 \n",
"165635 1.0 0.0 \n",
"496523 1.0 0.0 \n",
"182496 1.0 0.0 \n",
"554977 1.0 0.0 \n",
"\n",
" verification_status_joint_Not Verified \\\n",
"70467 0.0 \n",
"165635 0.0 \n",
"496523 0.0 \n",
"182496 0.0 \n",
"554977 0.0 \n",
"\n",
" verification_status_joint_Source Verified \\\n",
"70467 0.0 \n",
"165635 0.0 \n",
"496523 0.0 \n",
"182496 0.0 \n",
"554977 0.0 \n",
"\n",
" verification_status_joint_Verified \n",
"70467 0.0 \n",
"165635 0.0 \n",
"496523 0.0 \n",
"182496 0.0 \n",
"554977 0.0 \n",
"\n",
"[5 rows x 84 columns]"
]
},
"execution_count": 86,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# impute rows with NaN with a 0 for now\n",
"X2 = X.fillna(value = 0)\n",
"X2.head()"
]
},
{
"cell_type": "code",
"execution_count": 87,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"from sklearn.preprocessing import MinMaxScaler\n",
"\n",
"Scaler = MinMaxScaler()\n",
"\n",
"X2[['loan_amnt', 'funded_amnt', 'funded_amnt_inv', 'int_rate',\n",
" 'installment', 'annual_inc', 'dti', 'delinq_2yrs', 'inq_last_6mths',\n",
" 'mths_since_last_delinq', 'mths_since_last_record', 'open_acc',\n",
" 'pub_rec', 'revol_bal', 'revol_util', 'total_acc', 'out_prncp',\n",
" 'out_prncp_inv', 'total_pymnt', 'total_pymnt_inv', 'total_rec_prncp',\n",
" 'total_rec_int', 'total_rec_late_fee', 'recoveries',\n",
" 'collection_recovery_fee', 'last_pymnt_amnt',\n",
" 'collections_12_mths_ex_med', 'mths_since_last_major_derog',\n",
" 'policy_code', 'annual_inc_joint', 'dti_joint', 'acc_now_delinq',\n",
" 'tot_coll_amt', 'tot_cur_bal', 'open_acc_6m', 'open_il_6m',\n",
" 'open_il_12m', 'open_il_24m', 'mths_since_rcnt_il', 'total_bal_il',\n",
" 'il_util', 'open_rv_12m', 'open_rv_24m', 'max_bal_bc', 'all_util',\n",
" 'total_rev_hi_lim', 'inq_fi', 'total_cu_tl', 'inq_last_12m']] = Scaler.fit_transform(X2[['loan_amnt', 'funded_amnt', 'funded_amnt_inv', 'int_rate',\n",
" 'installment', 'annual_inc', 'dti', 'delinq_2yrs', 'inq_last_6mths',\n",
" 'mths_since_last_delinq', 'mths_since_last_record', 'open_acc',\n",
" 'pub_rec', 'revol_bal', 'revol_util', 'total_acc', 'out_prncp',\n",
" 'out_prncp_inv', 'total_pymnt', 'total_pymnt_inv', 'total_rec_prncp',\n",
" 'total_rec_int', 'total_rec_late_fee', 'recoveries',\n",
" 'collection_recovery_fee', 'last_pymnt_amnt',\n",
" 'collections_12_mths_ex_med', 'mths_since_last_major_derog',\n",
" 'policy_code', 'annual_inc_joint', 'dti_joint', 'acc_now_delinq',\n",
" 'tot_coll_amt', 'tot_cur_bal', 'open_acc_6m', 'open_il_6m',\n",
" 'open_il_12m', 'open_il_24m', 'mths_since_rcnt_il', 'total_bal_il',\n",
" 'il_util', 'open_rv_12m', 'open_rv_24m', 'max_bal_bc', 'all_util',\n",
" 'total_rev_hi_lim', 'inq_fi', 'total_cu_tl', 'inq_last_12m']])"
]
},
{
"cell_type": "code",
"execution_count": 88,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>loan_amnt</th>\n",
" <th>funded_amnt</th>\n",
" <th>funded_amnt_inv</th>\n",
" <th>int_rate</th>\n",
" <th>installment</th>\n",
" <th>annual_inc</th>\n",
" <th>dti</th>\n",
" <th>delinq_2yrs</th>\n",
" <th>inq_last_6mths</th>\n",
" <th>mths_since_last_delinq</th>\n",
" <th>...</th>\n",
" <th>verification_status_Source Verified</th>\n",
" <th>verification_status_Verified</th>\n",
" <th>pymnt_plan_n</th>\n",
" <th>initial_list_status_f</th>\n",
" <th>initial_list_status_w</th>\n",
" <th>application_type_INDIVIDUAL</th>\n",
" <th>application_type_JOINT</th>\n",
" <th>verification_status_joint_Not Verified</th>\n",
" <th>verification_status_joint_Source Verified</th>\n",
" <th>verification_status_joint_Verified</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>70467</th>\n",
" <td>0.362319</td>\n",
" <td>0.362319</td>\n",
" <td>0.370000</td>\n",
" <td>0.183777</td>\n",
" <td>0.293954</td>\n",
" <td>0.015803</td>\n",
" <td>0.033828</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>...</td>\n",
" <td>0.0</td>\n",
" <td>1.0</td>\n",
" <td>1.0</td>\n",
" <td>1.0</td>\n",
" <td>0.0</td>\n",
" <td>1.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>165635</th>\n",
" <td>0.217391</td>\n",
" <td>0.217391</td>\n",
" <td>0.228571</td>\n",
" <td>0.329109</td>\n",
" <td>0.186030</td>\n",
" <td>0.009469</td>\n",
" <td>0.025962</td>\n",
" <td>0.058824</td>\n",
" <td>0.000000</td>\n",
" <td>0.141732</td>\n",
" <td>...</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>1.0</td>\n",
" <td>1.0</td>\n",
" <td>0.0</td>\n",
" <td>1.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>496523</th>\n",
" <td>0.275362</td>\n",
" <td>0.275362</td>\n",
" <td>0.285714</td>\n",
" <td>0.224757</td>\n",
" <td>0.226795</td>\n",
" <td>0.008468</td>\n",
" <td>0.037233</td>\n",
" <td>0.058824</td>\n",
" <td>0.000000</td>\n",
" <td>0.110236</td>\n",
" <td>...</td>\n",
" <td>0.0</td>\n",
" <td>1.0</td>\n",
" <td>1.0</td>\n",
" <td>1.0</td>\n",
" <td>0.0</td>\n",
" <td>1.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>182496</th>\n",
" <td>0.449275</td>\n",
" <td>0.449275</td>\n",
" <td>0.457143</td>\n",
" <td>0.442755</td>\n",
" <td>0.398936</td>\n",
" <td>0.014803</td>\n",
" <td>0.027003</td>\n",
" <td>0.000000</td>\n",
" <td>0.214286</td>\n",
" <td>0.267717</td>\n",
" <td>...</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>1.0</td>\n",
" <td>0.0</td>\n",
" <td>1.0</td>\n",
" <td>1.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>554977</th>\n",
" <td>0.275362</td>\n",
" <td>0.275362</td>\n",
" <td>0.285714</td>\n",
" <td>0.108576</td>\n",
" <td>0.217407</td>\n",
" <td>0.012302</td>\n",
" <td>0.012594</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>...</td>\n",
" <td>0.0</td>\n",
" <td>1.0</td>\n",
" <td>1.0</td>\n",
" <td>0.0</td>\n",
" <td>1.0</td>\n",
" <td>1.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"<p>5 rows × 84 columns</p>\n",
"</div>"
],
"text/plain": [
" loan_amnt funded_amnt funded_amnt_inv int_rate installment \\\n",
"70467 0.362319 0.362319 0.370000 0.183777 0.293954 \n",
"165635 0.217391 0.217391 0.228571 0.329109 0.186030 \n",
"496523 0.275362 0.275362 0.285714 0.224757 0.226795 \n",
"182496 0.449275 0.449275 0.457143 0.442755 0.398936 \n",
"554977 0.275362 0.275362 0.285714 0.108576 0.217407 \n",
"\n",
" annual_inc dti delinq_2yrs inq_last_6mths \\\n",
"70467 0.015803 0.033828 0.000000 0.000000 \n",
"165635 0.009469 0.025962 0.058824 0.000000 \n",
"496523 0.008468 0.037233 0.058824 0.000000 \n",
"182496 0.014803 0.027003 0.000000 0.214286 \n",
"554977 0.012302 0.012594 0.000000 0.000000 \n",
"\n",
" mths_since_last_delinq ... \\\n",
"70467 0.000000 ... \n",
"165635 0.141732 ... \n",
"496523 0.110236 ... \n",
"182496 0.267717 ... \n",
"554977 0.000000 ... \n",
"\n",
" verification_status_Source Verified verification_status_Verified \\\n",
"70467 0.0 1.0 \n",
"165635 0.0 0.0 \n",
"496523 0.0 1.0 \n",
"182496 0.0 0.0 \n",
"554977 0.0 1.0 \n",
"\n",
" pymnt_plan_n initial_list_status_f initial_list_status_w \\\n",
"70467 1.0 1.0 0.0 \n",
"165635 1.0 1.0 0.0 \n",
"496523 1.0 1.0 0.0 \n",
"182496 1.0 0.0 1.0 \n",
"554977 1.0 0.0 1.0 \n",
"\n",
" application_type_INDIVIDUAL application_type_JOINT \\\n",
"70467 1.0 0.0 \n",
"165635 1.0 0.0 \n",
"496523 1.0 0.0 \n",
"182496 1.0 0.0 \n",
"554977 1.0 0.0 \n",
"\n",
" verification_status_joint_Not Verified \\\n",
"70467 0.0 \n",
"165635 0.0 \n",
"496523 0.0 \n",
"182496 0.0 \n",
"554977 0.0 \n",
"\n",
" verification_status_joint_Source Verified \\\n",
"70467 0.0 \n",
"165635 0.0 \n",
"496523 0.0 \n",
"182496 0.0 \n",
"554977 0.0 \n",
"\n",
" verification_status_joint_Verified \n",
"70467 0.0 \n",
"165635 0.0 \n",
"496523 0.0 \n",
"182496 0.0 \n",
"554977 0.0 \n",
"\n",
"[5 rows x 84 columns]"
]
},
"execution_count": 88,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"X2.head()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### d. Partition the data into train and testing "
]
},
{
"cell_type": "code",
"execution_count": 89,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"x_train, x_test, y_train, y_test = train_test_split(X2, y, test_size=.3, random_state=123)"
]
},
{
"cell_type": "code",
"execution_count": 90,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"(31058, 84)\n",
"(31058,)\n",
"(13311, 84)\n",
"(13311,)\n"
]
}
],
"source": [
"print(x_train.shape)\n",
"print(y_train.shape)\n",
"print(x_test.shape)\n",
"print(y_test.shape)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## 5. Building the k Nearest Neighbor Classifier\n",
"* experiment with different values for neighbors"
]
},
{
"cell_type": "code",
"execution_count": 91,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"KNeighborsClassifier(algorithm='auto', leaf_size=30, metric='euclidean',\n",
" metric_params=None, n_jobs=1, n_neighbors=10, p=2,\n",
" weights='uniform')"
]
},
"execution_count": 91,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# start out with the number of classes for neighbors\n",
"data_knn = KNeighborsClassifier(n_neighbors = 10, metric='euclidean')\n",
"data_knn"
]
},
{
"cell_type": "code",
"execution_count": 92,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"KNeighborsClassifier(algorithm='auto', leaf_size=30, metric='euclidean',\n",
" metric_params=None, n_jobs=1, n_neighbors=10, p=2,\n",
" weights='uniform')"
]
},
"execution_count": 92,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"data_knn.fit(x_train, y_train)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### a. predict on the test data using the knn model created above"
]
},
{
"cell_type": "code",
"execution_count": 93,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"array(['Current', 'Current', 'Current', ..., 'Current', 'Current',\n",
" 'Fully Paid'], dtype=object)"
]
},
"execution_count": 93,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"data_knn.predict(x_test)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### b. Evaluating the classifier model using R squared"
]
},
{
"cell_type": "code",
"execution_count": 94,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Training data R-squared:\n",
"0.829351535836\n",
"Test data R-squared:\n",
"0.804146946135\n"
]
}
],
"source": [
"# R-square from training and test data\n",
"rsquared_train = data_knn.score(x_train, y_train)\n",
"rsquared_test = data_knn.score(x_test, y_test)\n",
"print ('Training data R-squared:')\n",
"print(rsquared_train)\n",
"print ('Test data R-squared:')\n",
"print(rsquared_test)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### c. Confusion Matrix"
]
},
{
"cell_type": "code",
"execution_count": 96,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"The Confusion matrix:\n",
" [[ 79 480 0 0 0 104 0 0 0 0]\n",
" [ 14 8779 0 0 0 308 0 0 0 0]\n",
" [ 0 20 0 0 0 0 0 0 0 0]\n",
" [ 1 6 0 0 0 0 0 0 0 0]\n",
" [ 1 12 0 0 0 11 0 0 0 0]\n",
" [ 19 1204 0 0 0 1846 0 0 0 0]\n",
" [ 0 91 0 0 0 4 0 0 0 0]\n",
" [ 0 147 0 0 0 0 0 0 0 0]\n",
" [ 0 37 0 0 0 2 0 0 0 0]\n",
" [ 1 141 0 0 0 4 0 0 0 0]]\n"
]
}
],
"source": [
"# confusion matrix\n",
"from sklearn.metrics import confusion_matrix\n",
"\n",
"knn_confusion_matrix = confusion_matrix(y_true = y_test, y_pred = data_knn.predict(x_test))\n",
"print(\"The Confusion matrix:\\n\", knn_confusion_matrix)"
]
},
{
"cell_type": "code",
"execution_count": 99,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"image/png": "iVBORw0KGgoAAAANSUhEUgAAAW0AAAGYCAYAAACJXrOIAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nelQAAAAASUVORK5CYII=\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x1179c2978>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"# visualize the confusion matrix\n",
"# http://scikit-learn.org/stable/auto_examples/model_selection/plot_confusion_matrix.html\n",
"plt.matshow(knn_confusion_matrix, cmap = plt.cm.Blues)\n",
"plt.title(\"KNN Confusion Matrix\\n\")\n",
"#plt.xticks([0,1], ['No', 'Yes'])\n",
"#plt.yticks([0,1], ['No', 'Yes'])\n",
"plt.ylabel('True label')\n",
"plt.xlabel('Predicted label')\n",
"for y in range(knn_confusion_matrix.shape[0]):\n",
" for x in range(knn_confusion_matrix.shape[1]):\n",
" plt.text(x, y, '{}'.format(knn_confusion_matrix[y, x]),\n",
" horizontalalignment = 'center',\n",
" verticalalignment = 'center',)\n",
"plt.show()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### d. Classification Report"
]
},
{
"cell_type": "code",
"execution_count": 98,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
" precision recall f1-score support\n",
"\n",
"Charged Off 0.69 0.12 0.20 663\n",
" Current 0.80 0.96 0.88 9101\n",
" Default 0.00 0.00 0.00 20\n",
"Does not meet the credit policy. Status:Charged Off 0.00 0.00 0.00 7\n",
"Does not meet the credit policy. Status:Fully Paid 0.00 0.00 0.00 24\n",
" Fully Paid 0.81 0.60 0.69 3069\n",
"In Grace Period 0.00 0.00 0.00 95\n",
" Issued 0.00 0.00 0.00 147\n",
"Late (16-30 days) 0.00 0.00 0.00 39\n",
"Late (31-120 days) 0.00 0.00 0.00 146\n",
"\n",
"avg / total 0.77 0.80 0.77 13311\n",
"\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"/Users/jasminedumas/anaconda/lib/python3.5/site-packages/sklearn/metrics/classification.py:1074: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples.\n",
" 'precision', 'predicted', average, warn_for)\n"
]
}
],
"source": [
"#Generate the classification report\n",
"from sklearn.metrics import classification_report\n",
"knn_classify_report = classification_report(y_true = y_test, \n",
" y_pred = data_knn.predict(x_test))\n",
"print(knn_classify_report)"
]
},
{
"cell_type": "markdown",
"metadata": {
"collapsed": true
},
"source": [
"___\n",
"fin."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": []
}
],
"metadata": {
"anaconda-cloud": {},
"kernelspec": {
"display_name": "Python [default]",
"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": "3.5.2"
}
},
"nbformat": 4,
"nbformat_minor": 0
}
| mit |
wzxiong/DAVIS-Machine-Learning | lectures/lecture17-18/Theano test 2.ipynb | 2 | 43558 | {
"cells": [
{
"cell_type": "code",
"execution_count": 420,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"import theano\n",
"import numpy as np\n",
"import pickle\n",
"from theano import tensor as T\n",
"from matplotlib import pyplot as plt"
]
},
{
"cell_type": "code",
"execution_count": 435,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"with open('theano_test_data.pl','r') as datafile:\n",
" Xdata,ydata = pickle.load(datafile)"
]
},
{
"cell_type": "code",
"execution_count": 436,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"p = 2\n",
"H = 4\n",
"x = T.vector('x')\n",
"W1 = theano.shared(value = np.random.randn(p*H).reshape((H,p)), name= 'W1')\n",
"w2 = theano.shared(value = np.random.randn(H), name= 'w2')"
]
},
{
"cell_type": "code",
"execution_count": 437,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"u1 = T.dot(W1,x)"
]
},
{
"cell_type": "code",
"execution_count": 438,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"h = T.nnet.relu(u1)\n",
"u2 = T.dot(h,w2)\n",
"y = T.scalar('y')\n",
"prob = T.nnet.sigmoid(u2)\n",
"R = - y * T.log(prob) - (1 - y) * T.log(1 - prob)"
]
},
{
"cell_type": "code",
"execution_count": 439,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"w2g = T.grad(R,w2)\n",
"W1g = T.grad(R,W1)"
]
},
{
"cell_type": "code",
"execution_count": 440,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"learn_rate = .05"
]
},
{
"cell_type": "code",
"execution_count": 441,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"W_updates = [(W1, W1 - learn_rate * W1g),\n",
" (w2, w2 - learn_rate * w2g)]"
]
},
{
"cell_type": "code",
"execution_count": 442,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"grad_step = theano.function([x,y],R,updates=W_updates)"
]
},
{
"cell_type": "code",
"execution_count": 443,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"((1000L, 2L), 0.0)"
]
},
"execution_count": 443,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"Xdata.shape, ydata[0]"
]
},
{
"cell_type": "code",
"execution_count": 444,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"n = Xdata.shape[0]\n",
"for i in range(n):\n",
" grad_step(Xdata[i,:],ydata[i])"
]
},
{
"cell_type": "code",
"execution_count": 445,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"(array([[-1.54568534, 0.64928966],\n",
" [ 0.17317346, -1.67084628],\n",
" [-1.35370085, 0.8111976 ],\n",
" [ 1.58425059, -0.3717367 ]]),\n",
" array([-1.53440713, 0.89179048, 0.78306905, -1.52970686]))"
]
},
"execution_count": 445,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"W1.get_value(), w2.get_value()"
]
},
{
"cell_type": "code",
"execution_count": 446,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"yhat = T.gt(prob,.5)"
]
},
{
"cell_type": "code",
"execution_count": 447,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"ypred = np.array([prob.eval({x: X}) > .5 for X in Xdata])"
]
},
{
"cell_type": "code",
"execution_count": 448,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"image/png": "iVBORw0KGgoAAAANSUhEUgAAAgMAAAFkCAYAAAC9wjgoAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nyYKHXAAAAABJRU5ErkJggg==\n",
"text/plain": [
"<matplotlib.figure.Figure at 0xaf820f0>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"plt.scatter(Xdata[ypred==1.,0],Xdata[ypred==1.,1])\n",
"plt.scatter(Xdata[ypred==0.,0],Xdata[ypred==0.,1],c='r')\n",
"plt.show()"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": []
}
],
"metadata": {
"kernelspec": {
"display_name": "Python [default]",
"language": "python",
"name": "python2"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 2
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython2",
"version": "2.7.12"
}
},
"nbformat": 4,
"nbformat_minor": 1
}
| mit |
lukasbentkamp/ErrorPro | projects/interactive test.ipynb | 2 | 104966 | {
"cells": [
{
"cell_type": "code",
"execution_count": 2,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"import sys\n",
"sys.path.append('../')\n",
"sys.path.append('../../')\n",
"\n",
"from errorpro.interactive import *\n",
"init(locals())\n"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"[ 1.05 0.93333333]\n",
"[ 0.01443376 0.03118048]\n"
]
}
],
"source": [
"from scipy.optimize import curve_fit\n",
"from numpy import float_\n",
"def func(x, m, b):\n",
" return m*x+b\n",
"xdata = (float_([[1,2,3],[1,2,3]])).flatten()\n",
"ydata = (float_([[2,3,4.1],[2,3,4.1]])).flatten()\n",
"params_opt, params_covar = curve_fit(func, xdata, ydata)\n",
"print(params_opt)\n",
"print(np.sqrt(np.diag(params_covar)))"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"%%eq\n",
"z = 2 <0.1> [m]\n"
]
},
{
"cell_type": "code",
"execution_count": 11,
"metadata": {
"collapsed": false
},
"outputs": [
{
"ename": "TypeError",
"evalue": "<lambda>() takes 0 positional arguments but 1 was given",
"output_type": "error",
"traceback": [
"\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
"\u001b[1;31mTypeError\u001b[0m Traceback (most recent call last)",
"\u001b[1;32mD:\\Projekte\\Python\\ErrorPro\\errorpro\\core.py\u001b[0m in \u001b[0;36m<module>\u001b[1;34m()\u001b[0m\n\u001b[0;32m 1\u001b[0m \u001b[1;32mfrom\u001b[0m \u001b[0msympy\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mutilities\u001b[0m \u001b[1;32mimport\u001b[0m \u001b[0mlambdify\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m----> 2\u001b[1;33m \u001b[0mlambdify\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mz\u001b[0m\u001b[1;33m**\u001b[0m\u001b[1;36m2\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mfree_symbols\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m(\u001b[0m\u001b[0mz\u001b[0m\u001b[1;33m**\u001b[0m\u001b[1;36m2\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mmodules\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;34m\"numpy\"\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;36m1\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m",
"\u001b[1;32mC:\\Users\\Lukas\\Anaconda3\\lib\\site-packages\\numpy\\__init__.py\u001b[0m in \u001b[0;36m<lambda>\u001b[1;34m(_0)\u001b[0m\n",
"\u001b[1;31mTypeError\u001b[0m: <lambda>() takes 0 positional arguments but 1 was given"
]
}
],
"source": [
"from sympy.utilities import lambdify\n",
"lambdify((z**2).free_symbols, (z**2), modules=\"numpy\")"
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {
"collapsed": false
},
"outputs": [
{
"ename": "TypeError",
"evalue": "<lambda>() takes 0 positional arguments but 1 was given",
"output_type": "error",
"traceback": [
"\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
"\u001b[1;31mTypeError\u001b[0m Traceback (most recent call last)",
"\u001b[1;32mD:\\Projekte\\Python\\ErrorPro\\errorpro\\core.py\u001b[0m in \u001b[0;36m<module>\u001b[1;34m()\u001b[0m\n\u001b[1;32m----> 1\u001b[1;33m \u001b[0mget_ipython\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mrun_cell_magic\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m'eq'\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;34m''\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;34m'a = z**2'\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m",
"\u001b[1;32mC:\\Users\\Lukas\\Anaconda3\\lib\\site-packages\\IPython\\core\\interactiveshell.py\u001b[0m in \u001b[0;36mrun_cell_magic\u001b[1;34m(self, magic_name, line, cell)\u001b[0m\n\u001b[0;32m 2291\u001b[0m \u001b[0mmagic_arg_s\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mvar_expand\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mline\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mstack_depth\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 2292\u001b[0m \u001b[1;32mwith\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mbuiltin_trap\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 2293\u001b[1;33m \u001b[0mresult\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mfn\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mmagic_arg_s\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mcell\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 2294\u001b[0m \u001b[1;32mreturn\u001b[0m \u001b[0mresult\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 2295\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n",
"\u001b[1;32mD:\\Projekte\\Python\\ErrorPro\\errorpro\\interactive.py\u001b[0m in \u001b[0;36meq\u001b[1;34m(line, cell)\u001b[0m\n\u001b[0;32m 74\u001b[0m \u001b[0mcalculation\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mline\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 75\u001b[0m \u001b[1;32melse\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m---> 76\u001b[1;33m \u001b[0mcalculation\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mcell\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m",
"\u001b[1;32mD:\\Projekte\\Python\\ErrorPro\\errorpro\\interactive.py\u001b[0m in \u001b[0;36mcalculation\u001b[1;34m(calc)\u001b[0m\n\u001b[0;32m 67\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 68\u001b[0m \u001b[1;31m# execute\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m---> 69\u001b[1;33m \u001b[0mns\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0minterpreter\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0minterpret\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0msyntax_tree\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mns\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 70\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 71\u001b[0m \u001b[1;33m@\u001b[0m\u001b[0mregister_line_cell_magic\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
"\u001b[1;32mD:\\Projekte\\Python\\ErrorPro\\errorpro\\interpreter.py\u001b[0m in \u001b[0;36minterpret\u001b[1;34m(program, namespace)\u001b[0m\n\u001b[0;32m 16\u001b[0m \u001b[0merror\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mparse_expr\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0merror\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mnamespace\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 17\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m---> 18\u001b[1;33m \u001b[0mnamespace\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0mcommand\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mname\u001b[0m\u001b[1;33m]\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0massign\u001b[0m \u001b[1;33m(\u001b[0m\u001b[0mvalue\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0merror\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mcommand\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0munit\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mcommand\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mname\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mcommand\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mlongname\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 19\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 20\u001b[0m \u001b[1;32melif\u001b[0m \u001b[0mcommand\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mparseinfo\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mrule\u001b[0m \u001b[1;33m==\u001b[0m \u001b[1;34m\"multi_assignment\"\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
"\u001b[1;32mD:\\Projekte\\Python\\ErrorPro\\errorpro\\core.py\u001b[0m in \u001b[0;36massign\u001b[1;34m(value, error, unit, name, longname, value_unit, error_unit, ignore_dim)\u001b[0m\n\u001b[0;32m 64\u001b[0m \u001b[1;32mif\u001b[0m \u001b[0misinstance\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mvalue\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mExpr\u001b[0m\u001b[1;33m)\u001b[0m \u001b[1;32mand\u001b[0m \u001b[1;32mnot\u001b[0m \u001b[0mvalue\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mis_number\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 65\u001b[0m \u001b[0mvalue_formula\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mvalue\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m---> 66\u001b[1;33m \u001b[0mvalue\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mget_value\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mvalue_formula\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 67\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 68\u001b[0m \u001b[1;32mif\u001b[0m \u001b[0mignore_dim\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
"\u001b[1;32mD:\\Projekte\\Python\\ErrorPro\\errorpro\\quantities.py\u001b[0m in \u001b[0;36mget_value\u001b[1;34m(expr)\u001b[0m\n\u001b[0;32m 77\u001b[0m \u001b[1;32mraise\u001b[0m \u001b[0mRuntimeError\u001b[0m \u001b[1;33m(\u001b[0m\u001b[1;34m\"quantity '%s' doesn't have a value, yet.\"\u001b[0m \u001b[1;33m%\u001b[0m \u001b[0mvar\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mname\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 78\u001b[0m \u001b[0mdepValues\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mappend\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mvar\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mvalue\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m---> 79\u001b[1;33m \u001b[1;32mreturn\u001b[0m \u001b[0mcalcFunction\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m*\u001b[0m\u001b[0mdepValues\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 80\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 81\u001b[0m \u001b[1;32mdef\u001b[0m \u001b[0mget_error\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mexpr\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
"\u001b[1;32mC:\\Users\\Lukas\\Anaconda3\\lib\\site-packages\\numpy\\__init__.py\u001b[0m in \u001b[0;36m<lambda>\u001b[1;34m(_0)\u001b[0m\n",
"\u001b[1;31mTypeError\u001b[0m: <lambda>() takes 0 positional arguments but 1 was given"
]
}
],
"source": [
"%%eq\n",
"a = z**2"
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/html": [
"Displaying: $a$<div width=20px/><button onclick='var e = document.getElementById(\"d1\").style;if (e.display==\"none\"){e.display=\"block\";}else {e.display=\"none\";}'>Data</button><button onclick='var e = document.getElementById(\"d2\").style;if (e.display==\"none\"){e.display=\"block\";}else {e.display=\"none\";}'>LaTeX</button><hr/><div id=\"d1\" style=\"display:block\"> <table style=\"border:0;width:100%;border-collapse:collapse;\">\n",
"\t<tr style=\"border:0;border-top:1px solid black;border-bottom:1px solid black;\">\n",
"\t\t<td style=\"border:0;border-left:1px solid black;border-right:1px solid black;text-align:center;\">$a \\; \\mathrm{\\left[1\\right]}$</td>\n",
"\t</tr>\n",
"\t<tr style=\"border:0;border-top:1px solid black;border-bottom:1px solid black;\">\n",
"\t\t<td style=\"border:0;border-left:1px solid black;border-right:1px solid black;text-align:center;\">$-0.76 \\pm 0.27$</td>\n",
"\t</tr>\n",
"</table> </div><br><div id=\"d2\" style=\"display:none\"> \\begin{table}[H]\n",
"\\centering\n",
"\t\\begin{tabular}{|c|}\n",
"\t\\hline\n",
"\t$a \\; \\mathrm{\\left[1\\right]}$\\\\ \\hline\n",
"\t$-0.76 \\pm 0.27$\\\\ \\hline\n",
"\t\\end{tabular}\n",
"\\end{table} </div>"
],
"text/plain": [
"a"
]
},
"execution_count": 4,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"z = assign(2,0.1,name=\"z\")\n",
"a = assign(sin(z**2),name=\"a\")\n",
"a"
]
},
{
"cell_type": "code",
"execution_count": 1,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"import sys\n",
"sys.path.append('../')\n",
"sys.path.append('../../')\n",
"\n",
"from errorpro.interactive import *\n",
"init(locals())\n"
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"[[x, y], m, n] m*x + n*y\n",
"lambda x, p0, p1: f(x[0], x[1], p0, p1)\n",
"[[ 1. 1. 1. 2. 2. 2. 3. 3. 3.]\n",
" [ 1. 2. 3. 1. 2. 3. 1. 2. 3.]]\n",
"[ 1. 2. 3. 4. 5. 6. 7. 8. 9.]\n"
]
},
{
"data": {
"text/plain": [
"<mpl_toolkits.mplot3d.art3d.Path3DCollection at 0x8765830>"
]
},
"execution_count": 5,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"image/png": "iVBORw0KGgoAAAANSUhEUgAAAV0AAADtCAYAAAAcNaZ2AAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\ndDkcDsdGuE+Xw+FwbISLLofD4djI/wd52qYAKh2+GwAAAABJRU5ErkJggg==\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x84f96b0>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"x = assign([1,2,3],name='x')\n",
"y = assign([1,2,3],name='y')\n",
"z = assign([[1,2,3],[4,5,6],[7,8,9]], name='z')\n",
"m, n, b = params('m n b')\n",
"fit(m*x+n*y, (x,y), z, [m,n])\n",
"\n",
"import matplotlib.pyplot as plt\n",
"from mpl_toolkits.mplot3d import Axes3D\n",
"fig = plt.figure()\n",
"ax = fig.add_subplot(111, projection='3d')\n",
"xd=np.float_([1,1,1,2,2,2,3,3,3])\n",
"yd=np.float_([1,2,3,1,2,3,1,2,3])\n",
"ax.scatter(xd,yd,[1,2,3,4,5,6,7,8,9])\n",
"ax.scatter(xd,yd,m.value*xd+n.value*yd,c=\"r\")"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"%%eq\n",
"{\n",
"t [s], E [J]\n",
" 1 6.3\n",
" 2 7.4\n",
" 3 7.9\n",
" 4 8.7\n",
" 5 9.5\n",
" 6 10.3\n",
"}\n",
"A = 1 [J]\n",
"t0 = 4 [s]"
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"<function <lambda> at 0x085F9618> [ 1. 2. 3. 4. 5. 6.] [ 6.3 7.4 7.9 8.7 9.5 10.3] None [5.9717717349376302, 10.837378935839991] False\n"
]
},
{
"data": {
"image/png": "iVBORw0KGgoAAAANSUhEUgAAAbEAAAEPCAYAAADMEPq0AAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\njDHGBKz/B4i8IBrQAc4kAAAAAElFTkSuQmCC\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x8a77cd0>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"from errorpro.dimensions.dimensions import Dimension\n",
"#from errorpro.dimensions.solvers import dim_solve\n",
"#from sympy import symbols, exp\n",
"#x, y, z = symbols('x y z')\n",
"#dim_solve(x*exp(y/z), Dimension(length=1), {'y':Dimension(time=1)})\n",
"\n",
"fit(A*exp(t/t0), t, E, [A,t0])\n",
"\n",
"plot([t,E], [t, A*exp(t/t0)], xunit=\"ms\",yunit=\"kW*h\")\n",
"\n"
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"array([ 1., 2., 3., 4., 5., 6.])"
]
},
"execution_count": 7,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"a = [1,3,4,5,6,7]\n",
"b = [2.1,0,-0.1,-0.2,-0.3,-0.5]\n",
"c = [3,-3,5,6,7,8]\n",
"short = assign([3,4,5])\n",
"E = assign([a,b,c],[0.2,3,4,1,0.5,2],name=\"E\")\n",
"F = assign([a,b,c],name=\"F\")\n",
"#np.float_([[2,3,4,5,6],[34,4,5,6,7,4]])\n",
"t.value"
]
},
{
"cell_type": "code",
"execution_count": 10,
"metadata": {
"collapsed": false,
"scrolled": true
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"[[t, NoName_1], m, n, b] NoName_1*n + b + m*t\n",
"lambda x, p0, p1, p2: f(x[0], x[1], p0, p1, p2)\n",
"[[ 1. 1. 1. 2. 2. 2. 3. 3. 3. 4. 4. 4. 5. 5. 5. 6. 6. 6.]\n",
" [ 3. 4. 5. 3. 4. 5. 3. 4. 5. 3. 4. 5. 3. 4. 5. 3. 4. 5.]]\n",
"[ 1. 3. 4. 5. 6. 7. 2.1 0. -0.1 -0.2 -0.3 -0.5 3. -3. 5.\n",
" 6. 7. 8. ]\n"
]
},
{
"data": {
"text/plain": [
"<mpl_toolkits.mplot3d.art3d.Path3DCollection at 0xf1c370>"
]
},
"execution_count": 10,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"image/png": "iVBORw0KGgoAAAANSUhEUgAAAV0AAADtCAYAAAAcNaZ2AAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\n8n/rP+LrHLcLJQAAAABJRU5ErkJggg==\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x84e9610>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"\n",
"m,n,b = params(\"m n b\")\n",
"fit(m*t + n*short + b, (t,short), E, [m,n,b])\n",
"\n",
"import matplotlib.pyplot as plt\n",
"from mpl_toolkits.mplot3d import Axes3D\n",
"fig = plt.figure()\n",
"ax = fig.add_subplot(111, projection='3d')\n",
"from errorpro.fitting import cartesian\n",
"o = cartesian([t.value, short.value])\n",
"ax.scatter(o[0],o[1],E.value.flatten())\n",
"ax.scatter(o[0],o[1],m.value*o[0]+n.value*o[1]+b.value,c=\"r\")"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": []
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"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": "3.4.3"
}
},
"nbformat": 4,
"nbformat_minor": 0
}
| bsd-3-clause |
ChimeraCoder/intro-to-scipy-and-numpy | NumPy.ipynb | 1 | 17547 | {
"metadata": {
"name": ""
},
"nbformat": 3,
"nbformat_minor": 0,
"worksheets": [
{
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Why NumPy?\n",
"--------------------\n",
"\n",
"\n",
"####Python can be slow:"
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"from __future__ import division, print_function\n",
"import itertools"
],
"language": "python",
"metadata": {},
"outputs": []
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"lst1 = range(1000000)\n",
"lst2 = lst1[::-1] #Reverse the list"
],
"language": "python",
"metadata": {},
"outputs": []
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"%timeit [l1 + l2 for l1, l2 in itertools.izip(lst1, lst2)]"
],
"language": "python",
"metadata": {},
"outputs": []
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"#Cycles per addition (approx)\n",
"((109e-3)*(2.4e9))/1000000"
],
"language": "python",
"metadata": {},
"outputs": []
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Why is Python so slow?\n",
"------------------------"
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"from IPython.core.display import Image\n",
"Image(filename=\"python_memory_model.png\")"
],
"language": "python",
"metadata": {},
"outputs": []
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"###Can we do any better?\n",
"\n",
"\n",
"\n",
"\n",
"\n",
"\n",
"\n",
"\n",
"\n",
"\n",
"\n",
"\n",
"\n",
"\n",
"\n",
"\n",
"\n",
"\n",
"\n",
"\n",
"\n",
"\n",
"\n"
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"import numpy as np"
],
"language": "python",
"metadata": {},
"outputs": []
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"arr1 = np.array(lst1)\n",
"arr2 = np.array(lst2)"
],
"language": "python",
"metadata": {},
"outputs": []
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"%timeit arr1+arr2"
],
"language": "python",
"metadata": {},
"outputs": []
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"((1.97e-3)*(2.4e9))/1000000"
],
"language": "python",
"metadata": {},
"outputs": []
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"Image(filename=\"numpy_memory_model.png\")\n",
"\n"
],
"language": "python",
"metadata": {},
"outputs": []
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"####But, there are some tradeoffs to this."
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"a = np.array([(2<<30)-1],dtype=np.int32)"
],
"language": "python",
"metadata": {},
"outputs": []
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"a"
],
"language": "python",
"metadata": {},
"outputs": []
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"a+1"
],
"language": "python",
"metadata": {},
"outputs": []
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"####Uh-oh."
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"a = np.array([-.5])"
],
"language": "python",
"metadata": {},
"outputs": []
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"a"
],
"language": "python",
"metadata": {},
"outputs": []
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"a/0"
],
"language": "python",
"metadata": {},
"outputs": []
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"a**.5"
],
"language": "python",
"metadata": {},
"outputs": []
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"###Performance is not a free lunch either"
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"temperatures_f = np.array([i for i in xrange(32,212)])"
],
"language": "python",
"metadata": {},
"outputs": []
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"temperatures_c = (temperatures_f -32)*5/9.0"
],
"language": "python",
"metadata": {},
"outputs": []
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"####Each of the above three arithmetic operations creates a temporary value"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"###Numpy offers a number of convenient ways to create arrays"
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"a = np.arange(0, 20, 2, dtype=None)\n"
],
"language": "python",
"metadata": {},
"outputs": []
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"np.empty((4,5), dtype=float, order=None)"
],
"language": "python",
"metadata": {},
"outputs": []
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"np.zeros((4,5), dtype=float, order=None)\n"
],
"language": "python",
"metadata": {},
"outputs": []
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"np.ones((4,5), dtype=float, order=None)\n"
],
"language": "python",
"metadata": {},
"outputs": []
},
{
"cell_type": "code",
"collapsed": true,
"input": [
"np.asarray([[i for i in xrange(20)], [j for j in xrange(10)]], dtype=None)\n",
"#These will fail as \"collection\" and iterable is not defined\n",
"#np.array(collection, dtype=None, copy=True, order=None)\n",
"#np.fromiter(iterable, dtype, count=-1)"
],
"language": "python",
"metadata": {},
"outputs": []
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"a = np.arange(12)\n",
"a"
],
"language": "python",
"metadata": {},
"outputs": []
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"a = a.reshape(3,4)\n",
"a"
],
"language": "python",
"metadata": {},
"outputs": []
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"(a*10).reshape(2,6)"
],
"language": "python",
"metadata": {},
"outputs": []
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"a"
],
"language": "python",
"metadata": {},
"outputs": []
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"a * [2,4,6,8] #The 4-vector will be broadcast"
],
"language": "python",
"metadata": {},
"outputs": []
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"a * [2,3,4] #This will cause an error, as a 3-vector cannot be broadcast (the dimensions do not match)"
],
"language": "python",
"metadata": {},
"outputs": []
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"####We can get views of the data by indexing\n"
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"a = np.arange(12)\n",
"a"
],
"language": "python",
"metadata": {},
"outputs": []
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"b = a[::2]\n",
"b"
],
"language": "python",
"metadata": {},
"outputs": []
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"b[2] = -1\n",
"b"
],
"language": "python",
"metadata": {},
"outputs": []
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"a"
],
"language": "python",
"metadata": {},
"outputs": []
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"b.flags['OWNDATA']"
],
"language": "python",
"metadata": {},
"outputs": []
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"b.base is a"
],
"language": "python",
"metadata": {},
"outputs": []
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"####We can index by a list of ints, and get an array of those items"
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"a = np.arange(10)*10\n",
"a"
],
"language": "python",
"metadata": {},
"outputs": []
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"b = a[[4,3,-2]]\n",
"b"
],
"language": "python",
"metadata": {},
"outputs": []
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"b.flags[\"OWNDATA\"] #Note that this gives us a copy of the data"
],
"language": "python",
"metadata": {},
"outputs": []
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"####We can index by a list of boolean values as well"
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"a = a.reshape((5,2))"
],
"language": "python",
"metadata": {},
"outputs": []
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"a[(a%3)==0].shape"
],
"language": "python",
"metadata": {},
"outputs": []
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"b = ((a%3)==0)"
],
"language": "python",
"metadata": {},
"outputs": []
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"a[b]"
],
"language": "python",
"metadata": {},
"outputs": []
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"###Let's dive a bit deeper into the memory layout"
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"a = np.arange(3000000)\n",
"a.shape = (5,3,200000)\n",
"a"
],
"language": "python",
"metadata": {},
"outputs": []
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"b = a.swapaxes(1,2) #Swap the last two axes\n",
"print(b.shape)\n",
"print(a.shape)\n",
"print(b.flags[\"OWNDATA\"])"
],
"language": "python",
"metadata": {},
"outputs": []
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"#### When changing the shape, it helps to remember how arrays are laid out in memory"
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"a.shape = (5,600000) "
],
"language": "python",
"metadata": {},
"outputs": []
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"a.shape = (5,3,200000) #Reset the shape of a, before we reshape a different way\n"
],
"language": "python",
"metadata": {},
"outputs": []
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"a.shape = (1000000,3)"
],
"language": "python",
"metadata": {},
"outputs": []
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"print(b.shape) \n",
"b.shape = (1000000,3)"
],
"language": "python",
"metadata": {},
"outputs": []
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"#### If we take a look at the flags of the arrays, we can see why this error message happened"
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"a.flags"
],
"language": "python",
"metadata": {},
"outputs": []
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"b.flags"
],
"language": "python",
"metadata": {},
"outputs": []
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"###We can also do some fun graphing with matplotlib"
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"import random\n",
"%pylab inline --no-import-all"
],
"language": "python",
"metadata": {},
"outputs": []
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"pylab.ion()\n",
"pylab.figure()\n",
"pylab.plot([random.gauss(10, 3) for i in xrange(30)], 'g')\n",
"pylab.ioff()"
],
"language": "python",
"metadata": {},
"outputs": []
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"####SymPy lets us do symbolic manipulation"
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"from sympy import symbols, limit, log, integrate, Integral, sqrt"
],
"language": "python",
"metadata": {},
"outputs": []
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"x, y = symbols('x y')\n"
],
"language": "python",
"metadata": {},
"outputs": []
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"limit (x*log (x),x,0)"
],
"language": "python",
"metadata": {},
"outputs": []
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"limit (x*log (x),x,20)"
],
"language": "python",
"metadata": {},
"outputs": []
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"integrate(x/(x**2+2*x+1), x)"
],
"language": "python",
"metadata": {},
"outputs": []
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"from sympy import latex, init_printing"
],
"language": "python",
"metadata": {},
"outputs": []
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"integrate(x/(x**2+2*x+1), x)"
],
"language": "python",
"metadata": {},
"outputs": []
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"#### That works, but it's a bit ugly. Can we do any better?"
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"init_printing()"
],
"language": "python",
"metadata": {},
"outputs": []
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"integrate(x/(x**2+2*x+1), x)"
],
"language": "python",
"metadata": {},
"outputs": []
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"Integral(sqrt(1/x), x)"
],
"language": "python",
"metadata": {},
"outputs": []
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"###We can solve equations symbolically"
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"from sympy import solve, Eq\n",
"solve(Eq(x**2, 1), x)"
],
"language": "python",
"metadata": {},
"outputs": []
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"###DiffEqs? No sweat!"
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"from sympy import dsolve, Function, sin\n",
"f, g = symbols('f g', cls=Function)\n",
"diffeq = Eq(f(x).diff(x, x) - 2*f(x).diff(x) + f(x), sin(x))\n",
"diffeq"
],
"language": "python",
"metadata": {},
"outputs": []
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"dsolve(diffeq, f(x))"
],
"language": "python",
"metadata": {},
"outputs": []
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"from sympy import Matrix\n",
"M = Matrix(( [1, 2, 1], [6, -1, 0], [-1, -2, -1] ))\n",
"M.eigenvals()\n"
],
"language": "python",
"metadata": {},
"outputs": []
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"M.eigenvects()"
],
"language": "python",
"metadata": {},
"outputs": []
},
{
"cell_type": "code",
"collapsed": true,
"input": [
"M = Matrix(( [1, 2, 3], [3, 6, 2], [2, 0, 1] ))\n",
"M.eigenvals()"
],
"language": "python",
"metadata": {},
"outputs": []
}
],
"metadata": {}
}
]
} | gpl-3.0 |
par2/lamana | docs/components.ipynb | 1 | 32733 | {
"cells": [
{
"cell_type": "code",
"execution_count": 2,
"metadata": {
"collapsed": false,
"nbsphinx": "hidden"
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Last Run: 2016-07-26 17:33:26\n"
]
}
],
"source": [
"# Hidden TimeStamp\n",
"import time, datetime\n",
"st = datetime.datetime.fromtimestamp(time.time()).strftime('%Y-%m-%d %H:%M:%S')\n",
"print('Last Run: {}'.format(st))"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Key Package Components"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Core Module: `input_`"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### `Geometry` class\n",
"\n",
"This class is designed for parsing a user input string. This *input string* is assumed to be a *geometry string* and is converted to Geometry object. \n",
"\n",
" lamana.input_.Geometry(<input_str>) --> <Geometry object>\n",
" \n",
"When an input string is formatted, it becomes a *geometry string*. The accepted format is the [**General Convention**](lpep.ipynb#LPEP-001:-Implementing-Coding-and-Package-Standards) representing characteristic laminae types, i.e. outer-[inner_i]-middle. \n",
"\n",
"A `Geometry object` is combining mixed Pythonic types - specifically a namedtuple comprising floats, a list and a string (optional). We summarize below:\n",
"\n",
"- *input string*: raw user input \n",
"- *geometry string*: formatted laminate geometry, e.g. `'400.0-[200.0]-800.0'`\n",
"- *Geometry object*: `Geometry` class instance e.g. `<Geometry object (400-[200]-800)>`\n",
"\n",
"Names referencing geometry strings are typically lower-case: \n",
"\n",
"- `g`, `geo_inputs`, `geos` or `geos_full`, \n",
"- `geos = ['400-[200]-800', '400-[100,100]-400S']` \n",
"\n",
"Names referencing `Geometry` objects are typically capatlized: \n",
"\n",
"- `G`, `Geo_objects`, `Geos` or `Geos_full`, \n",
"- `G = la.input_.Geometry(FeatureInput)`"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### `BaseDefaults` class\n",
"\n",
"This class is essentially a storage for common geometry strings and Geometry objects. Placing them here enables simple inheritance of starter objects when using the API. \n",
"\n",
"\n",
"There are two main dicts which are stored as instance attributes: `geo_inputs` and `Geo_objects`\n",
"\n",
"#### `geo_inputs`\n",
"\n",
"This is a simple dict of common geometry strings with keys named by the number of plies. Again the number of plies is determined by $$2(outer + inner) + middle$$ \n",
"\n",
"Here is an example `geo_inputs` dict:\n",
"\n",
"```\n",
"geo_inputs = {\n",
" '1-ply': ['0-0-2000', '0-0-1000'],\n",
" '2-ply': ['1000-0-0'],\n",
" '3-ply': ['600-0-800', '600-0-400S'],\n",
" '4-ply': ['500-500-0', '400-[200]-0'],\n",
" '5-ply': ['400-200-800', '400-[200]-800', '400-200-400S'],\n",
" '6-ply': ['400-[100,100]-0', '500-[250,250]-0'],\n",
" '7-ply': ['400-[100,100]-800', '400-[100,100]-400S'],\n",
" '9-ply': ['400-[100,100,100]-800'],\n",
" '10-ply': ['500-[50,50,50,50]-0'],\n",
" '11-ply': ['400-[100,100,100,100]-800'],\n",
" '13-ply': ['400-[100,100,100,100,100]-800'],\n",
"}\n",
"```\n",
"Additional keys are added to this dict such as 'geos_even', 'geos_odd' and 'geos_all', which create new key-value pairs of groups for even, odd and all geometry strings. These added keys dynamically append values.\n",
"\n",
"Note the naming placement of 's':\n",
"- \"geo_input**s**\" is the base dict\n",
"- \"geo**s**_<group>\" is a grouping of existing dict values appended to the dict. \n",
"\n",
"Therefore an author or developer could extend either the base or appended dict items.\n",
"\n",
"#### `Geo_objects`\n",
"\n",
"This is a lazy dict. All entries of `geo_inputs` are automatically converted and stored as Geometry objects. The purpose here is to eliminate the added step of calling Geometry to convert strings. Both this dict and the `geo_inputs` dict are created using similar private methods, so there mechanisms are parallel.\n",
"\n",
"#### Subclassing `BaseDefaults`\n",
"\n",
"The remaining defaults such as `load_params`, `mat_props` and `FeatureInput` are specific to experimental setups and cannot be generalized effectively. However, there is a `get_FeatureInput()` method to help format this object and defaults to certain values if nonne are provided. Additionally, this class can be subclassed to a custom `Defaults` class by the author. This has a number of benefits for storing custom start values. See the [Author Documentation](writecustom.ipynb#What-are-Defaults?) for examples of subclassing."
]
},
{
"cell_type": "raw",
"metadata": {
"raw_mimetype": "text/restructuredtext"
},
"source": [
".. important ::\n",
" \n",
" DEV: Only add geometry strings to `geo_inputs`. Removing or \"trimming\" these dicts may break tests."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"---"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Feature Module: `distributions`"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### `Case` class\n",
"\n",
"The `Case` class translates user information into managable, analytical units. A `Case` object is:\n",
"\n",
"1. instantiated \n",
"2. user info is applied as geometry strings, model name, etc.\n",
"3. method and properties are accessed, such as `plot()` and `total`\n",
"\n",
"Here is an idiomatic example of the latter characteristics:\n",
"\n",
" >>> case = la.distributions.Case(load_params, mat_props)\n",
" >>> case.apply(geo_strings=None, model='Wilson_LT', **kwargs)\n",
" >>> case.plot(**kwargs)\n",
"\n",
"The `case` instance accepts loading and material information, then sets up their associated dicts. Specific geometry strings and a model are applied to the `case` instance. This `apply()` method generates `LaminateModel` objects (`FeatureInput` objects are also made). Information is parsed, calculated (such as layer thicknesses) and stored in attributes. These attributes and methods are then accessible for performing analysis, most importantly the `plot()` method.\n",
"\n",
"Therefore, you can think of a case as an analytical unit comprising start up data converted to `LaminateModel` objects.\n",
"\n",
"### `Cases` class\n",
"\n",
"The `Cases` class supplies options for manipulating multiple case objects. For example, set operations can be performed on multiple cases. In this context, each `case` is termed a `caselet` and typically correlated with a matplotlib subplot. Here is an idiomatic example:\n",
"\n",
" >>> import lamana as la\n",
" \n",
" >>> bdft = la.input_.BaseDefaults()\n",
" >>> cases = Cases(bdft.geo_inputs['geos_all'], ps=[2,3,4])\n",
"\n",
"The latter code builds cases for all geometry strings contained in the `BaseDefaults()` class, one for each `p` number of datapoints. Therefore in this example *dozens* of analytical units are built with only three lines of code. See [LPEP 002](lpep.ipynb#LPEP-002:--Extending-Cases-with-Patterns) and [LPEP 003](lpep.ipynb#LPEP-003:--A-humble-case-for-caselets) for motivations and details regarding `Cases`. "
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"---"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Core Module: `constructs`"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Principally, the `constructs` module builds a `LaminateModel` object. Technically a `LaminateModel` is a [`pandas`](http://pandas.pydata.org/) DataFrames representing a physical laminate with helpful attributes. DataFrames were chosen as the backend object because they allow for powerful data manipulation analyses and database/spreadsheet-like visualizations with simple methods. \n",
"\n",
"Additionally, the `constructs` module computes laminate dimensional columns and compiles theoretical calculations handled by the complementary `theories` module. Conveniently, all of this data is contained in tabular form within the DataFrame. The column names are closely related to computational variables defined in the next sub-section.\n",
"\n",
"### Preface: Variable Classifications\n",
"\n",
"Before we discuss the laminate structure, here we distinguish two ubiquitous variable categories used internally: *Laminate* and *Model* variables. In a full laminate DataFrame, these categories comprise variables that are represented by columns. The categories variables, columns and corresponding modules are illustrated in the image below and described in greater detail:\n",
"\n",
"\n",
"\n",
"An image of the output for a DataFrame and their labeled categories of columns (IDs, dimensionals and models). The first two categories are computed by `constructs` classess; the models columns are computed by `theories` classes and models. The highlighted blue text indicates data supplied by user/author interaction. Groups of rows are colored with alternating red and orange colors to distinguish separate layers.\n",
"\n",
"#### What distinguishes \"Laminate\" variables from \"Model\" variables\n",
"\n",
"- **Laminate** (or `constructs`) variables are responsible for building the laminate [stack](#First:-The-Stack-Class) and defining dimensions of the laminate. Internally, these varibles will be semantically distinguished with one trailing underscore.\n",
" 1. **ID**: variables related to layer and row identifications \n",
" 1. `layer_`, `side_`, `matl_`, `type_`, `t_`\n",
" 2. **Dimensional**: variables of heights relative to cross-sectional planes\n",
" 1. `label_`, `h_`, `d_`, `intf_`, `k_`, `Z_`, `z_` \n",
"- **Model** (or `theories`) variables: all remaining variables are relevant for LT calculations and defined from a given model. Since these variables are model-specific, theres is no particular semantic or naming format. \n",
"\n",
"The finer granularity seen with model variables is not essential for typcial API use, but may be helpful when authoring custom code that integrates with LamAna. \n",
"\n",
"#### Further Details of Model Variables\n",
"\n",
"For more detailed discussions, model variables can be further divided into sub-categories. There common subsets are as follows:\n",
"\n",
" 1. **User**: global variables delibrately set by the user at startup\n",
" 2. **Inline**: variables used per lamina at a kth level (row)\n",
" 3. **Global**: variables applied to the laminate, accessible by ks\n",
"\n",
"Although model variables are often particular to a chosen model, e.g Wilson_LT, there are some general trends that may be adopted. Some model variables are provided at startup by the user (user_vars). Some variables are calculated for each row of the data within the table (inline_vars). Some variables are calculated by the designated laminate theory model, which provide constants for remaining calculations (global_vars). Global values would display as the same number for every row. These constants are thus removed from the DataFrame, but they are stored internally within a `dict`. The details of this storage are coded within each model module. \n",
"\n",
"Global values are of particular importance to `FeatureInput` objects and when exporting meta data as dashboards in spreadsheets. In contrast, Inline values alter directly with the dimensional values thoroughout the lamainate thickness. Names of common variables used in `distributions` are organized below:\n",
"\n",
"*Model Variable Subsets*\n",
"\n",
" Model_vars = {user_vars, inline_vars, global_vars}\n",
"\n",
"*Examples of Subsets of Model Variables*\n",
"\n",
"- user_vars = [`mat_props`, `load_params`]\n",
"- global_vars = [`v_eq`, `D_11T`, `D_12T`, `M_r`, `M_t`, `D_11p`, `D_12n`, `K_r`, `K_t`]\n",
"- inline_vars = [`Q11`, `Q12`, `D11`, `D12`, `strain_r`, `strain_t`,\n",
" `stress_r`, `stress_t`, `stress_f`]\n",
"\n",
"TIP: Aside from user variables, all others are found as headers for columns \n",
"in a DataFrame (or spreadsheet)."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### The `LaminateModel` Architecture\n",
"\n",
"This section will describe in greater detail how `LaminateModel`s are constructed. \n",
"\n",
"When the user calls `case.apply()`, a number of objects series of inherited objects are made. The phases for building a `LaminateModel` object are outlined below and reflected the architecture of `lamana.constructs.LaminateModel` class.\n",
"\n",
"- Phase 1: build a `Stack`; a primitive laminate of order layers\n",
"- Phase 2: build a `Laminate`; calculate Laminate dimensional values (LFrame)\n",
"- Phase 3: build a `LaminateModel`; calculate laminate theory Model values (LMFrame)\n",
"\n",
"#### Phase 1: The `Stack` Class\n",
"\n",
"The purpose of the `Stack` class is to build a skeletal, precusor of a primitive `Laminate` object. This class houses methods for parsing Geometry objects, ordering layers, adding materials labels for each layer and setting *expected* stress states for each tensile or compressive side. `Stack` returns a namedtuple containing stack-related information (described below).\n",
"\n",
"For a given `Geometry` object instance the `Stack().StackTuple` method creates a namedtuple of the stack information. This object contains attributes to access the:\n",
"\n",
"- stack `order`\n",
"- the number of plies, `nplies`\n",
"- the technical `name` for the laminate, \"4-ply\", \"5-ply\"\n",
"- a convenient `alias` if any, e.g. \"Bilayer\", \"Trilayer\"\n",
"\n",
"The `stack` attribute accesses a dict of the laminate layers ordered from bottom to top (tensile to compressive layers). Now although Python dicts are unsorted, this particular dict is ordered because each layer is enumerated and stored as keys to perserve the order, layer thickness and layer type (sometimes referred as \"ltype\"). \n",
"\n",
"```python\n",
"\n",
"Examples\n",
"--------\n",
">>> import LamAna as la\n",
">>> G = la.input_.Geometry(['400-200-800'])\n",
">>> G\n",
"<Geometry object (400-[200]-800)>\n",
"\n",
"Create a StackTuple and access its attributes\n",
">>> st = constructs.Stack(G).StackTuple # converts G to a namedtuple\n",
">>> st.order # access namedtuple attributes\n",
"{1: [400.0, 'outer'],\n",
" 2: [200.0, 'inner']\n",
" 3: [800.0, 'middle']\n",
" 4: [200.0, 'inner']\n",
" 5: [400.0, 'outer']}\n",
">>> st.nplies\n",
"5\n",
">>> st.name\n",
"'5-ply'\n",
">>> st.alias\n",
"'standard'\n",
"\n",
"```\n",
"\n",
"#### Phase 2: The `Laminate` class\n",
"\n",
"The `Laminate` class prepares the geometric calcuations of the laminate. \n",
"\n",
"`Laminate` inherits from `Stack` and builds an LFrame object based on the skeletal layout of a stack parsed by and returned from the `Stack` class. A `Geometry` object, material parameters and geometric parameters are all passed from the user in as a single `FeatureInput` object - a dict of useful information that is passed between modules. See [*More on `FeatureInput`*](#More-on-FeatureInput) for details. Stack information is stored in an instance attribute called `Snapshot` and then converted to a set of DataFrames. \n",
"\n",
"Therefore, the IDs and dimensional data are determined and computed by `Stack` and `Laminate`. Combined, this information builds an LFrame.\n",
"\n",
"#### Phase 3: The `LaminateModel`\n",
"\n",
"`LaminateModel` object contains all dimensional information of a physical `Laminate` and all theoretical calculations using a laminate theory `Model`, e.g. stress/strain. \n",
"`LaminateModel._build_LMFrame()` calls `handshake` and tries to pass in an instance of self (black arrow). The self at this point is a primitive `Laminate` DataFrame (an LFrame), which comprises IDs and Dimensional columns only. As a `Laminate`, the author of a model has full access to its attributes. From here, `theories.handshakes()` searches within the models directory for a model (grey, dashed arrows) specified by the user at the time of instantiation, i.e. `Case.apply(*args, model=<model_name>).` These computations update the Laminate DataFrame (`Laminate.LFrame`), creating a final `LaminateModel`. The complete workflow is summarized below."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Additional Details\n",
"\n",
"#### More on Material Stacking Order\n",
"\n",
"The material order is initially defined by the user `mat_props` dict in `distributions` and automatically parsed in the `input_` module. Extracting order from a dict is not trivial, so the default sorting is alphabetical order. This order is handled by converting the dict to a pandas index. See `Stack.add_materials()` method for more details.\n",
"\n",
"As of 0.4.3d4, the user can partially override the default ordering by setting the `materials` property in the Case instance. This allows simple control of the stacking order in the final laminate stack and `Laminate` objects. At the moment, a list of materials is cycled through; more customizations have not been implemented yet.\n",
"\n",
"```python\n",
">>> case.material\n",
"['HA', 'PSu'] # alphabetical order\n",
">>> case.material = ['PSu', 'HA'] # overriding order \n",
">>> case.material\n",
"['PSu', 'HA']\n",
">>> case.apply(...) \n",
"<materials DataFrame> # cycles the stacking order\n",
"\n",
"```\n",
"\n",
"#### More on `Laminate`\n",
"\n",
"Using `Laminate._build_snapshot()`, the instance stack dict is converted to a DataFrame (`Snapshot`), giving a primitive view of the laminate geometry, idenfiers (IDs) and stacking order. This \"snapshot\" has the following ID columns of infornation, which are accessible to the user in a `Case` instance (see `distributions.Case.snapshot`):\n",
"\n",
" Variables addressed: `layer_, matl_, type_, t_`\n",
"\n",
"From this snapshot, the DataFrame can is updated with new information. For example, the sides on which to expected tensile and compressive stresses are located (`side_`) are assigned to a laminate through the `Laminate._set_stresses()` method. This function accounts for DataFrames with even and odd rows. For odd rows, 'None' is assigned to the neutral axis, implying \"no stress\".\n",
"\n",
" Variables addressed: `side_`"
]
},
{
"cell_type": "raw",
"metadata": {
"raw_mimetype": "text/restructuredtext"
},
"source": [
".. note ::\n",
"\n",
" This stress assignment is a general designation, coarsely determined by which side of the netural axis a row is found. The rigorous or finite stress state must be calculated through other analytical tools means such as Finite Element Analysis. "
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Here are similarities between the laminate data columns and the its objects:\n",
"\n",
"- `Snapshot`: primiate DataFrame of the Stack (see materials, layer info order).\n",
"- `LFrame`: updated `Snapshot` of IDs and dimensionals.\n",
"- `LMFrame`: updated LFrame with models computed columns.\n",
"\n",
"\n",
"\n",
"`LMFrame` is the paramount data structure of interest containing all IDs, Dimensional and Model variables and `p` number of rows pertaining to data points within a given lamina. \n",
"\n",
"Dimensional variable columns are populated through the `Laminate._build_LFrame()` method, which contains algorithms for calculating realative and absolute heights, thicknesses and midplane distances relative to the neutral axis. These columns contain dimensional data that are determined independent from the laminate theory model. \n",
"\n",
" Variables addresed: `label_, h_, d_, intf_, k_, Z_, z_`\n",
"\n",
"These variables are defined in the Laminate class docstring. See [*More on label_*](#More-on-label_) to understand the role of points, `p` and their relationship to DataFrame rows. \n",
"\n",
"Finally Data variable columns are populated using `Laminate._build_LMFrame()`. These columnns contain data based on calculations from laminate theory for a selected model. Here global_vars and inline_vars are calculated.\n",
"\n",
" Variables addressed:\n",
" --------------------\n",
" global_vars = [`v_eq, D_11T, D_12T, M_r, M_t, D_11p, D_12n, K_r, K_t`] --> FeatureInput['Global'] (dict entry)\n",
" \n",
" inline_vars = [`Q11, Q12, D11, D12, strain_r, strain_t, stress_r, stress_t, stress_f`] --> LaminateModel object (DataFrame)\n",
" \n",
"\n",
"###### More on `FeatureInput`\n",
"\n",
"A Feature module defines a `FeatureInput` object. \n",
"\n",
"For `distributions`, it is defined in `Case`. `FeatureInput`s contain information that is passed between objects. For instance, this object transfers user input data in `distributions` (converted in `input_`) to the `constructs` module to build the laminate stack and populate ID and dimensional columns. A FeatureInput from `distributions` looks like the following (as of 0.4.4b).\n",
"```python\n",
"FeatureInput = {\n",
" 'Geometry': <Geometry object>,\n",
" 'Loading': <load_params dict>,\n",
" 'Materials': <mat_props dict>,\n",
" 'Custom': <undefined>,\n",
" 'Model': <string>,\n",
" 'Globals': <dict>,\n",
"}\n",
"``` \n",
"After calculating model data, the \"Globals\" key is updated containing all necessary `globabl_vars`. These variables are constant and are necessary for further calculations of `inline_vars`. Here is an example of Global variables key-value pair in FeatureInput.\n",
"\n",
"```python\n",
"FeatureInput['Globals'] = [v_eq, D_11T, D_12T, M_r, M_t, D_11p, D_12n, K_r, K_t]\n",
"```\n",
"\n",
"###### More on `label_`\n",
"\n",
"See [LPEP 001.02](lpep.ipynb#LPEP-001:-Implementing-Coding-and-Package-Standards) for standards of API units.\n",
"\n",
"For this explanation, imagine we transverse the absolute height of the laminate at different cross-sectional planes. The values of inline stress points are calculated along different planes throughout the laminate thickness. What happens at interfaces where two materials meet with different stresses? How are these two stress points differentiated in a DataFrame or in a plot? For plotting purposes, we need to define diferent types of points. Here we define some rulse and four types of points found within a (i.e. DataFrame rows):\n",
"\n",
"1. interfacial - point on unbound outer surfaces and bound internal surfaces.\n",
"2. internal - point with the lamina thickness between interfaces \n",
"3. discontinuity - point on bounded interfaces pertaining to an adjacent lamina\n",
"4. neutralaxis - the middle, symmetric axial plane\n",
"\n",
"How these points are distributed depends on their locations within each lamina and whether they are located on the tensile or compressive `side_`. The neutral axis exists in physical laminates, but they are only represented as a row in DataFrames of odd ply, odd p laminates; they are not displayed in even laminates. The image below illustrates the different points from above with respect to `k_` (the fractional height for a given layer).\n",
"\n",
"\n",
"\n",
"Notice various layers have different point types.\n",
"\n",
"- Middle layers have two interfacial points, no discontinuities and a neutral axis.\n",
"- All other layers have one interfacial point with a discontinuity if p >= 2.\n",
"- All layers may (or may not) have internal points.\n",
"- Monoliths do not have discontinuities"
]
},
{
"cell_type": "raw",
"metadata": {
"raw_mimetype": "text/restructuredtext"
},
"source": [
".. note::\n",
"\n",
" Only the interfacial points can be theoreticlly verified, representing the maximum principal strains and stresses. The internal and discontinuity points are merely used by matplotlib to connect the points, assuming a linear stress distribution. "
]
},
{
"cell_type": "raw",
"metadata": {
"raw_mimetype": "text/restructuredtext"
},
"source": [
".. note::\n",
"\n",
" The midplane z height (`z_`) for discontinuities assumes a non-zero, lower limit value equal to the Z_ height of the bounding layer. This value should be verified."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"###### More on `IndeterminateError`"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"An `IndeterminateError` is thrown in cases where values cannot be calculated. An `INDET` keyword is given as values in DataFrame cells. An example for such an error is determining the stress state `side_` for a monolith with one data point (nplies=1, p=1). From a design perspective, the location of the point is ambiguous, either one one interface, but more intuitively at the neutral access. At such a position, the value of stress would report zero, which is misleading for the true stress state of the monolith. Therefore, the `InderminateError` is thrown, recommending at least p = 2 for disambiguated stress calculations. "
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"---"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Core Module: `theories`"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Laminate theory is merged with dimensional data to create a `LaminateModel`.\n",
"\n",
"### `LaminateModel` Handling\n",
"\n",
"For clarity, an illustration of `LaminateModel` handling is shown. \n",
"\n",
"The `Laminate` DataFrame (LFrame) is passed from `constructs` to `theories`. If successful, the `LaminateModel` is returned to `constructs`; otherwise an exception is caught in `constructs` and a `ModelError` is raised. Further up, this error initiates a rollback to an LFrame, handle in the Feature module.\n",
"\n",
""
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"'Image Intramodular component should be updated.'"
]
},
"execution_count": 3,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"\"\"\"Image Intramodular component should be updated.\"\"\""
]
},
{
"cell_type": "raw",
"metadata": {
"raw_mimetype": "text/restructuredtext"
},
"source": [
".. note::\n",
"\n",
" The term repr for <`LaminateModel object`> remains constant refering to a post-theories operation, whether LMFrame is updated with Model columns or not."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"See the [constructs section](#Phase-2:-The-Laminate-class) for how LaminateModel is made.\n",
"\n",
"A model is simply a module containing code that handles laminate theory calculations. The purpose of the model is to update the primitive LFame with columns of LT calculations. `handshake()` automatically distinguishes whether the author implemented a class-style or function-style model. The **most important hook method/function is `_use_model_()`**, which must be present somewhere inside the model module and must return a tuple containing:\n",
"\n",
" - updated Laminate DataFrame with model data columns (a.k.a. `LaminateModel`)\n",
" - `FeatureInput` with updated `'Globals'` key.\n",
" \n",
"`'Globals'` is a dict of calculated constants, used in exported reports [see output_ section](#Core-Module:-output_). \n",
"\n",
"Post-handshake, the self instance of the `LaminateModel` is updated with the new `LaminateModel` and `FeatureInput` (green arrow). Otherwise exceptions are raised. A commom excpetion are for Laminates with `p`=1, which detects an INDET value in middle layers. Handling these exceptions is done in the other modules. \n",
"\n",
"### Custom Models\n",
"\n",
"Sometimes Classical Laminate Theory needs to be modified to fit a specific set of constraints or boundary conditions. The LamAna package has powerful, extensible options for integrating user user-defined (authored) implementations of their own custom laminate theory models. It is common for a custom model to be named by the author, suffixed by the characters \"`_LT`\"). \n",
"\n",
"A library of these custom models, tests and pre-defined defaults are stored in the `models` directory (sub-package). Code for calculations, related exceptions, `FeatureInput` variables and defaults are stored in a Models module. `theories` then merges the model calculations with the passed in `Laminate` to calculate data columns in the `LaminateModel` object. This exchange is possbile since the `theories` module \"handshakes\" with the `constructs` module, and the selected model from the `models` sub-package. "
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"---"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Core Module: `output_`"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"A summary of `output` objects\n",
"\n",
"| Object | Purpose |\n",
"|:------ |:-------- |\n",
"| `SinglePlot` | Stress distribution for a single geometry |\n",
"| `MultiPlot` | Stress distributions for a multiple geometries |\n",
"| `HalfPlot` | Partial plot of either compression or tension side |\n",
"| `QuarterPlot` | Partial halfplot excluding side without data |\n",
"| `PanelPlot` | A series of subplots side-by-side |\n",
"| `RatioPlot` | Ratio thickness plot; prinicipal stress vs. ratio |\n",
"| `PredictPlot` | Plot of experimental failure load or stress vs. middle layer princ. stress |"
]
},
{
"cell_type": "raw",
"metadata": {
"collapsed": true,
"raw_mimetype": "text/restructuredtext"
},
"source": [
".. note::\n",
"\n",
" Development is in alpha stayus for this module. Therefore these objects are not yet implemented. The majority of plotting options are handled by temporary private functions called _distribplot() and _multiplot(). "
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"The `utils.tools.export()` function is used to save regular or temporary file versions of .xslx or .csv files. Files are automatically stored in the default export folder. More details are shown in the Demonstrations file."
]
}
],
"metadata": {
"celltoolbar": "Raw Cell Format",
"kernelspec": {
"display_name": "Python [py27]",
"language": "python",
"name": "Python [py27]"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 2
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython2",
"version": "2.7.12"
},
"toc": {
"toc_cell": false,
"toc_number_sections": false,
"toc_threshold": "2",
"toc_window_display": false
}
},
"nbformat": 4,
"nbformat_minor": 0
}
| bsd-3-clause |
kylelillie/repeatSales | repeat-sales method.ipynb | 1 | 1981 | {
"cells": [
{
"cell_type": "code",
"execution_count": 67,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"['LINC_NBR', 'TITLE_NBR', 'Year', 'MM', 'VALUE', 'Municipality']\n"
]
}
],
"source": [
"import pandas as pd\n",
"\n",
"codes = pd.read_csv('muni_codes.csv')\n",
"months = pd.read_csv('months.csv')\n",
"\n",
"data = pd.read_csv('net_LandTitles.csv',low_memory=False, encoding='Latin-1', dtype={'MUNI_CODE':str})\n",
"\n",
"data = data.merge(codes, how='left', on='MUNI_CODE')\n",
"data = data.merge(months, how='left', on='Month')\n",
"\n",
"data = data[['LINC_NBR','TITLE_NBR','Year','MM','VALUE','Municipality']]\n",
"data.sort_values(by=['LINC_NBR','TITLE_NBR','Year','MM'], ascending=True, inplace=True)\n",
"\n",
"print (list(data))"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"data['YR_$D'] = data.groupby(['LINC_NBR'])['VALUE'].transform(lambda x: x.diff())\n",
"data['YR_%D'] = data.groupby(['LINC_NBR'])['VALUE'].transform(lambda x: x.pct_change())\n",
"data['TITLES'] = data.groupby(['Municipality'])['TITLE_NBR'].transform(lambda x: x.count())"
]
},
{
"cell_type": "code",
"execution_count": 61,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"data.to_csv('repeat_sales.csv',index=False)"
]
}
],
"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": "3.4.5"
}
},
"nbformat": 4,
"nbformat_minor": 2
}
| mit |
domitry/cytoscape-ipy | example/demo.ipynb | 1 | 12023 | {
"metadata": {
"name": ""
},
"nbformat": 3,
"nbformat_minor": 0,
"worksheets": [
{
"cells": [
{
"cell_type": "code",
"collapsed": false,
"input": [
"import cytoscape.main as cy"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 1
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"cy.init_ipynb()"
],
"language": "python",
"metadata": {},
"outputs": [
{
"html": [
"<script>\n",
"if(window['cytoscape'] === undefined){\n",
" var paths = {\n",
"\tcytoscape: 'http://cytoscape.github.io/cytoscape.js/api/cytoscape.js-latest/cytoscape.min',\n",
" };\n",
" \n",
" console.log('Begin loading all JavaScript libs...');\n",
" require.config({paths: paths});\n",
"\n",
" require(['cytoscape'], function(cytoscape){\n",
"\twindow['cytoscape'] = cytoscape;\n",
"\tconsole.log('Finished loading jQuery and Cytoscape.js.');\n",
"\n",
"\tvar event = document.createEvent(\"HTMLEvents\");\n",
"\tevent.initEvent(\"load_cytoscape\", true, false);\n",
"\twindow.dispatchEvent(event);\n",
" });\n",
"}\n",
"</script>\n"
],
"metadata": {},
"output_type": "display_data",
"text": [
"<IPython.core.display.HTML at 0x18ec610>"
]
}
],
"prompt_number": 2
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"cy.plot()"
],
"language": "python",
"metadata": {},
"outputs": [
{
"html": [
"<!DOCTYPE html>\n",
"<html>\n",
"<head>\n",
"<meta charset=utf-8 />\n",
"<title>Animated BFS</title>\n",
"<style type=\"text/css\">\n",
"body { \n",
" font: 14px helvetica neue, helvetica, arial, sans-serif;\n",
"}\n",
"\n",
"#cyd71a5498-4e69-4c63-a2e0-fbcf2df3ede0 {\n",
" height: 500px;\n",
" width: 500px;\n",
" position: absolute;\n",
" left: 0;\n",
" top: 0;\n",
"}\n",
"</style>\n",
"\n",
"<script>\n",
"(function(){\n",
"\n",
"function render(){\n",
"$('#cyd71a5498-4e69-4c63-a2e0-fbcf2df3ede0').cytoscape({\n",
" style: cytoscape.stylesheet()\n",
" .selector('node')\n",
" .css({\n",
" 'content': 'data(id)'\n",
" })\n",
" .selector('edge')\n",
" .css({\n",
" 'target-arrow-shape': 'triangle',\n",
" 'width': 4,\n",
" 'line-color': '#ddd',\n",
" 'target-arrow-color': '#ddd'\n",
" })\n",
" .selector('.highlighted')\n",
" .css({\n",
" 'background-color': '#61bffc',\n",
" 'line-color': '#61bffc',\n",
" 'target-arrow-color': '#61bffc',\n",
" 'transition-property': 'background-color, line-color, target-arrow-color',\n",
" 'transition-duration': '0.5s'\n",
" }),\n",
" \n",
" elements: {\n",
" nodes: [{\"data\": {\"id\": \"a\"}}, {\"data\": {\"id\": \"b\"}}, {\"data\": {\"id\": \"c\"}}, {\"data\": {\"id\": \"d\"}}, {\"data\": {\"id\": \"e\"}}],\n",
" \n",
" edges: [{\"data\": {\"source\": \"a\", \"id\": \"a\", \"weight\": 1, \"target\": \"e\"}}, {\"data\": {\"source\": \"a\", \"id\": \"ab\", \"weight\": 3, \"target\": \"b\"}}, {\"data\": {\"source\": \"b\", \"id\": \"be\", \"weight\": 4, \"target\": \"e\"}}, {\"data\": {\"source\": \"b\", \"id\": \"bc\", \"weight\": 5, \"target\": \"c\"}}, {\"data\": {\"source\": \"c\", \"id\": \"ce\", \"weight\": 6, \"target\": \"e\"}}, {\"data\": {\"source\": \"c\", \"id\": \"cd\", \"weight\": 2, \"target\": \"d\"}}, {\"data\": {\"source\": \"d\", \"id\": \"de\", \"weight\": 7, \"target\": \"e\"}}]\n",
" },\n",
" \n",
" layout: {\n",
" name: 'breadthfirst',\n",
" directed: true,\n",
" roots: '#a',\n",
" padding: 10\n",
" },\n",
" \n",
" ready: function(){\n",
" window.cy = this;\n",
" \n",
" var bfs = cy.elements().bfs('#a', function(){}, true);\n",
" \n",
" var i = 0;\n",
" var highlightNextEle = function(){\n",
" bfs.path[i].addClass('highlighted');\n",
" \n",
" if( i < bfs.path.length ){\n",
" i++;\n",
" setTimeout(highlightNextEle, 1000);\n",
" }\n",
" };\n",
" \n",
" // kick off first highlight\n",
" highlightNextEle();\n",
" }\n",
"});\n",
"};\n",
"\n",
"var before_render = function(){\n",
" if(window['cytoscape'] === undefined){\n",
" console.log(\"wait!\");\n",
"\t window.addEventListener(\"load_cytoscape\", before_render);\n",
" }else{\n",
"\t console.log(\"begin rendering!\");\n",
"\t render();\n",
" }\n",
"}\n",
"\n",
"before_render();\n",
"\n",
"})();\n",
"</script>\n",
"</head>\n",
"<body>\n",
" <div id=\"cyd71a5498-4e69-4c63-a2e0-fbcf2df3ede0\"></div>\n",
" <!-- When only #uuid div is placed on this page, the height of output-box on ipynb will be 0px. One line below will prevent that. -->\n",
" <div id=\"dammy\" style=\"width:700px;height:500px\">\n",
"</body>\n",
"</html>"
],
"metadata": {},
"output_type": "display_data",
"text": [
"<IPython.core.display.HTML at 0x1a90c50>"
]
}
],
"prompt_number": 3
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"nodes = [\n",
" { 'data': { 'id': 'a' } },\n",
" { 'data': { 'id': 'b' } },\n",
" { 'data': { 'id': 'c' } },\n",
" { 'data': { 'id': 'd' } },\n",
" { 'data': { 'id': 'e' } },\n",
" { 'data': { 'id' : 'f'} }\n",
"]\n",
"edges = [\n",
" { 'data': { 'id': 'a', 'weight': 1, 'source': 'a', 'target': 'e' } },\n",
" { 'data': { 'id': 'ab', 'weight': 3, 'source': 'a', 'target': 'b' } },\n",
" { 'data': { 'id': 'be', 'weight': 4, 'source': 'b', 'target': 'e' } },\n",
" { 'data': { 'id': 'bc', 'weight': 5, 'source': 'b', 'target': 'c' } },\n",
" { 'data': { 'id': 'ce', 'weight': 6, 'source': 'c', 'target': 'e' } },\n",
" { 'data': { 'id': 'cd', 'weight': 2, 'source': 'c', 'target': 'd' } },\n",
" { 'data': { 'id': 'de', 'weight': 7, 'source': 'd', 'target': 'e' } },\n",
" { 'data': { 'id': 'df', 'weight': 7, 'source': 'd', 'target': 'f' } },\n",
" { 'data': { 'id': 'fc', 'weight': 3, 'source': 'f', 'target': 'c' } },\n",
" { 'data': { 'id': 'fe', 'weight': 5, 'source': 'f', 'target': 'e' } }\n",
"]\n",
"cy.plot(nodes=nodes, edges=edges)"
],
"language": "python",
"metadata": {},
"outputs": [
{
"html": [
"<!DOCTYPE html>\n",
"<html>\n",
"<head>\n",
"<meta charset=utf-8 />\n",
"<title>Animated BFS</title>\n",
"<style type=\"text/css\">\n",
"body { \n",
" font: 14px helvetica neue, helvetica, arial, sans-serif;\n",
"}\n",
"\n",
"#cycee16a87-eeb2-46ca-8558-e37620f9b5e7 {\n",
" height: 500px;\n",
" width: 500px;\n",
" position: absolute;\n",
" left: 0;\n",
" top: 0;\n",
"}\n",
"</style>\n",
"\n",
"<script>\n",
"(function(){\n",
"\n",
"function render(){\n",
"$('#cycee16a87-eeb2-46ca-8558-e37620f9b5e7').cytoscape({\n",
" style: cytoscape.stylesheet()\n",
" .selector('node')\n",
" .css({\n",
" 'content': 'data(id)'\n",
" })\n",
" .selector('edge')\n",
" .css({\n",
" 'target-arrow-shape': 'triangle',\n",
" 'width': 4,\n",
" 'line-color': '#ddd',\n",
" 'target-arrow-color': '#ddd'\n",
" })\n",
" .selector('.highlighted')\n",
" .css({\n",
" 'background-color': '#61bffc',\n",
" 'line-color': '#61bffc',\n",
" 'target-arrow-color': '#61bffc',\n",
" 'transition-property': 'background-color, line-color, target-arrow-color',\n",
" 'transition-duration': '0.5s'\n",
" }),\n",
" \n",
" elements: {\n",
" nodes: [{\"data\": {\"id\": \"a\"}}, {\"data\": {\"id\": \"b\"}}, {\"data\": {\"id\": \"c\"}}, {\"data\": {\"id\": \"d\"}}, {\"data\": {\"id\": \"e\"}}, {\"data\": {\"id\": \"f\"}}],\n",
" \n",
" edges: [{\"data\": {\"source\": \"a\", \"id\": \"a\", \"weight\": 1, \"target\": \"e\"}}, {\"data\": {\"source\": \"a\", \"id\": \"ab\", \"weight\": 3, \"target\": \"b\"}}, {\"data\": {\"source\": \"b\", \"id\": \"be\", \"weight\": 4, \"target\": \"e\"}}, {\"data\": {\"source\": \"b\", \"id\": \"bc\", \"weight\": 5, \"target\": \"c\"}}, {\"data\": {\"source\": \"c\", \"id\": \"ce\", \"weight\": 6, \"target\": \"e\"}}, {\"data\": {\"source\": \"c\", \"id\": \"cd\", \"weight\": 2, \"target\": \"d\"}}, {\"data\": {\"source\": \"d\", \"id\": \"de\", \"weight\": 7, \"target\": \"e\"}}, {\"data\": {\"source\": \"d\", \"id\": \"df\", \"weight\": 7, \"target\": \"f\"}}, {\"data\": {\"source\": \"f\", \"id\": \"fc\", \"weight\": 3, \"target\": \"c\"}}, {\"data\": {\"source\": \"f\", \"id\": \"fe\", \"weight\": 5, \"target\": \"e\"}}]\n",
" },\n",
" \n",
" layout: {\n",
" name: 'breadthfirst',\n",
" directed: true,\n",
" roots: '#a',\n",
" padding: 10\n",
" },\n",
" \n",
" ready: function(){\n",
" window.cy = this;\n",
" \n",
" var bfs = cy.elements().bfs('#a', function(){}, true);\n",
" \n",
" var i = 0;\n",
" var highlightNextEle = function(){\n",
" bfs.path[i].addClass('highlighted');\n",
" \n",
" if( i < bfs.path.length ){\n",
" i++;\n",
" setTimeout(highlightNextEle, 1000);\n",
" }\n",
" };\n",
" \n",
" // kick off first highlight\n",
" highlightNextEle();\n",
" }\n",
"});\n",
"};\n",
"\n",
"var before_render = function(){\n",
" if(window['cytoscape'] === undefined){\n",
" console.log(\"wait!\");\n",
"\t window.addEventListener(\"load_cytoscape\", before_render);\n",
" }else{\n",
"\t console.log(\"begin rendering!\");\n",
"\t render();\n",
" }\n",
"}\n",
"\n",
"before_render();\n",
"\n",
"})();\n",
"</script>\n",
"</head>\n",
"<body>\n",
" <div id=\"cycee16a87-eeb2-46ca-8558-e37620f9b5e7\"></div>\n",
" <!-- When only #uuid div is placed on this page, the height of output-box on ipynb will be 0px. One line below will prevent that. -->\n",
" <div id=\"dammy\" style=\"width:700px;height:500px\">\n",
"</body>\n",
"</html>"
],
"metadata": {},
"output_type": "display_data",
"text": [
"<IPython.core.display.HTML at 0x1b07e10>"
]
}
],
"prompt_number": 8
}
],
"metadata": {}
}
]
} | mit |
Subsets and Splits
No community queries yet
The top public SQL queries from the community will appear here once available.